Disk-based implementation of
 Functional Pruning Optimal Partitioning with up-down constraints
 <doi:10.18637/jss.v101.i10> for single-sample peak calling
 (independently for each sample and genomic problem),
 can handle huge data sets (10^7 or more).

A quadratic time dynamic programming algorithm
 can be used to compute an approximate solution to the problem of
 finding the most likely changepoints
 with respect to the Poisson likelihood, subject
 to a constraint on the number of segments, and the changes which must
 alternate: up, down, up, down, etc. For more info read
 <http://proceedings.mlr.press/v37/hocking15.html>
 "PeakSeg: constrained optimal segmentation and supervised penalty learning
 for peak detection in count data" by TD Hocking et al,
 proceedings of ICML2015.

Jointly segment several ChIP-seq samples to find the peaks 
 which are the same and different across samples. The fast approximate
 maximum Poisson likelihood algorithm is described in
 "PeakSegJoint: fast supervised peak detection via joint segmentation
 of multiple count data samples"
 <doi:10.48550/arXiv.1506.01286>
 by TD Hocking and G Bourque.

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 "Constrained Dynamic Programming and Supervised Penalty Learning 
 Algorithms for Peak Detection"
 <https://jmlr.org/papers/v21/18-843.html> 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.