Package 'distributional'

Description

Usage

cdf(x, q, ..., log = FALSE)

## S3 method for class 'distribution'
cdf(x, q, ...)

Arguments

Description

A generic function for computing the covariance of an object.

Usage

covariance(x, ...)

Arguments

See Also

covariance.distribution(), variance()

Description

Returns the empirical covariance of the probability distribution. If the method does not exist, the covariance of a random sample will be returned.

Usage

## S3 method for class 'distribution'
covariance(x, ...)

Arguments

Description

Computes the probability density function for a continuous distribution, or the probability mass function for a discrete distribution.

Usage

## S3 method for class 'distribution'
density(x, at, ..., log = FALSE)

Arguments

Description

Bernoulli distributions are used to represent events like coin flips when there is single trial that is either successful or unsuccessful. The Bernoulli distribution is a special case of the Binomial() distribution with n = 1.

Usage

dist_bernoulli(prob)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Bernoulli random variable with parameter p = $p$. Some textbooks also define $q = 1 - p$, or use $\pi$ instead of $p$.

The Bernoulli probability distribution is widely used to model binary variables, such as 'failure' and 'success'. The most typical example is the flip of a coin, when $p$ is thought as the probability of flipping a head, and $q = 1 - p$ is the probability of flipping a tail.

Support: $\{0, 1\}$

Mean: $p$

Variance: $p \cdot (1 - p) = p \cdot q$

Probability mass function (p.m.f):

$P(X = x) = p^x (1 - p)^{1-x} = p^x q^{1-x}$

Cumulative distribution function (c.d.f):

$P(X \le x) = \left \{ \begin{array}{ll} 0 & x < 0 \\ 1 - p & 0 \leq x < 1 \\ 1 & x \geq 1 \end{array} \right.$

Moment generating function (m.g.f):

$E(e^{tX}) = (1 - p) + p e^t$

Examples

dist <- dist_bernoulli(prob = c(0.05, 0.5, 0.3, 0.9, 0.1))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_beta(shape1, shape2)

Arguments

See Also

stats::Beta

Examples

dist <- dist_beta(shape1 = c(0.5, 5, 1, 2, 2), shape2 = c(0.5, 1, 3, 2, 5))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Binomial distributions are used to represent situations can that can be thought as the result of $n$ Bernoulli experiments (here the $n$ is defined as the size of the experiment). The classical example is $n$ independent coin flips, where each coin flip has probability p of success. In this case, the individual probability of flipping heads or tails is given by the Bernoulli(p) distribution, and the probability of having $x$ equal results ($x$ heads, for example), in $n$ trials is given by the Binomial(n, p) distribution. The equation of the Binomial distribution is directly derived from the equation of the Bernoulli distribution.

Usage

dist_binomial(size, prob)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

The Binomial distribution comes up when you are interested in the portion of people who do a thing. The Binomial distribution also comes up in the sign test, sometimes called the Binomial test (see stats::binom.test()), where you may need the Binomial C.D.F. to compute p-values.

In the following, let $X$ be a Binomial random variable with parameter size = $n$ and p = $p$. Some textbooks define $q = 1 - p$, or called $\pi$ instead of $p$.

Support: $\{0, 1, 2, ..., n\}$

Mean: $np$

Variance: $np \cdot (1 - p) = np \cdot q$

Probability mass function (p.m.f):

$P(X = k) = {n \choose k} p^k (1 - p)^{n-k}$

Cumulative distribution function (c.d.f):

$P(X \le k) = \sum_{i=0}^{\lfloor k \rfloor} {n \choose i} p^i (1 - p)^{n-i}$

Moment generating function (m.g.f):

$E(e^{tX}) = (1 - p + p e^t)^n$

Examples

dist <- dist_binomial(size = 1:5, prob = c(0.05, 0.5, 0.3, 0.9, 0.1))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_burr(shape1, shape2, rate = 1, scale = 1/rate)

Arguments

See Also

actuar::Burr

Examples

dist <- dist_burr(shape1 = c(1,1,1,2,3,0.5), shape2 = c(1,2,3,1,1,2))
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Categorical distributions are used to represent events with multiple outcomes, such as what number appears on the roll of a dice. This is also referred to as the 'generalised Bernoulli' or 'multinoulli' distribution. The Cateogorical distribution is a special case of the Multinomial() distribution with n = 1.

