Package {extras}

Title:	Helper Functions for Bayesian Analyses
Version:	0.10.0
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, extraDistr, 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
Config/roxygen2/version:	8.0.0.9000
NeedsCompilation:	no
Packaged:	2026-07-15 16:41:06 UTC; nicolehill
Author:	Nicole Hill [aut, cre], Joe Thorley [aut], Kirill Müller [ctb], Nadine Hussein [ctb], Stefano Mezzini [aut], Poisson Consulting [cph, fnd]
Maintainer:	Nicole Hill <nicole@poissonconsulting.ca>
Repository:	CRAN
Date/Publication:	2026-07-15 19:30:02 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:

• Nicole Hill nicole@poissonconsulting.ca (ORCID)

• Joe Thorley joe@poissonconsulting.ca (ORCID)

• Stefano Mezzini (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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), dev_skewnorm(), dev_student()

Examples

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

Skew-Lognormal Deviances

Description

Skew-Lognormal Deviances

Usage

dev_skewlnorm(x, meanlog = 0, sdlog = 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_skewnorm(), dev_student()

Examples

dev_skewlnorm(exp(-2:2))
dev_skewlnorm(exp(-2:2), 0, 1, 5)
dev_skewlnorm(exp(-2:2), 0, 1, 5, res = TRUE)

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_skewlnorm(), 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_skewlnorm(), dev_skewnorm()

Examples

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

Direction of a distribution

Description

The direction of a distribution is the side (left/right) that the distribution's center falls on, relative to a threshold. The center can be calculated using a user-specified function, including the median (default), mean, geometric mean, mode, or any other custom function. By convention, values below the threshold fall to the left, while values above the threshold fall to the right. Center estimates equal to the threshold are assumed to fall to the right.

Usage

direction(x, estimate = xtr_median, threshold = 0, na_rm = FALSE)

Arguments

Value

A string indicating if at least half of the observations are above the threshold ("right") or not ("left"), or NA_character_ in the case of missing values when na_rm = FALSE.

See Also

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

Examples

direction(c(1, 2, 3))
direction(c(-1))
direction(c(0, 0, 0))
direction(c(-100, 1, 1))
direction(c(-100, 1, 1), mean)
direction(c(100, 0.01, 0.01), function(.x) exp(mean(log(.x))))

Directional information

Description

The occurrence of an event (or lack thereof) transmits more or less information depending on the event's probability.

Usage

directional_information(
  x,
  ...,
  side = "median",
  threshold = 0,
  threshold_split = "proportional",
  skeptical = TRUE,
  na_rm = FALSE
)

p2info(p, n = Inf)

Arguments

Details

Quantifies the information about direction in a posterior distribution based on the directional probability. This function calculates such information using the difference in the probability of direction (see probability_direction()), after converting each probability to bits (also see svalue().

When skeptical = TRUE (default), one sample is added to the empty side, giving bounds of \pm \log_2(n) rather than \pm n, to mimic the behaviour of pvalue() and svalue(). When skeptical = FALSE, information is instead clamped to [-n, n], which is assumes the MCMC samples are independent and representative.

Value

A number indicating the directional information in bits. If x has NA values but na_rm is FALSE, returns NA_real.

Functions

• directional_information(): Calculate the directional information from a posterior distribution.

• p2info(): Calculate the information from a vector of probabilities.

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: direction(), kurtosis(), lower(), probability_direction(), pvalue(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

directional_information(0)
directional_information(1) # one coin flip of information
directional_information(c(1, 1)) # two coin flips
directional_information(c(1, 1, -1)) # x[2] and x[3] cancel out
directional_information(c(1, 1, -1, -1)) # both sides cancel out
directional_information(rnorm(1e3, mean = 0))
directional_information(rnorm(1e3, mean = 1))
directional_information(rnorm(1e3, mean = 10)) # all coin flips are positive
directional_information(rnorm(1e3, mean = -10)) # all coin flips are negative
directional_information(rnorm(1e3, mean = 1e3)) # only quantiles matter
directional_information(rnorm(1e6, mean = 1e3)) # more `x` implies more info
directional_information(rep(1, 1000)) # skeptical = TRUE (default) gives log2(n)
directional_information(rep(1, 1000), skeptical = FALSE) # skeptical = FALSE gives n

p2info(seq(0, 1, by = 0.1))
p2info(seq(0, 1, by = 0.1), n = 10) # limit information to be in [-10, 10]

Skew-Lognormal Distribution

Description

The skew-lognormal distribution of a value x whose natural logarithm follows a Skew-Normal distribution with location meanlog, scale sdlog and shape. It reduces to the Log-Normal distribution when shape = 0.

