command line stuff

1. sorting the command line
2. printing multiple paragraphs for the description of each class, if that's what the docstring contains
3. more commands

```
$ msmb -h
usage: msmb [-h]  ...

Construct models of high-dimensional time-series data.

optional arguments:
 -h, --help             show this help message and exit

commands:
 
  AtomIndices           Create index file for atoms or distance pairs.
  AtomPairsFeaturizer   Featurizer based on atom pair distances.
  ContactFeaturizer     Featurizer based on residue-residue distances.
  DRIDFeaturizer        Featurizer based on distribution of reciprocal interatomic distances (DRID).
  DihedralFeaturizer    Featurizer based on dihedral angles.
  SuperposeFeaturizer   Featurizer based on euclidian atom distances to reference structure.
  KCenters              K-Centers clustering.
  KMeans                K-Means clustering.
  KMedoids              K-Medoids clustering.
  MiniBatchKMeans       Mini-Batch K-Means clustering.
  MiniBatchKMedoids     Mini-Batch K-Medoids clustering.
  PCA                   Principal component analysis (PCA).
  tICA                  Time-structure Independent Component Analysis (tICA).
  AlanineDipeptide      Alanine dipeptide dataset.
  DoubleWell            Brownian dynamics on a 1D double well potential.
  FsPeptide             Fs peptide (implicit solvent) dataset.
  MetEnkephalin         Loader for the met-enkephalin dataset.
  QuadWell              Brownian dynamics on a 1D four well potential.
  GaussianFusionHMM     Reversible Gaussian Hidden Markov Model L1-Fusion Regularization.
  BayesianMarkovStateModel
                        Bayesian Markov state model.
```


```
$ msmb PCA -h
usage: msmb PCA [-h] --inp INP [--out OUT] [--transformed TRANSFORMED]
                [--n_components N_COMPONENTS] [--no-copy] [--no-whiten]

Principal component analysis (PCA). Linear dimensionality reduction using
Singular Value Decomposition of the data and keeping only the most significant
singular vectors to project the data to a lower dimensional space.

This implementation uses the scipy.linalg implementation of the singular value
decomposition. It only works for dense arrays and is not scalable to large
dimensional data.

The time complexity of this implementation is ``O(n ** 3)`` assuming n ~
n_samples ~ n_features.
```