`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.

## Installation

To install the developmental version from GitHub

```
# install.packages("remotes")
remotes::install_github("poissonconsulting/extras")
```

## Demonstration

### Summarise MCMC Samples

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
```

### Distributions

Implemented distributions with functions to draw random samples,
calculate log-likelihoods, and calculate deviance residuals for
include:

- Bernoulli
- Beta-binomial
- Gamma
- Gamma-Poisson
- Zero-inflated gamma-Poisson
- Log-Normal
- Negative Binomial
- Normal
- Poisson
- Zero-inflated Poisson
- Skew Normal
- Student’s t

### R translations

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
```

### Numericise R Objects

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
```

## References

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.

## Contribution

Please report any issues.

Pull
requests are always welcome.

## Code of Conduct

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.