Usage

dist_categorical(prob, outcomes = NULL)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Categorical random variable with probability parameters p = $\{p_1, p_2, \ldots, p_k\}$.

The Categorical probability distribution is widely used to model the occurance of multiple events. A simple example is the roll of a dice, where $p = \{1/6, 1/6, 1/6, 1/6, 1/6, 1/6\}$ giving equal chance of observing each number on a 6 sided dice.

Support: $\{1, \ldots, k\}$

Mean: $p$

Variance: $p \cdot (1 - p) = p \cdot q$

Probability mass function (p.m.f):

$P(X = i) = p_i$

Cumulative distribution function (c.d.f):

The cdf() of a categorical distribution is undefined as the outcome categories aren't ordered.

Examples

dist <- dist_categorical(prob = list(c(0.05, 0.5, 0.15, 0.2, 0.1), c(0.3, 0.1, 0.6)))

dist

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

# The outcomes aren't ordered, so many statistics are not applicable.
cdf(dist, 4)
quantile(dist, 0.7)
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

dist <- dist_categorical(
  prob = list(c(0.05, 0.5, 0.15, 0.2, 0.1), c(0.3, 0.1, 0.6)),
  outcomes = list(letters[1:5], letters[24:26])
)

generate(dist, 10)

density(dist, "a")
density(dist, "z", log = TRUE)

Description

The Cauchy distribution is the student's t distribution with one degree of freedom. The Cauchy distribution does not have a well defined mean or variance. Cauchy distributions often appear as priors in Bayesian contexts due to their heavy tails.

Usage

dist_cauchy(location, scale)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Cauchy variable with mean ⁠location =⁠ $x_0$ and scale = $\gamma$.

Support: $R$, the set of all real numbers

Mean: Undefined.

Variance: Undefined.

Probability density function (p.d.f):

$f(x) = \frac{1}{\pi \gamma \left[1 + \left(\frac{x - x_0}{\gamma} \right)^2 \right]}$

Cumulative distribution function (c.d.f):

$F(t) = \frac{1}{\pi} \arctan \left( \frac{t - x_0}{\gamma} \right) + \frac{1}{2}$

Moment generating function (m.g.f):

Does not exist.

See Also

stats::Cauchy

Examples

dist <- dist_cauchy(location = c(0, 0, 0, -2), scale = c(0.5, 1, 2, 1))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Chi-square distributions show up often in frequentist settings as the sampling distribution of test statistics, especially in maximum likelihood estimation settings.

Usage

dist_chisq(df, ncp = 0)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a $\chi^2$ random variable with df = $k$.

Support: $R^+$, the set of positive real numbers

Mean: $k$

Variance: $2k$

Probability density function (p.d.f):

$f(x) = \frac{1}{\sqrt{2 \pi \sigma^2}} e^{-(x - \mu)^2 / 2 \sigma^2}$

Cumulative distribution function (c.d.f):

The cumulative distribution function has the form

$F(t) = \int_{-\infty}^t \frac{1}{\sqrt{2 \pi \sigma^2}} e^{-(x - \mu)^2 / 2 \sigma^2} dx$

but this integral does not have a closed form solution and must be approximated numerically. The c.d.f. of a standard normal is sometimes called the "error function". The notation $\Phi(t)$ also stands for the c.d.f. of a standard normal evaluated at $t$. Z-tables list the value of $\Phi(t)$ for various $t$.

Moment generating function (m.g.f):

$E(e^{tX}) = e^{\mu t + \sigma^2 t^2 / 2}$

See Also

stats::Chisquare

Examples

dist <- dist_chisq(df = c(1,2,3,4,6,9))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

The degenerate distribution takes a single value which is certain to be observed. It takes a single parameter, which is the value that is observed by the distribution.

Usage

dist_degenerate(x)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a degenerate random variable with value x = $k_0$.

Support: $R$, the set of all real numbers

Mean: $k_0$

Variance: $0$

Probability density function (p.d.f):

$f(x) = 1 for x = k_0$

$f(x) = 0 for x \neq k_0$

Cumulative distribution function (c.d.f):

The cumulative distribution function has the form

$F(x) = 0 for x < k_0$

$F(x) = 1 for x \ge k_0$

Moment generating function (m.g.f):

$E(e^{tX}) = e^{k_0 t}$

Examples

dist_degenerate(x = 1:5)

Description

Usage

dist_exponential(rate)

Arguments

See Also

stats::Exponential

Examples

dist <- dist_exponential(rate = c(2, 1, 2/3))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_f(df1, df2, ncp = NULL)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Gamma random variable with parameters shape = $\alpha$ and rate = $\beta$.

