Title: Two and Three Dimensional Kolmogorov-Smirnov Two-Sample Tests
Description: The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample
test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3,
are provided to compute the test statistics in two and three dimensional spaces, respectively.
Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is

Title: Estimation of the peak-to-trough ratio of a seasonal variation
Author: Anette Luther Christensen <anluc@rn.dk>
Maintainer: Anette Luther Christensen <anluc@rn.dk>
Description: Estimation of the peak-to-trough ratio of a seasonal

Depends: R (>= 2.14.1) , rms , sspir , KFAS , Matrix, animation

Depends: R (>= 2.15.1) , rms , sspir , KFAS , Matrix, animation

Title: Compute the Annotation Error of Peak Calls
Description: Chromatin immunoprecipitation DNA sequencing results in genomic
tracks that show enriched regions or peaks where proteins are bound.
This package implements fast C code that computes the true and false
positives with respect to a database of annotated regions.

Title: Compute the Label Error of Peak Calls
positives with respect to a database of annotated region labels.

Author: Toby Dylan Hocking, Guillem Rigaill
Title: Dynamic Programming Algorithm for Peak Detection in ChIP-Seq
Description: A quadratic time dynamic programming algorithm
can be used to compute an approximate solution to the problem of
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
"PeakSeg: constrained optimal segmentation and supervised penalty learning
for peak detection in count data" by TD Hocking et al,
Suggests: ggplot2 (>= 2.0), testthat, penaltyLearning

Title: Disk-Based Implementation of PeakSegFPOP
Functional Pruning Optimal Partitioning with up-down constraints
<arXiv:1810.00117> for single-sample peak calling
(independently for each sample and genomic problem),
can handle huge data sets (10^7 or more).
Imports: data.table (>= 1.9.8), parallel