Title:	Helper Functions for Bayesian Analyses
Version:	0.8.0
Description:	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.
License:	MIT + file LICENSE
URL:	https://poissonconsulting.github.io/extras/, https://github.com/poissonconsulting/extras
BugReports:	https://github.com/poissonconsulting/extras/issues
Depends:	R (≥ 4.0.0)
Imports:	chk, lifecycle, stats
Suggests:	covr, ggplot2, hms, knitr, memoise, rlang, rmarkdown, scales, sn, testthat (≥ 3.0.0), tibble, tidyr, viridis, withr
VignetteBuilder:	knitr
Config/Needs/website:	poissonconsulting/poissontemplate
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.3.2.9000
NeedsCompilation:	no
Packaged:	2025-01-13 19:44:08 UTC; nicolehill
Author:	Nicole Hill [aut, cre], Joe Thorley [aut], Kirill Müller [ctb], Nadine Hussein [ctb], Poisson Consulting [cph, fnd]
Maintainer:	Nicole Hill <nicole@poissonconsulting.ca>
Repository:	CRAN
Date/Publication:	2025-01-13 20:10:02 UTC

extras: Helper Functions for Bayesian Analyses

Description

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.

Author(s)

Maintainer: Nicole Hill nicole@poissonconsulting.ca (ORCID)

Authors:

• Joe Thorley joe@poissonconsulting.ca (ORCID)

Other contributors:

• Kirill Müller (ORCID) [contributor]

• Nadine Hussein (ORCID) [contributor]

• Poisson Consulting [copyright holder, funder]

See Also

Useful links:

• https://poissonconsulting.github.io/extras/

• https://github.com/poissonconsulting/extras

• Report bugs at https://github.com/poissonconsulting/extras/issues

As List

Description

Coerces an object to an list. All attributes are removed except any names.

Usage

as_list(x, ...)

## Default S3 method:
as_list(x, ...)

Arguments

Value

A list.

Examples

as_list(1:3)
as_list(c(x = 1, y = 2))

As List

Description

Coerces an object to an list. All attributes are removed except any names.

Usage

as_list_unnamed(x, ...)

## Default S3 method:
as_list_unnamed(x, ...)

Arguments

Value

A list.

Examples

as_list_unnamed(1:3)
as_list_unnamed(c(x = 1, y = 2))

Check Index

Description

Checks if an object is a vector of one or more positive integer values.

Usage

chk_index(x, x_name = NULL)

vld_index(x)

Arguments

Value

The chk_ function throws an informative error if the test fails.

The vld_ function returns a flag indicating whether the test was met.

Functions

• vld_index(): Validate Index

Examples

x <- c(2L, 1L)
chk_index(x)
y <- c(2L, -1L)
try(chk_index(y))
vld_index(c(-1))
vld_index(c(3L, 1L))

Check Indices

Description

Checks if an object is a list of indices ie vectors of one or more positive integer values.

Usage

chk_indices(x, x_name = NULL)

vld_indices(x)

Arguments

Value

The chk_ function throws an informative error if the test fails.

The vld_ function returns a flag indicating whether the test was met.

Functions

• vld_indices(): Validate Indices

Examples

x <- list(c(2L, 1L))
chk_indices(x)
y <- c(2L, 1L)
try(chk_indices(y))
vld_indices(c(3L, 1L))
vld_indices(list(c(3L, 1L)))

Check Parameter Names

Description

Checks if valid parameter names.

Usage

chk_pars(x, x_name = NULL)

vld_pars(x)

Arguments

Details

The character vector must consist of values that start with an alpha and only include alphanumeric characters and '_' or '.'.

Missing values and duplicates are permitted.

Value

The chk_ function throws an informative error if the test fails.

The vld_ function returns a flag indicating whether the test was met.

Functions

• vld_pars(): Validate Parameter Names

Examples

x <- c("x", "a1._", "X")
chk_pars(x)
y <- c("x[1]", "a1", "a1", "._0")
try(chk_pars(y))
vld_pars(c("x", "a1._", "X"))
vld_pars(c("x[1]", "a1", "a1", "._0"))

Bernoulli Distribution

Description

Bernoulli Distribution

Usage

dbern(x, prob, log = FALSE)

pbern(q, prob, lower.tail = TRUE, log = FALSE)

qbern(p, prob, lower.tail = TRUE, log = FALSE)

rbern(n, prob)

Arguments

Value

An numeric vector of the random samples.

Examples

dbern(1, 0.5)
pbern(0.75, 0.5)
qbern(0.1, 0.5)
rbern(1, 0.5)

Bernoulli Deviances

Description

Bernoulli Deviances

Usage

dev_bern(x, prob = 0.5, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_bern(c(TRUE, FALSE), 0.7)

Beta-Binomial Deviances

Description

This parameterization of the beta-binomial distribution uses an expected probability parameter, prob, and a dispersion parameter, theta. The parameters of the underlying beta mixture are alpha = (2 * prob) / theta and beta = (2 * (1 - prob)) / theta. This parameterization of theta is unconventional, but has useful properties when modelling. When theta = 0, the beta-binomial reverts to the binomial distribution. When theta = 1 and prob = 0.5, the parameters of the beta distribution become alpha = 1 and beta = 1, which correspond to a uniform distribution for the beta-binomial probability parameter.

