Repository: pzivich/Delicatessen (language: python)

Issue:
Generalized Additive Models [EE]
**Is your feature request related to a problem? Please describe.**
 No, this is only an enhancement.

**Describe the solution you'd like**
Add estimating equations for generalized additive models. This is fairly straightforward as I already have L2-penalized regression implemented. All that really needs to be added is functionalities for different basis functions (like splines). There are some options I need to think through further though (e.g., applying different basis functions to different elements, generating predicted values from the GAM, as the predictors will be internally transformed)

**Describe alternatives you've considered**
Use outside GAM implementations, but this would be a nice thing to have. Also would put `delicatessen` closer so flexible ML models (a benefit in bioinformatics uptake).

**Additional context**
None



Discussion / hints:
A by-hand example of GAMs is now available. Overall feature still to be picked up.

https://github.com/pzivich/Delicatessen/blob/main/examples/Generalized-Additive-Model.ipynb

Fix patch:
diff --git a/delicatessen/estimating_equations/__init__.py b/delicatessen/estimating_equations/__init__.py
index de2a8ee..9612a7a 100644
--- a/delicatessen/estimating_equations/__init__.py
+++ b/delicatessen/estimating_equations/__init__.py
@@ -8,7 +8,8 @@
 
 from .regression import (ee_regression,
                          ee_robust_regression,
-                         ee_ridge_regression, ee_lasso_regression, ee_elasticnet_regression, ee_bridge_regression)
+                         ee_ridge_regression, ee_lasso_regression, ee_elasticnet_regression, ee_bridge_regression,
+                         ee_additive_regression)
 
 from .survival import (ee_exponential_model, ee_weibull_model,
                        ee_exponential_measure, ee_weibull_measure,
diff --git a/delicatessen/estimating_equations/regression.py b/delicatessen/estimating_equations/regression.py
index 77b9e66..74cf9d3 100644
--- a/delicatessen/estimating_equations/regression.py
+++ b/delicatessen/estimating_equations/regression.py
@@ -1,7 +1,9 @@
 import warnings
 import numpy as np
 
-from delicatessen.utilities import logit, inverse_logit, identity, robust_loss_functions
+from delicatessen.utilities import (logit, inverse_logit, identity,
+                                    robust_loss_functions,
+                                    additive_design_matrix)
 from delicatessen.estimating_equations.processing import generate_weights
 
 #################################################################
@@ -20,8 +22,9 @@ def ee_regression(theta, X, y, model, weights=None):
     Logistic regression uses the inverse-logit function, :math:`\text{expit}(u) = 1 / (1 + \exp(u))`. Finally, Poisson
     regression is :math:`\exp(u)`.
 
-    Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a
-    3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's.
+    Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if
+    X is a 3-by-n matrix, then :math:`\theta` will be a 1-by-3 array. The code is general to allow for an arbitrary
+    number of X's (as long as there is enough support in the data).
 
     Note
     ----
@@ -152,9 +155,9 @@ def ee_robust_regression(theta, X, y, model, k, loss='huber', weights=None, uppe
     occurring is zero.
 
 
-    Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a
-    3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as
-    long as there is enough support in the data).
+    Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if
+    X is a 3-by-n matrix, then :math:`\theta` will be a 1-by-3 array. The code is general to allow for an arbitrary
+    number of X's (as long as there is enough support in the data).
 
     Note
     ----
@@ -276,16 +279,20 @@ def ee_robust_regression(theta, X, y, model, k, loss='huber', weights=None, uppe
 # Penalized Regression Estimating Equations
 
 
-def ee_ridge_regression(theta, y, X, model, penalty, weights=None, center=0.):
+def ee_ridge_regression(theta, X, y, model, penalty, weights=None, center=0.):
     r"""Estimating equations for ridge regression. Ridge regression applies an L2-regularization through a squared
-    magnitude penalty. The estimating equation(s) is
+    magnitude penalty. The estimating equation for Ridge linear regression is
 
     .. math::
 
-        \sum_{i=1}^n \left{(Y_i - X_i^T \theta) X_i - \lambda \theta \right} = 0
+        \sum_{i=1}^n \left[(Y_i - X_i^T \theta) X_i - \lambda \theta \right] = 0
 
     where :math:`\lambda` is the penalty term.
 
+    Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if
+    X is a 3-by-n matrix, then :math:`\theta` will be a 1-by-3 array. The code is general to allow for an arbitrary
+    number of X's (as long as there is enough support in the data).
+
     Note
     ----
     The 'strength' of the penalty term is indicated by :math:`\lambda`, which is the ``penalty`` argument scaled (or
@@ -390,29 +397,18 @@ def ee_ridge_regression(theta, y, X, model, penalty, weights=None, center=0.):
 
     References
     ----------
-    Fu WJ. (1998). Penalized regressions: the Bridge versus the LASSO. Journal of Computational and Graphical
-    Statistics, 7(3), 397-416.
+    Fu WJ. (1998). Penalized regressions: the Bridge versus the LASSO. *Journal of Computational and Graphical
+    Statistics*, 7(3), 397-416.
 
-    Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132.
+    Fu WJ. (2003). Penalized estimating equations. *Biometrics*, 59(1), 126-132.
     """
-    # Preparation of input shapes and object types
-    X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center)
-
-    # Determining transformation function to use for the regression model
-    transform = _model_transform_(model=model)      # Looking up corresponding transformation
-    pred_y = transform(np.dot(X, beta))             # Generating predicted values
+    # Calling internal bridge penalized regression for implementation
+    return ee_bridge_regression(theta=theta,
+                                X=X, y=y, model=model, weights=weights,
+                                penalty=penalty, gamma=2, center=center)
 
-    # Allowing for a weighted penalized regression model
-    w = generate_weights(weights=weights, n_obs=X.shape[0])
-
-    # Creating penalty term for ridge regression (bridge with gamma=2 is the special case of ridge)
-    penalty_terms = _bridge_penalty_(theta=theta, n_obs=X.shape[0], penalty=penalty, gamma=2, center=center)
 
-    # Output b-by-n matrix
-    return w*(((y - pred_y) * X).T - penalty_terms[:, None])    # Score function with penalty term subtracted off
-
-
-def ee_lasso_regression(theta, y, X, model, penalty, epsilon=3.e-3, weights=None, center=0.):
+def ee_lasso_regression(theta, X, y, model, penalty, epsilon=3.e-3, weights=None, center=0.):
     r"""Estimating equation for an approximate LASSO (least absolute shrinkage and selection operator) regressor. LASSO
     regression applies an L1-regularization through a magnitude penalty.
 
@@ -422,11 +418,11 @@ def ee_lasso_regression(theta, y, X, model, penalty, epsilon=3.e-3, weights=None
     estimate the variance in all settings.
 
 
-    The estimating equation for the approximate LASSO is
+    The estimating equation for the approximate LASSO linear regression is
 
     .. math::
 
-        \sum_{i=1}^n \left{(Y_i - X_i^T \theta) X_i - \lambda (1 + \epsilon) | \theta |^{\epsilon} sign(\theta) \right}
+        \sum_{i=1}^n \left[(Y_i - X_i^T \theta) X_i - \lambda (1 + \epsilon) | \theta |^{\epsilon} sign(\theta) \right]
         = 0
 
     where :math:`\lambda` is the penalty term.
@@ -436,8 +432,8 @@ def ee_lasso_regression(theta, y, X, model, penalty, epsilon=3.e-3, weights=None
     LASSO may not be possible to implement due to the existence of multiple solutions
 
     Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if
-    X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of
-    X's (as long as there is enough support in the data).
+    X is a 3-by-n matrix, then :math:`\theta` will be a 1-by-3 array. The code is general to allow for an arbitrary
+    number of X's (as long as there is enough support in the data).
 
     Note
     ----
@@ -545,31 +541,21 @@ def ee_lasso_regression(theta, y, X, model, penalty, epsilon=3.e-3, weights=None
 
     References
     ----------
-    Fu WJ. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical
-    Statistics, 7(3), 397-416.
+    Fu WJ. (1998). Penalized regressions: the Bridge versus the LASSO. *Journal of Computational and Graphical
+    Statistics*, 7(3), 397-416.
 
-    Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132.
+    Fu WJ. (2003). Penalized estimating equations. *Biometrics*, 59(1), 126-132.
     """
-    # Preparation of input shapes and object types
-    X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center)
-
-    # Determining transformation function to use for the regression model
-    transform = _model_transform_(model=model)      # Looking up corresponding transformation
-    pred_y = transform(np.dot(X, beta))             # Generating predicted values
-
-    # Allowing for a weighted penalized regression model
-    w = generate_weights(weights=weights, n_obs=X.shape[0])
-
-    # Creating penalty term for ridge regression (bridge with gamma=2 is the special case of ridge)
     if epsilon < 0:
         raise ValueError("epsilon must be greater than zero for the approximate LASSO")
-    penalty_terms = _bridge_penalty_(theta=theta, n_obs=X.shape[0], penalty=penalty, gamma=1+epsilon, center=center)
 
-    # Output b-by-n matrix
-    return w*(((y - pred_y) * X).T - penalty_terms[:, None])    # Score function with penalty term subtracted off
+    # Calling internal bridge penalized regression for implementation
+    ee_bridge_regression(theta=theta,
+                         X=X, y=y, model=model, weights=weights,
+                         penalty=penalty, gamma=1+epsilon, center=center)
 
 
-def ee_elasticnet_regression(theta, y, X, model, penalty, ratio, epsilon=3.e-3, weights=None, center=0.):
+def ee_elasticnet_regression(theta, X, y, model, penalty, ratio, epsilon=3.e-3, weights=None, center=0.):
     r"""Estimating equations for Elastic-Net regression. Elastic-Net applies both L1- and L2-regularization at a
     pre-specified ratio. Notice that the L1 penalty is based on an approximation. See ``ee_lasso_regression`` for
     further details on the approximation for the L1 penalty.
@@ -580,18 +566,18 @@ def ee_elasticnet_regression(theta, y, X, model, penalty, ratio, epsilon=3.e-3,
     used to estimate the variance in all settings.
 
 
-    The estimating equation for Elastic-Net with the approximate L1 penalty is
+    The estimating equation for Elastic-Net linear regression with the approximate L1 penalty is
 
     .. math::
 
-        \sum_{i=1}^n \left{ (Y_i - X_i^T \theta) X_i - \lambda r (1 + \epsilon)
-        | \theta |^{\epsilon} sign(\theta) - \lambda (1-r) \theta \right} = 0
+        \sum_{i=1}^n \left[ (Y_i - X_i^T \theta) X_i - \lambda r (1 + \epsilon)
+        | \theta |^{\epsilon} sign(\theta) - \lambda (1-r) \theta \right] = 0
 
     where :math:`\lambda` is the penalty term and :math:`r` is the ratio for the L1 vs L2 penalty.
 
     Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if
-    X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of
-    X's (as long as there is enough support in the data).
+    X is a 3-by-n matrix, then :math:`\theta` will be a 1-by-3 array. The code is general to allow for an arbitrary
+    number of X's (as long as there is enough support in the data).
 
     Note
     ----
@@ -701,10 +687,10 @@ def ee_elasticnet_regression(theta, y, X, model, penalty, ratio, epsilon=3.e-3,
 
     References
     ----------
-    Fu WJ. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical
-    Statistics, 7(3), 397-416.
+    Fu WJ. (1998). Penalized regressions: the Bridge versus the LASSO. *Journal of Computational and Graphical
+    Statistics*, 7(3), 397-416.
 
-    Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132.
+    Fu WJ. (2003). Penalized estimating equations. *Biometrics*, 59(1), 126-132.
     """
     # Preparation of input shapes and object types
     X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center)
@@ -730,7 +716,7 @@ def ee_elasticnet_regression(theta, y, X, model, penalty, ratio, epsilon=3.e-3,
     return w * (((y - pred_y) * X).T - penalty_terms[:, None])  # Score function with penalty term subtracted off
 
 
-def ee_bridge_regression(theta, y, X, model, penalty, gamma, weights=None, center=0.):
+def ee_bridge_regression(theta, X, y, model, penalty, gamma, weights=None, center=0.):
     r"""Estimating equation for bridge penalized regression. The bridge penalty is a generalization of penalized
     regression, that includes L1 and L2-regularization as special cases.
 
@@ -741,17 +727,17 @@ def ee_bridge_regression(theta, y, X, model, penalty, gamma, weights=None, cente
     :math:`\gamma<2`. Therefore, the bread (and sandwich) cannot be used to estimate the variance in those settings.
 
