Description: An efficient and flexible system to solve sparse mixed model
equations, for models that are often used in statistical genetics.
Important applications are the use of splines to model spatial or temporal
trends. Another application area is mixed model QTL analysis for
multiparental populations, allowing for heterogeneous residual variance and
random design matrices with Identity-By-Descent (IBD) probabilities.
Imports: agridat, ggplot2, maps, Matrix, methods, Rcpp (>= 0.10.4), sp,
Author: Martin Boer [aut] (<https://orcid.org/0000-0002-1879-4588>),
Maintainer: Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>

Title: Repeated Measurement Models for Discrete Times
person("Julie", "Forman", role = "aut", email = "jufo@sund.ku.dk", comment = c(ORCID = "0000-0001-7368-0869"))
Description: Companion R package for the course "Statistical analysis
of correlated and repeated measurements for health science
researchers" taught by the section of Biostatistics of the University
of Copenhagen. It provides functions for computing summary statistics
and obtaining graphical displays of longitudinal data, as well as for
statistical modeling and statistical inference using linear mixed model.
Imports: emmeans, lava, Matrix, multcomp, numDeriv, reshape2, sandwich
Suggests: AICcmodavg, data.table, nlmeU, optimx, Publish, qqtest,
Collate: '0-onload.R' 'LMMstar-package.R' 'LMMstar.options.R' 'anova.R'
'autoplot.R' 'baselineAdjustment.R' 'coef.R' 'confint.R' 'df.R'
'doc-data.R' 'emmeans.R' 'estimate.R' 'fitted.R' 'formula.R'
'getCoef.R' 'getVarCov.R' 'iid.R' 'information.R' 'lmm.R'
'multcomp.R' 'nobs.R' 'precompute.R' 'predict.R' 'print.R'

Description: Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
taught by the section of Biostatistics of the University of Copenhagen.
It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, unstructured).
Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite degrees of freedom.
Normalized residuals are provided to assess model misspecification.
Statistical inference can be performed for arbitrary linear combination(s) of model coefficients.
Predictions can be computed conditional to covariates only or also to outcome values.
Imports: emmeans, ggplot2, lava, Matrix, multcomp, numDeriv, reshape2,
'doc-data.R' 'dummy.coef.R' 'emmeans.R' 'estimate.R' 'fitted.R'
'formula.R' 'getCoef.R' 'getVarCov.R' 'iid.R' 'information.R'
'levels.R' 'lmm.R' 'logLik.R' 'model.frame.R' 'model.matrix.R'
'model.tables.R' 'moments.R' 'multcomp.R' 'nobs.R' 'plot.R'
'structure.R' 'summarize.R' 'summary.R' 'utils.R' 'vcov.R'