Authors@R: c(person(given = "Alberto", family = "Fern�ndez",
Author: Alberto Fern�ndez [aut, cre] (<https://orcid.org/0000-0002-1241-1646>),

Title: Read and Write 'MTrackJ Data Files'
Authors@R: person("Gerhard", "Burger", email = "burger.ga@gmail.com", role = c("aut", "cre"))
Description: 'MTrackJ' is an 'ImageJ' plugin for motion tracking and analysis (see
and writes 'MTrackJ Data Files' ('.mdf', see
2D data and read/writes cluster, point, and channel information. If desired,
generates track identifiers that are unique over the clusters.
See the project page for more information and examples.

Suggests: testthat, tools, tibble, spelling
Author: Gerhard Burger [aut, cre] (<https://orcid.org/0000-0003-1062-5576>)

Title: Missing Data Imputation Using Gaussian Copulas
Description: Provides functions to impute missing values using Gaussian
copulas for mixed data types as described by Christoffersen et al.
(2021) <arXiv:2102.02642>. The method is related to Hoff (2007)
but differs by making a direct approximation of the log marginal likelihood
using an extended version of the Fortran code created by Genz and Bretz
(2002) <doi:10.1198/106186002394> in addition to also support multinomial
LinkingTo: Rcpp, RcppArmadillo, testthat, BH, psqn

Title: Multivariate Double Hierarchical Generalized Linear Models
Description: Allows different models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
Depends: R (>= 3.2.0), methods, Matrix, numDeriv, boot, mvtnorm

Author: Youngjo Lee, Marek Molas, Maengseok Noh
Description: Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.