contextual: Simulation and Analysis of Contextual Multi-Armed Bandit Policies

Facilitates the simulation and evaluation of context-free and contextual multi-Armed Bandit policies or algorithms to ease the implementation, evaluation, and dissemination of both existing and new bandit algorithms and policies.

Version:	0.9.8.4
Depends:	R (≥ 3.5.0)
Imports:	R6 (≥ 2.3.0), data.table, R.devices, foreach, doParallel, itertools, iterators, Formula, rjson
Suggests:	testthat, RCurl, splitstackshape, covr, knitr, here, rmarkdown, devtools, ggplot2, vdiffr
Published:	2020-07-25
Author:	Robin van Emden [aut, cre], Maurits Kaptein [ctb]
Maintainer:	Robin van Emden <robinvanemden at gmail.com>
BugReports:	https://github.com/Nth-iteration-labs/contextual/issues
License:	GPL-3
URL:	https://github.com/Nth-iteration-labs/contextual
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	contextual results

Downloads:

Reference manual:	contextual.pdf
Vignettes:	Demo: Basic Synthetic cMAB Policies Demo: Offline cMAB LinUCB evaluation Demo: MAB Replication Eckles & Kaptein (Bootstrap Thompson Sampling) Demo: Basic Epsilon Greed Getting started: running simulations Demo: MAB Policies Comparison Demo: MovieLens 10M Dataset Demo: Offline cMAB: CarsKit DePaul Movie Dataset Offline evaluation: Replication of Li et al 2010 Demo: Bandits, Propensity Weighting & Simpson's Paradox in R Demo: Replication Sutton & Barto, Reinforcement Learning: An Introduction, Chapter 2 Demo: Replication of John Myles White, Bandit Algorithms for Website Optimization
Package source:	contextual_0.9.8.4.tar.gz
Windows binaries:	r-devel: contextual_0.9.8.4.zip, r-release: contextual_0.9.8.4.zip, r-oldrel: contextual_0.9.8.4.zip
macOS binaries:	r-release (arm64): contextual_0.9.8.4.tgz, r-release (x86_64): contextual_0.9.8.4.tgz, r-oldrel: contextual_0.9.8.4.tgz
Old sources:	contextual archive

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