Support: $x \in (0, \infty)$

Mean: $\frac{\alpha}{\beta}$

Variance: $\frac{\alpha}{\beta^2}$

Probability density function (p.m.f):

$f(x) = \frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{\alpha - 1} e^{-\beta x}$

Cumulative distribution function (c.d.f):

$f(x) = \frac{\Gamma(\alpha, \beta x)}{\Gamma{\alpha}}$

Moment generating function (m.g.f):

$E(e^{tX}) = \Big(\frac{\beta}{ \beta - t}\Big)^{\alpha}, \thinspace t < \beta$

See Also

stats::FDist

Examples

dist <- dist_f(df1 = c(1,2,5,10,100), df2 = c(1,1,2,1,100))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Several important distributions are special cases of the Gamma distribution. When the shape parameter is 1, the Gamma is an exponential distribution with parameter $1/\beta$. When the $shape = n/2$ and $rate = 1/2$, the Gamma is a equivalent to a chi squared distribution with n degrees of freedom. Moreover, if we have $X_1$ is $Gamma(\alpha_1, \beta)$ and $X_2$ is $Gamma(\alpha_2, \beta)$, a function of these two variables of the form $\frac{X_1}{X_1 + X_2}$ $Beta(\alpha_1, \alpha_2)$. This last property frequently appears in another distributions, and it has extensively been used in multivariate methods. More about the Gamma distribution will be added soon.

Usage

dist_gamma(shape, rate, scale = 1/rate)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Gamma random variable with parameters shape = $\alpha$ and rate = $\beta$.

Support: $x \in (0, \infty)$

Mean: $\frac{\alpha}{\beta}$

Variance: $\frac{\alpha}{\beta^2}$

Probability density function (p.m.f):

$f(x) = \frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{\alpha - 1} e^{-\beta x}$

Cumulative distribution function (c.d.f):

$f(x) = \frac{\Gamma(\alpha, \beta x)}{\Gamma{\alpha}}$

Moment generating function (m.g.f):

$E(e^{tX}) = \Big(\frac{\beta}{ \beta - t}\Big)^{\alpha}, \thinspace t < \beta$

See Also

stats::GammaDist

Examples

dist <- dist_gamma(shape = c(1,2,3,5,9,7.5,0.5), rate = c(0.5,0.5,0.5,1,2,1,1))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

The Geometric distribution can be thought of as a generalization of the dist_bernoulli() distribution where we ask: "if I keep flipping a coin with probability p of heads, what is the probability I need $k$ flips before I get my first heads?" The Geometric distribution is a special case of Negative Binomial distribution.

Usage

dist_geometric(prob)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Geometric random variable with success probability p = $p$. Note that there are multiple parameterizations of the Geometric distribution.

Support: 0 < p < 1, $x = 0, 1, \dots$

Mean: $\frac{1-p}{p}$

Variance: $\frac{1-p}{p^2}$

Probability mass function (p.m.f):

$P(X = x) = p(1-p)^x,$

Cumulative distribution function (c.d.f):

$P(X \le x) = 1 - (1-p)^{x+1}$

Moment generating function (m.g.f):

$E(e^{tX}) = \frac{pe^t}{1 - (1-p)e^t}$

See Also

stats::Geometric

Examples

dist <- dist_geometric(prob = c(0.2, 0.5, 0.8))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

The GEV distribution function with parameters $\code{location} = a$, $\code{scale} = b$ and $\code{shape} = s$ is

Usage

dist_gev(location, scale, shape)

Arguments

Details

$F(x) = \exp\left[-\{1+s(x-a)/b\}^{-1/s}\right]$

for $1+s(x-a)/b > 0$, where $b > 0$. If $s = 0$ the distribution is defined by continuity, giving

$F(x) = \exp\left[-\exp\left(-\frac{x-a}{b}\right)\right]$

The support of the distribution is the real line if $s = 0$, $x \geq a - b/s$ if $s \neq 0$, and $x \leq a - b/s$ if $s < 0$.

The parametric form of the GEV encompasses that of the Gumbel, Frechet and reverse Weibull distributions, which are obtained for $s = 0$, $s > 0$ and $s < 0$ respectively. It was first introduced by Jenkinson (1955).

