Package: binsegRcpp 2025.5.13

binsegRcpp: Efficient Implementation of Binary Segmentation

Standard template library containers are used to implement an efficient binary segmentation algorithm, which is log-linear on average and quadratic in the worst case.

Authors:Toby Dylan Hocking [aut, cre]

binsegRcpp_2025.5.13.tar.gz
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binsegRcpp_2025.5.13.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
binsegRcpp/json (API)
NEWS

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

Bug tracker:https://github.com/tdhock/binsegrcpp/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

4.26 score 7 stars 13 scripts 872 downloads 20 exports 2 dependencies

Last updated from:9b00471974. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK180
linux-devel-x86_64OK187
source / vignettesOK240
linux-release-arm64OK129
linux-release-x86_64OK131
macos-release-arm64OK101
macos-release-x86_64OK410
macos-oldrel-arm64OK130
macos-oldrel-x86_64OK546
windows-develOK111
windows-releaseOK119
windows-oldrelOK104
wasm-releaseOK112

Exports:binsegbinseg_normalbinseg_normal_cvcase.colorscase.sizescum_mediandepth_first_interfaceget_complexityget_complexity_best_heuristic_equal_breadth_fullget_complexity_best_heuristic_equal_depth_fullget_complexity_best_optimal_costget_complexity_best_optimal_treeget_complexity_empiricalget_complexity_extremeget_complexity_worstget_distribution_infoget_tree_empiricalrandom_set_vecsize_to_splitstree_layout

Dependencies:data.tableRcpp

Comparisons with other packages

Rendered fromcomparisons.Rmdusingknitr::knitron May 13 2026.

Last update: 2022-03-31
Started: 2022-03-31

Cross-validation

Rendered fromcross-validation.Rmdusingknitr::knitron May 13 2026.

Last update: 2022-05-12
Started: 2022-01-21

Readme and manuals

Help Manual

Help pageTopics
Binary segmentationbinseg binsegRcpp
binseg interfacebinseg_interface
Binary segmentation, normal change in meanbinseg_normal
Binary segmentation, normal change in mean, cross-validation for model selectionbinseg_normal_cv
case colorscase.colors
case sizescase.sizes
check sizescheck_sizes
coef binseg normal cvcoef.binseg_normal_cv
coef binsegRcppcoef.binsegRcpp
cum mediancum_median
cum median interfacecum_median_interface
depth first interfacedepth_first_interface
get complexityget_complexity
get complexity best heuristic equal breadth fullget_complexity_best_heuristic_equal_breadth_full
get complexity best heuristic equal depth fullget_complexity_best_heuristic_equal_depth_full
get complexity best optimal costget_complexity_best_optimal_cost
get complexity best optimal splitsget_complexity_best_optimal_splits
get complexity best optimal treeget_complexity_best_optimal_tree
get complexity empiricalget_complexity_empirical
get complexity extremeget_complexity_extreme
get complexity worstget_complexity_worst
get distribution infoget_distribution_info
get tree empiricalget_tree_empirical
plot binseg normal cvplot.binseg_normal_cv
plot binsegRcppplot.binsegRcpp
plot complexityplot.complexity
print binseg normal cvprint.binseg_normal_cv
print binsegRcppprint.binsegRcpp
qp xqp.x
random set vecrandom_set_vec
size to splitssize_to_splits
tree layouttree_layout