in feature and parameter selection, and parameter estimation.
Use of parallel computing for cross validation and subsampling is supported through
the 'foreach' and 'doParallel' packages. Development version is on github, please report package issues on
Depends: R (>= 3.0.0), sglOptim (== 1.3.0), Matrix

Title: Linear Multiple Output Sparse Group Lasso
finds the sparse group lasso penalized maximum likelihood estimator. This
result in feature and parameter selection, and parameter estimation. Use of
parallel computing for cross validation and subsampling is supported through the
'foreach' and 'doParallel' packages. Development version is on GitHub, please
Depends: R (>= 3.2.4), Matrix, sglOptim (== 1.3.5)

Description: Linear multiple output using sparse group lasso. The
algorithm finds the sparse group lasso penalized maximum
likelihood estimator. This result in feature and parameter
selection, and parameter estimation. Use of parallel computing
for cross validation and subsampling is supported through the
'foreach' and 'doParallel' packages. Development version is on
GitHub, please report package issues on GitHub.
Depends: R (>= 3.2.4), Matrix, sglOptim (== 1.3.6)

Description: Calculates the (localised) length of the shorth and

Authors@R: c(person("Dongyoung", "Go", role = c("aut"),
person("Jina", "Park", role = c("aut", "cre"),
person("Ickhoon", "Jin", role = c("ctb"),
person("Minjeong", "Jeon", role = "ctb",
Description: Analysis of dichotomous and continuous response data using latent factor by both 1PL LSIRM and 2PL LSIRM as described in Jeon et al. (2021) <doi:10.1007/s11336-021-09762-5>. It includes original 1PL LSIRM and 2PL LSIRM provided for binary response data and its extension for continuous response data. Bayesian model selection with spike-and-slab prior and method for dealing data with missing value under missing at random, missing completely at random are also supported. Various diagnostic plots are available to inspect the latent space and summary of estimated parameters.
Imports: Rcpp (>= 1.0.5), MCMCpack, ggplot2, GPArotation