References

Jenkinson, A. F. (1955) The frequency distribution of the annual maximum (or minimum) of meteorological elements. Quart. J. R. Met. Soc., 81, 158–171.

See Also

gev

Examples

dist <- dist_gev(location = 0, scale = 1, shape = 0)

Description

The generalised g-and-h distribution is a flexible distribution used to model univariate data, similar to the g-k distribution. It is known for its ability to handle skewness and heavy-tailed behavior.

Usage

dist_gh(A, B, g, h, c = 0.8)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a g-and-h random variable with parameters A, B, g, h, and c.

Support: $(-\infty, \infty)$

Mean: Not available in closed form.

Variance: Not available in closed form.

Probability density function (p.d.f):

The g-and-h distribution does not have a closed-form expression for its density. Instead, it is defined through its quantile function:

$Q(u) = A + B \left( 1 + c \frac{1 - \exp(-gz(u))}{1 + \exp(-gz(u))} \right) \exp(h z(u)^2/2) z(u)$

where $z(u) = \Phi^{-1}(u)$

Cumulative distribution function (c.d.f):

The cumulative distribution function is typically evaluated numerically due to the lack of a closed-form expression.

See Also

gk::dgh, dist_gk

Examples

dist <- dist_gh(A = 0, B = 1, g = 0, h = 0.5)
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

The g-and-k distribution is a flexible distribution often used to model univariate data. It is particularly known for its ability to handle skewness and heavy-tailed behavior.

Usage

dist_gk(A, B, g, k, c = 0.8)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a g-k random variable with parameters A, B, g, k, and c.

Support: $(-\infty, \infty)$

Mean: Not available in closed form.

Variance: Not available in closed form.

Probability density function (p.d.f):

The g-k distribution does not have a closed-form expression for its density. Instead, it is defined through its quantile function:

$Q(u) = A + B \left( 1 + c \frac{1 - \exp(-gz(u))}{1 + \exp(-gz(u))} \right) (1 + z(u)^2)^k z(u)$

where $z(u) = \Phi^{-1}(u)$, the standard normal quantile of u.

Cumulative distribution function (c.d.f):

The cumulative distribution function is typically evaluated numerically due to the lack of a closed-form expression.

See Also

gk::dgk, dist_gh

Examples

dist <- dist_gk(A = 0, B = 1, g = 0, k = 0.5)
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

The GPD distribution function with parameters $\code{location} = a$, $\code{scale} = b$ and $\code{shape} = s$ is

Usage

dist_gpd(location, scale, shape)

Arguments

Details

$F(x) = 1 - \left(1+s(x-a)/b\right)^{-1/s}$

for $1+s(x-a)/b > 0$, where $b > 0$. If $s = 0$ the distribution is defined by continuity, giving

$F(x) = 1 - \exp\left(-\frac{x-a}{b}\right)$

The support of the distribution is $x \geq a$ if $s \geq 0$, and $a \leq x \leq a -b/s$ if $s < 0$.

The Pickands–Balkema–De Haan theorem states that for a large class of distributions, the tail (above some threshold) can be approximated by a GPD.

See Also

gpd

Examples

dist <- dist_gpd(location = 0, scale = 1, shape = 0)

Description

The Gumbel distribution is a special case of the Generalized Extreme Value distribution, obtained when the GEV shape parameter $\xi$ is equal to 0. It may be referred to as a type I extreme value distribution.

Usage

dist_gumbel(alpha, scale)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Gumbel random variable with location parameter mu = $\mu$, scale parameter sigma = $\sigma$.

Support: $R$, the set of all real numbers.

Mean: $\mu + \sigma\gamma$, where $\gamma$ is Euler's constant, approximately equal to 0.57722.

Median: $\mu - \sigma\ln(\ln 2)$.

Variance: $\sigma^2 \pi^2 / 6$.

Probability density function (p.d.f):

$f(x) = \sigma ^ {-1} \exp[-(x - \mu) / \sigma] \exp\{-\exp[-(x - \mu) / \sigma] \}$

for $x$ in $R$, the set of all real numbers.

Cumulative distribution function (c.d.f):

In the $\xi = 0$ (Gumbel) special case

$F(x) = \exp\{-\exp[-(x - \mu) / \sigma] \}$

for $x$ in $R$, the set of all real numbers.