 
-    The estimating equation for bridge penalized regression is
+    The estimating equation for bridge penalized linear regression is
 
     .. math::
 
-        \sum_{i=1}^n \left{ (Y_i - X_i^T \theta) X_i - \lambda \gamma | \theta |^{\gamma - 1} sign(\theta) \right} = 0
+        \sum_{i=1}^n \left[ (Y_i - X_i^T \theta) X_i - \lambda \gamma | \theta |^{\gamma - 1} sign(\theta) \right] = 0
 
     where :math:`\lambda` is the penalty term and :math:`\gamma` is a tuning parameter.
 
     Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if
-    X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of
-    X's (as long as there is enough support in the data).
+    X is a 3-by-n matrix, then :math:`\theta` will be a 1-by-3 array. The code is general to allow for an arbitrary
+    number of X's (as long as there is enough support in the data).
 
     Note
     ----
@@ -860,10 +846,10 @@ def ee_bridge_regression(theta, y, X, model, penalty, gamma, weights=None, cente
 
     References
     ----------
-    Fu WJ. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical
-    Statistics, 7(3), 397-416.
+    Fu WJ. (1998). Penalized regressions: the Bridge versus the LASSO. *Journal of Computational and Graphical
+    Statistics*, 7(3), 397-416.
 
-    Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132.
+    Fu WJ. (2003). Penalized estimating equations. *Biometrics*, 59(1), 126-132.
     """
     # Preparation of input shapes and object types
     X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center)
@@ -882,6 +868,224 @@ def ee_bridge_regression(theta, y, X, model, penalty, gamma, weights=None, cente
     return w * (((y - pred_y) * X).T - penalty_terms[:, None])  # Score function with penalty term subtracted off
 
 
+#################################################################
+# Flexible Regression Estimating Equations
+
+
+def ee_additive_regression(theta, X, y, specifications, model, weights=None):
+    r"""Estimating equation for Generalized Additive Models (GAMs). GAMs are an extension of generalized linear models
+    that allow for more flexible specifications of relationships of continuous variables. This flexibility is
+    accomplished via splines. To further control the flexibility, the spline terms are penalized.
+
+    Note
+    ----
+    The implemented GAM uses L2-penalization. This penalization only applies to the generated spline terms
+    (i.e., penalization decreases the 'wiggliness' of the estimated relationships).
+
+
+    The estimating equation for a generalized additive linear regression model is
+
+    .. math::
+
+        \sum_{i=1}^n \left[ (Y_i - f(X_i)^T \theta) f(X_i) - \lambda \theta \right] = 0
+
+    where :math:`\lambda` is the penalty term.
+
+    While this looks similar to Ridge regression, there are two important differences: the function :math:`f()` and
+    how :math:`\lambda` is defined. First, the function :math:`f()` denotes a general vector function. For spline terms,
+    this function defines the basis functions for the splines (set via the ``specifications`` parameter). For non-spline
+    terms, this is the identity function (i.e., no changes are made to the input). This setup allows for terms
+    to be selectively modeled using splines (e.g., categorical features are not modeled using splines). Next, the
+    penalty term, :math:`\lambda`, is only non-zero for :math:`\theta` that correspond to parameters for splines (i.e.,
+    only the spline terms are penalized). This is distinction from default Ridge regression, which penalizes all terms
+    in the model.
+
+    Note
+    ----
+    Originally, GAMs were implemented via splines with a knot at each unique values of :math:`X`. More recently, GAMs
+    use a more moderate amount of knots to improve computationally efficiency. Both versions can be implemented by
+    ``ee_additive_regression`` through setting the knot locations.
+
+
+    Here, :math:`\theta` is a 1-by-(b+k) array, where b is the distinct covariates included as part of X and the k
+    distinct spline basis functions. For example, if X is a 2-by-n matrix with a 10-knot natural spline for the second
+    column in X, then :math:`\theta` will be a 1-by-(2+9) array. The code is general to allow for an arbitrary
+    number of X variables and spline knots.
+
+    Parameters
+    ----------
+    theta : ndarray, list, vector
+        Parameter values. Number of values should match the number of columns in the additive design matrix.
+    X : ndarray, list, vector
+        2-dimensional vector of n observed values for b variables.
+    y : ndarray, list, vector
+        1-dimensional vector of n observed values.
+    specifications : list, dict, None
+        A list of dictionaries that define the hyperparameters for the spline (e.g., number of knots, strength of
+        penalty). For terms that should not have splines, ``None`` should be specified instead (see examples below).
+        Each dictionary supports the following parameters:
+        "knots", "natural", "power", "penalty"
+        * knots (list): controls the position of the knots, with knots are placed at given locations. There is no
+            default, so must be specified by the user.
+        * natural (bool): controls whether to generate natural (restricted) or unrestricted splines.
+            Default is ``True``, which corresponds to natural splines.
+        * power (float): controls the power to raise the spline terms to. Default is 3, which corresponds to cubic
+            splines.
+        * penalty (float): penalty term (:math:`\lambda`) applied to each corresponding spline basis term. Default is 0,
+            which applies no penalty to the spline basis terms.
+    model : str
+        Type of regression model to estimate. Options are ``'linear'`` (linear regression), ``'logistic'`` (logistic
+        regression), and ``'poisson'`` (Poisson regression).
+    weights : ndarray, list, vector, None, optional
+        1-dimensional vector of n weights. Default is ``None``, which assigns a weight of 1 to all observations.
+
+    Returns
+    -------
+    array :
+        Returns a (b+k)-by-n NumPy array evaluated for the input ``theta``
+
+    Examples
+    --------
+    Construction of a estimating equation(s) with ``ee_additive_regression`` should be done similar to the following
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from scipy.stats import logistic
+    >>> from delicatessen import MEstimator
+    >>> from delicatessen.estimating_equations import ee_additive_regression
+    >>> from delicatessen.utilities import additive_design_matrix, regression_predictions
+
+    Some generic data to estimate a generalized additive model
+
+    >>> n = 2000
+    >>> data = pd.DataFrame()
+    >>> x = np.random.uniform(-5, 5, size=n)
+    >>> data['X'] = x
+    >>> data['Y1'] = np.exp(x+0.5)) + np.abs(x) + np.random.normal(scale=1.5, size=n)
+    >>> data['Y2'] = np.random.binomial(n=1, p=logistic.cdf(np.exp(np.sin(x+0.5)) + np.abs(x)), size=n)
+    >>> data['Y3'] = np.random.poisson(lam=np.exp(np.exp(np.sin(x+0.5)) + np.abs(x)), size=n)
+    >>> data['C'] = 1
+
+    Note that ``C`` here is set to all 1's. This will be the intercept in the regression. Further, notice that the
+    relationship between ``X`` and the various ``Y``'s is not linear.
+
+    The design matrix for linear regression would be ``X = np.asarray(d[['C', 'X']])``. As the intercept is a constant,
+    we only want spline terms to be applied to ``'X'`` column. To define the spline specifications, we create the
+    following list
+
+    >>> specs = [None, {"knots": [-4, -3, -2, -1, 0, 1, 2, 3, 4], "penalty": 10}]
+
+    This tells ``ee_additive_regression`` to not generate a spline term for the first column in the input design matrix
+    and to generate a default spline with knots at the specified locations and penalty of 10 for the second column in
+    the input design matrix. Interally, the design matrix processing is done by the ``additive_design_matrix`` utility
+    function. We can see what the output of that function looks like via
+
+    >>> Xa_design = additive_design_matrix(X=np.asarray(data[['C', 'X']]), specifications=specs)
+
+    That output matrix is the corresponding design matrix. Use of the ``additive_design_matrix`` utility will be
+    demonstrated later for generating predictions from the estimated parameters.
+
+    Now psi, or the stacked estimating equations.
+
+    >>> def psi(theta):
+    >>>     x, y = data[['C', 'X']], data['Y']
+    >>>     return ee_additive_regression(theta=theta, X=x, y=y, model='linear', specifications=specs)
+
+    Calling the M-estimator. Note that the input initial values depends on the number of splines being generated. To
+    easily determine the number of initial values, we can use the shape of the previously generated design matrix
+    (``Xa_design``)
+
+    >>> n_params = Xa_design.shape[1]
+    >>> estr = MEstimator(stacked_equations=psi, init=[0., ]*n_params)
+    >>> estr.estimate()
+
+    Inspecting the parameter estimates, variance, and confidence intervals
+
+    >>> estr.theta
+    >>> estr.variance
+    >>> estr.confidence_intervals()
+
+    While all these estimates can be easily inspected, interpretting them is not easy. Instead, we can generate
+    predictions from the GAM and plot the estimated regression line. To do this, we will first create a new design
+    matrix where ``'X'`` is evenly spaced over a range of values
+
+    >>> p = pd.DataFrame()
+    >>> p['X'] = np.linspace(-5, 5, 200)
+    >>> p['C'] = 1
+    >>> Xa_pred = additive_design_matrix(X=np.asarray(p[['C', 'X']]), specifications=specs)
+
+    To generate the predicted values of Y (and the corresponding confidence intervals), we do the following
+
+    >>> yhat = regression_predictions(Xa_pred, theta=estr.theta, covariance=estr.variance)
+
+    For further details, see the ``regression_predictions`` utility function documentation.
+
+    Other optional specifications are available for the spline terms. Here, we will specify an unrestricted quadratic
+    spline with a penalty of 5.5 for the second column of the design matrix.
+
+    >>> specs = [None, {"knots": [-4, -2, 0, 2, 4], "natural": False, "power": 2, "penalty": 5.5}]
+
+    See the documentation of ``additive_design_matrix`` for additional examples of how to specify the additive design
+    matrix and corresponding splines.
+
+    Lastly, knots could be placed at each unique observation via
+
+    >>> specs = [None, {"knots": np.unique(data['X']), "penalty": 500}]
+
+    Note that the penalty is increased here (as the number of knots has dramatically increased).
+
+    A GAM for a binary outcome (i.e., logistic regression) can be implemented as follows
+
+    >>> specs = [None, {"knots": [-4, -3, -2, -1, 0, 1, 2, 3, 4], "penalty": 10}]
+    >>> Xa_design = additive_design_matrix(X=np.asarray(data[['C', 'X']]), specifications=specs)
+    >>> n_params = Xa_design.shape[1]
+
+    >>> def psi(theta):
+    >>>     x, y = data[['C', 'X']], data['Y2']
+    >>>     return ee_additive_regression(theta=theta, X=x, y=y, model='logistic', specifications=specs)
+
+    >>> estr = MEstimator(stacked_equations=psi, init=[0.]*n_params)
+    >>> estr.estimate(solver='lm', maxiter=5000)
+
+    A GAM for count outcomes (i.e., Poisson regression) can be implemented as follows
+
+    >>> specs = [None, {"knots": [-4, -3, -2, -1, 0, 1, 2, 3, 4], "penalty": 10}]
+    >>> Xa_design = additive_design_matrix(X=np.asarray(data[['C', 'X']]), specifications=specs)
+    >>> n_params = Xa_design.shape[1]
+
+    >>> def psi(theta):
+    >>>     x, y = data[['C', 'X']], data['Y3']
+    >>>     return ee_additive_regression(theta=theta, X=x, y=y, model='poisson', specifications=specs)
+
+    >>> estr = MEstimator(stacked_equations=psi, init=[0.]*n_params)
+    >>> estr.estimate(solver='lm', maxiter=5000)
+
+    Weighted models can be estimated by specifying the optional ``weights`` argument.
+
+    References
+    ----------
+    Fu WJ. (2003). Penalized estimating equations. *Biometrics*, 59(1), 126-132.
+
+    Hastie TJ. (2017). Generalized additive models. *In Statistical models in S* (pp. 249-307). Routledge.
+
+    Marx BD, & Eilers PH. (1998). Direct generalized additive modeling with penalized likelihood.
+    *Computational Statistics & Data Analysis*, 28(2), 193-209.
+
+    Wild CJ, & Yee TW. (1996). Additive extensions to generalized estimating equation methods.
+    *Journal of the Royal Statistical Society: Series B (Methodological)*, 58(4), 711-725.
+    """
+    # Compute the design matrix for the additive model
+    Xa, penalty = additive_design_matrix(X=X,                               # Create the additive design matrix
+                                         specifications=specifications,     # ... with the provided specifications
+                                         return_penalty=True)               # ... and return corresponding penalties
+
+    # Apply spline-penalized regression
+    return ee_bridge_regression(theta=theta, y=y, X=Xa,                     # Call bridge reg with additive design
+                                model=model, penalty=penalty,               # ... matrix and processed penalties
+                                gamma=2, weights=weights,                   # ... and set gamma=2 (Ridge reg)
+                                center=0.)                                  # ... with splines ALWAYS penalized to zero
+
+
 #################################################################
 # Utility functions for regression equations
 
diff --git a/delicatessen/mestimation.py b/delicatessen/mestimation.py
index 921e0a2..bef38a1 100644
--- a/delicatessen/mestimation.py
+++ b/delicatessen/mestimation.py
@@ -3,7 +3,6 @@
 from scipy.misc import derivative
 from scipy.optimize import approx_fprime
 from scipy.stats import norm
-from copy import copy
 
 
 class MEstimator:
@@ -364,7 +363,6 @@ def _mestimation_answer_(self, theta):
             b-by-1 array, which is the sum over n for each b.
         """
         # Option for the subset argument
-        # TODO this subset breaks the bread matrix...
         if self._subset_ is None:                      # If NOT subset then,
             full_theta = theta                         # ... then use the full input theta
         else:                                          # If subset then,
diff --git a/delicatessen/utilities.py b/delicatessen/utilities.py
index 8be6483..00f7368 100644
--- a/delicatessen/utilities.py
+++ b/delicatessen/utilities.py
@@ -1,4 +1,6 @@
+import warnings
 import numpy as np
+from scipy.stats import norm
 
 
 def logit(prob):
@@ -160,8 +162,6 @@ def robust_loss_functions(residual, loss, k, a=None, b=None):
     if not isinstance(loss, str):
         raise ValueError("The provided loss function should be a string.")
 
