Package: mcp 0.3.4

mcp: Regression with Multiple Change Points

Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.

Authors:Jonas Kristoffer Lindeløv [aut, cre]

mcp_0.3.4.tar.gz
mcp_0.3.4.zip(r-4.7)mcp_0.3.4.zip(r-4.6)mcp_0.3.4.zip(r-4.5)
mcp_0.3.4.tgz(r-4.6-any)mcp_0.3.4.tgz(r-4.5-any)
mcp_0.3.4.tar.gz(r-4.7-any)mcp_0.3.4.tar.gz(r-4.6-any)
mcp_0.3.4.tgz(r-4.5-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mcp/json (API)
NEWS

# Install 'mcp' in R:

install.packages('mcp', repos = c('https://lindeloev.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lindeloev/mcp/issues

Pkgdown/docs site:https://lindeloev.github.io

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

Datasets:

demo_fit - Example 'mcpfit' for examples

On CRAN:

jags cpp

6.94 score 118 stars 1 packages 123 scripts 555 downloads 4 mentions 20 exports 59 dependencies

Last updated from:e5b1370879. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	212
source / vignettes	OK	200
linux-release-x86_64	OK	205
macos-release-arm64	OK	118
macos-oldrel-arm64	OK	130
windows-devel	OK	166
windows-release	OK	166
windows-oldrel	OK	169
wasm-release	OK	124

Exports:bernoulli criterion exponential fixef get_segment_table hypothesis ilogit is.mcpfit logit loo mcp mcp_example negbinomial phi plot_pars pp_check probit ranef sd_to_prec waic

Dependencies:abind arrayhelpers backports bayesplot checkmate cli coda codetools cpp11 digest distributional dplyr farver future future.apply generics ggdist ggplot2 ggridges globals glue gtable isoband labeling lattice lifecycle listenv loo magrittr matrixStats numDeriv parallelly patchwork pillar pkgconfig plyr posterior purrr quadprog R6 RColorBrewer Rcpp reshape2 rjags rlang S7 scales stringi stringr svUnit tensorA tibble tidybayes tidyr tidyselect utf8 vctrs viridisLite withr

Help page	Topics
Bernoulli family for mcp	bernoulli
Compute information criteria for model comparison	criterion LOO loo loo.mcpfit WAIC waic waic.mcpfit
Example 'mcpfit' for examples	demo_fit
Exponential family for mcp	exponential
Expected Values from the Posterior Predictive Distribution	fitted fitted.mcpfit
Test hypotheses on mcp objects.	hypothesis hypothesis.mcpfit
Inverse logit function	ilogit
Checks if argument is an 'mcpfit' object	is.mcpfit
Logit function	logit
Fit Multiple Linear Segments And Their Change Points	mcp
Get example models and data	mcp_example
Class 'mcpfit' of models fitted with the 'mcp' package	mcpfit mcpfit-class
Negative binomial for mcp	negbinomial
Inverse probit function	phi
Plot individual parameters	plot_pars
Plot full fits	plot plot.mcpfit
Posterior Predictive Checks For Mcpfit Objects	pp_check pp_check.mcpfit
Samples from the Posterior Predictive Distribution	predict predict.mcpfit
Print mcplist	print.mcplist
Nice printing texts	print.mcptext
Probit function	probit
Compute Residuals From Mcpfit Objects	resid resid.mcpfit residuals residuals.mcpfit
Transform a prior from SD to precision.	sd_to_prec
Summarise mcpfit objects	fixed.effects fixef fixef.mcpfit print print.mcpfit random.effects ranef ranef.mcpfit summary summary.mcpfit

Package: mcp 0.3.4

mcp: Regression with Multiple Change Points

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)