See Also

actuar::Gumbel

Examples

dist <- dist_gumbel(alpha = c(0.5, 1, 1.5, 3), scale = c(2, 2, 3, 4))
dist


mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

To understand the HyperGeometric distribution, consider a set of $r$ objects, of which $m$ are of the type I and $n$ are of the type II. A sample with size $k$ ($k<r$) with no replacement is randomly chosen. The number of observed type I elements observed in this sample is set to be our random variable $X$.

Usage

dist_hypergeometric(m, n, k)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a HyperGeometric random variable with success probability p = $p = m/(m+n)$.

Support: $x \in { \{\max{(0, k-n)}, \dots, \min{(k,m)}}\}$

Mean: $\frac{km}{n+m} = kp$

Variance: $\frac{km(n)(n+m-k)}{(n+m)^2 (n+m-1)} = kp(1-p)(1 - \frac{k-1}{m+n-1})$

Probability mass function (p.m.f):

$P(X = x) = \frac{{m \choose x}{n \choose k-x}}{{m+n \choose k}}$

Cumulative distribution function (c.d.f):

$P(X \le k) \approx \Phi\Big(\frac{x - kp}{\sqrt{kp(1-p)}}\Big)$

See Also

stats::Hypergeometric

Examples

dist <- dist_hypergeometric(m = rep(500, 3), n = c(50, 60, 70), k = c(100, 200, 300))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_inflated(dist, prob, x = 0)

Arguments

Description

Usage

dist_inverse_exponential(rate)

Arguments

See Also

actuar::InverseExponential

Examples

dist <- dist_inverse_exponential(rate = 1:5)
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_inverse_gamma(shape, rate = 1/scale, scale)

Arguments

See Also

actuar::InverseGamma

Examples

dist <- dist_inverse_gamma(shape = c(1,2,3,3), rate = c(1,1,1,2))
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_inverse_gaussian(mean, shape)

Arguments

See Also

actuar::InverseGaussian

Examples

dist <- dist_inverse_gaussian(mean = c(1,1,1,3,3), shape = c(0.2, 1, 3, 0.2, 1))
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_logarithmic(prob)

Arguments

See Also

actuar::Logarithmic

Examples

dist <- dist_logarithmic(prob = c(0.33, 0.66, 0.99))
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

A continuous distribution on the real line. For binary outcomes the model given by $P(Y = 1 | X) = F(X \beta)$ where $F$ is the Logistic cdf() is called logistic regression.

Usage

dist_logistic(location, scale)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Logistic random variable with location = $\mu$ and scale = $s$.

Support: $R$, the set of all real numbers

Mean: $\mu$

Variance: $s^2 \pi^2 / 3$

Probability density function (p.d.f):

$f(x) = \frac{e^{-(\frac{x - \mu}{s})}}{s [1 + \exp(-(\frac{x - \mu}{s})) ]^2}$

Cumulative distribution function (c.d.f):

$F(t) = \frac{1}{1 + e^{-(\frac{t - \mu}{s})}}$

Moment generating function (m.g.f):

$E(e^{tX}) = e^{\mu t} \beta(1 - st, 1 + st)$

where $\beta(x, y)$ is the Beta function.

See Also

stats::Logistic

Examples

dist <- dist_logistic(location = c(5,9,9,6,2), scale = c(2,3,4,2,1))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

The log-normal distribution is a commonly used transformation of the Normal distribution. If $X$ follows a log-normal distribution, then $\ln{X}$ would be characteristed by a Normal distribution.

Usage

dist_lognormal(mu = 0, sigma = 1)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $Y$ be a Normal random variable with mean mu = $\mu$ and standard deviation sigma = $\sigma$. The log-normal distribution $X = exp(Y)$ is characterised by:

Support: $R+$, the set of all real numbers greater than or equal to 0.

Mean: $e^(\mu + \sigma^2/2$

Variance: $(e^(\sigma^2)-1) e^(2\mu + \sigma^2$

Probability density function (p.d.f):

$f(x) = \frac{1}{x\sqrt{2 \pi \sigma^2}} e^{-(\ln{x} - \mu)^2 / 2 \sigma^2}$

Cumulative distribution function (c.d.f):

The cumulative distribution function has the form

$F(x) = \Phi((\ln{x} - \mu)/\sigma)$

Where $Phi$ is the CDF of a standard Normal distribution, N(0,1).

