_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-cotram 0.5-2
Propagated dependencies: r-variables@1.1-1 r-tram@1.2-1 r-survival@3.7-0 r-qrng@0.0-10 r-mlt@1.6-3 r-matrix@1.7-1 r-basefun@1.2-2 r-alabama@2023.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: http://ctm.R-forge.R-project.org
Licenses: GPL 2
Synopsis: Count Transformation Models
Description:

Count transformation models featuring parameters interpretable as discrete hazard ratios, odds ratios, reverse-time discrete hazard ratios, or transformed expectations. An appropriate data transformation for a count outcome and regression coefficients are simultaneously estimated by maximising the exact discrete log-likelihood using the computational framework provided in package mlt', technical details are given in Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>. The package also contains an experimental implementation of multivariate count transformation models with an application to multi-species distribution models <DOI:10.48550/arXiv.2201.13095>.

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