-    # TODO add check that a < b < k for Hampel
-
     # Huber function
     elif loss.lower() == "huber":
         xr = np.clip(residual, a_min=-k, a_max=k)
@@ -181,6 +181,8 @@ def robust_loss_functions(residual, loss, k, a=None, b=None):
     elif loss.lower() == "hampel":
         if a is None or b is None:
             raise ValueError("The 'hampel' loss function requires the optional `a` and `b` arguments")
+        if not a < b < k:
+            raise ValueError("The 'hampel' loss function requires that a < b < k")
         xr = np.where(np.abs(residual) < a, residual, np.nan)
         xr = np.where((a <= residual) & (residual < b), a, xr)
         xr = np.where((-a >= residual) & (residual > -b), -a, xr)
@@ -194,3 +196,375 @@ def robust_loss_functions(residual, loss, k, a=None, b=None):
 
     # Returning the updated values
     return xr
+
+
+def regression_predictions(X, theta, covariance, alpha=0.05):
+    r"""Generate predicted values of the outcome given a design matrix, point estimates, and covariance matrix.
+    This functionality computes :math:`\hat{Y}`, :math:`\hat{Var}\left(\hat{Y}\right)`, and corresponding Wald-type
+    :math:`(1 - \alpha) \times` 100% confidence intervals from estimated coefficients and covariance of a regression
+    model given a set of specific covariate values.
+
+    This function is a helper function to compute the predictions from a regression model for a set of given :math:`X`
+    values. Importantly, this method allows for the variance of :math:`\hat{Y}` to be estimated without having to expand
+    the estimating equations. As such, this functionality is meant to be used after ``MEstimator`` has been used to
+    estimate the coefficients (i.e., this function is for use after the M-estimator has computed the results for the
+    chosen regression model). Therefore, this function should be viewed as a post-processing functionality.
+
+    Note
+    ----
+    No tranformations are applied by this function. So, input from a logistic model will generate the *log-odds* of the
+    outcome (not probability).
+
+
+    Parameters
+    ----------
+    X : ndarray, list, vector
+        2-dimensional vector of values to generate predicted variances for. The number of columns must match the number
+        of coefficients / parameters in ``theta``.
+    theta : ndarray
+        Estimated coefficients from ``delicatessen.MEstimator.theta``.
+    covariance : ndarray
+        Estimated covariance matrix from ``delicatessen.MEstimator.variance``.
+    alpha : float, optional
+        The :math:`\alpha` level for the corresponding confidence intervals. Default is 0.05, which calculate the
+        95% confidence intervals. Notice that :math:`0<\alpha<1`.
+
+    Returns
+    -------
+    array :
+        Returns a n-by-4 NumPy array with the 4 columns correspond to the predicted outcome, variance of the predictied
+        outcome, lower confidence limit, and upper confidence limit.
+
+    Examples
+    --------
+    The following is a simple example demonstrating how this function can be used to plot a regression line and
+    corresponding 95% confidence intervals.
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> import matplotlib.pyplot as plt
+    >>> from delicatessen import MEstimator
+    >>> from delicatessen.estimating_equations import ee_regression
+    >>> from delicatessen.utilities import regression_predictions
+
+    Some generic data to estimate the regression model with
+
+    >>> n = 50
+    >>> data = pd.DataFrame()
+    >>> data['X'] = np.random.normal(size=n)
+    >>> data['Z'] = np.random.normal(size=n)
+    >>> data['Y'] = 0.5 + 2*data['X'] - 1*data['Z'] + np.random.normal(loc=0, size=n)
+    >>> data['C'] = 1
+
+    Estimating the corresponding regression model parameters
+
+    >>> def psi(theta):
+    >>>     return ee_regression(theta=theta, X=data[['C', 'X', 'Z']], y=data['Y'], model='linear')
+
+    >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0.,])
+    >>> estr.estimate(solver='lm')
+
+    To create a line plot of our regression line, we need to first create a new set of covariate values that are evenly
+    spaced across the range of the predictor values. Here, we will plot the relationship between ``Z`` and ``Y`` while
+    holding ``X=0``.
+
+    >>> pred = pd.DataFrame()
+    >>> pred['Z'] = np.linspace(np.min(data['Z']), np.max(data['Z']), 200)
+    >>> pred['X'] = 0
+    >>> pred['C'] = 1
+
+    Now the predicted values of the outcome, and confidence intervals to plot
+
+    >>> Xp = pred[['C', 'X', 'Z']]
+    >>> yhat = regression_predictions(X=Xp, theta=estr.theta, covariance=estr.variance)
+
+    Finally, the predicted values can be plotted (using matplotlib)
+
+    >>> plt.plot(pred['Z'], yhat[:, 0], '-', color='blue')
+    >>> plt.fill_between(pred['Z'], yhat[:, 2], yhat[:, 3], alpha=0.25, color='blue')
+    >>> plt.show()
+
+    For predicting with a Poisson or logistic model, one may want to transform the predicted values and confidence
+    intervals to another measure. For the logistic model, the predicted log-odds can easily be transformed using
+    ``delicatessen.utilities.inverse_logit``. For the Poisson model, predictions can easily be transformed using
+    ``numpy.exp``.
+    """
+    # Check valid alpha value is being provided
+    if not 0 < alpha < 1:
+        raise ValueError("`alpha` must be 0 < a < 1")
+
+    # Setup inputs for matrix multiplications
+    x = np.asarray(X)
+    b = np.asarray(theta)
+    c = np.asarray(covariance)
+
+    # Predicted Y
+    yhat = np.dot(x, b)
+
+    # Predicted Y variance / standard error
+    yhat_var = np.sum(np.dot(x, c) * x,
+                      axis=1)
+
+    # Confidence limit of predictions
+    yhat_se = np.sqrt(yhat_var)                        # Taking square root to get SE
+    z_alpha = norm.ppf(1 - alpha/2, loc=0, scale=1)    # Z_alpha value for CI
+    lower_ci = yhat - z_alpha*yhat_se                  # Lower CI
+    upper_ci = yhat + z_alpha*yhat_se                  # Upper CI
+
+    # Return estimates and variance
+    return np.vstack([yhat, yhat_var, lower_ci, upper_ci]).T
+
+
+def spline(variable, knots, power=3, restricted=True):
+    r"""Generate generic polynomial spline terms for a given NumPy array and pre-specified knots. Default is restricted
+    cubic splines but unrestricted splines to different polynomial terms can also be specified.
+
+    Parameters
+    ----------
+    variable : ndarray, vector, list
+        1-dimensional vector of observed values. Input should consists of the variable to generate spline terms for
+    knots : ndarray, vector, list
+        1-dimensional vector of pre-specified knot locations. All knots should be between the minimum and maximum
+        values of the input variable
+    power : int, float, optional
+        Power or polynomial term to use for the splines. Default is 3, which corresponds to cubic splines
+    restricted : bool, optional
+        Whether to generate restricted or unrestricted splines. Default is True, which corresponds to restricted
+        splines. Restricted splines return one less column than the number of knots, whereas unrestricted splines
+        return the same number of columns as knots
+
+    Returns
+    -------
+    ndarray :
+        A 2-dimensional array of the spline terms in ascending order of the knots.
+
+    Examples
+    --------
+
+    References
+    ----------
+
+    """
+    # Processing input and output arrays
+    knots = sorted(knots)                           # Sorting the order of the knots
+    n_cols = len(knots)                             # Column number is based on the number of provided knots
+    x = np.asarray(variable)                        # Converting input variable to another array
+    spline_terms = np.empty((x.shape[0], n_cols))   # Creating the output spline array
+
+    # Generating each spline with it's corresponding term
+    for i in range(n_cols):
+        spline_terms[:, i] = np.where(x > knots[i], (x - knots[i])**power, 0)
+        spline_terms[:, i] = np.where(np.isnan(x), np.nan, spline_terms[:, i])
+
+    # Logic for unrestricted and restricted splines
+    if restricted is False:
+        return spline_terms
+    else:
+        for i in range(n_cols - 1):
+            spline_terms[:, i] = np.where(x > knots[i], spline_terms[:, i] - spline_terms[:, -1], 0)
+            spline_terms[:, i] = np.where(np.isnan(x), np.nan, spline_terms[:, i])
+        return spline_terms[:, :-1]
+
+
+def additive_design_matrix(X, specifications, return_penalty=False):
+    r"""Generate an additive design matrix for generalized additive models (GAM) given a set of spline specifications to
+    apply.
+
+    Note
+    ----
+    This function is interally called by ``ee_additive_regression``. This function can also be called to aid in easily
+    generating predicted values.
+
+    Parameters
+    ----------
+    X : ndarray, vector, list
+        Input independent variable data.
+    specifications : ndarray, vector, list
+        A list of dictionaries that define the hyperparameters for the spline (e.g., number of knots, strength of
+        penalty). For terms that should not have splines, ``None`` should be specified instead (see examples below).
+        Each dictionary supports the following parameters:
+        "knots", "natural", "power", "penalty"
+        * knots (list): controls the position of the knots, with knots are placed at given locations. There is no
+            default, so must be specified by the user.
+        * natural (bool): controls whether to generate natural (restricted) or unrestricted splines.
+            Default is ``True``, which corresponds to natural splines.
+        * power (float): controls the power to raise the spline terms to. Default is 3, which corresponds to cubic
+            splines.
+        * penalty (float): penalty term (:math:`\lambda`) applied to each corresponding spline basis term. Default is 0,
+            which applies no penalty to the spline basis terms.
+    return_penalty : bool, optional
+        Whether the list of the corresponding penalty terms should also be returned. This functionality is used
+        internally to create the list of penalty terms to provide the Ridge regression model, where only the spline
+        terms are penalized. Default is False.
+
+    Returns
+    -------
+    array :
+        Returns a (b+k)-by-n design matrix as a NumPy array, where b is the number of columns in the input array and k
+        is determined by the specifications of the spline basis functions.
+
+    Examples
+    --------
+    Construction of a design matrix for an additive model should be done similar to the following
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from delicatessen.utilities import additive_design_matrix
+
+    Some generic data to estimate a generalized additive model
+
+    >>> n = 200
+    >>> d = pd.DataFrame()
+    >>> d['X'] = np.random.normal(size=n)
+    >>> d['Z'] = np.random.normal(size=n)
+    >>> d['W'] = np.random.binomial(n=1, p=0.5, size=n)
+    >>> d['C'] = 1
+
+    To begin, consider the simple input design matrix of ``d[['C', 'X']]``. This initial design matrix consists of an
+    intercept term and a continuous term. Here, we will specify a natural spline with 20 knots for the second term only
+
+    >>> x_knots = np.linspace(np.min(d['X'])+0.1, np.max(d['X'])-0.1, 20)
+    >>> specs = [None, {"knots": x_knots, "penalty": 10}]
+    >>> Xa_design = additive_design_matrix(X=d[['C', 'X']], specifications=specs)
+
+    Note
+    ----
+    The knot locations must always be specified by the user.
+
+
+    Other optional specifications are also available. Here, we will specify an unrestricted quadratic spline with a
+    penalty of 5.5 for the second column of the design matrix.
+
+    >>> specs = [None, {"knots": [-2, -1, 0, 1, 2], "natural": False, "power": 2, "penalty": 5.5}]
+    >>> Xa_design = additive_design_matrix(X=d[['C', 'X']], specifications=specs)
+
+    Now consider the input design matrix of ``d[['C', 'X', 'Z', 'W']]``. This initial design matrix consists of an
+    intercept, two continuous, and a categorical term. Here, we will specify splines for both continuous terms
+
+    >>> x_knots = np.linspace(np.min(d['X'])+0.1, np.max(d['X'])-0.1, 20)
+    >>> z_knots = np.linspace(np.min(d['Z'])+0.1, np.max(d['Z'])-0.1, 10)
+    >>> specs = [None,                              # Intercept term
+    >>>          {"knots": x_knots, "penalty": 25}, # X (continuous)
+    >>>          {"knots": z_knots, "penalty": 15}, # Z (continuous)
+    >>>          None]                              # W (categorical)
+    >>> Xa_design = additive_design_matrix(X=d[['C', 'X', 'Z', 'W']], specifications=specs)
+
+    Notice that the two continuous terms have different spline specifications.
+
+    Finally, we could opt to only generate a spline basis for one of the continuous variables
+
+    >>> specs = [None,                              # Intercept term
+    >>>          {"knots": x_knots, "penalty": 25}, # X (continuous)
+    >>>          None,                              # Z (continuous)
+    >>>          None]                              # W (categorical)
+    >>> Xa_design = additive_design_matrix(X=d[['C', 'X', 'Z', 'W']], specifications=specs)
+
+    Specification of splines can be modified and paired in a variety of ways. These are determined by the object type
+    in the specification list, and the input dictionary for the spline terms.
+    """
+    def generate_spline(variable, specification):
+        """Internal function to call the spline functionality. This function merely calls the spline function with the
+        corresponding specifications. This was built as an internal function to simply future maintenance.
+
+        Parameters
+        ----------
+        variable : ndarray
+            Column of variables to generate the splines for
+        specification : dict
+            Dictionary of the processed spline specification for the corresponding variable.
+
+        Returns
+        -------
+        ndarray :
+            Returns the object returned by the spline (the basis matrix of the spline terms for the column)
+        """
+        return spline(variable=variable,                      # Pass variable to spline function
+                      knots=specification["knots"],           # ... with knot locations
+                      power=specification["power"],           # ... to the power
+                      restricted=specification["natural"])    # ... and whether to restrict
+
+    def generate_default_spline_parameters(specification):
+        """Internal function to process the input specification dictionary of spline parameters. Namely, ensure that
+        'knots' is specified, fill in any other empty parameters, and check for additional keys given.
+
+        Parameters
+        ----------
+        specification : dict
+            Dictionary of the input spline specifications to check and process.
+
+        Returns
+        -------
+        dict :
+            Processed dictionary of the spline hyperparameters
+        """
+        # Setup for dict processing
+        keys = specification.keys()                                   # Extract keys from the input dictionary
+        defaults = {"knots": None,                                    # Default values (knots must be provided)
+                    "natural": True,                                  # ... default to restricted splines
+                    "power": 3,                                       # ... default to cubic splines
+                    "penalty": 0}                                     # ... default to NO penalty on spline terms
+
+        # Checking the keys in the input dictionary against the expected keys
+        expected_keys = ["knots", "natural", "power", "penalty", ]    # List of keys expected to occur in the dict
+        extra_keys = [param for param in keys if param not in expected_keys]
+        if len(extra_keys) != 0:
+            warnings.warn("The following keys were found in the specification: " + str(extra_keys)
+                          + ". These keys are not supported and are being ignored.",
+                          UserWarning)
+
+        # Managing knot keyword
+        if "knots" not in keys:
+            raise ValueError("`knots` must be specified.")
+        # Managing keyword for natural / restricted splines
+        if "natural" not in keys:
+            specification["natural"] = defaults["natural"]
+        # Managing keyword for power to raise splines to
+        if "power" not in keys:
+            specification["power"] = defaults["power"]
+        # Managing keyword for penalty to apply to splines
+        if "penalty" not in keys:
+            specification["penalty"] = defaults["penalty"]
+
+        # Returning processed spline parameters dictionary
+        return specification
+
+    # Extract meta-data from input
+    X = np.asarray(X)                               # Convert to NumPy array (when user interacts directly with func)
+    n_cols = X.shape[1]                             # Number of columns in the input data
+    n_obs = X.shape[0]                              # Number of observations in the input data
+    if isinstance(specifications, dict):            # Expanding to list if only a single dict is provided
+        specifications = [specifications, ]*n_cols
+    elif specifications is None:                    # When None is given as specification, ignore splines (linear reg)
+        specifications = [specifications, ]*n_cols
+    else:                                           # Otherwise check dimensions of specifications and cols match
+        if len(specifications) != n_cols:
+            raise ValueError("The number of input specifications (" + str(len(specifications)) +
+                             ") and the number of columns (" + str(n_cols) + ") do not match")
+
+    # Generate spline terms for each column
+    Xt = []                                              # Placeholder storage for generated spline terms
+    penalties = []                                       # Placeholder storage for corresponding penalty terms
+    for col_id in range(n_cols):                         # Loop through all the columns by their index number
+        xvar = X[:, col_id]                              # ... extract corresponding column by ID
+        xspec = specifications[col_id]                   # ... extract specification by ID
+
+        # Linear term
+        Xt.append(xvar.reshape(n_obs, 1))                # ... always add the corresponding linear term
+        penalties.append(0)                              # ... always have penalty of zero for the linear term
+
+        # Spline generation
+        if xspec is not None:                            # ... when given a specification != None, generate splines
+            # Processing input spline specifications to adhere to conventions
+            spec_i = generate_default_spline_parameters(specification=xspec)
+            # Generate the spline matrix using the spline function and specification
+            spline_matrix = generate_spline(variable=X[:, col_id],
+                                            specification=spec_i)
+            # Add spline terms to storage
+            Xt.append(spline_matrix)                                      # ... add matrix of splines to list
+            penalties += [spec_i["penalty"], ]*spline_matrix.shape[1]     # ... add penalty terms from specifications
+
+    # Return the transformed design matrix
+    if return_penalty:                      # If return penalty
+        return np.hstack(Xt), penalties     # ... return additive design matrix AND list of penalty terms
+    else:                                   # Otherwise
+        return np.hstack(Xt)                # ... only return the additive design matrix
diff --git a/delicatessen/version.py b/delicatessen/version.py
index 4802e90..f901408 100644
--- a/delicatessen/version.py
+++ b/delicatessen/version.py
@@ -1,1 +1,1 @@
-__version__ = "1.0"
+__version__ = "1.1"
diff --git a/docs/Built-in Equations.rst b/docs/Built-in Equations.rst
index efe8722..ad16a4b 100644
--- a/docs/Built-in Equations.rst
+++ b/docs/Built-in Equations.rst
@@ -486,6 +486,96 @@ and :math:`\gamma=2` is the Ridge penalty. Here, we use a value larger than 2 fo
 Different penalty terms can be assigned to each coefficient. Furthermore, the ``center`` argument can be used to
 penalize towards non-zero values for all or some of the coefficients.
 