See Also

stats::Lognormal

Examples

dist <- dist_lognormal(mu = 1:5, sigma = 0.1)

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

# A log-normal distribution X is exp(Y), where Y is a Normal distribution of
# the same parameters. So log(X) will produce the Normal distribution Y.
log(dist)

Description

A placeholder distribution for handling missing values in a vector of distributions.

Usage

dist_missing(length = 1)

Arguments

Examples

dist <- dist_missing(3L)

dist
mean(dist)
variance(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_mixture(..., weights = numeric())

Arguments

Examples

dist_mixture(dist_normal(0, 1), dist_normal(5, 2), weights = c(0.3, 0.7))

Description

The multinomial distribution is a generalization of the binomial distribution to multiple categories. It is perhaps easiest to think that we first extend a dist_bernoulli() distribution to include more than two categories, resulting in a dist_categorical() distribution. We then extend repeat the Categorical experiment several ($n$) times.

Usage

dist_multinomial(size, prob)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X = (X_1, ..., X_k)$ be a Multinomial random variable with success probability p = $p$. Note that $p$ is vector with $k$ elements that sum to one. Assume that we repeat the Categorical experiment size = $n$ times.

Support: Each $X_i$ is in ${0, 1, 2, ..., n}$.

Mean: The mean of $X_i$ is $n p_i$.

Variance: The variance of $X_i$ is $n p_i (1 - p_i)$. For $i \neq j$, the covariance of $X_i$ and $X_j$ is $-n p_i p_j$.

Probability mass function (p.m.f):

$P(X_1 = x_1, ..., X_k = x_k) = \frac{n!}{x_1! x_2! ... x_k!} p_1^{x_1} \cdot p_2^{x_2} \cdot ... \cdot p_k^{x_k}$

Cumulative distribution function (c.d.f):

Omitted for multivariate random variables for the time being.

Moment generating function (m.g.f):

$E(e^{tX}) = \left(\sum_{i=1}^k p_i e^{t_i}\right)^n$

See Also

stats::Multinomial

Examples

dist <- dist_multinomial(size = c(4, 3), prob = list(c(0.3, 0.5, 0.2), c(0.1, 0.5, 0.4)))

dist
mean(dist)
variance(dist)

generate(dist, 10)

# TODO: Needs fixing to support multiple inputs
# density(dist, 2)
# density(dist, 2, log = TRUE)

Description

Usage

dist_multivariate_normal(mu = 0, sigma = diag(1))

Arguments

See Also

mvtnorm::dmvnorm, mvtnorm::qmvnorm

Examples

dist <- dist_multivariate_normal(mu = list(c(1,2)), sigma = list(matrix(c(4,2,2,3), ncol=2)))
dimnames(dist) <- c("x", "y")
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, cbind(2, 1))
density(dist, cbind(2, 1), log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)
quantile(dist, 0.7, type = "marginal")

Description

A generalization of the geometric distribution. It is the number of failures in a sequence of i.i.d. Bernoulli trials before a specified number of successes (size) occur. The probability of success in each trial is given by prob.

Usage

dist_negative_binomial(size, prob)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Negative Binomial random variable with success probability prob = $p$ and the number of successes size = $r$.

Support: $\{0, 1, 2, 3, ...\}$

Mean: $\frac{p r}{1-p}$

Variance: $\frac{pr}{(1-p)^2}$

Probability mass function (p.m.f):

$f(k) = {k + r - 1 \choose k} \cdot (1-p)^r p^k$

Cumulative distribution function (c.d.f):

Too nasty, omitted.

Moment generating function (m.g.f):

$\left(\frac{1-p}{1-pe^t}\right)^r, t < -\log p$

See Also

stats::NegBinomial

Examples

dist <- dist_negative_binomial(size = 10, prob = 0.5)

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)
support(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

The Normal distribution is ubiquitous in statistics, partially because of the central limit theorem, which states that sums of i.i.d. random variables eventually become Normal. Linear transformations of Normal random variables result in new random variables that are also Normal. If you are taking an intro stats course, you'll likely use the Normal distribution for Z-tests and in simple linear regression. Under regularity conditions, maximum likelihood estimators are asymptotically Normal. The Normal distribution is also called the gaussian distribution.

Usage

dist_normal(mu = 0, sigma = 1, mean = mu, sd = sigma)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Normal random variable with mean mu = $\mu$ and standard deviation sigma = $\sigma$.

