Computes optimal changepoint models using the
 Poisson likelihood for non-negative count data,
 subject to the PeakSeg constraint:
 the first change must be up, second change down, third change up, etc.
 For more info about the models and algorithms,
 read "A log-linear time algorithm for constrained changepoint detection"
 <arXiv:1703.03352> by TD Hocking et al.

Supports maximum likelihood inference for the Pearson VII
    distribution with shape parameter 3/2 and free location and scale
    parameters. This distribution is relevant when estimating the velocity of
    processive motor proteins with random detachment.

Implementation of the Pearson distribution system, including full
  support for the (d,p,q,r)-family of functions for probability distributions 
  and fitting via method of moments and maximum likelihood method.

The Pearson-ICA algorithm is a mutual information-based
        method for blind separation of statistically independent source
        signals. It has been shown that the minimization of mutual
        information leads to iterative use of score functions, i.e.
        derivatives of log densities. The Pearson system allows
        adaptive modeling of score functions. The flexibility of the
        Pearson system makes it possible to model a wide range of
        source distributions including asymmetric distributions. The
        algorithm is designed especially for problems with asymmetric
        sources but it works for symmetric sources as well.

Validation of risk predictions obtained from survival models and
    competing risk models based on censored data using inverse weighting and
    cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.

It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.

Data files and documentation for PEDiatric vALidation oF vAriableS
    in TBI (PEDALFAST).  The data was used in "Functional Status Scale in
    Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett,
    Dixon, et al  (2016) <doi:10.1097/PCC.0000000000000934>.