+Flexible Regression
+=============================
+The previous regression models generally rely on strong parametric assumptions (unless explicitly relaxed by the user
+through the specified design matrix). An alternative is to use more flexible regression models, which place less strict
+parametric assumptions on the model. Here, we will demonstrate flexible models for linear regression, but logistic and
+Poisson regression are also supported (through the :code:`model` argument).
+
+To demonstrate application of the flexible regression models, consider the following simulated data set
+
+.. code::
+
+    from delicatessen.estimating_equations import (ee_additive_regression,
+                                                   )
+    from delicatessen.utilities import additive_design_matrix
+
+    n = 2000
+    d = pd.DataFrame()
+    d['X'] = np.random.uniform(-5, 5, size=n)
+    d['Z'] = np.random.binomial(n=1, p=0.5, size=n)
+    d['Y'] = 2*d['Z'] + np.exp(np.sin(d['X'] + 0.5)) + np.abs(d['X']) + np.random.normal(size=n)
+    d['C'] = 1
+
+Here, there the relationship between X and Y is nonlinear. The flexible regression models will attempt to capture this
+flexibility without the user having to directly specify the functional form.
+
+Generalized Additive Model
+----------------------------
+Generalized Additive Models (GAMs) are an extension of Generalized Linear Models (GLMs) that replace linear terms
+in the model with an arbitrary (but user-specified) function. For the GLM, we might consider the following model
+
+.. math::
+
+    Y_i = \beta_0 + \beta_1 Z_i + \beta_2 X_i + \epsilon_i
+
+However, this model assumes that the relationship between X and Y is linear (which we known to be untrue in this case).
+GAMs work by replacing the linear term with a spline function. For the GAM, we might consider the following model
+
+.. math::
+
+    Y_i = \beta_0 + \beta_1 Z_i + \beta_2 X_i + \sum_k \beta_k f_k(X_k) + \epsilon_i
+
+Here, X was replaced with a set of function. Those functions define a pre-specified number of spline terms. These
+spline terms allow for the relationship between X and Y to be modeled in a flexible but smooth way. However, this
+flexibility is not free. If our splines are complex, the GAM can overfit the data. To help prevent this issue, GAMs
+generally use penalized splines, where the coefficients for the spline terms are penalized. ``delicatessen`` uses L2
+penalization and allows various specifications for the splines.
+
+The main trick of the GAM is to generate a new design matrix for the additive model based on some input design matrix
+and spline specifications. This is done (internally) by the ``additive_design_matrix`` function. This can also be
+directly called
+
+.. code::
+
+    x_knots = np.linspace(-4.75, 4.75, 30)
+    specs = [None,
+             None,
+             {"knots": x_knots, "penalty": 20},
+             ]
+    Xa = additive_design_matrix(X=data[['C', 'Z', 'X']], specifications=specs)
+
+Here, a design matrix is return where the first two columns (C and Z) have no spline terms generated. For the last
+column (X), a natural cubic spline with 30 evenly spaced knots and a penalty of 20 is generated. So the output design
+matrix will consist of the C,Z,X columns followed by the 29 column basis of the splines.
+
+To implement a GAM, the estimating equations can be specified as
+
+.. code::
+
+    def psi(theta):
+        return ee_additive_regression(theta=theta,
+                                      X=d[['C', 'X']], y=d['Y'],
+                                      specifications=specs,
+                                      model='linear')
+
+Here, the previously defined spline specifications are provided. Internally, ``ee_additive_regression`` calls the
+``additive_design_matrix``, so this design matrix does not have to be provided by the user. However, pre-computing the
+design matrix is helpful for determining the number of initial values. To determine the number of initial values to
+provide ``MEstimator``, we can check the number of columns in ``Xa``. In the following, we use the number of columns to
+generate a list of starting values.
+
+.. code::
+
+    estr = MEstimator(psi, init=[0, ]*Xa.shape[1])
+    estr.estimate(solver='lm', maxiter=10000)
+
+Multiple splines, different types of splines, or varying penalty strengths can also be specified. These specifications
+are all done through the list of dictionaries provided in the ``specifications`` arguments. Any element with a
+dictionary will have splines generated and any ``None`` element will only have the main term returned. See the
+``ee_additive_regression`` and ``additive_design_matrix`` reference pages for further examples.
+
 Survival
 =============================
 Suppose each person has two unique times: their event time (:math:`T_i`) and their censoring time (:math:`C_i`).
diff --git a/docs/Reference/Estimating Equations.rst b/docs/Reference/Estimating Equations.rst
index 338dc23..d4fb04a 100644
--- a/docs/Reference/Estimating Equations.rst
+++ b/docs/Reference/Estimating Equations.rst
@@ -33,6 +33,7 @@ Regression
   ee_lasso_regression
   ee_elasticnet_regression
   ee_bridge_regression
+  ee_additive_regression
 
 
 Survival
diff --git a/docs/Reference/Utilities.rst b/docs/Reference/Utilities.rst
index 2ec3a7c..9043832 100644
--- a/docs/Reference/Utilities.rst
+++ b/docs/Reference/Utilities.rst
@@ -3,8 +3,8 @@ Utilities
 For manipulation of output or inputs, there are several basic utility functionalities for transformation of variables
 or predicted parameters. These are used internally by the estimating equations but are also made available for users.
 
