`partitionsRank.Rd`

Generate the rank (lexicographically) of partitions/compositions. These functions are the complement to

`partitions/compositionsSample`

. See the examples below.GMP support allows for exploration of partitions/compositions of vectors with many elements.

```
partitionsRank(..., v, repetition = FALSE, freqs = NULL, target = NULL)
compositionsRank(..., v, repetition = FALSE, freqs = NULL,
target = NULL, weak = FALSE)
```

- ...
vectors or matrices to be ranked.

- v
Source vector. If

`v`

is a positive integer, it will be converted to the sequence`1:v`

. If`v`

is a negative integer, it will be converted to the sequence`v:-1`

. All atomic types are supported (See`is.atomic`

).- repetition
Logical value indicating whether partitions/compositions should be with or without repetition. The default is

`FALSE`

.- freqs
A vector of frequencies used for producing all partitions of a multiset of

`v`

. Each element of`freqs`

represents how many times each element of the source vector,`v`

, is repeated. It is analogous to the`times`

argument in`rep`

. The default value is`NULL`

.- target
Number to be partitioned. If

`NULL`

,`max(v)`

will be used.- weak
(Compositions only) Logical flag indicating whether to allow terms of the sequence to be zero.

These algorithms rely on efficiently ranking the \(n^{th}\) lexicographical partition.

A vector of class `integer`

, `numeric`

, or `bigz`

determined by the total number of partitions/compositions

`v`

must be supplied.

```
mySamp = partitionsSample(30, 8, TRUE, n = 5, seed = 10, namedSample = TRUE)
myRank = partitionsRank(mySamp, v = 30, repetition = TRUE)
all.equal(as.integer(rownames(mySamp)), myRank)
#> [1] TRUE
```