Support: $R$, the set of all real numbers

Mean: $\mu$

Variance: $\sigma^2$

Probability density function (p.d.f):

$f(x) = \frac{1}{\sqrt{2 \pi \sigma^2}} e^{-(x - \mu)^2 / 2 \sigma^2}$

Cumulative distribution function (c.d.f):

The cumulative distribution function has the form

$F(t) = \int_{-\infty}^t \frac{1}{\sqrt{2 \pi \sigma^2}} e^{-(x - \mu)^2 / 2 \sigma^2} dx$

but this integral does not have a closed form solution and must be approximated numerically. The c.d.f. of a standard Normal is sometimes called the "error function". The notation $\Phi(t)$ also stands for the c.d.f. of a standard Normal evaluated at $t$. Z-tables list the value of $\Phi(t)$ for various $t$.

Moment generating function (m.g.f):

$E(e^{tX}) = e^{\mu t + \sigma^2 t^2 / 2}$

See Also

stats::Normal

Examples

dist <- dist_normal(mu = 1:5, sigma = 3)

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_pareto(shape, scale)

Arguments

See Also

actuar::Pareto

Examples

dist <- dist_pareto(shape = c(10, 3, 2, 1), scale = rep(1, 4))
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_percentile(x, percentile)

Arguments

Examples

dist <- dist_normal()
percentiles <- seq(0.01, 0.99, by = 0.01)
x <- vapply(percentiles, quantile, double(1L), x = dist)
dist_percentile(list(x), list(percentiles*100))

Description

Poisson distributions are frequently used to model counts.

Usage

dist_poisson(lambda)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a Poisson random variable with parameter lambda = $\lambda$.

Support: $\{0, 1, 2, 3, ...\}$

Mean: $\lambda$

Variance: $\lambda$

Probability mass function (p.m.f):

$P(X = k) = \frac{\lambda^k e^{-\lambda}}{k!}$

Cumulative distribution function (c.d.f):

$P(X \le k) = e^{-\lambda} \sum_{i = 0}^{\lfloor k \rfloor} \frac{\lambda^i}{i!}$

Moment generating function (m.g.f):

$E(e^{tX}) = e^{\lambda (e^t - 1)}$

See Also

stats::Poisson

Examples

dist <- dist_poisson(lambda = c(1, 4, 10))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_poisson_inverse_gaussian(mean, shape)

Arguments

See Also

actuar::PoissonInverseGaussian

Examples

dist <- dist_poisson_inverse_gaussian(mean = rep(0.1, 3), shape = c(0.4, 0.8, 1))
dist


mean(dist)
variance(dist)
support(dist)
generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

Description

Usage

dist_sample(x)

Arguments

Examples

# Univariate numeric samples
dist <- dist_sample(x = list(rnorm(100), rnorm(100, 10)))

dist
mean(dist)
variance(dist)
skewness(dist)
generate(dist, 10)

density(dist, 1)

# Multivariate numeric samples
dist <- dist_sample(x = list(cbind(rnorm(100), rnorm(100, 10))))
dimnames(dist) <- c("x", "y")

dist
mean(dist)
variance(dist)
generate(dist, 10)
quantile(dist, 0.4) # Returns the marginal quantiles
cdf(dist, matrix(c(0.3,9), nrow = 1))

Description

The Student's T distribution is closely related to the Normal() distribution, but has heavier tails. As $\nu$ increases to $\infty$, the Student's T converges to a Normal. The T distribution appears repeatedly throughout classic frequentist hypothesis testing when comparing group means.

Usage

dist_student_t(df, mu = 0, sigma = 1, ncp = NULL)

Arguments

Details

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let $X$ be a central Students T random variable with df = $\nu$.

Support: $R$, the set of all real numbers

Mean: Undefined unless $\nu \ge 2$, in which case the mean is zero.

Variance:

$\frac{\nu}{\nu - 2}$

Undefined if $\nu < 1$, infinite when $1 < \nu \le 2$.

Probability density function (p.d.f):

$f(x) = \frac{\Gamma(\frac{\nu + 1}{2})}{\sqrt{\nu \pi} \Gamma(\frac{\nu}{2})} (1 + \frac{x^2}{\nu} )^{- \frac{\nu + 1}{2}}$

See Also

stats::TDist

Examples

dist <- dist_student_t(df = c(1,2,5), mu = c(0,1,2), sigma = c(1,2,3))

dist
mean(dist)
variance(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)