Package index • distributions3

Generic functions for S3 distribution objects

pdf() log_pdf() pmf(): Evaluate the probability density of a probability distribution

cdf(): Evaluate the cumulative distribution function of a probability distribution

random() simulate(<distribution>): Draw a random sample from a probability distribution

variance() skewness() kurtosis(): Compute the moments of a probability distribution

support(): Return the support of a distribution

is_discrete() is_continuous(): Determine whether a distribution is discrete or continuous

suff_stat(): Compute the sufficient statistics of a distribution from data

fit_mle(): Fit a distribution to data

is_distribution(): Is an object a distribution?

prodist(): Extracting fitted or predicted probability distributions from models

simulate(<default>): Simulate responses from fitted model objects

Utilities, tools, and data

apply_dpqr() make_support() make_positive_integer(): Utilities for distributions3 objects

log_likelihood() likelihood(): Compute the (log-)likelihood of a probability distribution given data

FIFA2018: Goals scored in all 2018 FIFA World Cup matches

Visualization infrastructure

plot(<distribution>): Plot the p.m.f, p.d.f or c.d.f. of a univariate distribution

plot_cdf(): Plot the CDF of a distribution

plot_pdf(): Plot the PDF of a distribution

stat_auc() geom_auc(): Fill out area under the curve for a plotted PDF

Bernoulli distribution

Bernoulli(): Create a Bernoulli distribution

cdf(<Bernoulli>): Evaluate the cumulative distribution function of a Bernoulli distribution

fit_mle(<Bernoulli>): Fit a Bernoulli distribution to data

pdf(<Bernoulli>) log_pdf(<Bernoulli>): Evaluate the probability mass function of a Bernoulli distribution

quantile(<Bernoulli>): Determine quantiles of a Bernoulli distribution

random(<Bernoulli>): Draw a random sample from a Bernoulli distribution

suff_stat(<Bernoulli>): Compute the sufficient statistics for a Bernoulli distribution from data

support(<Bernoulli>): Return the support of the Bernoulli distribution

Beta distribution

Beta(): Create a Beta distribution

cdf(<Beta>): Evaluate the cumulative distribution function of a Beta distribution

pdf(<Beta>) log_pdf(<Beta>): Evaluate the probability mass function of a Beta distribution

quantile(<Beta>): Determine quantiles of a Beta distribution

random(<Beta>): Draw a random sample from a Beta distribution

support(<Beta>): Return the support of the Beta distribution

Binomial distribution

Binomial(): Create a Binomial distribution

cdf(<Binomial>): Evaluate the cumulative distribution function of a Binomial distribution

fit_mle(<Binomial>): Fit a Binomial distribution to data

pdf(<Binomial>) log_pdf(<Binomial>): Evaluate the probability mass function of a Binomial distribution

quantile(<Binomial>): Determine quantiles of a Binomial distribution

random(<Binomial>): Draw a random sample from a Binomial distribution

suff_stat(<Binomial>): Compute the sufficient statistics for the Binomial distribution from data

support(<Binomial>): Return the support of the Binomial distribution

Categorical distribution

Categorical(): Create a Categorical distribution

cdf(<Categorical>): Evaluate the cumulative distribution function of a Categorical distribution

pdf(<Categorical>) log_pdf(<Categorical>): Evaluate the probability mass function of a Categorical discrete distribution

quantile(<Categorical>): Determine quantiles of a Categorical discrete distribution

random(<Categorical>): Draw a random sample from a Categorical distribution

Cauchy distribution

Cauchy(): Create a Cauchy distribution

cdf(<Cauchy>): Evaluate the cumulative distribution function of a Cauchy distribution

pdf(<Cauchy>) log_pdf(<Cauchy>): Evaluate the probability mass function of a Cauchy distribution

quantile(<Cauchy>): Determine quantiles of a Cauchy distribution

random(<Cauchy>): Draw a random sample from a Cauchy distribution

support(<Cauchy>): Return the support of the Cauchy distribution

Chi-square distribution

ChiSquare(): Create a Chi-Square distribution

cdf(<ChiSquare>): Evaluate the cumulative distribution function of a chi square distribution