-Transformations
--------------------
+Data transformations
+---------------------
 
 .. currentmodule:: delicatessen.utilities
 
@@ -15,3 +15,13 @@ Transformations
   inverse_logit
   identity
   robust_loss_functions
+  spline
+  regression_predictions
+
+Design matrices
+---------------------
+
+.. autosummary::
+  :toctree: generated/
+
+  additive_design_matrix
diff --git a/docs/Reference/generated/delicatessen.estimating_equations.regression.ee_additive_regression.rst b/docs/Reference/generated/delicatessen.estimating_equations.regression.ee_additive_regression.rst
new file mode 100644
index 0000000..eec29b1
--- /dev/null
+++ b/docs/Reference/generated/delicatessen.estimating_equations.regression.ee_additive_regression.rst
@@ -0,0 +1,6 @@
+﻿delicatessen.estimating\_equations.regression.ee\_additive\_regression
+======================================================================
+
+.. currentmodule:: delicatessen.estimating_equations.regression
+
+.. autofunction:: ee_additive_regression
\ No newline at end of file
diff --git a/docs/Reference/generated/delicatessen.utilities.additive_design_matrix.rst b/docs/Reference/generated/delicatessen.utilities.additive_design_matrix.rst
new file mode 100644
index 0000000..0a4b13d
--- /dev/null
+++ b/docs/Reference/generated/delicatessen.utilities.additive_design_matrix.rst
@@ -0,0 +1,6 @@
+﻿delicatessen.utilities.additive\_design\_matrix
+===============================================
+
+.. currentmodule:: delicatessen.utilities
+
+.. autofunction:: additive_design_matrix
\ No newline at end of file
diff --git a/docs/Reference/generated/delicatessen.utilities.regression_predictions.rst b/docs/Reference/generated/delicatessen.utilities.regression_predictions.rst
new file mode 100644
index 0000000..755a7ab
--- /dev/null
+++ b/docs/Reference/generated/delicatessen.utilities.regression_predictions.rst
@@ -0,0 +1,6 @@
+﻿delicatessen.utilities.regression\_predictions
+==============================================
+
+.. currentmodule:: delicatessen.utilities
+
+.. autofunction:: regression_predictions
\ No newline at end of file
diff --git a/docs/Reference/generated/delicatessen.utilities.spline.rst b/docs/Reference/generated/delicatessen.utilities.spline.rst
new file mode 100644
index 0000000..fb6666a
--- /dev/null
+++ b/docs/Reference/generated/delicatessen.utilities.spline.rst
@@ -0,0 +1,6 @@
+﻿delicatessen.utilities.spline
+=============================
+
+.. currentmodule:: delicatessen.utilities
+
+.. autofunction:: spline
\ No newline at end of file
diff --git a/examples/Boos-Stefanski-Ch7.ipynb b/examples/Boos-Stefanski-Ch7.ipynb
index bcf875c..1a00bef 100644
--- a/examples/Boos-Stefanski-Ch7.ipynb
+++ b/examples/Boos-Stefanski-Ch7.ipynb
@@ -24,7 +24,7 @@
       "NumPy version:        1.22.2\n",
       "SciPy version:        1.9.2\n",
       "Pandas version:       1.4.1\n",
-      "Delicatessen version: 1.0\n"
+      "Delicatessen version: 1.1\n"
      ]
     }
    ],
diff --git a/examples/Generalized-Additive-Model.ipynb b/examples/Generalized-Additive-Model.ipynb
index 5148b04..87afd76 100644
--- a/examples/Generalized-Additive-Model.ipynb
+++ b/examples/Generalized-Additive-Model.ipynb
@@ -7,9 +7,9 @@
    "source": [
     "# Generalized Additive Model\n",
     "\n",
-    "The following is a brief illustrative example of how generalized additive models (GAMs) can be implemented in `delicatessen` by-hand. \n",
+    "The following is a brief illustrative example of how generalized additive models (GAMs) can be implemented in `delicatessen`. For demonstration, we use data on motorcycle impacts from Silverman (1985). \n",
     "\n",
-    "Paul Zivich (2023/01/02)"
+    "Code by Paul Zivich"
    ]
   },
   {
@@ -26,7 +26,7 @@
       "SciPy version:        1.9.2\n",
       "Pandas version:       1.4.1\n",
       "Matplotlib version:   3.5.1\n",
-      "Delicatessen version: 1.0\n"
+      "Delicatessen version: 1.1\n"
      ]
     }
    ],
@@ -39,10 +39,13 @@
     "\n",
     "import delicatessen as deli\n",
     "from delicatessen import MEstimator\n",
-    "from delicatessen.estimating_equations import ee_ridge_regression\n",
+    "from delicatessen.estimating_equations import (ee_regression, \n",
+    "                                               ee_ridge_regression, \n",
+    "                                               ee_additive_regression)\n",
+    "from delicatessen.utilities import additive_design_matrix, regression_predictions\n",
     "\n",
     "%matplotlib inline\n",
-    "mpl.rcParams['figure.dpi']= 100\n",
+    "mpl.rcParams['figure.dpi']= 200\n",
     "np.random.seed(202301)\n",
     "\n",
     "print(\"NumPy version:       \", np.__version__)\n",
@@ -57,7 +60,7 @@
    "id": "9bd5a57f",
    "metadata": {},
    "source": [
-    "For illustrative purposes, we will simulate some data with a non-linear relationship between $Y$ and $X$. "
+    "Loading the motorcycle data (the data can be found in a few different R libraries and other online resources about GAMs). "
    ]
   },
   {
@@ -67,12 +70,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "n = 2000\n",
+    "# Loading data\n",
+    "d = pd.read_csv(\"data/silverman.csv\")\n",
+    "d['intercept'] = 1\n",
     "\n",
-    "d = pd.DataFrame()\n",
-    "d['X'] = np.random.uniform(-5, 5, size=n)\n",
-    "d['C'] = 1\n",
-    "d['Y'] = np.exp(np.sin(d['X'] + 0.5)) + np.abs(d['X'])"
+    "# Formating data for delicatessen\n",
+    "X = np.asarray(d[['intercept', 'times']])\n",
+    "y = np.asarray(d['accel'])\n",
+    "\n",
+    "# Formating data for plots\n",
+    "p = pd.DataFrame()\n",
+    "p['times'] = np.linspace(np.min(d['times']), np.max(d['times']), 200)\n",
+    "p['intercept'] = 1\n",
+    "Xp = np.asarray(p[['intercept', 'times']])"
    ]
   },
   {
@@ -83,19 +93,9 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x255ff9b0a00>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "<Figure size 1200x800 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -105,48 +105,53 @@
     }
    ],
    "source": [
-    "plt.plot(d['X'], d['Y'], 'o')"
+    "plt.plot(d['times'], d['accel'], '.', color='k')\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Acceleration (g)\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f316130b",
+   "id": "699fac56",
    "metadata": {},
    "source": [
-    "Now we will add some random Gaussian noise to $Y$ for our regression models. With the random noise, the underlying functional form will not be as easy to see."
+    "As easily seen in the scatterplot, the relationship between time and acceleration is non-linear. We can show this even further by fitting a regression model with a linear relationship between the variables. We will do that here"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "58288e97",
+   "id": "88e09bad",
    "metadata": {},
    "outputs": [],
    "source": [
-    "d['Y'] = d['Y'] + np.random.normal(scale=1.5, size=n)"
+    "def psi(theta):\n",
+    "    return ee_regression(theta=theta, X=X, y=y, model='linear')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "63d2168b",
+   "id": "08166eb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estr = MEstimator(psi, init=[0, 0])\n",
+    "estr.estimate(solver='lm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2e157eda",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x255ffe86640>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "<Figure size 1200x800 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -156,159 +161,366 @@
     }
    ],
    "source": [
-    "plt.plot(d['X'], d['Y'], 'o')"
+    "# Predicted values and confidence intervals for plot\n",
+    "pred_y = regression_predictions(Xp, estr.theta, estr.variance)\n",
+    "\n",
+    "# Plot\n",
+    "plt.plot(d['times'], d['accel'], '.', color='k')\n",
+    "plt.plot(p['times'], pred_y[:, 0], color='orange')\n",
+    "plt.fill_between(p['times'],\n",
+    "                 pred_y[:, 2], pred_y[:, 3],\n",
+    "                 color='orange', alpha=0.3)\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Acceleration (g)\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "699fac56",
+   "id": "d628e122",
    "metadata": {},
    "source": [
     "## Implementing a GAM\n",
     "\n",
-    "### Processing Data\n",
+    "As of v1.1, `delicatessen` has a built-in functionality for GAMs. We will use that built-in functionality here. GAMs are implemented through the use of $L_2$-penalized splines. For implementation, the user needs to provide the knots (number or locations). Other optional arguments include the power of the splines, whether to restrict, and the strength of the penalty.\n",
     "\n",
-    "To get our GAM working, we need to generate splines for the continuous terms. Below is a simple function to generate generic splines at pre-specified knot locations. The default `term=3` means that cubic splines are being generated."
+    "First, we define our estimating equations. The ``ee_additive_regression`` model builds the additive model design matrix for us, so we only need to pass the design matrix for the linear model above. However, we also need to specify some spline options. For the intercept term, we do not want any splines. For times, we will specify use of 12 knots and a rather strong penalty of 2000. This penalty will prevent the GAM from being too 'wiggly'."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "555455b9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def spline(variable, knots, term=3):\n",
-    "    # Processing input and output arrays\n",
-    "    x = np.asarray(variable)               # Converting input variable to another array\n",
-    "    V = np.empty((x.shape[0], len(knots)))   # Creating the output spline array\n",
-    "\n",
-    "    # Generating each spline with it's corresponding term\n",
-    "    for i in range(len(knots)):\n",
-    "        V[:, i] = np.where(x > knots[i], (x - knots[i])**term, 0)\n",
-    "        V[:, i] = np.where(np.isnan(x), np.nan, V[:, i])\n",
-    "\n",
-    "    # Return splines\n",
-    "    return V"
+    "# Defining specifications for splines to built the additive design matrix\n",
+    "knot_locs = np.percentile(d['times'], q=np.linspace(2.5, 97.5, 11)).tolist()\n",
+    "specs = [None,                                               # Spec for 1st term (intercept)\n",
+    "         {\"knots\": knot_locs, \"penalty\": 2000, \"power\": 3}]  # Spec for 2nd term (times)\n",
+    "\n",
+    "# Defining the estimating equation\n",
+    "def psi(theta):\n",
+    "    return ee_additive_regression(theta=theta, \n",
+    "                                  X=X, y=y, \n",
+    "                                  model='linear',\n",
+    "                                  specifications=specs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "0e2853c8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "knots = [-4, -2, -1, 0, 1, 2, 4]\n",
-    "cols = ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7']\n",
-    "\n",
-    "d[cols] = spline(d['X'], knots=knots, term=3)"
+    "estr = MEstimator(psi, init=[0, 0, ] + [0, ]*10)\n",
+    "estr.estimate(solver='lm', maxiter=2000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "eb2e6d1e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "y = np.asarray(d['Y'])\n",
-    "X = np.asarray(d[['C', 'X'] + cols])"
+    "# Creating the corresponding additive design matrix\n",
+    "Xpa = additive_design_matrix(p[['intercept', 'times']],\n",
+    "                            specifications=specs)\n",
+    "\n",
+    "# Generating predicted values\n",
+    "pred_y = regression_predictions(Xpa, estr.theta, estr.variance)\n",
+    "\n",
+    "# Plot\n",
+    "plt.plot(d['times'], d['accel'], '.', color='k')\n",
+    "plt.plot(p['times'], pred_y[:, 0], color='orange')\n",
+    "plt.fill_between(p['times'],\n",
+    "                 pred_y[:, 2], pred_y[:, 3],\n",
+    "                 color='orange', alpha=0.3)\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Acceleration (g)\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0589e23a",
+   "id": "6b6d3264",
    "metadata": {},
    "source": [
-    "### Penalized Regression\n",
+    "Here, we can see that the GAM does a much better job of following the shape of the scatterplot.\n",
     "\n",
-    "GAM use penalized regression. Here, we will use $L_2$ penalization for our GAM. For that, we can use the built-in estimating equations for Ridge regression in `delicatessen`. Importantly, we will *not* be penalizing the intercept but all other terms will be penalized."
+    "## Impact of the Penalty\n",
+    "To show how the penalty impacts the wiggliness of the GAM, let's increase the GAM penalty by a large amount. As this penalty is only applied to the spline terms, we would expect to see the regression line become smoother. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "3c7c9345",
+   "execution_count": 10,
+   "id": "0922047a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "# Defining specifications for splines to built the additive design matrix\n",
+    "knot_locs = np.percentile(d['times'], q=np.linspace(2.5, 97.5, 11)).tolist()\n",
+    "specs = [None,                                               # Spec for 1st term (intercept)\n",
+    "         {\"knots\": knot_locs, \"penalty\": 1000000, \"power\": 3}]  # Spec for 2nd term (times)\n",
+    "\n",
+    "# Defining the estimating equation\n",
     "def psi(theta):\n",
-    "    return ee_ridge_regression(theta=theta,\n",
-    "                               y=y,\n",
-    "                               X=X,\n",
-    "                               model='linear',\n",
-    "                               penalty=[0, ] + [2, ]*8)"
+    "    return ee_additive_regression(theta=theta, \n",
+    "                                  X=X, y=y, \n",
+    "                                  model='linear',\n",
+    "                                  specifications=specs)\n",
+    "\n",
+    "estr = MEstimator(psi, init=[0, 0, ] + [0, ]*10)\n",
+    "estr.estimate(solver='lm', maxiter=2000)\n",
+    "\n",
+    "# Creating the corresponding additive design matrix\n",
+    "Xpa = additive_design_matrix(p[['intercept', 'times']],\n",
+    "                            specifications=specs)\n",
+    "\n",
+    "# Generating predicted values\n",
+    "pred_y = regression_predictions(Xpa, estr.theta, estr.variance)\n",
+    "\n",
+    "# Plot\n",
+    "plt.plot(d['times'], d['accel'], '.', color='k')\n",
+    "plt.plot(p['times'], pred_y[:, 0], color='orange')\n",
+    "plt.fill_between(p['times'],\n",
+    "                 pred_y[:, 2], pred_y[:, 3],\n",
+    "                 color='orange', alpha=0.3)\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Acceleration (g)\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6b6d3264",
+   "id": "82ea0de2",
    "metadata": {},
    "source": [
-    "### Estimation\n",
-    "\n",
-    "Finally, we can estimate the GAM. Here, we use the Levenberg-Marquette root-finding algorithm."
+    "Here, we can see the estimated function from the GAM is smoother than the previous GAM. To continue along this trend, let's crank up the penalty further."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "0922047a",
+   "execution_count": 11,
+   "id": "c7c3fb6c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "estr = MEstimator(psi, init=[0., ]*9)\n",
-    "estr.estimate(solver='lm')"
+    "# Defining specifications for splines to built the additive design matrix\n",
+    "knot_locs = np.percentile(d['times'], q=np.linspace(2.5, 97.5, 11)).tolist()\n",
+    "specs = [None,                                               # Spec for 1st term (intercept)\n",
+    "         {\"knots\": knot_locs, \"penalty\": 1e11, \"power\": 3}]  # Spec for 2nd term (times)\n",
+    "\n",
+    "# Defining the estimating equation\n",
+    "def psi(theta):\n",
+    "    return ee_additive_regression(theta=theta, \n",
+    "                                  X=X, y=y, \n",
+    "                                  model='linear',\n",