Usage

dev_beta_binom(x, size = 1, prob = 0.5, theta = 0, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_beta_binom(c(0, 1, 2), 10, 0.5, 0.1)

Binomial Deviances

Description

Binomial Deviances

Usage

dev_binom(x, size = 1, prob = 0.5, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_binom(c(0, 1, 2), 2, 0.3)

Gamma Deviances

Description

Gamma Deviances

Usage

dev_gamma(x, shape = 1, rate = 1, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_gamma(c(0, 1, 2), 1, 2)

Gamma-Poisson Deviances

Description

Gamma-Poisson Deviances

Usage

dev_gamma_pois(x, lambda = 1, theta = 0, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_gamma_pois(c(1, 3, 4), 3, 2)

Zero-Inflated Gamma-Poisson Deviances

Description

Zero-Inflated Gamma-Poisson Deviances

Usage

dev_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

Examples

dev_gamma_pois_zi(c(1, 3, 4), 3, 2)

Log-Normal Deviances

Description

Log-Normal Deviances

Usage

dev_lnorm(x, meanlog = 0, sdlog = 1, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_lnorm(exp(-2:2))

Negative Binomial Deviances

Description

Negative Binomial Deviances

Usage

dev_neg_binom(x, lambda = 1, theta = 0, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_neg_binom(c(1, 2, 5), 2, 3)

Normal Deviances

Description

Normal Deviances

Usage

dev_norm(x, mean = 0, sd = 1, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_norm(c(-2:2))

Poisson Deviances

Description

Poisson Deviances

Usage

dev_pois(x, lambda, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_pois(c(1, 3, 4), 3)

Zero-Inflated Poisson Deviances

Description

Zero-Inflated Poisson Deviances

Usage

dev_pois_zi(x, lambda, prob = 0, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_skewnorm(), dev_student()

Examples

dev_pois_zi(c(1, 3, 4), 3)

Skew Normal Deviances

Description

Skew Normal Deviances

Usage

dev_skewnorm(x, mean = 0, sd = 1, shape = 0, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_student()

Examples

dev_skewnorm(c(-2:2))
dev_skewnorm(-2:2, 0, 1, 5)
dev_skewnorm(-2:2, 0, 1, 5, res = TRUE)

Student's t Deviances

Description

Student's t Deviances

Usage

dev_student(x, mean = 0, sd = 1, theta = 0, res = FALSE)

Arguments

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_beta_binom(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm()

Examples

dev_student(c(1, 3.5, 4), 3)

Skew-Normal Distribution

Description

Skew-Normal Distribution

Usage

dskewnorm(x, mean = 0, sd = 1, shape = 0, log = FALSE)

pskewnorm(q, mean = 0, sd = 1, shape = 0)

qskewnorm(p, mean = 0, sd = 1, shape = 0)

rskewnorm(n = 1, mean = 0, sd = 1, shape = 0)

Arguments

Value

dskewnorm gives the density, pskewnorm gives the distribution function, qskewnorm gives the quantile function, and rskewnorm generates random deviates. pskewnorm and qskewnorm use the lower tail probability.

Examples

dskewnorm(x = -2:2, mean = 0, sd = 1, shape = 0.1)
dskewnorm(x = -2:2, mean = 0, sd = 1, shape = -1)
qskewnorm(p = c(0.1, 0.4), mean = 0, sd = 1, shape = 0.1)
qskewnorm(p = c(0.1, 0.4), mean = 0, sd = 1, shape = -1)
pskewnorm(q = -2:2, mean = 0, sd = 1, shape = 0.1)
pskewnorm(q = -2:2, mean = 0, sd = 1, shape = -1)
rskewnorm(n = 3, mean = 0, sd = 1, shape = 0.1)
rskewnorm(n = 3, mean = 0, sd = 1, shape = -1)

Exponential Transformation of Base 10

Description

Returns the transformation of 10^x.

Usage

exp10(x)

Arguments

Value

A numeric atomic object with the value of 10^x.

See Also

Other translations: exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- c(5, 10.5)
exp10(x)

Exponential Transformation of Base 2

Description

Returns the transformation of 2^x.

Usage

exp2(x)

Arguments

Value

A numeric atomic object with the value of 2^x.

See Also

Other translations: exp10(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- c(5, 10.5)
exp2(x)

Absolute

Description

Computes the absolute value of x. Used in TMB as replacement for abs() which is seemingly ambiguous.

Usage

fabs(x)

Arguments

Details

A wrapper on abs⁠()⁠.

Value

A numeric vector of the corresponding absolute values.

See Also

Other translations: exp10(), exp2(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

fabs(c(0, -1, 2))

Fill All Values

Description

Fills all of an object's (missing and non-missing) values while preserving the object's dimensionality and class.

Usage

fill_all(x, value, ...)

## S3 method for class 'logical'
fill_all(x, value = FALSE, nas = TRUE, ...)

## S3 method for class 'integer'
fill_all(x, value = 0L, nas = TRUE, ...)

## S3 method for class 'numeric'
fill_all(x, value = 0, nas = TRUE, ...)

## S3 method for class 'character'
fill_all(x, value = "0", nas = TRUE, ...)

Arguments

Details

It should only be defined for objects with values of consistent class ie not standard data.frames.

