Functions for using runif() and rnorm() with randtoolbox generators
Description
These functions allow to set some of the random number generators from randtoolbox
package to be used instead of the standard generator in the functions, which use
random numbers, for example runif()
, rnorm()
, sample()
and set.seed()
.
Usage
1 2 3 4 5 6  set.generator(name=c("congruRand", "WELL", "MersenneTwister", "default"),
parameters=NULL, seed=NULL, ..., only.dsc=FALSE)
put.description(description)
get.description()

Arguments
name 
A character string for the RNG name. 
parameters 
A numeric or character vector describing a specific RNG from the
family specified by the 
seed 
A number, whose value is an integer between 
... 
Arguments describing named components of the vector of parameters,
if argument 
only.dsc 
Logical. If 
description 
A list describing a specific RNG as created by

Details
Random number generators provided by R extension packages are set using
RNGkind("usersupplied")
. The package randtoolbox assumes that
this function is not called by the user directly and is called from
the functions set.generator()
and put.description()
.
Random number generators in randtoolbox are represented at the R level by a list
containing mandatory components name, parameters, state
and possibly an
optional component authors
. The function set.generator()
internally
creates this list from the user supplied information and then runs put.description()
on this list, which initializes the generator. If the generator is initialized, then the
function get.description()
may be used to get the actual state of the generator,
which may be stored in an R object and used later in put.description()
to continue
the sequence of the random numbers from the point, where get.description()
was called. This may be used to generate several independent streams of random numbers
generated by different generators.
The component parameters
is a character or a numeric vector, whose structure
is different for different types of the generators. This vector may be passed
to set.generator()
, if it is prepared before its call, however, it is
also possible to pass its named components via the ...
parametr of
set.generator()
and the vector parameters
is created internally.
If the vector parameters
is not supplied and the arguments in ...
are not sufficient to create it, an error message is produced.
Linear congruential generators
Parameters for the linear congruential generators (name="congruRand"
)
are integers represented either as a character or a numeric vector. The
components are
 mod
The modulus.
 mult
The multiplier.
 incr
The increment.
WELL generators
Parameters for the WELL generators is a character vector with components
 order
The number of bits in the internal state. Possible values are 512, 521, 607, 800, 1024, 19937, 21701, 23209, 44497.
 version
The version letter "a", "b", or "c" to be appended to the order.
The concatenation of order
and version
should belong to
"512a", "521a", "521b", "607a", "607b", "800a", "800b", "1024a", "1024b",
"19937a", "19937b", "19937c", "21701a", "23209a", "23209b", "44497a", "44497b"
.
When order and version are specified in ...
parametr of set.generator()
,
then the parameter order
is optional and if missing, it is assumed that the
parameter version
contains also the number of bits in the internal state
and the combination belongs to the list above.
Mersenne Twister 2002 generator
Parameters for the Mersenne Twister 2002 is a character vector with components
 initialization
Either "init2002" or "array2002". The initialization to be used.
 resolution
Either 53 or 32. The number of random bits in each number.
Value
set.generator()
with the parameter only.dsc=TRUE
and
get.description()
return the list describing a generator.
put.description()
with the parameter only.dsc=TRUE
(the default)
and put.description()
return NULL
.
Author(s)
Petr Savicky and Christophe Dutang
See Also
RNGkind
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  #set WELL19937a
set.generator("WELL", version="19937a", seed=12345)
runif(5)
#Store the current state and generate 10 random numbers
storedState < get.description()
x < runif(10)
#Park Miller congruential generator
set.generator(name="congruRand", mod=2^311, mult=16807, incr=0, seed=12345)
runif(5)
setSeed(12345)
congruRand(5, dim=1, mod=2^311, mult=16807, incr=0)
# the Knuth Lewis RNG
set.generator(name="congruRand", mod="4294967296", mult="1664525", incr="1013904223", seed=1)
runif(5)
setSeed(1)
congruRand(5, dim=1, mod=4294967296, mult=1664525, incr=1013904223)
#Restore the generator from storedState and regenerate the same numbers
put.description(storedState)
x == runif(10)
# generate the same random numbers as in Matlab
set.generator("MersenneTwister", initialization="init2002", resolution=53, seed=12345)
runif(5)
# [1] 0.9296161 0.3163756 0.1839188 0.2045603 0.5677250
# Matlab commands rand('twister', 12345); rand(1, 5) generate the same numbers,
# which in short format are 0.9296 0.3164 0.1839 0.2046 0.5677
#Restore the original R setting
set.generator("default")
RNGkind()

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