# randompack

This package provides R bindings to the C library Randompack, a random number
generation toolkit that also includes interfaces for Julia, Fortran, and Python.
Randompack exposes a collection of modern RNG engines, including
xoshiro256++/\*\*, PCG64 DXSM, sfc64, ranlux++, Philox, and ChaCha20, together
with a range of probability distributions, both integer and continuous. The
library allows matching random draws across platforms and supported language
interfaces. It provides unbounded and bounded integer draws, permutations,
sampling without replacement, and 14 continuous distributions, ranging from
basic ones (uniform, normal, exponential), through commonly used distributions
(beta, gamma), to more specialized ones (such as skew-normal). Multivariate
normal sampling is also supported.

Through SIMD instructions on modern CPUs, the inherently fast default engine
xoshiro256++ delivers high throughput for bulk generation, typically running
3–10 times faster than base R and about 1.5 times faster than dqrng (a modern,
high-performance RNG package) for uniform, normal, and exponential draws.

For more information, including implementation details, benchmarking results,
and documentation of engines and distributions, see the main project readme file
at https://github/jonasson2/randompack. The same page also links to
DEVELOPMENT.md, which contains setup and development instructions, including
details specific to the R interface.

## Cross-platform consistency

Given the same engine and seed, samples obtained on different platforms
(programming language/computer/compiler/OS/architecture) agree. For uniform,
normal, exponential, and integer distributions the agreement is bit-exact (x ==
y holds). For the remaining distributions, samples agree to within ca. 2 ulp. If
the `bitexact` parameter is set to `TRUE` the agreement is bit-exact for all
distributions.

## Usage

### Installation, setup, and seeding

``` r
install.packages("randompack")  # From CRAN
library(randompack)             #

rng <- randompack_rng()         # default engine (x256++simd)
rng <- randompack_rng("pcg64")  # specified engine; rng is randomized by default
randompack_engines()            # list available engines
rng$seed(123)                   # deterministic seed
rng$seed(123, spawn_key=c(1,2)) # independent substreams
rng$randomize()                 # seed from system entropy
rng2 <- rng$duplicate()         # identical independent copy
```

### Continuous distributions

``` r
x <- rng$unif(100)            # 100 draws from U(0,1)
y <- rng$unif(100, 2, 5)      # 100 draws from U(2,5)
s <- rng$unif(1)              # scalar draw
z <- rng$normal(5)            # 5 standard normal draws
t <- rng$normal(5, 2, 3)      # 5 draws from N(2,3)
u <- rng$beta(50, 2, 5)       # 50 draws from the Beta(2,5) distribution
```

### Discrete distributions

``` r
x <- rng$int(100, 1, 6)       # integers in [1,6]
p <- rng$perm(10)             # permutation of 1:10
s <- rng$sample(20, 5)        # 5-element sample from 1:20
```

### Multivariate normal

``` r
Sigma <- matrix(c(1.0, 0.2, 0.2, 2.0), 2, 2)
X <- rng$mvn(100, Sigma)                       # zero mean
Y <- rng$mvn(50, Sigma, mu=c(1.0, 2.0))        # specified mean
```

### State control and serialization

``` r
rngc <- randompack_rng("chacha20")
rngp <- randompack_rng("philox")
rngq <- randompack_rng("pcg64")
rngr <- randompack_rng("ranlux++")
rngs <- randompack_rng("sfc64")
rngw <- randompack_rng("cwg128")
rngx <- randompack_rng("x256**")
rngy <- randompack_rng("x256**")
rngz <- randompack_rng("squares")

rngq$pcg64_set_inc(c(3, 0, 5, 0))         # change PCG stream increment
rngq$advance(2**16)                       # advance pcg64 by 2^16 steps
rngq.advance([2**16, 0])                  # also advances by 2^16 steps
rngq.jump(16)                             # and also advances by 2^16 steps
rngr$jump(32)                             # jump ranlux++ state by 2^32 steps
rngx$jump(128)                            # jump x256** state by 2^128 steps
rngc$chacha_set_nonce(c(7, 11, 13))       # change ChaCha20 nonce
rngw$cwg128_set_weyl(c(3, 0, 5, 0))       # change CWG128 Weyl increment            
rngs$sfc64_set_abc(c(7, 0, 11, 0, 13, 0)) # change a, b, c                          
rngp$philox_set_key(c(4, 6))              # set Philox key                          
rngz$squares_set_key(c(4, 0))             # change squares key
rngx$set_state(c(1, 2, 3, 4))             # general state setter                    

state <- rngx$serialize()                 # copy engine state of rngx
rngy$deserialize(state)                   # and put in rngy (engines much match)

rng <- randompack_rng(bitexact=TRUE)     # make agreement across platforms exact
rng <- randompack_rng('philox', bitexact=TRUE) # bitexact with specified engine
rng <- randompack_rng(full_mantissa = TRUE)    # enable full 53-bit mantissa
                                               # (52 bit is default)
```