Value

The modified object.

Methods (by class)

• fill_all(logical): Fill All for logical Objects

• fill_all(integer): Fill All for integer Objects

• fill_all(numeric): Fill All for numeric Objects

• fill_all(character): Fill All for character Objects

See Also

Other fill: fill_na()

Examples

# logical
fill_all(c(TRUE, NA, FALSE))
fill_all(c(TRUE, NA, FALSE, nas = FALSE))
fill_all(c(TRUE, NA, FALSE, value = NA))

# integer
fill_all(matrix(1:4, nrow = 2), value = -1)

# numeric
fill_all(c(1, 4, NA), value = TRUE)
fill_all(c(1, 4, NA), value = TRUE, nas = FALSE)

# character
fill_all(c("some", "words"), value = TRUE)

Fill Missing Values

Description

Fills all of an object's missing values while preserving the object's dimensionality and class.

Usage

fill_na(x, value, ...)

## S3 method for class 'logical'
fill_na(x, value = FALSE, ...)

## S3 method for class 'integer'
fill_na(x, value = 0L, ...)

## S3 method for class 'numeric'
fill_na(x, value = 0, ...)

## S3 method for class 'character'
fill_na(x, value = "0", ...)

Arguments

Details

It should only be defined for objects with values of consistent class ie not standard data.frames.

Value

The modified object.

Methods (by class)

• fill_na(logical): Fill Missing Values for logical Objects

• fill_na(integer): Fill Missing Values for integer Objects

• fill_na(numeric): Fill Missing Values for numeric Objects

• fill_na(character): Fill Missing Values for character Objects

See Also

Other fill: fill_all()

Examples

# logical
fill_na(c(TRUE, NA))

# integer
fill_na(c(1L, NA), 0)

# numeric
fill_na(c(1, NA), Inf)

# character
fill_na(c("text", NA))
fill_na(matrix(c("text", NA)), value = Inf)

Inverse Log Transformation

Description

Inverse log transforms a numeric atomic object.

Usage

ilog(x)

Arguments

Details

A wrapper on exp(value).

Value

A numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- 1
ilog(x)

Inverse Log Base 10 Transformation

Description

Inverse log transforms a numeric atomic object with base 10.

Usage

ilog10(x)

Arguments

Details

A wrapper on exp10(value).

Value

A numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- c(2, 4.5)
ilog10(x)

Inverse Log Base 2 Transformation

Description

Inverse log transforms a numeric atomic object with base 2.

Usage

ilog2(x)

Arguments

Details

A wrapper on exp2(value).

Value

A numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- c(2, 4.5)
ilog2(x)

Inverse Logistic Transformation

Description

Inverse logistically transforms a numeric atomic object.

Usage

ilogit(x)

Arguments

Details

A wrapper on stats::plogis().

Value

A numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

ilogit(c(-1, 0, 5))

Inverse Logistic Transformation

Description

Inverse logistically transforms a numeric atomic object.

Usage

inv_logit(x)

Arguments

Details

A wrapper on stats::plogis().

Value

A numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

inv_logit(c(-1, 0, 5))

Inverse Odds

Description

Calculates the probabilities for odds.

Usage

inv_odds(x)

Arguments

Value

A numeric object of the the probabilities for each odd.

See Also

Other odds: log_odds(), log_odds<-(), log_odds_ratio(), odds(), odds<-(), odds_ratio()

Examples

inv_odds(c(0, 1, 9, 9999))

Inverse Logistic Transformation

Description

Inverse logistically transforms a numeric atomic object.

Usage

invlogit(x)

Arguments

Details

A wrapper on stats::plogis().

Value

A numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

invlogit(c(-1, 0, 5))

Kurtosis

Description

Kurtosis

Usage

kurtosis(x, na_rm = FALSE)

Arguments

Value

A number.

See Also

Other summary: lower(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

kurtosis(1:10)

Log Transformation

Description

Replaces a object with the exponent of value.

Usage

log(x) <- value

Arguments

Details

A wrapper on exp(value).

Value

Called for the side effect of updating x.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- NULL
log(x) <- 0.5
x

Log Base 10 Transformation

Description

Replaces a object with the base 10 exponent of value.

Usage

log10(x) <- value

Arguments

Details

A wrapper on exp10(value).

Value

Called for the side effect of updating x.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log2<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- NULL
log10(x) <- c(0.5, 5)
x

Log Base 2 Transformation

Description

Replaces a object with the base 2 exponent of value.

Usage

log2(x) <- value

Arguments

Details

A wrapper on exp2(value).

Value

Called for the side effect of updating x.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log<-(), logit(), logit<-(), phi(), pow(), step()

Examples

x <- NULL
log2(x) <- c(0.5, 5)
x

Bernoulli Log-Likelihood

Description

Bernoulli Log-Likelihood

Usage

log_lik_bern(x, prob = 0.5)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_bern(c(TRUE, FALSE), 0.7)

Beta-Binomial Log-Likelihood

Description

This parameterization of the beta-binomial distribution uses an expected probability parameter, prob, and a dispersion parameter, theta. The parameters of the underlying beta mixture are alpha = (2 * prob) / theta and beta = (2 * (1 - prob)) / theta. This parameterization of theta is unconventional, but has useful properties when modelling. When theta = 0, the beta-binomial reverts to the binomial distribution. When theta = 1 and prob = 0.5, the parameters of the beta distribution become alpha = 1 and beta = 1, which correspond to a uniform distribution for the beta-binomial probability parameter.

