Information Matrices for 'lmeStruct' and 'glsStruct' Objects
c(person(given = "James",
family = "Pustejovsky",
role = "aut",
email = "jepusto@gmail.com",
comment = c(ORCID = "0000-0003-0591-9465")),
person(given = "Man",
family = "Chen",
role = c("aut", "cre"),
email = "manchen9005@gmail.com"))
Provides analytic derivatives and information matrices for
fitted linear mixed effects (lme) models and generalized least squares (gls) models
estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively.
The package includes functions for estimating the sampling variance-covariance of variance
component parameters using the inverse Fisher information. The variance components include
the parameters of the random effects structure (for lme models), the variance structure,
and the correlation structure. The expected and average forms of the Fisher information matrix
are used in the calculations, and models estimated by full maximum likelihood or
restricted maximum likelihood are supported. The package also includes a function for estimating
standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>)
based on fitted lme or gls models.
covr, testthat (>= 2.1.0), knitr, rmarkdown, scdhlm, dplyr,
tidyr, mlmRev, carData, lme4, merDeriv
James Pustejovsky [aut] (<https://orcid.org/0000-0003-0591-9465>),
Man Chen [aut, cre]

covr, testthat (>= 2.1.0), knitr, rmarkdown, scdhlm, mlmRev,
carData, lme4, Matrix, merDeriv

Fit negative binomial mixed-effect regression model.
Fit negative binomial mixed-effect regression model. For
the distribution of subject-specific random intercept,the gamma
or log-normal distributions or semi-parametric procedure are
allowed. For the safety monitoring, subject-specific
conditional probability index can be computed. For details, see
Zhao, Y., Li, D.K.B., Petkau, J.A., Riddehough, A. &
Traboulsee, A.  Detection of unusually large increases in MRI
lesion counts in multiple sclerosis patients.