+    "                                  specifications=specs)\n",
+    "\n",
+    "estr = MEstimator(psi, init=[0, 0, ] + [0, ]*10)\n",
+    "estr.estimate(solver='lm', maxiter=2000)\n",
+    "\n",
+    "# Creating the corresponding additive design matrix\n",
+    "Xpa = additive_design_matrix(p[['intercept', 'times']],\n",
+    "                            specifications=specs)\n",
+    "\n",
+    "# Generating predicted values\n",
+    "pred_y = regression_predictions(Xpa, estr.theta, estr.variance)\n",
+    "\n",
+    "# Plot\n",
+    "plt.plot(d['times'], d['accel'], '.', color='k')\n",
+    "plt.plot(p['times'], pred_y[:, 0], color='orange')\n",
+    "plt.fill_between(p['times'],\n",
+    "                 pred_y[:, 2], pred_y[:, 3],\n",
+    "                 color='orange', alpha=0.3)\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Acceleration (g)\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "82ea0de2",
+   "id": "ddfa16f1",
    "metadata": {},
    "source": [
-    "## Results\n",
+    "Here, the GAM is very similar to the linear regression we started with. This helps to show the more general relationship: as the penalty goes to infinity, the GAM will coincide with the linear model from before. \n",
+    "\n",
+    "To summarize, we want a penalty that prevents overfitting to the data but does not prevent the GAM from approximating the data. This selection of penalization is commonly done via cross-validation. However, use of cross-validation invalidates the variance estimation (including with the sandwich variance estimator). Therefore, I would recommend sacrificing the optimal penalty parameter (as determined by cross-validation) in favor of variance estimation with `delicatessen`.\n",
+    "\n",
+    "## GAM by-hand\n",
+    "GAMs can also be done by-hand with `delicatessen`. This allows for flexibility in specifications beyond the `ee_additive_regression` options or use with other models. Here, we will reapply the GAM from before.\n",
     "\n",
-    "As the estimated parameters of a GAM can be difficult to interpret, the following is a plot of the estimated relationship from the GAM. As we will see in the plot, the GAM closely approximates the true underlying function. How well the GAM approximates the true relationship will depend on the chosen knot locations and the strength of the penalty term."
+    "First, we will use the ``additive_design_matrix`` function to generate the corresponding design matrix with splines. We will also have the penalty returned into the `penalty` object. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "0e252eaf",
+   "execution_count": 12,
+   "id": "c028e924",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0,\n",
+       " 0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "p = pd.DataFrame()\n",
-    "p['X'] = np.linspace(-5, 5, 100)\n",
-    "p['C'] = 1\n",
-    "p[cols] = spline(p['X'], knots=knots, term=3)\n",
-    "Xp = np.asarray(p[['C', 'X'] + cols])\n",
-    "\n",
-    "y_pred = np.dot(Xp, estr.theta)\n"
+    "# Setting up the design matrix\n",
+    "knot_locs = np.percentile(d['times'], q=np.linspace(2.5, 97.5, 11)).tolist()\n",
+    "specs = [None,                                               # Spec for 1st term (intercept)\n",
+    "         {\"knots\": knot_locs, \"penalty\": 2e3, \"power\": 3}]  # Spec for 2nd term (times)\n",
+    "Xa, penalty = additive_design_matrix(d[['intercept', 'times']],\n",
+    "                                     specifications=specs,\n",
+    "                                     return_penalty=True)\n",
+    "penalty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87c0d3e5",
+   "metadata": {},
+   "source": [
+    "While the penalty could be specified by-hand, the `additive_design_matrix` also makes it easy to get the penalty. To show the advantage of by-hand implementations, we will adjust the penalty for the later spline terms. Specifically, we will apply a stronger penalty"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "46095efa",
+   "execution_count": 13,
+   "id": "e4d5815b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x255fff0c970>"
+       "[0,\n",
+       " 0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 2000.0,\n",
+       " 50000.0,\n",
+       " 50000.0,\n",
+       " 50000.0,\n",
+       " 50000.0,\n",
+       " 50000.0]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "for i in [7, 8, 9, 10, 11]:\n",
+    "    penalty[i] = 50000.\n",
+    "    \n",
+    "penalty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fffda61",
+   "metadata": {},
+   "source": [
+    "To implement the GAM, we will now use the built-in estimating equation for Ridge regression. This is the same penalization scheme used by the built-in GAM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "46095efa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def psi(theta):\n",
+    "    return ee_ridge_regression(theta=theta,\n",
+    "                               X=Xa, y=y, model='linear',\n",
+    "                               penalty=penalty)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8ba869e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estr = MEstimator(psi, init=[0, 0, ] + [0, ]*10)\n",
+    "estr.estimate(solver='lm', maxiter=2000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b7e15c23",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "<Figure size 1200x800 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -318,9 +530,22 @@
     }
    ],
    "source": [
-    "plt.plot(p['X'], np.exp(np.sin(p['X'] + 0.5)) + np.abs(p['X']), '--', color='k', label='True')\n",
-    "plt.plot(p['X'], y_pred, '-', color='blue', label='GAM')\n",
-    "plt.legend()"
+    "# Creating the corresponding additive design matrix\n",
+    "Xpa = additive_design_matrix(p[['intercept', 'times']],\n",
+    "                            specifications=specs)\n",
+    "\n",
+    "# Generating predicted values\n",
+    "pred_y = regression_predictions(Xpa, estr.theta, estr.variance)\n",
+    "\n",
+    "# Plot\n",
+    "plt.plot(d['times'], d['accel'], '.', color='k')\n",
+    "plt.plot(p['times'], pred_y[:, 0], color='orange')\n",
+    "plt.fill_between(p['times'],\n",
+    "                 pred_y[:, 2], pred_y[:, 3],\n",
+    "                 color='orange', alpha=0.3)\n",
+    "plt.xlabel(\"Time (ms)\")\n",
+    "plt.ylabel(\"Acceleration (g)\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
@@ -328,9 +553,15 @@
    "id": "af4fd56a",
    "metadata": {},
    "source": [
+    "Here, the end of the function (time > 40) is smoother than the first GAM but still allows for flexibility at the start. This provides a simple illustration of how GAMs can be implemented by-hand using custom penalities.\n",
+    "\n",
     "## Conclusions\n",
     "\n",
-    "GAMs are a flexible method that place fewer constraints on the functional forms of relationships via splines. These splines don't require us to know the true underlying functional form. Instead, the higher-order splines (with well-chosen knots) can serve as close approximations. To implement GAMs, `delicatessen` can easily be used with a little pre-processing of the input data."
+    "GAMs are a flexible method that place fewer constraints on the functional forms of relationships via splines. These splines don't require us to know the true underlying functional form. Instead, the higher-order splines (with well-chosen knots) can serve as close approximations. To implement GAMs, `delicatessen` can easily implemented with a built-in functionality or by-hand using built-in pre-processing functions.\n",
+    "\n",
+    "### References\n",
+    "\n",
+    "Silverman BW. (1985). Some aspects of the spline smoothing approach to non‐parametric regression curve fitting. *Journal of the Royal Statistical Society: Series B (Methodological)*, 47(1), 1-21."
    ]
   }
  ],
diff --git a/examples/LifeScienceExamples.ipynb b/examples/LifeScienceExamples.ipynb
index 1fa8335..77e7268 100644
--- a/examples/LifeScienceExamples.ipynb
+++ b/examples/LifeScienceExamples.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "The following is the code to replicate the examples provided in Zivich PN, Klose M, Cole SR, Edwards JK, & Shook-Sa BE. (2022). Delicatessen: M-Estimation in Python. arXiv preprint arXiv:2203.11300.\n",
     "\n",
-    "Code written by Paul Zivich (last update: 2022/10/12)"
+    "Code written by Paul Zivich"
    ]
   },
   {
@@ -26,7 +26,7 @@
       "SciPy version:        1.9.2\n",
       "Pandas version:       1.4.1\n",
       "Matplotlib version:   3.5.1\n",
-      "Delicatessen version: 1.0\n"
+      "Delicatessen version: 1.1\n"
      ]
     }
    ],
diff --git a/examples/Morris-Trials-2022.ipynb b/examples/Morris-Trials-2022.ipynb
index f10297c..d8c235c 100644
--- a/examples/Morris-Trials-2022.ipynb
+++ b/examples/Morris-Trials-2022.ipynb
@@ -26,7 +26,7 @@
       "Versions\n",
       "NumPy:         1.22.2\n",
       "pandas:        1.4.1\n",
-      "Delicatessen:  1.0\n"
+      "Delicatessen:  1.1\n"
      ]
     }
    ],
@@ -53,32 +53,7 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.9.4\\lib\\site-packages\\pandas\\compat\\_optional.py:126\u001b[0m, in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, errors, min_version)\u001b[0m\n\u001b[0;32m    125\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 126\u001b[0m     module \u001b[38;5;241m=\u001b[39m \u001b[43mimportlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    127\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
-      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.9.4\\lib\\importlib\\__init__.py:127\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m    126\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m<frozen importlib._bootstrap>:1030\u001b[0m, in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
-      "File \u001b[1;32m<frozen importlib._bootstrap>:1007\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
-      "File \u001b[1;32m<frozen importlib._bootstrap>:984\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'xlrd'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_excel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata/GetTestedData.xls\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msheet_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Subsetting the desired columns\u001b[39;00m\n\u001b[0;32m      4\u001b[0m cols \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgroup\u001b[39m\u001b[38;5;124m'\u001b[39m,      \u001b[38;5;66;03m# Randomized arm\u001b[39;00m\n\u001b[0;32m      5\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124manytest\u001b[39m\u001b[38;5;124m'\u001b[39m,    \u001b[38;5;66;03m# Outcome 1 (any test)\u001b[39;00m\n\u001b[0;32m      6\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124manydiag\u001b[39m\u001b[38;5;124m'\u001b[39m,    \u001b[38;5;66;03m# Outcome 2 (any diagnosis)\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpartners\u001b[39m\u001b[38;5;124m'\u001b[39m,   \u001b[38;5;66;03m# Number of partners in <12 months\u001b[39;00m\n\u001b[0;32m     11\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124methnicgrp\u001b[39m\u001b[38;5;124m'\u001b[39m]  \u001b[38;5;66;03m# Ethnicity (5 categories)\u001b[39;00m\n",
-      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.9.4\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    306\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    307\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[0;32m    308\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    309\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m    310\u001b[0m     )\n\u001b[1;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.9.4\\lib\\site-packages\\pandas\\io\\excel\\_base.py:457\u001b[0m, in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, decimal, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(io, ExcelFile):\n\u001b[0;32m    456\u001b[0m     should_close \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 457\u001b[0m     io \u001b[38;5;241m=\u001b[39m \u001b[43mExcelFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mio\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    458\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;129;01mand\u001b[39;00m engine \u001b[38;5;241m!=\u001b[39m io\u001b[38;5;241m.\u001b[39mengine:\n\u001b[0;32m    459\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    460\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEngine should not be specified when passing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    461\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    462\u001b[0m     )\n",
-      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.9.4\\lib\\site-packages\\pandas\\io\\excel\\_base.py:1419\u001b[0m, in \u001b[0;36mExcelFile.__init__\u001b[1;34m(self, path_or_buffer, engine, storage_options)\u001b[0m\n\u001b[0;32m   1416\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m=\u001b[39m engine\n\u001b[0;32m   1417\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage_options \u001b[38;5;241m=\u001b[39m storage_options\n\u001b[1;32m-> 1419\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engines\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.9.4\\lib\\site-packages\\pandas\\io\\excel\\_xlrd.py:24\u001b[0m, in \u001b[0;36mXlrdReader.__init__\u001b[1;34m(self, filepath_or_buffer, storage_options)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;124;03mReader using xlrd engine.\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;124;03m    passed to fsspec for appropriate URLs (see ``_get_filepath_or_buffer``)\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     23\u001b[0m err_msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInstall xlrd >= 1.0.0 for Excel support\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 24\u001b[0m \u001b[43mimport_optional_dependency\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mxlrd\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merr_msg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(filepath_or_buffer, storage_options\u001b[38;5;241m=\u001b[39mstorage_options)\n",
-      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.9.4\\lib\\site-packages\\pandas\\compat\\_optional.py:129\u001b[0m, in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, errors, min_version)\u001b[0m\n\u001b[0;32m    127\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[0;32m    128\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 129\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[0;32m    130\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    131\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "\u001b[1;31mImportError\u001b[0m: Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "d = pd.read_excel(\"data/GetTestedData.xls\", sheet_name=1)\n",
     "\n",
@@ -138,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "bbb82143",
    "metadata": {},
    "outputs": [],
@@ -198,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f8b6e272",
    "metadata": {},
    "outputs": [],
@@ -235,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "5f148316",
    "metadata": {},
    "outputs": [],
@@ -247,10 +222,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "c020844a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Risk Difference: 0.260 (0.021)\n",
+      "Risk Ratio:      0.795 (0.075)\n"
+     ]
+    }
+   ],
    "source": [
     "std = np.sqrt(np.diag(estr.variance))\n",
     "fmt = '{:.3f}'\n",
@@ -287,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "7386a203",
    "metadata": {},
    "outputs": [],
@@ -327,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "bff602f0",
    "metadata": {},
    "outputs": [],
@@ -338,10 +322,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "1ddb5cdc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Risk Difference: 0.261 (0.021)\n",
+      "Risk Ratio:      0.805 (0.075)\n"
+     ]
+    }
+   ],
    "source": [
     "std = np.sqrt(np.diag(estr.variance))\n",
     "fmt = '{:.3f}'\n",


