Packaged: 2022-03-15 11:15:11 UTC; mauriciosarrias

Authors@R: c(person("Mauricio", "Sarrias", role = c("aut", "cre"), email = "msarrias86@gmail.com", comment = c(ORCID = "0000-0001-5932-4817")), person("Yves", "Croissant", role = "ctb"), person("Achim", "Zeileis", role = "ctb"))
Packaged: 2022-04-20 22:03:19 UTC; mauriciosarrias
Author: Mauricio Sarrias [aut, cre] (<https://orcid.org/0000-0001-5932-4817>),

Description: An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data.
Packaged: 2015-04-29 18:55:18 UTC; mauriciosarrias

Title: A Tool for Writing Cleaner, more Transparent Code
Maintainer: Matthew K. Lau <matthewklau@fas.harvard.edu>
Description: To create clearer, more concise code provides this
toolbox helps coders to isolate the essential parts of a script that
produces a chosen result, such as an object, tables and figures
written to disk and even warnings and errors. This work
was funded by US National Science Foundation grant
SSI-1450277 for applications of End-to-End Data Provenance.

Title: Graphical Toolbox for Clustering and Classification of Data
Authors@R: c(   person("Guillaume", "Wacquet", role = c("aut"),
person("Pierre-Alexandre", "Hebert", role = c("aut", "cre"),
person("Emilie", "Poisson", role = c("aut"),
person("Pierre", "Talon", role = c("aut"),
Description: Graphical toolbox for clustering and classification of data frames.
It proposes a graphical interface to process clustering and classification methods on features
data-frames, and to view initial data as well as resulted cluster or classes. According to the
level of available labels, different approaches are proposed: unsupervised clustering,
semi-supervised clustering and supervised classification.
To assess the processed clusters or classes, the toolbox can import and show some supplementary
data formats: either profile/time series, or images.
These added information can help the expert to label clusters (clustering), or to constrain data
frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by
Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by
Depends: R (>= 3.0.0), tcltk, tcltk2, tkrplot
Imports: class, cluster, conclust, corrplot, e1071, factoextra,
FactoMineR, ggplot2, grid, jpeg, MASS, mclust, mda, mmand,
nnet, png, randomForest, reshape, sp, stringi, stringr, tools