Type: Package
Package: mmrm
Title: Mixed Models for Repeated Measures
Version: 0.2.2
Authors@R: c(
    person("Daniel", "Sabanes Bove", , "daniel.sabanes_bove@roche.com", role = c("aut", "cre")),
    person("Julia", "Dedic", , "julia.dedic@roche.com", role = "aut"),
    person("Doug", "Kelkhoff", , "kelkhoff.douglas@gene.com", role = "aut"),
    person("Kevin", "Kunzmann", , "kevin.kunzmann@boehringer-ingelheim.com", role = "aut"),
    person("Brian Matthew", "Lang", , "brian.lang@msd.com", role = "aut"),
    person("Liming", "Li", , "liming.li@roche.com", role = "aut"),
    person("Ya", "Wang", , "ya.wang10@gilead.com", role = "aut"),
    person("Craig", "Gower-Page", , "craig.gower-page@roche.com", role = "ctb"),
    person("Boehringer Ingelheim Ltd.", role = c("cph", "fnd")),
    person("Gilead Sciences, Inc.", role = c("cph", "fnd")),
    person("F. Hoffmann-La Roche AG", role = c("cph", "fnd")),
    person("Merck Sharp & Dohme, Inc.", role = c("cph", "fnd"))
  )
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 and Schnell (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'.
License: Apache License 2.0
URL: https://openpharma.github.io/mmrm/
BugReports: https://github.com/openpharma/mmrm/issues
Depends: R (>= 4.0)
Imports: checkmate (>= 2.0), lifecycle, methods, nlme, numDeriv,
        parallel, Rcpp, Rdpack, stats, stringr, TMB (>= 1.9.1), utils
Suggests: emmeans (>= 1.6), estimability, knitr, parallelly (>=
        1.32.0), rmarkdown, testthat (>= 3.0.0), xml2
LinkingTo: Rcpp, RcppEigen, testthat, TMB (>= 1.9.1)
VignetteBuilder: knitr
RdMacros: Rdpack
biocViews:
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: true
NeedsCompilation: yes
RoxygenNote: 7.2.1
Collate: 'catch-routine-registration.R' 'component.R' 'data.R'
        'emmeans.R' 'fit.R' 'kenwardroger.R' 'mmrm-methods.R'
        'mmrm-package.R' 'utils.R' 'satterthwaite.R' 'testing.R'
        'tmb-methods.R' 'tmb.R'
Packaged: 2022-12-19 19:50:43 UTC; sabanesd
Author: Daniel Sabanes Bove [aut, cre],
  Julia Dedic [aut],
  Doug Kelkhoff [aut],
  Kevin Kunzmann [aut],
  Brian Matthew Lang [aut],
  Liming Li [aut],
  Ya Wang [aut],
  Craig Gower-Page [ctb],
  Boehringer Ingelheim Ltd. [cph, fnd],
  Gilead Sciences, Inc. [cph, fnd],
  F. Hoffmann-La Roche AG [cph, fnd],
  Merck Sharp & Dohme, Inc. [cph, fnd]
Maintainer: Daniel Sabanes Bove <daniel.sabanes_bove@roche.com>
Repository: CRAN
Date/Publication: 2022-12-20 09:50:02 UTC
Built: R 4.1.2; x86_64-apple-darwin17.0; 2022-12-21 12:31:01 UTC; unix
Archs: mmrm.so.dSYM
