Title: Integrative Analysis of Several Related Data Matrices
Authors@R: person("Jonatan", "Kallus", email = "kallus@chalmers.se",
Description: A generalization of principal component analysis for integrative
analysis. The method finds principal components that describe single matrices
or that are common to several matrices. The solutions are sparse. Rank of
solutions is automatically selected using cross validation. The method is
described in Kallus, Johansson, Nelander and Jörnsten (2019)

person(given = "Jonatan", family = "Kallus", role = c("aut")),
described in Kallus et al. (2019) <arXiv:1911.04927>.

Title: Monte-Carlo Methods for Prediction Functions
Description: Marginalizes prediction functions using Monte-Carlo integration and computes permutation importance.
Authors@R: person("Zachary", "Jones", email = "zmj@zmjones.com", role = c("aut", "cre"))
Suggests: testthat, rmarkdown, knitr, randomForest

Suggests: testthat, rmarkdown, knitr, randomForest, ggplot2, reshape2

Title: Various Similarity and Distance Metrics for Marked Point
Author: Hideitsu Hino, Ken Takano, Yuki Yoshikawa, and Noboru Murata
Description: Compute similarities and distances between marked point processes.

Title: Markov Modulated Poisson Process for Unsupervised Event
Depends: R (>= 3.0.2), expm, reshape2, stats, methods
Description: Time-series of count data occur in many different contexts. A
Markov-modulated Poisson process provides a framework for detecting
anomalous events using an unsupervised learning approach.

Title: Matrix Multiplication on Data.frames
Author: Steve Condylios [aut, cre, cph] (<https://orcid.org/0000-0003-0599-844X>)
Description: Simple helpers for matrix multiplication on data.frames. These allow for more
concise code during low level mathematical operations, and help ensure code is more

Title: Mixed Models for Repeated Measures
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("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)
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
Imports: checkmate (>= 2.0), lifecycle, methods, nlme, numDeriv,
parallel, Rdpack, stats, stringr, TMB (>= 1.9.1)
Suggests: emmeans (>= 1.6), estimability, knitr, rmarkdown, testthat
'emmeans.R' 'fit.R' 'mmrm-methods.R' 'mmrm-package.R' 'utils.R'