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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: 1.0.1
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, samplrData
Published: 2024-08-19
DOI: 10.32614/CRAN.package.samplr
Author: Lucas Castillo ORCID iD [aut, cre, cph], Yun-Xiao Li ORCID iD [aut, cph], Adam N Sanborn ORCID 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://github.com/lucas-castillo/samplr
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: samplr results

Documentation:

Reference manual: 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_1.0.1.tar.gz
Windows binaries: r-devel: samplr_1.0.1.zip, r-release: samplr_1.0.1.zip, r-oldrel: samplr_1.0.1.zip
macOS binaries: r-release (arm64): samplr_1.0.1.tgz, r-oldrel (arm64): samplr_1.0.1.tgz, r-release (x86_64): samplr_1.0.1.tgz, r-oldrel (x86_64): samplr_1.0.1.tgz
Old sources: samplr archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=samplr to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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