extras provides helper functions for Bayesian analyses.
In particular it provides functions to summarise vectors of MCMC (Monte Carlo Markov Chain) samples, draw random samples from various distributions and calculate deviance residuals as well as R translations of some BUGS (Bayesian Using Gibbs Sampling), JAGS (Just Another Gibbs Sampler), STAN and TMB (Template Model Builder) functions.
The extras package provides functions to summarise MCMC samples like svalue() which gives the surprisal value (Greenland, 2019)
library(extras) #> #> Attaching package: 'extras' #> The following object is masked from 'package:stats': #> #> step set.seed(1) x <- rnorm(100) svalue(rnorm(100)) #> [1] 0.3183615 svalue(rnorm(100, mean = 1)) #> [1] 1.704015 svalue(rnorm(100, mean = 2)) #> [1] 3.850857 svalue(rnorm(100, mean = 3)) #> [1] 5.073249
Implemented distributions with functions to draw random samples, calculate log-likelihoods, and calculate deviance residuals for include:
The package also provides R translations of BUGS (and JAGS) functions such as pow() and log<-.
pow(10, 2) #> [1] 100 mu <- NULL log(mu) <- 1 mu #> [1] 2.718282
Atomic vectors, matrices, arrays and data.frames of appropriate classes can be converted to numeric objects suitable for Bayesian analysis using the numericise() (and numericize()) function.
numericise(
data.frame(
logical = c(TRUE, FALSE),
factor = factor(c("blue", "green")),
Date = as.Date(c("2000-01-01", "2000-01-02")),
hms = hms::as_hms(c("00:00:02", "00:01:01"))
)
)
#> logical factor Date hms
#> [1,] 1 1 10957 2
#> [2,] 0 2 10958 61
For more information see the Get Started vignette.
To install the release version from CRAN.
install.packages("extras")
The website for the release version is at https://poissonconsulting.github.io/extras/.
To install the development version from GitHub
# install.packages("remotes")
remotes::install_github("poissonconsulting/extras")
or from r-universe.
install.packages("extras", repos = c("https://poissonconsulting.r-universe.dev", "https://cloud.r-project.org"))
Greenland, S. 2019. Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values. The American Statistician 73(sup1): 106–114.
Please report any issues.
Pull requests are always welcome.
Please note that the extras project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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