Title: Mixtures of Gaussian Graphical Models
Description: Mixtures of Gaussian graphical models for model-based clustering with sparse covariance and concentration matrices. See Fop, Murphy, and Scrucca (2018) <doi:10.1007/s11222-018-9838-y>.
Imports: foreach, GA (>= 3.1), mclust (>= 5.4), memoise, network, Rcpp

Title: Mixture of Multiple Highly Variable Feature Selection Methods
person(given = "Ruzhang", family = "Zhao", email = "ruzhangzhao@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3420-5771"))
Description: Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>.
Imports: scran, Seurat, Matrix, methods, SingleCellExperiment, scuttle
Packaged: 2022-10-07 15:11:14 UTC; zhaoruzhang
Author: Ruzhang Zhao [aut, cre] (<https://orcid.org/0000-0002-3420-5771>)

Title: Simulated Maximum Likelihood Estimation of Mixed Logit Models
Description: Specification and estimation of multinomial logit
models.  Large datasets and complex models are supported, with an
intuitive syntax.  Multinomial Logit Models, Mixed models, random
coefficients and Hybrid Choice are all supported.  For more
information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
Imports: maxLik, numDeriv, randtoolbox, Rcpp (>= 0.12.19), readr,
Suggests: knitr, mlogit, rmarkdown, testthat, texreg, xtable
Packaged: 2020-03-21 10:08:00 UTC; joemolloy

Authors@R: person("Joseph", "Molloy", email = "joseph.molloy@ivt.baug.ethz.ch",
Description: Specification and estimation of multinomial logit models.
Large datasets and complex models are supported, with an intuitive syntax.
Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported.
For more information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
Imports: stats, readr, stringr (>= 1.3.1), maxLik, numDeriv, Rcpp (>=
Suggests: testthat, knitr, rmarkdown, mlogit, xtable, texreg