knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
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.
install.packages("extras")
To install the developmental version from GitHub
# install.packages("remotes") remotes::install_github("poissonconsulting/extras")
The extras
package provides functions to summarise MCMC samples like svalue()
which gives the surprisal value (Greenland, 2019)
library(extras) set.seed(1) x <- rnorm(100) svalue(rnorm(100)) svalue(rnorm(100, mean = 1)) svalue(rnorm(100, mean = 2)) svalue(rnorm(100, mean = 3))
Implemented distributions include
The package also provides R translations of BUGS
(and JAGS
) functions such as pow()
and log<-
.
pow(10, 2) mu <- NULL log(mu) <- 1 mu
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")) ) )
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. https://doi.org/10.1080/00031305.2018.1529625.
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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