Suggests: testthat, ggplot2, future.apply, future, knitr

Title: Disk-Based Constrained Change-Point Detection

Suggests: testthat, ggplot2, future.apply, future, knitr, markdown

<doi:10.18637/jss.v101.i10> for single-sample peak calling

Title: Joint Peak Detection in Several ChIP-Seq Samples
Description: 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
<arXiv:1506.01286> by TD Hocking and G Bourque.
Imports: data.table (>= 1.9.4), PeakError, parallel, penaltyLearning

Suggests: testthat, ggplot2 (>= 2.0), microbenchmark

Title: Optimal Segmentation Subject to Up-Down Constraints
Description: Computes optimal changepoint models using the
Poisson likelihood for non-negative count data,
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"
Suggests: PeakSegDP (>= 2016.08.06), ggplot2, testthat, data.table (>=

Description: Spectrum manipulation: background estimation, Markov
smoothing, deconvolution and peaks search functions. Ported

Description: 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
Packaged: Fri Sep 18 23:24:44 2009; mbecker

Author: Martin Becker and Stefan Kl��ner

Description: 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.

Package: PearsonICA
Type: Package
Title: Independent component analysis using score functions from the Pearson system
Version: 1.2-2
Date: 2006-06-29
Author: Juha Karvanen
Maintainer: Juha Karvanen <juha.karvanen@ktl.fi>
Description: 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.
License: GPL 2
Packaged: Thu Jun 29 11:07:19 2006; jkau