_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-corncob 0.4.2
Propagated dependencies: r-vgam@1.1-13 r-trust@0.1-8 r-scales@1.4.0 r-rlang@1.1.6 r-numderiv@2016.8-1.1 r-magrittr@2.0.3 r-ggplot2@3.5.2 r-dplyr@1.1.4 r-detectseparation@0.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/statdivlab/corncob
Licenses: GPL 2+
Synopsis: Count Regression for Correlated Observations with the Beta-Binomial
Description:

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.

Total results: 1