DESCRIPTION

Type: Package
Package: mmrm
Title: Mixed Models for Repeated Measures
Version: 0.2.2.9034
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", , "doug.kelkhoff@roche.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("Christian", "Stock", , "christian.stock@boehringer-ingelheim.com", role = "aut"),
    person("Ya", "Wang", , "ya.wang10@gilead.com", role = "aut"),
    person("Craig", "Gower-Page", , "craig.gower-page@roche.com", role = "ctb"),
    person("Dan", "James", , "dan.james@astrazeneca.com", role = "aut"),
    person("Jonathan", "Sidi", , "yoni.sidi@sagerx.com", role = "aut"),
    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")),
    person("AstraZeneca plc", 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, Schnell et al. (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,
    Matrix,
    methods,
    nlme,
    parallel,
    Rcpp,
    Rdpack,
    stats,
    stringr,
    TMB (>= 1.9.1),
    utils
Suggests:
    cli,
    clubSandwich,
    clusterGeneration,
    dplyr,
    emmeans (>= 1.6),
    estimability,
    generics,
    ggplot2,
    glmmTMB,
    hardhat,
    knitr,
    lme4,
    MASS,
    microbenchmark,
    mockery,
    parallelly (>= 1.32.0),
    parsnip,
    purrr,
    rmarkdown,
    sasr,
    scales,
    testthat (>= 3.0.0),
    tidymodels,
    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
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
SystemRequirements: C++14
Collate:
    'catch-routine-registration.R'
    'component.R'
    'cov_struct.R'
    'data.R'
    'empirical.R'
    'fit.R'
    'kenwardroger.R'
    'mmrm-methods.R'
    'mmrm-package.R'
    'utils.R'
    'residual.R'
    'utils-nse.R'
    'utils-formula.R'
    'satterthwaite.R'
    'testing.R'
    'tmb-methods.R'
    'tmb.R'
    'interop-emmeans.R'
    'interop-parsnip.R'
    'zzz.R'