Usage

dskewlnorm(x, meanlog = 0, sdlog = 1, shape = 0, log = FALSE)

pskewlnorm(q, meanlog = 0, sdlog = 1, shape = 0)

qskewlnorm(p, meanlog = 0, sdlog = 1, shape = 0)

rskewlnorm(n = 1, meanlog = 0, sdlog = 1, shape = 0)

Arguments

Value

dskewlnorm gives the density, pskewlnorm gives the distribution function, qskewlnorm gives the quantile function, and rskewlnorm generates random deviates. pskewlnorm and qskewlnorm use the lower tail probability.

Examples

dskewlnorm(x = 1:5, meanlog = 0, sdlog = 1, shape = 0.1)
dskewlnorm(x = 1:5, meanlog = 0, sdlog = 1, shape = -1)
qskewlnorm(p = c(0.1, 0.4), meanlog = 0, sdlog = 1, shape = 0.1)
qskewlnorm(p = c(0.1, 0.4), meanlog = 0, sdlog = 1, shape = -1)
pskewlnorm(q = 1:5, meanlog = 0, sdlog = 1, shape = 0.1)
pskewlnorm(q = 1:5, meanlog = 0, sdlog = 1, shape = -1)
rskewlnorm(n = 3, meanlog = 0, sdlog = 1, shape = 0.1)
rskewlnorm(n = 3, meanlog = 0, sdlog = 1, shape = -1)

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: direction(), directional_information(), lower(), probability_direction(), 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(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

Examples

log_lik_bern(c(TRUE, FALSE), 0.7)

Beta Log-Likelihood

Description

Beta Log-Likelihood

Usage

log_lik_beta(x, alpha = 1, beta = 1, tlower = 0, tupper = 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_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

Examples

log_lik_beta(c(0, 0.5, 0.7, 1), 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,
  tlower = 0,
  tupper = Inf,
  memoize = FALSE
)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

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, tlower = 0, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

Examples

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

Exponential Log-Likelihood

Description

The exponential distribution is parameterized in terms of the rate.

Usage

log_lik_exp(x, rate = 1, tlower = 0, tupper = Inf)

Arguments

Value

A numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

Examples

log_lik_exp(c(0, 1, 2), 2)

Gamma Log-Likelihood

Description

Gamma Log-Likelihood

Usage

log_lik_gamma(x, shape = 1, rate = 1, tlower = 0, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

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, tlower = 0, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

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,
  tlower = 0,
  tupper = Inf
)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

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, tlower = 0, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

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, tlower = 0, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

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, tlower = -Inf, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

Examples

log_lik_norm(c(-2:2))

Poisson Log-Likelihood

Description

Poisson Log-Likelihood

Usage

log_lik_pois(x, lambda = 1, tlower = 0, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

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, tlower = 0, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student(), log_lik_unif()

Examples

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

Skew-Lognormal Log-Likelihood

Description

Skew-Lognormal Log-Likelihood

Usage

log_lik_skewlnorm(
  x,
  meanlog = 0,
  sdlog = 1,
  shape = 0,
  tlower = 0,
  tupper = Inf
)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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(), log_lik_unif()

Examples

log_lik_skewlnorm(1:5)
log_lik_skewlnorm(1:5, shape = -2)
log_lik_skewlnorm(1:5, shape = 2)

Skew Normal Log-Likelihood

Description

Skew Normal Log-Likelihood

Usage

log_lik_skewnorm(x, mean = 0, sd = 1, shape = 0, tlower = -Inf, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_student(), log_lik_unif()

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, tlower = -Inf, tupper = Inf)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_unif()

Examples

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

Uniform Log-Likelihood

Description

Uniform Log-Likelihood

Usage

log_lik_unif(x, min = 0, max = 1)

Arguments

Value

An numeric vector of the corresponding log-likelihoods.

See Also

Other log_lik_dist: log_lik_bern(), log_lik_beta(), log_lik_beta_binom(), log_lik_binom(), log_lik_exp(), 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_skewlnorm(), log_lik_skewnorm(), log_lik_student()

Examples

log_lik_unif(c(0, 0.7, 1))

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: direction(), directional_information(), kurtosis(), probability_direction(), 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)

Bernoulli Cumulative Distribution Function

Description

Bernoulli Cumulative Distribution Function

Usage

prob_bern(x, prob = 0.5)

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_bern(c(TRUE, FALSE), 0.7)

Beta Cumulative Distribution Function

Description

Beta Cumulative Distribution Function

Usage

prob_beta(x, alpha = 1, beta = 1)

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

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

Beta-Binomial Cumulative Distribution Function

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

prob_beta_binom(x, size = 1, prob = 0.5, theta = 0)

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

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

Binomial Cumulative Distribution Function

Description

Binomial Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

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

Exponential Cumulative Distribution Function

Description

Exponential Cumulative Distribution Function

Usage

prob_exp(x, rate = 1)

