Imports: Rcpp (>= 1.0.5), MCMCpack, ggplot2, GPArotation, dplyr,

person("Gwanghee", "Kim", role = c("aut"),

Authors@R: person("Po-Hsien", "Huang", role = c("aut", "cre"),
Description: Conduct latent structure learning methods, particularly
structural equation modeling via penalized likelihood.

Description: Conduct structural equation modeling via penalized likelihood.

Imports: methods, ggplot2, reshape2, lavaan

Description: Fits structural equation modeling via penalized likelihood.

Title: Semi-Confirmatory Structural Equation Modeling via Penalized
Authors@R: c(person("Po-Hsien", "Huang", email = "psyphh@gmail.com", role = c("cre", "aut")), person("Wen-Hsin", "Hu", email = "loveakai@gmail.com", role = "aut"))
Description: Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with lasso or minimax concave penalty (MCP) developed by Huang, Chen, and Weng (2017) <doi:10.1007/s11336-017-9566-9>.
Collate: lslxData.R lslxModel.R lslxFitting.R lslx.R

Description: Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with lasso or minimax concave penalty (MCP).

Description: Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) <doi:10.18637/jss.v093.i07>.
Imports: stats, Rcpp, R6, ggplot2, lavaan

Description: Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with LASSO or minimax concave penalty (MCP).

Collate: lslxData.R lslxModel.R lslxFitting.R prelslx-class.R

Description: Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with elastic net (EN) or minimax concave penalty (MCP).

Description: Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS).

Title: Estimation of the log Likelihood of the Saturated Model
Authors@R: c(person("Humberto", "Llinas", role = c("aut")),
person("Omar", "Fabregas", role = c("aut")),
person("Jorge", "Villalba", email = "jlvia1191@gmail.com", role = c("aut", "cre")))
Description: When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.