_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-mcp 0.3.4
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tidybayes@3.0.7 r-tibble@3.2.1 r-stringr@1.5.1 r-rlang@1.1.4 r-rjags@4-16 r-patchwork@1.3.0 r-magrittr@2.0.3 r-loo@2.8.0 r-ggplot2@3.5.1 r-future-apply@1.11.3 r-future@1.34.0 r-dplyr@1.1.4 r-coda@0.19-4.1 r-bayesplot@1.11.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://lindeloev.github.io/mcp/
Licenses: GPL 2
Synopsis: Regression with Multiple Change Points
Description:

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>.

Total results: 10