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.

2017.08.15

remove binSum.

add cDPA_interface.

2017.06.20

depend on penaltyLearning, delete related code in this pkg.

2016.08.06

Suggest ggplot2 >= 2.0, new geom_tallrect implementation.

cDPA C code now computes total Poisson loss which is consistent with the PoissonLoss R function, and with other packages (Segmentor3IsBack, coseg). It has been significantly cleaned up (duplication removed).

2015.04.07

move joint segmentation code to PeakSegJoint package.

2015.04.02

multiSampleSegZoom C function.

R implementation of multiSampleSegSome.

2015.03.27

Scripts up to Step4 under exec/

2015.03.23

Error checking in binSum.

chrom and sample ratio features in exec/Step1.R

2015.03.16

multiSampleSeg Optimal and Heuristic C code.

2015.03.11

clusterPeaks C code.

LinDynProg.c renamed to cDPA to be consistent with paper.

2015.03.05

Do not use GSL headers for positive infinity cost; instead use INFINITY defined in "math.h"

2015.03.03

fista.R interval regression code.

binSum C code sets count to -1 for profiles that are too short for the number of bins requested.

2015.02.27

binSum C code for quickly computing sums over bins of constant size.

2014.11.10

warning when user requests X peaks but that model is infeasible.