pdf(<ChiSquare>) log_pdf(<ChiSquare>): Evaluate the probability mass function of a chi square distribution

quantile(<ChiSquare>): Determine quantiles of a chi square distribution

random(<ChiSquare>): Draw a random sample from a chi square distribution

support(<ChiSquare>): Return the support of the ChiSquare distribution

Erlang distribution

Erlang(): Create an Erlang distribution

cdf(<Erlang>): Evaluate the cumulative distribution function of an Erlang distribution

pdf(<Erlang>) log_pdf(<Erlang>): Evaluate the probability mass function of an Erlang distribution

quantile(<Erlang>): Determine quantiles of an Erlang distribution

random(<Erlang>): Draw a random sample from an Erlang distribution

support(<Erlang>): Return the support of the Erlang distribution

Exponential distribution

Exponential(): Create an Exponential distribution

cdf(<Exponential>): Evaluate the cumulative distribution function of an Exponential distribution

fit_mle(<Exponential>): Fit an Exponential distribution to data

pdf(<Exponential>) log_pdf(<Exponential>): Evaluate the probability density function of an Exponential distribution

quantile(<Exponential>): Determine quantiles of an Exponential distribution

random(<Exponential>): Draw a random sample from an Exponential distribution

suff_stat(<Exponential>): Compute the sufficient statistics of an Exponential distribution from data

support(<Exponential>): Return the support of the Exponential distribution

Fisher F distribution

FisherF(): Create an F distribution

cdf(<FisherF>): Evaluate the cumulative distribution function of an F distribution

pdf(<FisherF>) log_pdf(<FisherF>): Evaluate the probability mass function of an F distribution

quantile(<FisherF>): Determine quantiles of an F distribution

random(<FisherF>): Draw a random sample from an F distribution

support(<FisherF>): Return the support of the FisherF distribution

Frechet distribution

Frechet(): Create a Frechet distribution

cdf(<Frechet>): Evaluate the cumulative distribution function of a Frechet distribution

pdf(<Frechet>) log_pdf(<Frechet>): Evaluate the probability mass function of a Frechet distribution

quantile(<Frechet>): Determine quantiles of a Frechet distribution

random(<Frechet>): Draw a random sample from a Frechet distribution

support(<Frechet>): Return the support of the Frechet distribution

Generalized extreme value (GEV) distribution

GEV(): Create a Generalised Extreme Value (GEV) distribution

cdf(<GEV>): Evaluate the cumulative distribution function of a GEV distribution

pdf(<GEV>) log_pdf(<GEV>): Evaluate the probability mass function of a GEV distribution

quantile(<GEV>): Determine quantiles of a GEV distribution

random(<GEV>): Draw a random sample from a GEV distribution

support(<GEV>): Return the support of a GEV distribution

Generalized Pareto (GP) distribution

GP(): Create a Generalised Pareto (GP) distribution

cdf(<GP>): Evaluate the cumulative distribution function of a GP distribution

pdf(<GP>) log_pdf(<GP>): Evaluate the probability mass function of a GP distribution

quantile(<GP>): Determine quantiles of a GP distribution

random(<GP>): Draw a random sample from a GP distribution

support(<GP>): Return the support of the GP distribution

Gamma distribution

Gamma(): Create a Gamma distribution

cdf(<Gamma>): Evaluate the cumulative distribution function of a Gamma distribution

fit_mle(<Gamma>): Fit a Gamma distribution to data

pdf(<Gamma>) log_pdf(<Gamma>): Evaluate the probability mass function of a Gamma distribution

quantile(<Gamma>): Determine quantiles of a Gamma distribution

random(<Gamma>): Draw a random sample from a Gamma distribution

suff_stat(<Gamma>): Compute the sufficient statistics for a Gamma distribution from data

support(<Gamma>): Return the support of the Gamma distribution

Geometric distribution

Geometric(): Create a Geometric distribution

cdf(<Geometric>): Evaluate the cumulative distribution function of a Geometric distribution

