These functions can be used to define random variables in a
greta model. They return a variable greta array that follows the specified
distribution. This variable greta array can be used to represent a
parameter with prior distribution, combined into a mixture distribution
using `mixture`

, or used with `distribution`

to
define a distribution over a data greta array.

uniform(min, max, dim = NULL) normal(mean, sd, dim = NULL, truncation = c(-Inf, Inf)) lognormal(meanlog, sdlog, dim = NULL, truncation = c(0, Inf)) bernoulli(prob, dim = NULL) binomial(size, prob, dim = NULL) beta_binomial(size, alpha, beta, dim = NULL) negative_binomial(size, prob, dim = NULL) hypergeometric(m, n, k, dim = NULL) poisson(lambda, dim = NULL) gamma(shape, rate, dim = NULL, truncation = c(0, Inf)) inverse_gamma(alpha, beta, dim = NULL, truncation = c(0, Inf)) weibull(shape, scale, dim = NULL, truncation = c(0, Inf)) exponential(rate, dim = NULL, truncation = c(0, Inf)) pareto(a, b, dim = NULL, truncation = c(0, Inf)) student(df, mu, sigma, dim = NULL, truncation = c(-Inf, Inf)) laplace(mu, sigma, dim = NULL, truncation = c(-Inf, Inf)) beta(shape1, shape2, dim = NULL, truncation = c(0, 1)) cauchy(location, scale, dim = NULL, truncation = c(-Inf, Inf)) chi_squared(df, dim = NULL, truncation = c(0, Inf)) logistic(location, scale, dim = NULL, truncation = c(-Inf, Inf)) f(df1, df2, dim = NULL, truncation = c(0, Inf)) multivariate_normal(mean, Sigma, n_realisations = NULL, dimension = NULL) wishart(df, Sigma) lkj_correlation(eta, dimension = 2) multinomial(size, prob, n_realisations = NULL, dimension = NULL) categorical(prob, n_realisations = NULL, dimension = NULL) dirichlet(alpha, n_realisations = NULL, dimension = NULL) dirichlet_multinomial(size, alpha, n_realisations = NULL, dimension = NULL)

min, max | scalar values giving optional limits to |
---|---|

dim | the dimensions of the greta array to be returned, either a scalar or a vector of positive integers. See details. |

mean, meanlog, location, mu | unconstrained parameters |

sd, sdlog, sigma, lambda, shape, rate, df, scale, shape1, shape2, alpha, beta, df1, df2, a, b, eta | positive parameters, |

truncation | a length-two vector giving values between which to truncate
the distribution, similarly to the |

prob | probability parameter ( |

size, m, n, k | positive integer parameter |

Sigma | positive definite variance-covariance matrix parameter |

n_realisations | the number of independent realisation of a multivariate distribution |

dimension | the dimension of a multivariate distribution |

The discrete probability distributions (`bernoulli`

,
`binomial`

, `negative_binomial`

, `poisson`

,
`multinomial`

, `categorical`

, `dirichlet_multinomial`

) can
be used when they have fixed values (e.g. defined as a likelihood using
`distribution`

, but not as unknown variables.

For univariate distributions `dim`

gives the dimensions of the greta
array to create. Each element of the greta array will be (independently)
distributed according to the distribution. `dim`

can also be left at
its default of `NULL`

, in which case the dimension will be detected
from the dimensions of the parameters (provided they are compatible with
one another).

For multivariate distributions (`multivariate_normal()`

,
`multinomial()`

, `categorical()`

, `dirichlet()`

, and
`dirichlet_multinomial()`

) each row of the output and parameters
corresponds to an independent realisation. If a single realisation or
parameter value is specified, it must therefore be a row vector (see
example). `n_realisations`

gives the number of rows/realisations, and
`dimension`

gives the dimension of the distribution. Ie. a bivariate
normal distribution would be produced with ```
multivariate_normal(...,
dimension = 2)
```

. The dimension can usually be detected from the parameters.

`multinomial()`

does not check that observed values sum to
`size`

, and `categorical()`

does not check that only one of the
observed entries is 1. It's the user's responsibility to check their data
matches the distribution!

The parameters of `uniform`

must be fixed, not greta arrays. This
ensures these values can always be transformed to a continuous scale to run
the samplers efficiently. However, a hierarchical `uniform`

parameter
can always be created by defining a `uniform`

variable constrained
between 0 and 1, and then transforming it to the required scale. See below
for an example.

Wherever possible, the parameterisations and argument names of greta
distributions match commonly used R functions for distributions, such as
those in the `stats`

or `extraDistr`

packages. The following
table states the distribution function to which greta's implementation
corresponds:

greta | reference |

`uniform` | stats::dunif |

`normal` | stats::dnorm |

`lognormal` | stats::dlnorm |

`bernoulli` | extraDistr::dbern |

`binomial` | stats::dbinom |

`beta_binomial` | extraDistr::dbbinom |

`negative_binomial` | stats::dnbinom |

`hypergeometric` | stats::dhyper |

`poisson` | stats::dpois |

`gamma` | stats::dgamma |

`inverse_gamma` | extraDistr::dinvgamma |

`weibull` | stats::dweibull |

`exponential` | stats::dexp |

`pareto` | extraDistr::dpareto |

`student` | extraDistr::dlst |

`laplace` | extraDistr::dlaplace |

`beta` | stats::dbeta |

`cauchy` | stats::dcauchy |

`chi_squared` | stats::dchisq |

`logistic` | stats::dlogis |

`f` | stats::df |

`multivariate_normal` | mvtnorm::dmvnorm |

`multinomial` | stats::dmultinom |

`categorical` | stats::dmultinom (size = 1) |

`dirichlet` | extraDistr::ddirichlet |

`dirichlet_multinomial` | extraDistr::ddirmnom |

`wishart` | stats::rWishart |

`lkj_correlation` | rethinking::dlkjcorr |

# NOT RUN { # a uniform parameter constrained to be between 0 and 1 phi <- uniform(min = 0, max = 1) # a length-three variable, with each element following a standard normal # distribution alpha <- normal(0, 1, dim = 3) # a length-three variable of lognormals sigma <- lognormal(0, 3, dim = 3) # a hierarchical uniform, constrained between alpha and alpha + sigma, eta <- alpha + uniform(0, 1, dim = 3) * sigma # a hierarchical distribution mu <- normal(0, 1) sigma <- lognormal(0, 1) theta <- normal(mu, sigma) # a vector of 3 variables drawn from the same hierarchical distribution thetas <- normal(mu, sigma, dim = 3) # a matrix of 12 variables drawn from the same hierarchical distribution thetas <- normal(mu, sigma, dim = c(3, 4)) # a multivariate normal variable, with correlation between two elements # note that the parameter must be a row vector Sig <- diag(4) Sig[3, 4] <- Sig[4, 3] <- 0.6 theta <- multivariate_normal(t(rep(mu, 4)), Sig) # 10 independent replicates of that theta <- multivariate_normal(t(rep(mu, 4)), Sig, n_realisations = 10) # 10 multivariate normal replicates, each with a different mean vector, # but the same covariance matrix means <- matrix(rnorm(40), 10, 4) theta <- multivariate_normal(means, Sig, n_realisations = 10) dim(theta) # a Wishart variable with the same covariance parameter theta <- wishart(df = 5, Sigma = Sig) # }