When models are nonlinear, the distributions of the coefficients may not be known and may be non-Gaussian. These methods depict the marginal univariate and bivariate distributions of the coefficients non-parametrically. Solves general maximum likelihood problem using Sequential Quadratic Programming method . Linear and nonlinear constraints on parameters . Equality and inequality contraints on parameters .
Confidence limits for constrained problems .
Bootstrapping with histogram and surface plot output . Likelihood profile trace plots . Profile t plots . Weight observations . All rights reserved.