Usage

log_lik_beta_binom(x, size = 1, prob = 0.5, theta = 0, memoize = FALSE)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_beta_binom(c(0, 1, 2), 3, 0.5, 0)

Binomial Log-Likelihood

Description

Binomial Log-Likelihood

Usage

log_lik_binom(x, size = 1, prob = 0.5)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_binom(c(0, 1, 2), 2, 0.3)

Gamma Log-Likelihood

Description

Gamma Log-Likelihood

Usage

log_lik_gamma(x, shape = 1, rate = 1)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_gamma(c(0, 1, 2), 1, 2)

Gamma-Poisson Log-Likelihood

Description

Gamma-Poisson Log-Likelihood

Usage

log_lik_gamma_pois(x, lambda = 1, theta = 0)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_gamma_pois(c(0, 1, 2), 1, 1)

Zero-Inflated Gamma-Poisson Log-Likelihood

Description

Zero-Inflated Gamma-Poisson Log-Likelihood

Usage

log_lik_gamma_pois_zi(x, lambda = 1, theta = 0, prob = 0)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_gamma_pois_zi(c(1, 3, 4), 3, 1, prob = 0.5)

Log-Normal Log-Likelihood

Description

Log-Normal Log-Likelihood

Usage

log_lik_lnorm(x, meanlog = 0, sdlog = 1)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_lnorm(10, 0, 2)

Negative Binomial Log-Likelihood

Description

Negative Binomial Log-Likelihood

Usage

log_lik_neg_binom(x, lambda = 1, theta = 0)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_neg_binom(c(0, 1, 2), 2, 1)

Normal Log-Likelihood

Description

Normal Log-Likelihood

Usage

log_lik_norm(x, mean = 0, sd = 1)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_norm(c(-2:2))

Poisson Log-Likelihood

Description

Poisson Log-Likelihood

Usage

log_lik_pois(x, lambda = 1)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois_zi(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_pois(c(1, 3, 4), 3)

Zero-Inflated Poisson Log-Likelihood

Description

Zero-Inflated Poisson Log-Likelihood

Usage

log_lik_pois_zi(x, lambda = 1, prob = 0)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_pois_zi(c(1, 3, 4), 3, prob = 0.5)

Skew Normal Log-Likelihood

Description

Skew Normal Log-Likelihood

Usage

log_lik_skewnorm(x, mean = 0, sd = 1, shape = 0)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_student()

Examples

log_lik_skewnorm(c(-2:2))
log_lik_skewnorm(c(-2:2), shape = -2)
log_lik_skewnorm(c(-2:2), shape = 2)

Student's t Log-Likelihood

Description

Student's t Log-Likelihood

Usage

log_lik_student(x, mean = 0, sd = 1, theta = 0)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta_binom(), log_lik_binom(), log_lik_gamma(), log_lik_gamma_pois(), log_lik_gamma_pois_zi(), log_lik_lnorm(), log_lik_neg_binom(), log_lik_norm(), log_lik_pois(), log_lik_pois_zi(), log_lik_skewnorm()

Examples

log_lik_student(c(1, 3.5, 4), mean = 1, sd = 2, theta = 1 / 3)

Log Odds

Description

Calculates the log odds for probabilities.

Usage

log_odds(x)

Arguments

Value

A numeric object of the the log odds for each probability.

See Also

Other odds: inv_odds(), log_odds<-(), log_odds_ratio(), odds(), odds<-(), odds_ratio()

Examples

log_odds(c(0, 0.5, 0.9, 1))

Inverse Log Odds Transformation

Description

Replaces an object with the inverse log odds of value.

Usage

log_odds(x) <- value

Arguments

Value

Called for the side effect of updating x.

See Also

Other odds: inv_odds(), log_odds(), log_odds_ratio(), odds(), odds<-(), odds_ratio()

Examples

x <- NULL
log_odds(x) <- 0.5
x

Log-Odds Ratio

Description

Calculates the log odds ratio for two probabilities.

Usage

log_odds_ratio(x, x2)

Arguments

Value

A numeric object of the log odds ratios.

See Also

Other odds: inv_odds(), log_odds(), log_odds<-(), odds(), odds<-(), odds_ratio()

Examples

log_odds_ratio(0.5, 0.75)

Log Odds Ratio2

Description

Calculates the log odds ratio for a vector of two probabilities.

Usage

log_odds_ratio2(x)

Arguments

Value

A number.

See Also

Other odds fun2: odds_ratio2()

Examples

log_odds_ratio2(c(0.5, 0.9))
log_odds_ratio2(c(0.9, 0.5))

Logistic Transformation

Description

Logistic transforms a numeric atomic object.

Usage

logit(x)

Arguments

Details

A wrapper on stats::qlogis().

