Title: Modelling Continuous Report Visual Short-Term Memory Studies
Description: A set of utility functions for analysing and modelling data from
continuous report short-term memory experiments using either the 2-component
mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the
3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users
are also able to simulate from these models.
Imports: dplyr, ggplot2, rlang, tidyr, RColorBrewer
Copyright: Some functions have been adapted from Matlab code written by
Paul Bays (https://bayslab.com) published under GNU General
Packaged: 2021-07-30 12:41:36 UTC; jimgrange
Author: Jim Grange [aut, cre] (<https://orcid.org/0000-0002-8352-8390>),

Title: Mixture Models for Clustering and Classification
Author: Ryan P. Browne and Paul D. McNicholas
Description: An implementation of all 14 Gaussian parsimonious
clustering models (GPCMs) for model-based clustering and
Packaged: 2013-03-09 13:52:08 UTC; ryanbrowne

Author: Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas

Packaged: 2014-12-31 15:50:35 UTC; ryanbrowne

Title: Finite Gaussian Mixture Models for Clustering and Classification
X-CRAN-Comment: Orphaned and corrected on 2020-12-12

Packaged: 2018-02-13 18:42:39 UTC; ryanbrowne

Authors@R: c(person("Nik", "Pocuca", role=c("aut")), person("Ryan P.", "Browne", role = c("aut")), person("Paul D.", "McNicholas", role = c("aut", "cre"), email = "mcnicholas@math.mcmaster.ca"))
Description: An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
LinkingTo: Rcpp, RcppArmadillo, BH, RcppGSL

Description: An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.