Showing 10 of total 10 results (show query)
friendly
vcdExtra:'vcd' Extensions and Additions
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.
Maintained by Michael Friendly. Last updated 4 days ago.
categorical-data-visualizationgeneralized-linear-modelsmosaic-plots
24 stars 10.85 score 472 scripts 3 dependents
bioc
Category:Category Analysis
A collection of tools for performing category (gene set enrichment) analysis.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
annotationgopathwaysgenesetenrichment
7.93 score 183 scripts 16 dependents
andreasnordland
polle:Policy Learning
Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
Maintained by Andreas Nordland. Last updated 18 hours ago.
4 stars 5.80 score 6 scripts
rje42
contingency:Discrete Multivariate Probability Distributions
Provides an object class for dealing with many multivariate probability distributions at once, useful for simulation.
Maintained by Robin Evans. Last updated 7 months ago.
4.00 score 5 scripts
jeremyroos
gmgm:Gaussian Mixture Graphical Model Learning and Inference
Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.
Maintained by Jérémy Roos. Last updated 3 years ago.
bayesian-networksgaussian-mixture-modelsinferencemachine-learningprobabilistic-graphical-models
5 stars 3.40 score 7 scriptsbioc
diggit:Inference of Genetic Variants Driving Cellular Phenotypes
Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm
Maintained by Mariano J Alvarez. Last updated 5 months ago.
systemsbiologynetworkenrichmentgeneexpressionfunctionalpredictiongeneregulation
3.30 score 3 scripts
ycroissant
descstat:Tools for Descriptive Statistics
A toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.
Maintained by Yves Croissant. Last updated 4 years ago.
2 stars 3.00 score 1 scripts
cran
deal:Learning Bayesian Networks with Mixed Variables
Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
Maintained by Claus Dethlefsen. Last updated 2 years ago.
2.70 score
pettermostad
lestat:A Package for Learning Statistics
Some simple objects and functions to do statistics using linear models and a Bayesian framework.
Maintained by Petter Mostad. Last updated 7 years ago.
2.28 score 64 scripts 1 dependents