fit_mle(<Geometric>): Fit a Geometric distribution to data

pdf(<Geometric>) log_pdf(<Geometric>): Evaluate the probability mass function of a Geometric distribution

quantile(<Geometric>): Determine quantiles of a Geometric distribution

random(<Geometric>): Draw a random sample from a Geometric distribution

suff_stat(<Geometric>): Compute the sufficient statistics for the Geometric distribution from data

support(<Geometric>): Return the support of the Geometric distribution

Gumbel distribution

Gumbel(): Create a Gumbel distribution

cdf(<Gumbel>): Evaluate the cumulative distribution function of a Gumbel distribution

pdf(<Gumbel>) log_pdf(<Gumbel>): Evaluate the probability mass function of a Gumbel distribution

quantile(<Gumbel>): Determine quantiles of a Gumbel distribution

random(<Gumbel>): Draw a random sample from a Gumbel distribution

support(<Gumbel>): Return the support of the Gumbel distribution

Hurdle negative binomial distribution

dhnbinom() phnbinom() qhnbinom() rhnbinom(): The hurdle negative binomial distribution

HurdleNegativeBinomial(): Create a hurdle negative binomial distribution

cdf(<HurdleNegativeBinomial>): Evaluate the cumulative distribution function of a hurdle negative binomial distribution

pdf(<HurdleNegativeBinomial>) log_pdf(<HurdleNegativeBinomial>): Evaluate the probability mass function of a hurdle negative binomial distribution

quantile(<HurdleNegativeBinomial>): Determine quantiles of a hurdle negative binomial distribution

random(<HurdleNegativeBinomial>): Draw a random sample from a hurdle negative binomial distribution

support(<HurdleNegativeBinomial>): Return the support of the hurdle negative binomial distribution

Hurdle Poisson distribution

dhpois() phpois() qhpois() rhpois(): The hurdle Poisson distribution

HurdlePoisson(): Create a hurdle Poisson distribution

cdf(<HurdlePoisson>): Evaluate the cumulative distribution function of a hurdle Poisson distribution

pdf(<HurdlePoisson>) log_pdf(<HurdlePoisson>): Evaluate the probability mass function of a hurdle Poisson distribution

quantile(<HurdlePoisson>): Determine quantiles of a hurdle Poisson distribution

random(<HurdlePoisson>): Draw a random sample from a hurdle Poisson distribution

support(<HurdlePoisson>): Return the support of the hurdle Poisson distribution

Hypergeometric distribution

HyperGeometric(): Create a HyperGeometric distribution

cdf(<HyperGeometric>): Evaluate the cumulative distribution function of a HyperGeometric distribution

pdf(<HyperGeometric>) log_pdf(<HyperGeometric>): Evaluate the probability mass function of a HyperGeometric distribution

quantile(<HyperGeometric>): Determine quantiles of a HyperGeometric distribution

random(<HyperGeometric>): Draw a random sample from a HyperGeometric distribution

support(<HyperGeometric>): Return the support of the HyperGeometric distribution

Log-normal distribution

LogNormal(): Create a LogNormal distribution

cdf(<LogNormal>): Evaluate the cumulative distribution function of a LogNormal distribution

fit_mle(<LogNormal>): Fit a Log Normal distribution to data

pdf(<LogNormal>) log_pdf(<LogNormal>): Evaluate the probability mass function of a LogNormal distribution

quantile(<LogNormal>): Determine quantiles of a LogNormal distribution

random(<LogNormal>): Draw a random sample from a LogNormal distribution

suff_stat(<LogNormal>): Compute the sufficient statistics for a Log-normal distribution from data

support(<LogNormal>): Return the support of the LogNormal distribution

Logistic distribution

Logistic(): Create a Logistic distribution

cdf(<Logistic>): Evaluate the cumulative distribution function of a Logistic distribution

pdf(<Logistic>) log_pdf(<Logistic>): Evaluate the probability mass function of a Logistic distribution