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_exp(c(0, 1, 2), 2)

Gamma Cumulative Distribution Function

Description

Gamma Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_gamma(c(0, 1, 2), 1, 2)

Gamma-Poisson Cumulative Distribution Function

Description

Gamma-Poisson Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_gamma_pois(c(0, 1, 2), 1, 1)

Zero-Inflated Gamma-Poisson Cumulative Distribution Function

Description

Zero-Inflated Gamma-Poisson Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

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

Log-Normal Cumulative Distribution Function

Description

Log-Normal Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_lnorm(10, 0, 2)

Negative Binomial Cumulative Distribution Function

Description

Negative Binomial Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_neg_binom(c(0, 1, 2), 2, 1)

Normal Cumulative Distribution Function

Description

Normal Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_norm(c(-2:2))

Poisson Cumulative Distribution Function

Description

Poisson Cumulative Distribution Function

Usage

prob_pois(x, lambda = 1)

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_pois(c(1, 3, 4), 3)

Zero-Inflated Poisson Cumulative Distribution Function

Description

Zero-Inflated Poisson Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_skewlnorm(), prob_skewnorm(), prob_student(), prob_unif()

Examples

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

Skew-Lognormal Cumulative Distribution Function

Description

Skew-Lognormal Cumulative Distribution Function

Usage

prob_skewlnorm(x, meanlog = 0, sdlog = 1, shape = 0)

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewnorm(), prob_student(), prob_unif()

Examples

prob_skewlnorm(1:5)
prob_skewlnorm(1:5, shape = -2)
prob_skewlnorm(1:5, shape = 2)

Skew Normal Cumulative Distribution Function

Description

Skew Normal Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_student(), prob_unif()

Examples

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

Student's t Cumulative Distribution Function

Description

Student's t Cumulative Distribution Function

Usage

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

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_unif()

Examples

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

Uniform Cumulative Distribution Function

Description

Uniform Cumulative Distribution Function

Usage

prob_unif(x, min = 0, max = 1)

Arguments

Value

An numeric vector of the corresponding probabilities.

See Also

Other prob_dist: prob_bern(), prob_beta(), prob_beta_binom(), prob_binom(), prob_exp(), prob_gamma(), prob_gamma_pois(), prob_gamma_pois_zi(), prob_lnorm(), prob_neg_binom(), prob_norm(), prob_pois(), prob_pois_zi(), prob_skewlnorm(), prob_skewnorm(), prob_student()

Examples

prob_unif(c(0, 0.5, 1))

Probability of Direction

Description

The probability of direction (PD) is the proportion of the (posterior) distribution above (right) or below (left) a threshold.

Usage

probability_direction(x, side = "median", threshold = 0, na_rm = FALSE)

Arguments

Details

By default, the direction is based on the side of the median value, but it can be specified to measure support for specific hypotheses. A right-side PD of 0.9 indicates that the interval spanning from the threshold to infinity has a coverage of 90%. Can be used as a measure of certainty in the direction of the estimate (e.g., positive or negative when using a threshold of 0). NOTE: probability estimates of 0 or 1 are corrected towards 0.5 by adding or subtracting 1 / (length(x) + 1), where x is a vector of MCMC samples. Ideally, x should be large enough as to make the correction negligible.

Value

A number between 0 and 1. If x has NA values but na_rm is FALSE, returns NA_real.

References

Makowski, D., Ben-Shachar, M.S., Chen, S.H.A., and Lüdecke, D. 2019. Indices of Effect Existence and Significance in the Bayesian Framework. Front. Psychol. 10: 2767. doi:10.3389/fpsyg.2019.02767.

See Also

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

Examples

x <- rnorm(1e6, qnorm(0.05, lower.tail = TRUE))
probability_direction(x, side = "left")
probability_direction(x, side = "right") # = 1 - probability_direction(x, side = "left")
probability_direction(c(0, 0, 1), side = "right") # returns P(X >0) = 1/3 instead of P(X >= 0) = 1
probability_direction(c(1, 1), side = "right") # p = 1 - 1/(n+1)

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).

Usage

pvalue(x, ..., side = "both", threshold = 0, skeptical = TRUE, na_rm = FALSE)

Arguments

Details

A p-value of 0.05 indicates that the 95% CRI just includes the threshold value.

Note that the function contains the sample-size correction p_{c} = p * n / (n + 1) to avoid p-values of 0. The function can still return p-values of 1.

When skeptical = TRUE (default), a floor of 1 / (n + 1) is applied to avoid p-values of 0 when all samples are on one side of the threshold. When skeptical = FALSE, p-values of 0 are allowed.

To use as a measure of certainty in the direction of the estimate (i.e., positive or negative), see probability_direction().

For p-values converted to bits, see svalue().

To convert MCMC objects to information, see directional_information().

Value

A number between 0 and 1. If x has NA values but na_rm is FALSE, returns NA_real.

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: direction(), directional_information(), kurtosis(), lower(), probability_direction(), pzeros(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

x <- rnorm(1e6, qnorm(0.05, lower.tail = TRUE))
pvalue(x) # should be 0.05 * 2
pvalue(x, side = "left") # should be 0.95
pvalue(x, side = "right") # should be 0.05
pvalue(rep(1, 10)) # skeptical = TRUE (default) avoids p = 0
pvalue(rep(1, 10), skeptical = FALSE) # skeptical = FALSE allows p = 0

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: direction(), directional_information(), kurtosis(), lower(), probability_direction(), pvalue(), skewness(), svalue(), upper(), variance(), xtr_mean(), xtr_median(), xtr_sd(), zeros(), zscore()

Examples

pzeros(c(0:2))

Bernoulli Quantile Function

Description

Bernoulli Quantile Function

Usage

quant_bern(x, prob = 0.5)

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_bern(c(0.3, 0.8), 0.7)

Beta Quantile Function

Description

Beta Quantile Function

Usage

quant_beta(x, alpha = 1, beta = 1)

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_beta(c(0.1, 0.4, 0.6), 2, 3)

Binomial Quantile Function

Description

Binomial Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_binom(c(0.1, 0.4, 0.6), 2, 0.3)

Exponential Quantile Function

Description

Exponential Quantile Function

Usage

quant_exp(x, rate = 1)

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_exp(c(0.1, 0.4, 0.6), 2)

Gamma Quantile Function

Description

Gamma Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_gamma(c(0.1, 0.4, 0.6), 1, 2)

Gamma-Poisson Quantile Function

Description

Gamma-Poisson Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_gamma_pois(c(0.1, 0.4, 0.6), 1, 1)

Zero-Inflated Gamma-Poisson Quantile Function

Description

Zero-Inflated Gamma-Poisson Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_gamma_pois_zi(c(0.1, 0.4, 0.6), 3, 1, prob = 0.5)

Log-Normal Quantile Function

Description

Log-Normal Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_lnorm(c(0.1, 0.4, 0.6), 0, 2)

Negative Binomial Quantile Function

Description

Negative Binomial Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_neg_binom(c(0.1, 0.4, 0.6), 2, 1)

Normal Quantile Function

Description

Normal Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_norm(c(0.1, 0.4, 0.6))

Poisson Quantile Function

Description

Poisson Quantile Function

Usage

quant_pois(x, lambda = 1)

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_pois(c(0.1, 0.4, 0.6), 3)

Zero-Inflated Poisson Quantile Function

Description

Zero-Inflated Poisson Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_skewlnorm(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_pois_zi(c(0.1, 0.4, 0.6), 3, prob = 0.5)

Skew-Lognormal Quantile Function

Description

Skew-Lognormal Quantile Function

Usage

quant_skewlnorm(x, meanlog = 0, sdlog = 1, shape = 0)

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewnorm(), quant_student(), quant_unif()

Examples

quant_skewlnorm(c(0.1, 0.4, 0.6))
quant_skewlnorm(c(0.1, 0.4, 0.6), shape = -2)
quant_skewlnorm(c(0.1, 0.4, 0.6), shape = 2)

Skew Normal Quantile Function

Description

Skew Normal Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_student(), quant_unif()

Examples

quant_skewnorm(c(0.1, 0.4, 0.6))
quant_skewnorm(c(0.1, 0.4, 0.6), shape = -2)
quant_skewnorm(c(0.1, 0.4, 0.6), shape = 2)

Student's t Quantile Function

Description

Student's t Quantile Function

Usage

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

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_unif()

Examples

quant_student(c(0.1, 0.4, 0.6), mean = 1, sd = 2, theta = 1 / 3)

Uniform Quantile Function

Description

Uniform Quantile Function

Usage

quant_unif(x, min = 0, max = 1)

Arguments

Value

An numeric vector of the corresponding quantiles.

See Also

Other quant_dist: quant_bern(), quant_beta(), quant_binom(), quant_exp(), quant_gamma(), quant_gamma_pois(), quant_gamma_pois_zi(), quant_lnorm(), quant_neg_binom(), quant_norm(), quant_pois(), quant_pois_zi(), quant_skewlnorm(), quant_skewnorm(), quant_student()

Examples

quant_unif(c(0.1, 0.4, 0.6))

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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), 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_skewlnorm(), ran_skewnorm(), ran_student()

Examples

ran_pois_zi(10, prob = 0.5)

Skew-Lognormal Random Samples

Description

Skew-Lognormal Random Samples

Usage

ran_skewlnorm(n = 1, meanlog = 0, sdlog = 1, shape = 0)

Arguments