extras
provides helper functions for Bayesian
analyses.
In particular it provides functions to numericise R objects (coerce to numeric objects), summarise MCMC (Monte Carlo Markov Chain) samples 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")
::install_github("poissonconsulting/extras") remotes
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.
library(extras)
#>
#> Attaching package: 'extras'
#> The following object is masked from 'package:stats':
#>
#> step
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
The extras
package provides functions to summarise MCMC
samples like svalue()
which gives the surprisal
value (Greenland, 2019)
set.seed(1)
<- rnorm(100)
x 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
The package also provides R translations of BUGS
(and
JAGS
) functions such as pow()
and
log<-
.
pow(10, 2)
#> [1] 100
<- NULL
mu log(mu) <- 1
mu#> [1] 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.