quantile(<Logistic>): Determine quantiles of a Logistic distribution

random(<Logistic>): Draw a random sample from a Logistic distribution

support(<Logistic>): Return the support of the Logistic distribution

Multinomial distribution

Multinomial(): Create a Multinomial distribution

pdf(<Multinomial>) log_pdf(<Multinomial>): Evaluate the probability mass function of a Multinomial distribution

random(<Multinomial>): Draw a random sample from a Multinomial distribution

Negative binomial distribution

NegativeBinomial(): Create a negative binomial distribution

cdf(<NegativeBinomial>): Evaluate the cumulative distribution function of a negative binomial distribution

pdf(<NegativeBinomial>) log_pdf(<NegativeBinomial>): Evaluate the probability mass function of a NegativeBinomial distribution

quantile(<NegativeBinomial>): Determine quantiles of a NegativeBinomial distribution

random(<NegativeBinomial>): Draw a random sample from a negative binomial distribution

support(<NegativeBinomial>): Return the support of the NegativeBinomial distribution

Normal distribution

Normal(): Create a Normal distribution

cdf(<Normal>): Evaluate the cumulative distribution function of a Normal distribution

fit_mle(<Normal>): Fit a Normal distribution to data

pdf(<Normal>) log_pdf(<Normal>): Evaluate the probability mass function of a Normal distribution

quantile(<Normal>): Determine quantiles of a Normal distribution

random(<Normal>): Draw a random sample from a Normal distribution

suff_stat(<Normal>): Compute the sufficient statistics for a Normal distribution from data

support(<Normal>): Return the support of the Normal distribution

Poisson distribution

Poisson(): Create a Poisson distribution

cdf(<Poisson>): Evaluate the cumulative distribution function of a Poisson distribution

fit_mle(<Poisson>): Fit an Poisson distribution to data

pdf(<Poisson>) log_pdf(<Poisson>): Evaluate the probability mass function of a Poisson distribution

quantile(<Poisson>): Determine quantiles of a Poisson distribution

random(<Poisson>): Draw a random sample from a Poisson distribution

suff_stat(<Poisson>): Compute the sufficient statistics of an Poisson distribution from data

support(<Poisson>): Return the support of the Poisson distribution

PoissonBinomial distribution

PoissonBinomial(): Create a Poisson binomial distribution

cdf(<PoissonBinomial>): Evaluate the cumulative distribution function of a PoissonBinomial distribution

pdf(<PoissonBinomial>) log_pdf(<PoissonBinomial>): Evaluate the probability mass function of a PoissonBinomial distribution

quantile(<PoissonBinomial>): Determine quantiles of a PoissonBinomial distribution

random(<PoissonBinomial>): Draw a random sample from a PoissonBinomial distribution

support(<PoissonBinomial>): Return the support of the PoissonBinomial distribution

Reversed Weibull distribution

RevWeibull(): Create a reversed Weibull distribution

cdf(<RevWeibull>): Evaluate the cumulative distribution function of an RevWeibull distribution

pdf(<RevWeibull>) log_pdf(<RevWeibull>): Evaluate the probability mass function of an RevWeibull distribution

quantile(<RevWeibull>): Determine quantiles of a RevWeibull distribution

random(<RevWeibull>): Draw a random sample from an RevWeibull distribution

support(<RevWeibull>): Return the support of the RevWeibull distribution

Student’s T distribution

StudentsT(): Create a Student's T distribution

cdf(<StudentsT>): Evaluate the cumulative distribution function of a StudentsT distribution

pdf(<StudentsT>) log_pdf(<StudentsT>): Evaluate the probability mass function of a StudentsT distribution

quantile(<StudentsT>): Determine quantiles of a StudentsT distribution

random(<StudentsT>): Draw a random sample from a StudentsT distribution

support(<StudentsT>): Return the support of the StudentsT distribution

Tukey distribution

Tukey(): Create a Tukey distribution

cdf(<Tukey>): Evaluate the cumulative distribution function of a Tukey distribution