Test patch:
diff --git a/tests/test_estimating_equations.py b/tests/test_estimating_equations.py
index add855d..3bc0a08 100644
--- a/tests/test_estimating_equations.py
+++ b/tests/test_estimating_equations.py
@@ -11,6 +11,7 @@
 from delicatessen.estimating_equations import (ee_mean, ee_mean_variance, ee_mean_robust,
                                                # Regression models
                                                ee_regression, ee_robust_regression, ee_ridge_regression,
+                                               ee_additive_regression,
                                                # Survival models
                                                ee_exponential_model, ee_exponential_measure, ee_weibull_model,
                                                ee_weibull_measure, ee_aft_weibull, ee_aft_weibull_measure,
@@ -19,6 +20,8 @@
                                                # Causal inference
                                                ee_gformula, ee_ipw, ee_aipw)
 from delicatessen.data import load_inderjit
+from delicatessen.utilities import additive_design_matrix
+
 
 np.random.seed(236461)
 
@@ -134,6 +137,41 @@ def psi(theta):
         with pytest.raises(ValueError, match="Invalid input"):
             estr.estimate(solver='lm')
 
+    def test_error_robust(self):
+        n = 1000
+        data = pd.DataFrame()
+        data['x1'] = np.random.normal(size=n)
+        data['x2'] = data['x1'] + np.random.normal(scale=0.1, size=n)
+        data['c'] = 1
+        data['y'] = 5 + data['x1'] + np.random.normal(size=n)
+        Xvals = np.asarray(data[['c', 'x1', 'x2']])
+        yvals = np.asarray(data['y'])
+
+        def psi(theta):
+            return ee_robust_regression(theta, X=Xvals, y=yvals, model='logistic', k=5)
+
+        estr = MEstimator(psi, init=[5, 1, 1])
+        with pytest.raises(ValueError, match="only supports linear"):
+            estr.estimate(solver='lm')
+
+    def test_error_penalized(self):
+        n = 1000
+        data = pd.DataFrame()
+        data['x1'] = np.random.normal(size=n)
+        data['x2'] = data['x1'] + np.random.normal(scale=0.1, size=n)
+        data['c'] = 1
+        data['y'] = 5 + data['x1'] + np.random.normal(size=n)
+        Xvals = np.asarray(data[['c', 'x1', 'x2']])
+        yvals = np.asarray(data['y'])
+
+        def psi(theta):
+            return ee_ridge_regression(theta, X=Xvals, y=yvals, model='linear',
+                                       penalty=[0.5, 5.], weights=None)
+
+        estr = MEstimator(psi, init=[5, 1, 1])
+        with pytest.raises(ValueError, match="The penalty term must"):
+            estr.estimate(solver='lm')
+
     def test_ols(self):
         """Tests linear regression with the built-in estimating equation.
         """
@@ -313,23 +351,60 @@ def psi(theta):
                             np.asarray(wridge.params),
                             atol=1e-6)
 
-    def test_error_ridge(self):
+    def test_additive_ols(self):
         n = 1000
         data = pd.DataFrame()
         data['x1'] = np.random.normal(size=n)
-        data['x2'] = data['x1'] + np.random.normal(scale=0.1, size=n)
+        data['x2'] = np.random.normal(scale=0.1, size=n)
         data['c'] = 1
-        data['y'] = 5 + data['x1'] + np.random.normal(size=n)
+        data['y'] = 5 + data['x1'] + data['x2'] + 0.01*data['x2'] + np.random.normal(size=n)
         Xvals = np.asarray(data[['c', 'x1', 'x2']])
         yvals = np.asarray(data['y'])
+        spec = [None, {"knots": [-1, 0, 1], "penalty": 3}, {"knots": [-2, -1, 0, 1, 2], "penalty": 5}]
 
+        # Testing array of penalty terms
         def psi(theta):
-            return ee_ridge_regression(theta, X=Xvals, y=yvals, model='linear',
-                                       penalty=[0.5, 5.], weights=None)
+            return ee_additive_regression(theta, X=Xvals, y=yvals, model='linear', specifications=spec)
 
-        estr = MEstimator(psi, init=[5, 1, 1])
-        with pytest.raises(ValueError, match="The penalty term must"):
-            estr.estimate(solver='lm')
+        estr = MEstimator(psi, init=[5, 1, 0, 0, 1, 0, 0, 0, 0])
+        estr.estimate(solver='lm')
+
+        design, penalty = additive_design_matrix(X=Xvals, specifications=spec, return_penalty=True)
+        ridge = sm.OLS(yvals, design).fit_regularized(L1_wt=0., alpha=np.array(penalty)/Xvals.shape[0])
+
+        # Checking mean estimate
+        npt.assert_allclose(estr.theta,
+                            np.asarray(ridge.params),
+                            atol=1e-6)
+
+    def test_additive_wls(self):
+        n = 1000
+        data = pd.DataFrame()
+        data['x1'] = np.random.normal(size=n)
+        data['x2'] = np.random.normal(scale=0.1, size=n)
+        data['c'] = 1
+        data['y'] = 5 + data['x1'] + data['x2'] + 0.01*data['x2'] + np.random.normal(size=n)
+        Xvals = np.asarray(data[['c', 'x1', 'x2']])
+        yvals = np.asarray(data['y'])
+        spec = [None, {"knots": [-1, 0, 1], "penalty": 3}, {"knots": [-2, -1, 0, 1, 2], "penalty": 5}]
+        weights = np.random.uniform(0.1, 2.5, size=n)
+
+        # Testing array of penalty terms
+        def psi(theta):
+            return ee_additive_regression(theta, X=Xvals, y=yvals, model='linear',
+                                          specifications=spec, weights=weights)
+
+        estr = MEstimator(psi, init=[5, 1, 0, 0, 1, 0, 0, 0, 0])
+        estr.estimate(solver='lm')
+
+        design, penalty = additive_design_matrix(X=Xvals, specifications=spec, return_penalty=True)
+        ridge = sm.WLS(yvals, design, weights=weights).fit_regularized(L1_wt=0.,
+                                                                       alpha=np.array(penalty)/Xvals.shape[0])
+
+        # Checking mean estimate
+        npt.assert_allclose(estr.theta,
+                            np.asarray(ridge.params),
+                            atol=1e-6)
 
     def test_logistic(self):
         n = 1000
@@ -465,6 +540,35 @@ def psi_regression(theta):
                             np.asarray(lgt.params),
                             atol=1e-4)
 
+    def test_additive_logistic(self):
+        n = 1000
+        data = pd.DataFrame()
+        data['X'] = np.random.normal(size=n)
+        data['Z'] = np.random.normal(size=n)
+        data['Y'] = np.random.binomial(n=1, p=logistic.cdf(0.5 + 2*data['X'] - 1*data['Z']), size=n)
+        data['C'] = 1
+        Xvals = np.asarray(data[['C', 'X', 'Z']])
+        yvals = np.asarray(data['Y'])
+        spec = [None, {"knots": [-1, 0, 1], "penalty": 3}, {"knots": [-2, -1, 0, 1, 2], "penalty": 5}]
+
+        def psi_regression(theta):
+            return ee_additive_regression(theta, X=Xvals, y=yvals,
+                                          model='logistic',
+                                          specifications=spec)
+
+        mestimator = MEstimator(psi_regression, init=[0., 2., 0., 0., 1., 0., 0., 0., 0.])
+        mestimator.estimate(solver='lm', tolerance=1e-14)
+
+        design, penalty = additive_design_matrix(X=Xvals, specifications=spec, return_penalty=True)
+        f = sm.families.Binomial()
+        lgt = sm.GLM(yvals, design, family=f).fit_regularized(L1_wt=0., alpha=np.array(penalty)/Xvals.shape[0],
+                                                              tol=1e-14)
+
+        # Checking mean estimate
+        npt.assert_allclose(mestimator.theta,
+                            np.asarray(lgt.params),
+                            atol=1e-3)
+
     def test_poisson(self):
         """Tests Poisson regression by-hand with a single estimating equation.
         """
@@ -662,6 +766,34 @@ def psi_regression(theta):
                             np.asarray(lgt.params),
                             atol=5e-4)
 
+    def test_additive_poisson(self):
+        n = 1000
+        data = pd.DataFrame()
+        data['X'] = np.random.normal(size=n)
+        data['Z'] = np.random.normal(size=n)
+        data['Y'] = np.random.poisson(lam=np.exp(1 + 2*data['X'] - 1*data['Z']), size=n)
+        data['C'] = 1
+        Xvals = np.asarray(data[['C', 'X', 'Z']])
+        yvals = np.asarray(data['Y'])
+        spec = [None, {"knots": [-1, 0, 1], "penalty": 3}, {"knots": [-2, -1, 0, 1, 2], "penalty": 5}]
+
+        def psi_regression(theta):
+            return ee_additive_regression(theta, X=Xvals, y=yvals,
+                                          model='poisson',
+                                          specifications=spec)
+
+        mestimator = MEstimator(psi_regression, init=[0., 2., 0., 0., 1., 0., 0., 0., 0.])
+        mestimator.estimate(solver='lm', tolerance=1e-12)
+
+        design, penalty = additive_design_matrix(X=Xvals, specifications=spec, return_penalty=True)
+        f = sm.families.Poisson()
+        lgt = sm.GLM(yvals, design, family=f).fit_regularized(L1_wt=0., alpha=np.array(penalty)/Xvals.shape[0])
+
+        # Checking mean estimate
+        npt.assert_allclose(mestimator.theta,
+                            np.asarray(lgt.params),
+                            atol=1e-5)
+
 
 class TestEstimatingEquationsSurvival:
 
diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index 0673ef6..4a0c3b2 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -1,12 +1,39 @@
 import pytest
 import numpy as np
 import numpy.testing as npt
+import pandas as pd
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+from scipy.stats import logistic
 
-from delicatessen.utilities import inverse_logit, logit, robust_loss_functions
+from delicatessen import MEstimator
+from delicatessen.estimating_equations import ee_regression
+from delicatessen.utilities import (identity, inverse_logit, logit,
+                                    robust_loss_functions,
+                                    regression_predictions,
+                                    spline,
+                                    additive_design_matrix)
+
+np.random.seed(80958151)
+
+
+@pytest.fixture
+def design_matrix():
+    array = np.array([[1, 1, 1, 1, 1],
+                      [1, 5, 10, 15, 20],
+                      [1, 2, 3, 4, 5], ])
+    return array.T
 
 
 class TestFunctions:
 