Value

The logistically transformed numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit<-(), phi(), pow(), step()

Examples

logit(c(0.25, 0.5, 0.75))

Logistic Transformation

Description

Logistic Transformation

Usage

logit(x) <- value

Arguments

Details

A wrapper on stats::plogis(value).

Value

Called for the side effect of updating x.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), phi(), pow(), step()

Examples

x <- 1
logit(x) <- 0.5
x

Lower Credible Limit

Description

Calculates the quantile-based lower credible limit.

Usage

lower(x, conf_level = 0.95, na_rm = FALSE)

Arguments

Details

By default it returns the 95% credible limit which corresponds to the 2.5% quantile.

Value

A number.

See Also

Other summary: kurtosis(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

lower(as.numeric(0:100))

Numericise (or Numericize)

Description

Coerce an R object to a numeric atomic object.

Usage

numericise(x, ...)

numericize(x, ...)

## S3 method for class 'logical'
numericise(x, ...)

## S3 method for class 'integer'
numericise(x, ...)

## S3 method for class 'double'
numericise(x, ...)

## S3 method for class 'factor'
numericise(x, ...)

## S3 method for class 'Date'
numericise(x, ...)

## S3 method for class 'POSIXct'
numericise(x, ...)

## S3 method for class 'hms'
numericise(x, ...)

## S3 method for class 'matrix'
numericise(x, ...)

## S3 method for class 'array'
numericise(x, ...)

## S3 method for class 'data.frame'
numericise(x, ...)

Arguments

Details

numericize() is an alias for numericise. If you want to implement a method for a class "foo", implement numericise.foo().

Value

A numeric atomic object.

Methods (by class)

• numericise(logical): Numericise a logical Object

• numericise(integer): Numericise an integer Object

• numericise(double): Numericise an double Object

• numericise(factor): Numericise a factor

• numericise(Date): Numericise a Date vector

• numericise(POSIXct): Numericise a POSIXct vector

• numericise(hms): Numericise a hms vector

• numericise(matrix): Numericise a matrix

• numericise(array): Numericise an array

• numericise(data.frame): Numericise a data.frame

Examples

# logical
numericise(TRUE)
numericise(matrix(c(TRUE, FALSE), nrow = 2))

# integer
numericise(2L)

# double
numericise(c(1, 3))

# factor
numericise(factor(c("c", "a")))

# Date
numericise(as.Date("1972-01-01"))

# POSIXct
numericise(as.POSIXct("1972-01-01", tz = "UTC"))

# hms

numericise(hms::as_hms("00:01:03"))


# matrix
numericise(matrix(TRUE))

# array
numericise(array(TRUE))

# data.frame
numericise(data.frame(
  logical = c(TRUE, FALSE, NA),
  integer = 1:3,
  numeric = c(4, 10, NA),
  factor = as.factor(c("c", "A", "green"))
))

Odds

Description

Calculates the odds for probabilities.

Usage

odds(x)

Arguments

Value

A numeric object of the the odds for each probability.

See Also

Other odds: inv_odds(), log_odds(), log_odds<-(), log_odds_ratio(), odds<-(), odds_ratio()

Examples

odds(c(0, 0.5, 0.9, 1))

Inverse Odds Transformation

Description

Replaces an object with the inverse odds of value.

Usage

odds(x) <- value

Arguments

Value

Called for the side effect of updating x.

See Also

Other odds: inv_odds(), log_odds(), log_odds<-(), log_odds_ratio(), odds(), odds_ratio()

Examples

x <- NULL
odds(x) <- 0.5
x

Odds Ratio

Description

Calculates the odds ratio for two probabilities.

Usage

odds_ratio(x, x2)

Arguments

Value

A numeric object of the odds ratios.

See Also

Other odds: inv_odds(), log_odds(), log_odds<-(), log_odds_ratio(), odds(), odds<-()

Examples

odds_ratio(0.5, 0.75)

Odds Ratio2

Description

Calculates the odds ratio for a vector of two probabilities.

Usage

odds_ratio2(x)

Arguments

Value

A number.

See Also

Other odds fun2: log_odds_ratio2()

Examples

odds_ratio2(c(0.5, 0.9))
odds_ratio2(c(0.9, 0.5))

Parameter Pattern

Description

Parameter Pattern

Usage

par_pattern()

Value

A string of the regular expression for a parameter name.

Examples

par_pattern()

Parameter Descriptions

Description

Default parameter descriptions which may be overridden in individual functions.

Arguments

Details

A flag is a non-missing logical scalar.

A string is a non-missing character scalar.

Extreme Probability

Description

Calculates the probability that a cumulative distribution function probability is at least that extreme.

Usage

pextreme(x)

Arguments

Value

A numeric vector of values between 0 and 1.

See Also

Other residuals: sextreme()

Examples

pextreme(seq(0, 1, by = 0.1))

Phi

Description

The standard normal cumulative density function.

Usage

phi(x)

Arguments

Details

A wrapper on stats::pnorm().

Value

A numeric atomic object.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), pow(), step()

Examples

phi(0:2)

Power

Description

R equivalent to the power function.

Usage

pow(x, n)

Arguments

Details

Wrapper on x^n.

Value

A numeric atomic object of x raised to n.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), step()

Examples

pow(10, 2)

Proportional Change

Description

Calculates the proportional change for two sets of numbers.

