extras provides helper functions for Bayesian analyses.
In particular it provides functions to numericise R objects and
summarise MCMC samples as well as R translations of
To install the developmental version from GitHub
# install.packages("remotes") remotes::install_github("poissonconsulting/extras")
Atomic vectors, matrices, arrays and data.frames of appropriate classes
can be converted to numeric objects suitable for Bayesian analysis using
library(extras) 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
extras package provides functions to summarise MCMC samples like
svalue() which gives the surprisal value (Greenland, 2019)
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
The package also provides R translations of
functions such as
pow(10, 2) #>  100 mu <- NULL log(mu) <- 1 mu #>  2.718282
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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