Package: MSigSeg 0.2.0

Xuanyu Liu

MSigSeg: Multiple SIGnal SEGmentation

Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields. The segmentation(), lambda_estimator() functions are the main functions of this package.

Authors:Xuanyu Liu [aut, cre], Junbo Duan [aut]

MSigSeg_0.2.0.tar.gz
MSigSeg_0.2.0.tar.gz(r-4.5-noble)MSigSeg_0.2.0.tar.gz(r-4.4-noble)
MSigSeg_0.2.0.tgz(r-4.4-emscripten)MSigSeg_0.2.0.tgz(r-4.3-emscripten)
MSigSeg.pdf |MSigSeg.html
MSigSeg/json (API)

# Install 'MSigSeg' in R:
install.packages('MSigSeg', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • NCHSData - Influenza data set from CDC used as an example.
  • T16M - A chromosome sequencing data set used as an example.
  • T16P - A chromosome sequencing data set used as an example.
  • data_test - A simulated data set used for testing.
  • stock - A stock data set used as an example.

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 147 downloads 10 exports 68 dependencies

Last updated 1 years agofrom:d3f8d94090. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-linuxOKNov 07 2024

Exports:brkpsdata.inputdata.outputlambdalambda_estimatormulti_plotprintseg.lensegmentationsummary

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6RColorBrewerRcppRcppEigenrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr