_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
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r-mmrm 0.3.14
Propagated dependencies: r-tmb@1.9.15 r-tibble@3.2.1 r-testthat@3.2.1.1 r-stringr@1.5.1 r-rdpack@2.6.1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.0.13-1 r-nlme@3.1-166 r-matrix@1.7-1 r-lifecycle@1.0.4 r-generics@0.1.3 r-checkmate@2.3.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://openpharma.github.io/mmrm/
Licenses: ASL 2.0
Synopsis: Mixed Models for Repeated Measures
Description:

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using emmeans'.

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