Description: Given an adjacency matrix drawn from a Generalized Stochastic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.

Imports: softImpute, RSpectra, doParallel, Matrix, foreach
Suggests: knitr, rmarkdown, igraph, missSBM, RColorBrewer, combinat,

Description: A package for smoothing with shape constraints. This

Description: A package for smoothing with shape constraints. A smooth
function is regarded as a continuous function approximated by a
series of points. A shape constraint is regarded as a
piece-wise constraint on the sign of a smooth function's
zeroth, first, second or third derivative.

Description: An experimental package for representing shape constraints
(for sets of smooth functions) and transforming regularly
spaced series to satisfy those constraints. Shape constraints
include constraints on the signs of the zeroth, first, second

Title: Generalized Structure Component Analysis- Latent Class Analysis
person("Seohee", "Park", email = "hee6904@gmail.com",
Execute Latent Class Analysis (LCA) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2009) <doi:10.1007/s41237-019-00084-6>.
It estimates the parameters of latent class prevalence and item response probability in LCA
with a single line comment. It also provide graphs of item response probabilities.
Imports: gridExtra, ggplot2, stringr, progress, psych, fastDummies,

Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>.
It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.
fclust, MASS, devtools, foreach, doSNOW, nnet