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samplr: Compare Human Performance to Sampling Algorithms

Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at <https://sampling.warwick.ac.uk>.

Version:1.1.0
Depends:R (≥ 2.10)
Imports:Rcpp (≥ 1.0.6),ggplot2,latex2exp,pracma, stats,lme4,Rdpack,R6, graphics
LinkingTo:Rcpp,RcppArmadillo,RcppDist,testthat
Suggests:knitr,rmarkdown,testthat (≥ 3.0.0),vdiffr,bench,dplyr,tidyr,magrittr,mvtnorm,xml2,withr,samplrData
Published:2025-03-31
DOI:10.32614/CRAN.package.samplr
Author:Lucas CastilloORCID iD [aut, cre, cph], Yun-Xiao LiORCID iD [aut, cph], Adam N SanbornORCID iD [aut, cph], European Research Council (ERC) [fnd]
Maintainer:Lucas Castillo <lucas.castillo-marti at warwick.ac.uk>
BugReports:https://github.com/lucas-castillo/samplr/issues
License:CC BY 4.0
URL:https://lucas-castillo.github.io/samplr/
NeedsCompilation:yes
Materials:README,NEWS
CRAN checks:samplr results

Documentation:

Reference manual:samplr.html ,samplr.pdf
Vignettes:Simulations-of-the-Autocorrelated-Bayesian-Sampler (source,R code)
custom-density-functions (source,R code)
how-to-sample (source,R code)
multivariate-mixtures (source,R code)
samplr-package (source,R code)
supported-distributions (source,R code)
time-comparisons (source,R code)

Downloads:

Package source: samplr_1.1.0.tar.gz
Windows binaries: r-devel:samplr_1.1.0.zip, r-release:samplr_1.1.0.zip, r-oldrel:samplr_1.1.0.zip
macOS binaries: r-release (arm64):samplr_1.1.0.tgz, r-oldrel (arm64):samplr_1.1.0.tgz, r-release (x86_64):samplr_1.1.0.tgz, r-oldrel (x86_64):samplr_1.1.0.tgz
Old sources: samplr archive

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=samplrto link to this page.


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