Package: DRLAP2 0.1.1

DRLAP2: Dynamic Reinforcement Learning and Adaptive Progressive Censoring

Implements Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (MCMC) sampling algorithms for progressive censoring models, with support for dynamic reinforcement learning environment simulation and accelerated computational routines written in C++.

Authors:Okechukwu J. Obulezi [aut, cre]

DRLAP2_0.1.1.tar.gz
DRLAP2_0.1.1.zip(r-4.7-x86_64)DRLAP2_0.1.1.zip(r-4.7-arm64)DRLAP2_0.1.1.zip(r-4.6-x86_64)DRLAP2_0.1.1.zip(r-4.6-arm64)DRLAP2_0.1.1.zip(r-4.5-x86_64)
DRLAP2_0.1.1.tgz(r-4.6-x86_64)DRLAP2_0.1.1.tgz(r-4.6-arm64)DRLAP2_0.1.1.tgz(r-4.5-x86_64)DRLAP2_0.1.1.tgz(r-4.5-arm64)
DRLAP2_0.1.1.tar.gz(r-4.7-arm64)DRLAP2_0.1.1.tar.gz(r-4.7-x86_64)DRLAP2_0.1.1.tar.gz(r-4.6-arm64)DRLAP2_0.1.1.tar.gz(r-4.6-x86_64)
DRLAP2_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DRLAP2/json (API)

# Install 'DRLAP2' in R:
install.packages('DRLAP2', repos = c('https://obulezi-oj.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 218 downloads 6 exports 1 dependencies

Last updated from:1f8133cdb4. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK115
linux-devel-x86_64OK103
source / vignettesOK151
linux-release-arm64OK106
linux-release-x86_64OK90
macos-release-arm64OK91
macos-release-x86_64OK134
macos-oldrel-arm64OK106
macos-oldrel-x86_64OK302
windows-devel-arm64OK167
windows-devel-x86_64OK117
windows-release-arm64OK183
windows-release-x86_64OK75
windows-oldrel-x86_64OK81
wasm-releaseOK80

Exports:calc_rewardcompute_drl_sums_cppdrl_log_target_beta_cppfit_drl_bayesfit_drl_mleinit_drl_env

Dependencies:Rcpp