+    def test_identity_var(self):
+        """Checks the logit transformation of a single observation
+        """
+        npt.assert_allclose(identity(0.5), 0.5)
+        npt.assert_allclose(identity(0.25), 0.25)
+        npt.assert_allclose(identity(0.75), 0.75)
+
     def test_logit_var(self):
         """Checks the logit transformation of a single observation
         """
@@ -41,6 +68,12 @@ def test_logit_backtransform(self):
         npt.assert_allclose(logit(inverse_logit(logit(inverse_logit(logit(inverse_logit(original_logit)))))),
                             original_logit)
 
+    def test_identity_array(self):
+        """Checks the inverse logit transformation of an array
+        """
+        vals = np.array([0.5, 0.25, 0.75, 0.5])
+        npt.assert_allclose(identity(vals), vals)
+
     def test_logit_array(self):
         """Checks the inverse logit transformation of an array
         """
@@ -117,3 +150,260 @@ def test_rloss_hampel(self):
                            (4 - 3)/(4 - 2)*1])
 
         npt.assert_allclose(func, byhand)
+
+    def test_rloss_hampel_error(self):
+        residuals = np.array([-5, 1, 1.5, -1.3, 0.1, -2, 8, 3])
+
+        # All parameters are specified
+        with pytest.raises(ValueError, match="requires the optional"):
+            robust_loss_functions(residual=residuals, loss='hampel',
+                                  k=4)
+
+        # Ordering of parameters
+        with pytest.raises(ValueError, match="requires that a < b < k"):
+            robust_loss_functions(residual=residuals, loss='hampel',
+                                  k=-4, a=1, b=2)
+        with pytest.raises(ValueError, match="requires that a < b < k"):
+            robust_loss_functions(residual=residuals, loss='hampel',
+                                  k=4, a=1, b=-2)
+        with pytest.raises(ValueError, match="requires that a < b < k"):
+            robust_loss_functions(residual=residuals, loss='hampel',
+                                  k=1.5, a=1, b=2)
+
+    def test_regression_predictions_linear(self):
+        n = 500
+        data = pd.DataFrame()
+        data['X'] = np.random.normal(size=n)
+        data['Y'] = 0.5 + 2*data['X'] + np.random.normal(loc=0, size=n)
+        data['C'] = 1
+
+        p = pd.DataFrame()
+        p['X'] = np.linspace(-2.5, 2.5, 20)
+        p['C'] = 1
+
+        def psi(theta):
+            return ee_regression(theta,
+                                 X=data[['C', 'X']], y=data['Y'],
+                                 model='linear')
+
+        estr = MEstimator(psi, init=[0., 0.])
+        estr.estimate(solver='lm')
+        returned = regression_predictions(X=p[['C', 'X']], theta=estr.theta, covariance=estr.variance)
+
+        # Statsmodels function equivalent
+        glm = smf.glm("Y ~ X", data).fit(cov_type="HC1")
+        expected = np.asarray(glm.get_prediction(p).summary_frame())
+        expected[:, 1] = expected[:, 1]**2
+
+        npt.assert_allclose(returned, expected, atol=1e-6)
+
+    def test_regression_predictions_logit(self):
+        n = 1000
+        data = pd.DataFrame()
+        data['X'] = np.random.normal(size=n)
+        data['Y'] = np.random.binomial(n=1, p=logistic.cdf(-0.5 + 2*data['X']), size=n)
+        data['C'] = 1
+
+        p = pd.DataFrame()
+        p['X'] = np.linspace(-2.5, 2.5, 20)
+        p['C'] = 1
+
+        def psi(theta):
+            return ee_regression(theta,
+                                 X=data[['C', 'X']], y=data['Y'],
+                                 model='logistic')
+
+        estr = MEstimator(psi, init=[0., 0.])
+        estr.estimate(solver='lm')
+        returned = regression_predictions(X=p[['C', 'X']], theta=estr.theta, covariance=estr.variance)
+
+        # Comparing to statsmodels GLM (with robust covariance)
+        glm = smf.glm("Y ~ X", data, family=sm.families.Binomial()).fit(cov_type="HC1")
+        expected = np.asarray(glm.get_prediction(p).summary_frame())
+
+        npt.assert_allclose(returned[:, 0], logit(expected[:, 0]), atol=1e-6)
+        npt.assert_allclose(returned[:, 2], logit(expected[:, 2]), atol=1e-6)
+        npt.assert_allclose(returned[:, 3], logit(expected[:, 3]), atol=1e-6)
+
+    def test_regression_predictions_poisson(self):
+        n = 500
+        data = pd.DataFrame()
+        data['X'] = np.random.normal(size=n)
+        data['Y'] = np.random.poisson(lam=np.exp(-1 + 2*data['X']), size=n)
+        data['C'] = 1
+
+        p = pd.DataFrame()
+        p['X'] = np.linspace(-2.5, 2.5, 20)
+        p['C'] = 1
+
+        def psi(theta):
+            return ee_regression(theta,
+                                 X=data[['C', 'X']], y=data['Y'],
+                                 model='poisson')
+
+        estr = MEstimator(psi, init=[0., 0.])
+        estr.estimate(solver='lm')
+        returned = regression_predictions(X=p[['C', 'X']], theta=estr.theta, covariance=estr.variance)
+
+        # Comparing to statsmodels GLM (with robust covariance)
+        glm = smf.glm("Y ~ X", data, family=sm.families.Poisson()).fit(cov_type="HC1")
+        expected = np.asarray(glm.get_prediction(p).summary_frame())
+
+        npt.assert_allclose(returned[:, 0], np.log(expected[:, 0]), atol=1e-6)
+        npt.assert_allclose(returned[:, 2], np.log(expected[:, 2]), atol=1e-6)
+        npt.assert_allclose(returned[:, 3], np.log(expected[:, 3]), atol=1e-6)
+
+    def test_spline1(self):
+        vars = [1, 5, 10, 15, 20]
+
+        # Spline setup 1
+        expected = np.array([[0.0, 0.0, 0.0, 0.0, 4.0], ]).T
+        returned = spline(variable=vars, knots=[16, ], power=1, restricted=False)
+        npt.assert_allclose(returned, expected)
+
+        # Spline setup 2
+        expected = np.array([[0.0, 0.0, 0.0, 0.0, 16.0], ]).T
+        returned = spline(variable=vars, knots=[16, ], power=2, restricted=False)
+        npt.assert_allclose(returned, expected)
+
+        # Spline setup 3
+        expected = np.array([[0.0, 0.0, 0.0, 5.0**1.5, 10.0**1.5],
+                             [0.0, 0.0, 0.0, 0.0, 4.0**1.5]]).T
+        returned = spline(variable=vars, knots=[10, 16], power=1.5, restricted=False)
+        npt.assert_allclose(returned, expected)
+
+        # Spline setup 4
+        expected = np.array([[0.0, 0.0, 0.0, 5.0, 6.0], ]).T
+        returned = spline(variable=vars, knots=[10, 16], power=1, restricted=True)
+        npt.assert_allclose(returned, expected)
+
+        # Spline setup 5
+        expected = np.array([[0.0, 0.0, 5.0**2, 10.0**2, 15.0**2 - 4.0**2], ]).T
+        returned = spline(variable=vars, knots=[5, 16], power=2, restricted=True)
+        npt.assert_allclose(returned, expected)
+
+    def test_adm_error_noknots(self, design_matrix):
+        # Testing error when dictionary with no knots given
+        specs = [None,
+                 {"power": 2},
+                 None]
+        with pytest.raises(ValueError, match="`knots` must be"):
+            additive_design_matrix(X=design_matrix,
+                                   specifications=specs,
+                                   return_penalty=False)
+
+    def test_adm_error_negknots(self, design_matrix):
+        # Testing error when dictionary with negative number of knots given
+        specs = [None,
+                 {"knots": 2},
+                 None]
+        with pytest.raises(TypeError, match="not iterable"):
+            additive_design_matrix(X=design_matrix,
+                                   specifications=specs,
+                                   return_penalty=False)
+
+    def test_adm_error_misalign(self, design_matrix):
+        # Testing error matrix is misaligned
+        # Testing warning for extra spline arguments
+        specs = [None,
+                 {"knots": [-1, 1]},
+                 None]
+        with pytest.raises(ValueError, match="number of input"):
+            additive_design_matrix(X=design_matrix.T,
+                                   specifications=specs,
+                                   return_penalty=False)
+
+    def test_adm_warn_extras(self, design_matrix):
+        # Testing warning for extra spline arguments
+        specs = [None,
+                 {"knots": [-1, 1], "extra": 4},
+                 None]
+        with pytest.warns(UserWarning, match="following keys"):
+            additive_design_matrix(X=design_matrix,
+                                   specifications=specs,
+                                   return_penalty=False)
+
+    def test_adm_defaults(self, design_matrix):
+        # Testing default additive design matrix specifications
+        specs = [None,
+                 {"knots": [5, 16]},
+                 {"knots": [2, 4]}]
+        expected_matrix = np.array([[1, 1, 1, 1, 1],
+                                    [1, 5, 10, 15, 20],
+                                    [0.0, 0.0, 5.0**3, 10.0**3, 15.0**3 - 4.0**3],
+                                    [1, 2, 3, 4, 5],
+                                    [0, 0, 1, 2**3, 3**3 - 1]]).T
+        expected_penalty = np.array([0, 0, 0, 0, 0])
+        returned_matrix, returned_penalty = additive_design_matrix(X=design_matrix,
+                                                                   specifications=specs,
+                                                                   return_penalty=True)
+        npt.assert_allclose(returned_matrix, expected_matrix)
+        npt.assert_allclose(returned_penalty, expected_penalty)
+
+    def test_adm_v1(self, design_matrix):
+        specs = [None,
+                 {"knots": [16, ], "power": 1, "natural": False},
+                 None]
+        expected = np.array([[1, 1, 1, 1, 1],
+                             [1, 5, 10, 15, 20],
+                             [0, 0, 0, 0, 4],
+                             [1, 2, 3, 4, 5]]).T
+        returned = additive_design_matrix(X=design_matrix,
+                                          specifications=specs,
+                                          return_penalty=False)
+        npt.assert_allclose(returned, expected)
+
+    def test_adm_v2(self, design_matrix):
+        specs = [None,
+                 {"knots": [16, ], "power": 1, "natural": False},
+                 {"knots": [2, 4], "power": 2, "natural": True}]
+        expected = np.array([[1, 1, 1, 1, 1],
+                             [1, 5, 10, 15, 20],
+                             [0, 0, 0, 0, 4],
+                             [1, 2, 3, 4, 5],
+                             [0, 0, 1, 4, 3**2 - 1]]).T
+        returned = additive_design_matrix(X=design_matrix,
+                                          specifications=specs,
+                                          return_penalty=False)
+        npt.assert_allclose(returned, expected)
+
+    def test_adm_v3(self, design_matrix):
+        # Testing non-ascending order correction for knots
+        specs = [None,
+                 {"knots": [16, ], "power": 1, "natural": False},
+                 {"knots": [4, 2], "power": 2, "natural": True}]
+        expected = np.array([[1, 1, 1, 1, 1],
+                             [1, 5, 10, 15, 20],
+                             [0, 0, 0, 0, 4],
+                             [1, 2, 3, 4, 5],
+                             [0, 0, 1, 4, 3**2 - 1]]).T
+        returned = additive_design_matrix(X=design_matrix,
+                                          specifications=specs,
+                                          return_penalty=False)
+        npt.assert_allclose(returned, expected)
+
+    def test_adm_v4(self, design_matrix):
+        # Testing number of generated rows
+        specs = [None,
+                 {"knots": [16, ], "power": 2, "natural": False},
+                 {"knots": [2, 4], "power": 1, "natural": False}]
+        expected = np.array([[1, 1, 1, 1, 1],
+                             [1, 5, 10, 15, 20],
+                             [0, 0, 0, 0, 4**2],
+                             [1, 2, 3, 4, 5],
+                             [0, 0, 1, 2, 3],
+                             [0, 0, 0, 0, 1]]).T
+        returned = additive_design_matrix(X=design_matrix,
+                                          specifications=specs,
+                                          return_penalty=False)
+        npt.assert_allclose(returned, expected)
+
+    def test_adm_penalty(self, design_matrix):
+        specs = [None,
+                 {"knots": [16, ], "power": 1, "natural": False, "penalty": 3},
+                 {"knots": [2, 3, 4], "power": 2, "natural": True, "penalty": 5}]
+        expected = np.array([0, 0, 3, 0, 5, 5])
+        design, returned = additive_design_matrix(X=design_matrix,
+                                                  specifications=specs,
+                                                  return_penalty=True)
+        npt.assert_allclose(returned, expected)