Usage

proportional_change(x, x2)

Arguments

Value

A numeric object of the proportional change.

See Also

Other proportional: proportional_difference()

Examples

proportional_change(1, 2)
proportional_change(2, 1)

Proportional Change2

Description

Calculates the proportional change for a vector of two non-negative numbers.

Usage

proportional_change2(x)

Arguments

Value

A number.

See Also

Other proportional fun2: proportional_difference2()

Examples

proportional_change2(c(1, 2))
proportional_change2(c(2, 1))

Proportional Difference

Description

Calculates the proportional difference for two sets of numbers.

Usage

proportional_difference(x, x2)

Arguments

Value

A numeric object of the proportional change.

See Also

Other proportional: proportional_change()

Examples

proportional_difference(1, 2)
proportional_difference(2, 1)

Proportional Difference2

Description

Calculates the proportional difference for a vector of two non-negative numbers.

Usage

proportional_difference2(x)

Arguments

Value

A number.

See Also

Other proportional fun2: proportional_change2()

Examples

proportional_difference2(c(1, 2))
proportional_difference2(c(2, 1))

Bayesian P-Value

Description

A Bayesian p-value (p) is here defined in terms of the quantile-based (1-p) * 100% credible interval (CRI) that just includes a threshold (Kery and Schaub 2011). By default a p-value of 0.05 indicates that the 95% CRI just includes 0.

Usage

pvalue(x, threshold = 0, na_rm = FALSE)

Arguments

Value

A number between 0 and 1.

References

Kery, M., and Schaub, M. 2011. Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press, Boston. Available from https://www.vogelwarte.ch/en/research/population-biology/book-bpa/.

See Also

Other summary: kurtosis(), lower(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

pvalue(as.numeric(0:100))

Proportion of Zeros

Description

The proportion of zeros in an numeric object.

Usage

pzeros(x, na_rm = FALSE)

Arguments

Value

A number between 0 and 1.

See Also

Other summary: kurtosis(), lower(), pvalue(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

pzeros(c(0:2))

Bernoulli Random Samples

Description

Bernoulli Random Samples

Usage

ran_bern(n = 1, prob = 0.5)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_bern(10)

Beta-Binomial Random Samples

Description

This parameterization of the beta-binomial distribution uses an expected probability parameter, prob, and a dispersion parameter, theta. The parameters of the underlying beta mixture are alpha = (2 * prob) / theta and beta = (2 * (1 - prob)) / theta. This parameterization of theta is unconventional, but has useful properties when modelling. When theta = 0, the beta-binomial reverts to the binomial distribution. When theta = 1 and prob = 0.5, the parameters of the beta distribution become alpha = 1 and beta = 1, which correspond to a uniform distribution for the beta-binomial probability parameter.

Usage

ran_beta_binom(n = 1, size = 1, prob = 0.5, theta = 0)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_beta_binom(10, 1, 0.5, 0)

Binomial Random Samples

Description

Binomial Random Samples

Usage

ran_binom(n = 1, size = 1, prob = 0.5)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_binom(10)

Gamma Random Samples

Description

Gamma Random Samples

Usage

ran_gamma(n = 1, shape = 1, rate = 1)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_gamma(10)

Gamma-Poisson Random Samples

Description

Gamma-Poisson Random Samples

Usage

ran_gamma_pois(n = 1, lambda = 1, theta = 0)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_gamma_pois(10, theta = 1)

Zero-Inflated Gamma-Poisson Random Samples

Description

Zero-Inflated Gamma-Poisson Random Samples

Usage

ran_gamma_pois_zi(n = 1, lambda = 1, theta = 0, prob = 0)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_gamma_pois_zi(10, lambda = 3, theta = 1, prob = 0.5)

Log-Normal Random Samples

Description

Log-Normal Random Samples

Usage

ran_lnorm(n = 1, meanlog = 0, sdlog = 1)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_lnorm(10)

Negative Binomial Random Samples

Description

Identical to Gamma-Poisson Random Samples.

Usage

ran_neg_binom(n = 1, lambda = 1, theta = 0)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_neg_binom(10, theta = 1)

Normal Random Samples

Description

Normal Random Samples

Usage

ran_norm(n = 1, mean = 0, sd = 1)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_pois(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_norm(10)

Poisson Random Samples

Description

Poisson Random Samples

Usage

ran_pois(n = 1, lambda = 1)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois_zi(), ran_skewnorm(), ran_student()

Examples

ran_pois(10)

Zero-Inflated Poisson Random Samples

Description

Zero-Inflated Poisson Random Samples

Usage

ran_pois_zi(n = 1, lambda = 1, prob = 0)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_skewnorm(), ran_student()

Examples

ran_pois_zi(10, prob = 0.5)

Skew Normal Random Samples

Description

Skew Normal Random Samples

Usage

ran_skewnorm(n = 1, mean = 0, sd = 1, shape = 0)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_student()

Examples

ran_skewnorm(10, shape = -1)
ran_skewnorm(10, shape = 0)
ran_skewnorm(10, shape = 1)

Student's t Random Samples

Description

Student's t Random Samples

Usage

ran_student(n = 1, mean = 0, sd = 1, theta = 0)

Arguments

Value

A numeric vector of the random samples.

