Package: catmap
Version: 1.6
Date: 2009-02-05
Title: Case-control And Tdt Meta-Analysis Package
Author: Kristin K. Nicodemus <kristin.nicodemus@well.ox.ac.uk>
Maintainer: Kristin K. Nicodemus <kristin.nicodemus@well.ox.ac.uk>
Description: catmap is an R package that conducts fixed-effects
(inverse variance) and random-effects (DerSimonian and Laird,
1986) meta-analyses of case-control or family-based (TDT)
genetic data; in addition, it performs meta-analyses combining
these two types of study designs.  The fixed-effects model was
first described by Kazeem and Farrell (2005); the
random-effects model is described in Nicodemus (submitted).
License: GPL (>= 2)
Packaged: Sat May 2 16:38:43 2009; knicodemus
Repository: CRAN
Date/Publication: 2009-05-04 10:18:01

Title: Heatmap for Categorical Data using 'plotly'
person("Yves", "Mauron", email="ypmauron@gmail.com", role=c("aut", "cre")),
Description: Methods and plotting functions for displaying categorical data on an
interactive heatmap using 'plotly'. Provides functionality for strictly
categorical heatmaps, heatmaps illustrating categorized continuous data
and annotated heatmaps. Also, there are various options to interact with the x-axis
to prevent overlapping axis labels, e.g. via simple sliders or range sliders.
Besides the viewer pane, resulting plots can be saved as a standalone HTML file,
embedded in 'R Markdown' documents or in a 'Shiny' app.
Imports: plotly, tidyverse, dplyr, lubridate, magrittr, rlang

Suggests: testthat, viridis, lubridate, tidyverse, knitr, rmarkdown

Title: catnet: Categorical Bayesian Network Inference
Description: A package that handles discrete Bayesian network models
and provides inference using the frequentist approach
Maintainer: Nikolay Balov <nikolay_balov@urmc.rochester.edu>, Peter
Collate: catnet.class.R catnet.def.R graph2catnet.R catnet.dags.R

Title: Categorical Bayesian Network Inference

Description: A package that handles discrete Bayesian network models and provides inference using the frequentist approach

Description: Structure learning and parameter estimation of discrete Bayesian networks using likelihood-based criteria.  Exhaustive search for fixed node orders and stochastic search of optimal orders via simulated annealing algorithm are implemented.