Type: Package
Package: mmrm
Title: Mixed Models for Repeated Measures
Version: 0.1.5
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
    adjusted degrees of freedom, 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, Rdpack, stats, stringr, TMB (>= 1.9.1)
Suggests: emmeans (>= 1.6), estimability, knitr, rmarkdown, testthat
        (>= 3.0.0), xml2
LinkingTo: RcppEigen, testthat, TMB
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' 'mmrm-methods.R' 'mmrm-package.R' 'utils.R'
        'satterthwaite.R' 'tmb-methods.R' 'tmb.R'
Packaged: 2022-10-18 09:47:50 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: 2022-10-18 12:12:37 UTC