See Also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewnorm()

Examples

ran_student(10, theta = 1 / 2)

Bernoulli Residuals

Description

Bernoulli Residuals

Usage

res_bern(x, prob = 0.5, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_bern(c(TRUE, FALSE), 0.7)

Beta-Binomial Residuals

Description

This parameterization of the beta-binomial distribution uses an expected probability parameter, prob, and a dispersion parameter, theta. The parameters of the underlying beta mixture are alpha = (2 * prob) / theta and beta = (2 * (1 - prob)) / theta. This parameterization of theta is unconventional, but has useful properties when modelling. When theta = 0, the beta-binomial reverts to the binomial distribution. When theta = 1 and prob = 0.5, the parameters of the beta distribution become alpha = 1 and beta = 1, which correspond to a uniform distribution for the beta-binomial probability parameter.

Usage

res_beta_binom(
  x,
  size = 1,
  prob = 0.5,
  theta = 0,
  type = "dev",
  simulate = FALSE
)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_beta_binom(c(0, 1, 2), 4, 0.5, 0.1)

Binomial Residuals

Description

Binomial Residuals

Usage

res_binom(x, size = 1, prob = 0.5, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_binom(c(0, 1, 2), 2, 0.3)

Gamma Residuals

Description

Gamma Residuals

Usage

res_gamma(x, shape = 1, rate = 1, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_gamma(c(0, 1, 2), 1, 2)

Gamma-Poisson Residuals

Description

Gamma-Poisson Residuals

Usage

res_gamma_pois(x, lambda = 1, theta = 0, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_gamma_pois(c(0, 1, 2), 1, 1)

Zero-Inflated Gamma-Poisson Residuals

Description

Zero-Inflated Gamma-Poisson Residuals

Usage

res_gamma_pois_zi(
  x,
  lambda = 1,
  theta = 0,
  prob = 0,
  type = "dev",
  simulate = FALSE
)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_gamma_pois_zi(c(0, 1, 2), 1, 1, 0.5)

Log-Normal Residuals

Description

Log-Normal Residuals

Usage

res_lnorm(x, meanlog = 0, sdlog = 1, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_lnorm(10)

Negative Binomial Residuals

Description

Negative Binomial Residuals

Usage

res_neg_binom(x, lambda = 1, theta = 0, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_neg_binom(c(0, 1, 5), 2, 3)

Normal Residuals

Description

Normal Residuals

Usage

res_norm(x, mean = 0, sd = 1, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_pois(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_norm(c(-2:2))

Poisson Residuals

Description

Poisson Residuals

Usage

res_pois(x, lambda = 1, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois_zi(), res_skewnorm(), res_student()

Examples

res_pois(c(1, 3, 4), 3)

Zero-Inflated Poisson Residuals

Description

Zero-Inflated Poisson Residuals

Usage

res_pois_zi(x, lambda = 1, prob = 0, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_skewnorm(), res_student()

Examples

res_pois_zi(c(1, 3, 4), 6, 0.5, type = "raw")

Skew Normal Residuals

Description

Skew Normal Residuals

Usage

res_skewnorm(x, mean = 0, sd = 1, shape = 0, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_student()

Examples

res_skewnorm(c(-2:2))

Student's t Residuals

Description

Student's t Residuals

Usage

res_student(x, mean = 0, sd = 1, theta = 0, type = "dev", simulate = FALSE)

Arguments

Value

An numeric vector of the corresponding residuals.

See Also

Other res_dist: res_bern(), res_beta_binom(), res_binom(), res_gamma(), res_gamma_pois(), res_gamma_pois_zi(), res_lnorm(), res_neg_binom(), res_norm(), res_pois(), res_pois_zi(), res_skewnorm()

Examples

res_student(c(1, 3.5, 4), mean = 6, sd = 0.5, theta = 1 / 3, type = "raw")

Adjust Beta Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Beta distribution. The Beta distribution has a maximum variance of ⁠mean(x) * (1 - mean(x)⁠, where mean(x) = alpha / (alpha + beta). If the inputs produce a desired variance that is greater than the maximum possible variance, or provides alpha and/or beta parameters that are ⁠< 1⁠ and thus push more probability weight towards extreme probability values, this function returns alpha = 1 and beta = 1 (the uniform distribution).

Usage

sens_beta(alpha, beta, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_exp(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_beta(10, 10, 2)
sens_beta(10, 10, 0.8)

Adjust Exponential Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the exponential distribution. Due to the parameterization of this distribution, adjusting the standard deviation necessarily changes the mean value.

Usage

sens_exp(rate, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_exp(10, 2)
sens_exp(10, 0.8)

Adjust Gamma Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Gamma distribution.

Usage

sens_gamma(shape, rate, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_gamma(10, 2, 2)
sens_gamma(10, 2, 0.2)

Adjust Gamma-Poisson Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) the standard deviation of the Negative Binomial distribution. This function does not currently have the option to reduce the standard deviation.

Usage

sens_gamma_pois(lambda, theta, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_gamma_pois(10, 0.1, 2)

Adjust Zero-Inflated Gamma-Poisson Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Zero-Inflated Gamma-Poisson distribution.

