Random variables backed by arrays of arbitrary dimension

```
rvar(
x = double(),
dim = NULL,
dimnames = NULL,
nchains = 1L,
with_chains = FALSE
)
```

- x
(multiple options) The object to convert to an

`rvar`

:A vector of draws from a distribution.

An array where the first dimension represents draws from a distribution. The resulting

`rvar`

will have dimension`dim(x)[-1]`

; that is, everything except the first dimension is used for the shape of the variable, and the first dimension is used to index draws from the distribution (see**Examples**). Optionally, if`with_chains == TRUE`

, the first dimension indexes the iteration and the second dimension indexes the chain (see`with_chains`

).

- dim
(integer vector) One or more integers giving the maximal indices in each dimension to override the dimensions of the

`rvar`

to be created (see`dim()`

). If`NULL`

(the default),`dim`

is determined by the input.**NOTE:**This argument controls the dimensions of the`rvar`

, not the underlying array, so you cannot change the number of draws using this argument.- dimnames
(list) Character vectors giving the names in each dimension to override the names of the dimensions of the

`rvar`

to be created (see`dimnames()`

). If`NULL`

(the default), this is determined by the input.**NOTE:**This argument controls the names of the dimensions of the`rvar`

, not the underlying array.- nchains
(positive integer) The number of chains. The default is

`1`

.- with_chains
(logical) Does

`x`

include a dimension for chains? If`FALSE`

(the default), chains are not included, the first dimension of the input array should index draws, and the`nchains`

argument can be used to determine the number of chains. If`TRUE`

, the`nchains`

argument is ignored and the second dimension of`x`

is used to index chains. Internally, the array will be converted to a format without the chain index.

An object of class `"rvar"`

representing a random variable.

The `"rvar"`

class internally represents random variables as arrays of arbitrary
dimension, where the first dimension is used to index draws from the distribution.
Most mathematical operators and functions are supported, including efficient matrix
multiplication and vector and array-style indexing. The intent is that an `rvar`

works as closely as possible to how a base vector/matrix/array does, with a few
differences:

The default behavior when subsetting is not to drop extra dimensions (i.e. the default

`drop`

argument for`[`

is`FALSE`

, not`TRUE`

).Rather than base R-style recycling,

`rvar`

s use a limited form of broadcasting: if an operation is being performed on two vectors with different size of the same dimension, the smaller vector will be recycled up to the size of the larger one along that dimension so long as it has size 1.

For functions that expect base numeric arrays and for which `rvar`

s cannot be
used directly as arguments, you can use `rfun()`

or `rdo()`

to translate your
code into code that executes across draws from one or more random variables
and returns a random variable as output. Typically `rdo()`

offers the most
straightforward translation.

As `rfun()`

and `rdo()`

incur some performance cost, you can also operate directly
on the underlying array using the `draws_of()`

function. To re-use existing
random number generator functions to efficiently create `rvar`

s, use `rvar_rng()`

.

`as_rvar()`

to convert objects to `rvar`

s. See `rdo()`

, `rfun()`

, and
`rvar_rng()`

for higher-level interfaces for creating `rvar`

s.

```
set.seed(1234)
# To create a "scalar" `rvar`, pass a one-dimensional array or a vector
# whose length (here `4000`) is the desired number of draws:
x <- rvar(rnorm(4000, mean = 1, sd = 1))
x
#> rvar<4000>[1] mean ± sd:
#> [1] 1 ± 1
# Create random vectors by adding an additional dimension:
n <- 4 # length of output vector
x <- rvar(array(rnorm(4000 * n, mean = rep(1:n, each = 4000), sd = 1), dim = c(4000, n)))
x
#> rvar<4000>[4] mean ± sd:
#> [1] 1 ± 0.99 2 ± 0.99 3 ± 1.00 4 ± 1.02
# Create a random matrix:
rows <- 4
cols <- 3
x <- rvar(array(rnorm(4000 * rows * cols, mean = 1, sd = 1), dim = c(4000, rows, cols)))
x
#> rvar<4000>[4,3] mean ± sd:
#> [,1] [,2] [,3]
#> [1,] 1.00 ± 0.98 1.00 ± 1.00 0.97 ± 1.00
#> [2,] 1.00 ± 1.01 1.01 ± 1.02 0.99 ± 0.99
#> [3,] 1.02 ± 1.01 0.99 ± 1.00 1.00 ± 0.99
#> [4,] 1.01 ± 1.01 1.02 ± 1.00 1.00 ± 1.01
# If the input sample comes from multiple chains, we can indicate that using the
# nchains argument (here, 1000 draws each from 4 chains):
x <- rvar(rnorm(4000, mean = 1, sd = 1), nchains = 4)
x
#> rvar<1000,4>[1] mean ± sd:
#> [1] 0.97 ± 1
# Or if the input sample has chain information as its second dimension, we can
# use with_chains to create the rvar
x <- rvar(array(rnorm(4000, mean = 1, sd = 1), dim = c(1000, 4)), with_chains = TRUE)
x
#> rvar<1000,4>[1] mean ± sd:
#> [1] 1 ± 1
```