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mmrm: Mixed Models for Repeated Measures

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'.

Version:	0.3.16
Depends:	R (≥ 4.1)
Imports:	checkmate (≥ 2.0),generics,lifecycle,MASS,Matrix, methods,nlme, parallel,Rcpp,Rdpack, stats,stringr,tibble,TMB (≥ 1.9.1), utils
LinkingTo:	Rcpp,RcppEigen,testthat,TMB (≥ 1.9.1)
Suggests:	broom,broom.helpers,car (≥ 3.1.2),cli,clubSandwich,clusterGeneration,dplyr,emmeans (≥ 1.6),estimability,ggplot2,glmmTMB,hardhat,knitr,lme4,lmerTest,microbenchmark,mockery,parallelly (≥ 1.32.0),parsnip (≥1.1.0),purrr,rmarkdown,sasr,scales,testthat (≥ 3.0.0),tidymodels,withr,xml2
Published:	2025-12-09
DOI:	10.32614/CRAN.package.mmrm
Author:	Daniel Sabanes Bove [aut, cre], Liming Li [aut], Julia Dedic [aut], Doug Kelkhoff [aut], Kevin Kunzmann [aut], Brian Matthew Lang [aut], Christian Stock [aut], Ya Wang [aut], Craig Gower-Page [ctb], Dan James [aut], Jonathan Sidi [aut], Daniel Leibovitz [aut], Daniel D. Sjoberg [aut], Nikolas Ivan Krieger [aut], Lukas A. Widmer [ctb], Boehringer Ingelheim Ltd. [cph, fnd], Gilead Sciences, Inc. [cph, fnd], F. Hoffmann-La Roche AG [cph, fnd], Merck Sharp & Dohme, Inc. [cph, fnd], AstraZeneca plc [cph, fnd], inferential.biostatistics GmbH [cph, fnd]
Maintainer:	Daniel Sabanes Bove <daniel.sabanes_bove at rconis.com>
BugReports:	https://github.com/openpharma/mmrm/issues
License:	Apache License 2.0
URL:	https://openpharma.github.io/mmrm/
NeedsCompilation:	yes
Language:	en-US
Materials:	NEWS
In views:	ClinicalTrials,MixedModels
CRAN checks:	mmrm results

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