Usage

sens_gamma_pois_zi(lambda, theta, prob, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_gamma_pois_zi(10, 0.1, 0.3, 2)

Adjust Log-Normal Distribution Parameters for Sensitivity Analysis

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Log-Normal distribution. With high values of sdlog (i.e., ⁠> 9⁠), and sd_mult > 1, the mean of the adjusted distribution can be expected to have a mean value that is very different from the original mean, however, the proportional difference in these values should not be very different.

Usage

sens_lnorm(meanlog, sdlog, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_neg_binom(), sens_norm(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_lnorm(0, 1, 2)
sens_lnorm(0, 1, 0.8)

Adjust Negative Binomial Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) the standard deviation of the Negative Binomial distribution. This function does not currently have the option to reduce the standard deviation.

Usage

sens_neg_binom(lambda, theta, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_norm(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_neg_binom(10, 0.1, 2)

Adjust Normal Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Normal distribution without changing the mean.

Usage

sens_norm(mean, sd, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_pois(), sens_skewnorm(), sens_student()

Examples

sens_norm(10, 3, 2)
sens_norm(10, 3, 0.8)

Adjust Poisson Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Poisson distribution. Due to the parameterization of this distribution, adjusting the standard deviation necessarily changes the mean value.

Usage

sens_pois(lambda, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_skewnorm(), sens_student()

Examples

sens_pois(10, 2)
sens_pois(10, 0.8)

Adjust Skew Normal Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Skew Normal distribution without changing the mean.

Usage

sens_skewnorm(mean, sd, shape, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_pois(), sens_student()

Examples

sens_skewnorm(10, 3, -1, 2)
sens_skewnorm(10, 3, 3, 0.8)

Adjust Student's t Distribution Parameters for Sensitivity Analyses

Description

Expands (sd_mult > 1) or reduces (sd_mult < 1) the standard deviation of the Student's t distribution. Because the variance of this distribution is not defined for every degree of freedom, the adjustment to the standard deviation is approximate, and the mean of the adjusted distribution can be expected to have shifted.

Usage

sens_student(mean, sd, theta, sd_mult = 2)

Arguments

Value

A named list of the adjusted distribution's parameters.

See Also

Other sens_dist: sens_beta(), sens_exp(), sens_gamma(), sens_gamma_pois(), sens_gamma_pois_zi(), sens_lnorm(), sens_neg_binom(), sens_norm(), sens_pois(), sens_skewnorm()

Examples

sens_student(10, 3, 0.1, 2)
sens_student(10, 3, 0.1, 0.8)

Extreme Surprisal

Description

Calculates the surprisal (in bits) that a cumulative distribution function probability is at least that extreme.

Usage

sextreme(x, directional = FALSE)

Arguments

Value

A numeric vector of surprisal values.

See Also

Other residuals: pextreme()

Examples

sextreme(seq(0.1, 0.9, by = 0.1))
sextreme(seq(0.1, 0.9, by = 0.1), directional = TRUE)

Skewness

Description

Skewness

Usage

skewness(x, na_rm = FALSE)

Arguments

Value

A number.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

skewness(1:10)

Step

Description

Step

Usage

step(x)

Arguments

Value

A logical value.

See Also

Other translations: exp10(), exp2(), fabs(), ilog(), ilog10(), ilog2(), ilogit(), inv_logit(), invlogit(), log10<-(), log2<-(), log<-(), logit(), logit<-(), phi(), pow()

Examples

step(1)

Surprisal Value

Description

The surprisal value (Greenland 2019) is the pvalue expressed in terms of how many consecutive heads would have to be thrown on a fair coin in a single attempt to achieve the same probability.

Usage

svalue(x, threshold = 0, na_rm = FALSE)

Arguments

Value

A non-negative number.

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. doi:10.1080/00031305.2018.1529625.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

svalue(as.numeric(0:100))

Upper Credible Limit

Description

Calculates the quantile-based upper credible limit.

Usage

upper(x, conf_level = 0.95, na_rm = FALSE)

Arguments

Details

By default it returns the 95% credible limit which corresponds to the 97.5% quantile.

Value

A number.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), svalue(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

upper(as.numeric(0:100))

Variance

Description

Variance

Usage

variance(x, na_rm = FALSE)

Arguments

Value

A number.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), svalue(), upper(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

variance(1:10)

Mean

Description

Mean

Usage

xtr_mean(x, na_rm = FALSE)

Arguments

Value

A number.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

xtr_mean(1:10)

Median

Description

Median

Usage

xtr_median(x, na_rm = FALSE)

Arguments

Value

A number.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_sd(), zeros(), zscore()

Examples

xtr_mean(1:10)

Standard Deviation

Description

Standard Deviation

Usage

xtr_sd(x, na_rm = FALSE)

Arguments

Value

A number.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), zeros(), zscore()

Examples

xtr_sd(1:10)

Zeros

Description

The number of zeros in an numeric object.

Usage

zeros(x, na_rm = FALSE)

Arguments

Value

A non-negative integer.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zscore()

Examples

zeros(c(0:2))

Z-Score

Description

The Bayesian z-score is here defined as the number of standard deviations from the mean estimate to zero.

Usage

zscore(x, na_rm = FALSE)

Arguments

Value

A number.

See Also

Other summary: kurtosis(), lower(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros()

Examples

zscore(as.numeric(0:100))