Imports: dplyr, ggplot2, graphics, gridExtra, methods, plotrix, Rcpp,
Suggests: colorspace, knitr, scales, shinyBS, rmarkdown

Description: Simulate expected equilibrium length composition, yield-per-recruit, and
the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR
model to length data to estimate  selectivity, relative apical fishing mortality, and
the spawning potential ratio for data-limited fisheries.
See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the

Description: Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.

Title: Latent Class Analysis (LCA) with familial dependence in extended
Author: Arafat TAYEB <arafat.tayeb@ircm.qc.ca>, Alexandre BUREAU
Description: This package performs a Latent Class Analysis of
phenotypic measurements in pedigrees and a model selection
based on one of two methods: likelihood-based cross-validation
and Bayesian Information Criterion. It computes also individual
and triplet child-parents weights in a pedigree using an
upward-downward algorithm. It takes into account the familial
dependence defined by the pedigree structure by considering
that a class of a child depends on his parents classes via
triplet-transition probabilities of the classes. The package
handles the case where measurements are available on all
subjects and the case where measurements are available only on
symptomatic (i.e. affected) subjects. Distributions for
discrete (or ordinal) and continuous data are currently
implemented. The package can deal with missing data.

Title: Latent Class Analysis (LCA) with Familial Dependence in Extended
phenotypic measurements in pedigrees and model selection
and Bayesian Information Criterion. Computation of individual
and triplet child-parents weights in a pedigree is performed using an
upward-downward algorithm. The model takes into account the familial
Packaged: 2018-07-06 19:14:03 UTC; alexandrebureau

Title: Variable Selection for Latent Class Analysis
Description: Variable selection for latent class analysis for model-based clustering of multivariate categorical data.
The package implements a general framework for selecting the subset of variables with relevant clustering information
and discard those that are redundant and/or not informative. The variable selection method is based on the approach
Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search.
Concomitant covariates used to predict the class membership probabilities can also be included
in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.
Authors@R: c( person("Michael", "Fop", role = c("aut", "cre"), email = "michael.fop@ucd.ie"),
person("Thomas Brendan", "Murphy", role = "ctb", email = "brendan.murphy@ucd.ie") )
Imports: nnet, MASS, foreach, parallel, doParallel, GA, memoise
Suggests: knitr (>= 1.12), rmarkdown (>= 1.2)