quantile(<Tukey>): Determine quantiles of a Tukey distribution

random(<Tukey>): Draw a random sample from a Tukey distribution

support(<Tukey>): Return the support of the Tukey distribution

Uniform distribution

Uniform(): Create a Continuous Uniform distribution

cdf(<Uniform>): Evaluate the cumulative distribution function of a continuous Uniform distribution

pdf(<Uniform>) log_pdf(<Uniform>): Evaluate the probability mass function of a continuous Uniform distribution

quantile(<Uniform>): Determine quantiles of a continuous Uniform distribution

random(<Uniform>): Draw a random sample from a continuous Uniform distribution

support(<Uniform>): Return the support of the Uniform distribution

Weibull distribution

Weibull(): Create a Weibull distribution

cdf(<Weibull>): Evaluate the cumulative distribution function of a Weibull distribution

pdf(<Weibull>) log_pdf(<Weibull>): Evaluate the probability mass function of a Weibull distribution

quantile(<Weibull>): Determine quantiles of a Weibull distribution

random(<Weibull>): Draw a random sample from a Weibull distribution

support(<Weibull>): Return the support of the Weibull distribution

Zero-inflated negative binomial distribution

dzinbinom() pzinbinom() qzinbinom() rzinbinom(): The zero-inflated negative binomial distribution

ZINegativeBinomial(): Create a zero-inflated negative binomial distribution

cdf(<ZINegativeBinomial>): Evaluate the cumulative distribution function of a zero-inflated negative binomial distribution

pdf(<ZINegativeBinomial>) log_pdf(<ZINegativeBinomial>): Evaluate the probability mass function of a zero-inflated negative binomial distribution

quantile(<ZINegativeBinomial>): Determine quantiles of a zero-inflated negative binomial distribution

random(<ZINegativeBinomial>): Draw a random sample from a zero-inflated negative binomial distribution

support(<ZINegativeBinomial>): Return the support of the zero-inflated negative binomial distribution

Zero-inflated Poisson distribution

dzipois() pzipois() qzipois() rzipois(): The zero-inflated Poisson distribution

ZIPoisson(): Create a zero-inflated Poisson distribution

cdf(<ZIPoisson>): Evaluate the cumulative distribution function of a zero-inflated Poisson distribution

pdf(<ZIPoisson>) log_pdf(<ZIPoisson>): Evaluate the probability mass function of a zero-inflated Poisson distribution

quantile(<ZIPoisson>): Determine quantiles of a zero-inflated Poisson distribution

random(<ZIPoisson>): Draw a random sample from a zero-inflated Poisson distribution

support(<ZIPoisson>): Return the support of the zero-inflated Poisson distribution

Zero-truncated negative binomial distribution

dztnbinom() pztnbinom() qztnbinom() rztnbinom(): The zero-truncated negative binomial distribution

ZTNegativeBinomial(): Create a zero-truncated negative binomial distribution

cdf(<ZTNegativeBinomial>): Evaluate the cumulative distribution function of a zero-truncated negative binomial distribution

pdf(<ZTNegativeBinomial>) log_pdf(<ZTNegativeBinomial>): Evaluate the probability mass function of a zero-truncated negative binomial distribution

quantile(<ZTNegativeBinomial>): Determine quantiles of a zero-truncated negative binomial distribution

random(<ZTNegativeBinomial>): Draw a random sample from a zero-truncated negative binomial distribution

support(<ZTNegativeBinomial>): Return the support of the zero-truncated negative binomial distribution

Zero-truncated Poisson distribution

dztpois() pztpois() qztpois() rztpois(): The zero-truncated Poisson distribution

ZTPoisson(): Create a zero-truncated Poisson distribution

cdf(<ZTPoisson>): Evaluate the cumulative distribution function of a zero-truncated Poisson distribution

pdf(<ZTPoisson>) log_pdf(<ZTPoisson>): Evaluate the probability mass function of a zero-truncated Poisson distribution

quantile(<ZTPoisson>): Determine quantiles of a zero-truncated Poisson distribution

random(<ZTPoisson>): Draw a random sample from a zero-truncated Poisson distribution

support(<ZTPoisson>): Return the support of the zero-truncated Poisson distribution