This package is considered a duplicate. The official version of this package is found at:https://lindeloev.r-universe.dev/mcp

Package: mcp 0.3.4

Jonas Kristoffer Lindeløv

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.tar.gz(r-4.5-noble)mcp_0.3.4.tar.gz(r-4.4-noble)
mcp_0.3.4.tgz(r-4.4-emscripten)mcp_0.3.4.tgz(r-4.3-emscripten)
mcp.pdf |mcp.html✨
mcp/json (API)
NEWS

# Install 'mcp' in R:

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

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

Pkgdown 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

3.71 score 1 packages 85 scripts 523 downloads 4 mentions 20 exports 65 dependencies

Last updated 12 months agofrom:ee4040b642. Checks:2 OK. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 24 2025
R-4.5-linux	OK	Feb 24 2025

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 colorspace cpp11 digest distributional dplyr fansi farver future future.apply generics ggdist ggplot2 ggridges globals glue gtable isoband labeling lattice lifecycle listenv loo magrittr MASS Matrix matrixStats mgcv munsell nlme numDeriv parallelly patchwork pillar pkgconfig plyr posterior purrr quadprog R6 RColorBrewer Rcpp reshape2 rjags rlang 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)