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.
To install the developmental version from GitHub
# install.packages("remotes") remotes::install_github("poissonconsulting/extras")
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)) #>  0.3183615 svalue(rnorm(100, mean = 1)) #>  1.704015 svalue(rnorm(100, mean = 2)) #>  3.850857 svalue(rnorm(100, mean = 3)) #>  5.073249
Implemented distributions include
The package also provides R translations of
functions such as
pow(10, 2) #>  100 mu <- NULL log(mu) <- 1 mu #>  2.718282
Atomic vectors, matrices, arrays and data.frames of appropriate classes
can be converted to numeric objects suitable for Bayesian analysis using
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
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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