Package 'rangen'

Random Number Generators and Utilities

Description

Provides a collection of random number generators for common and custom distributions, along with utility functions for sampling and simulation.

Details

Package:	rangen
Type:	Package
Version:	0.0.1
Date:	2026-03-09
License:	GPL-3

Maintainers

Manos Papadakis <[email protected]>

Author(s)

• Manos Papadakis <[email protected]>

• Michail Tsagris <[email protected]>

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Random integer values simulation

Description

Random integer values simulation.

Usage

Sample.int(n, size = n, replace = FALSE) 
Sample(x, size = length(x), replace = FALSE)
colSample(x, size = rep_len(nrow(x), ncol(x)), 
replace = rep_len(FALSE, ncol(x)), parallel = FALSE, cores = 0)
rowSample(x, size = rep_len(ncol(x), nrow(x)), 
replace = rep_len(FALSE, nrow(x)), parallel = FALSE, cores = 0)

Arguments

x	A numeric vector for sampling or a matrix for column-row sampling.
n	This must be an integer value. The function will then draw random integer values from 1:n.
size	The number of integer values to sample.
replace	Do you want to sample with replacement? If yes, set this equal to TRUE.
parallel	Do you want to do it in parallel, for column - row major, in C++? TRUE or FALSE.
cores	Number of cores to use for parallelism. Valid only when argument parallel is set to TRUE. Default value is 0 and it means the maximum supported cores.

Details

These functions provide flexible sampling utilities similar in purpose to R's base functions sample.int and sample. Each function operates on a different structure:

• Sample.int: Generates a random sample of integers from 1 to n.

• Sample: Samples elements from the vector x.

• colSample: Performs column-wise sampling on a matrix or data frame, selecting size[i] elements from each column i.

• rowSample: Performs row-wise sampling on a matrix or data frame, selecting size[i] elements from each row i.

All functions support sampling with or without replacement. Parallel versions do not support seeding.

Value

Sample.int, Sample	A vector of sampled values.
colSample, rowSample	A matrix or data frame containing the sampled elements.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

sample, sample.int

Examples

# Sample integers from 1 to 10 with replacement (faster than base::sample.int)
x <- Sample.int(10, 1000, replace = TRUE)

# Sample from the vector 'x' (faster than base::sample)
xs <- Sample(x)

# Create a matrix and perform column-wise sampling
# 'size' must have the same length as the number of columns
mat <- matrix(1:20, nrow = 5, ncol = 4)
colSample(mat, size = rep(5, ncol(mat)), replace = rep(TRUE, ncol(mat)))

# Create a matrix and perform row-wise sampling
# 'size' must have the same length as the number of rows
rowSample(mat, size = rep(4, nrow(mat)), replace = rep(FALSE, nrow(mat)))

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Random values simulation from various distributions

Description

Functions to simulate random values from different probability distributions: uniform, beta, exponential, chi-squared, gamma, Cauchy, t-distribution, and geometric.

Usage

Runif(n, min = 0, max = 1) 
Rbeta(n, alpha, beta)
Rexp(n, rate = 1)
Rchisq(n, df)
Rgamma(n, shape, rate = 1)
Rcauchy(n, location = 0, scale = 1)
Rt(n, df, ncp)
Rgeom(n, prob)
Rpareto(n, shape = 1, scale = 1)
Rfrechet(n, lambda = 1, mu = 0, sigma = 1)
Rlaplace(n, mu = 0, sigma = 1)
Rgumbel(n, mu = 0, sigma = 1)
Rarcsine(n, min = 0, max = 1)
Rnorm(n, mean = 0, sd = 1)
Runif.mat(nrow, ncol, min = 0, max = 1) 
Rbeta.mat(nrow, ncol, alpha, beta)
Rexp.mat(nrow, ncol, rate = 1)
Rchisq.mat(nrow, ncol, df)
Rgamma.mat(nrow, ncol, shape, rate = 1)
Rcauchy.mat(nrow, ncol, location = 0, scale = 1)
Rt.mat(nrow, ncol, df, ncp)
Rgeom.mat(nrow, ncol, prob)
Rpareto.mat(nrow, ncol, shape = 1, scale = 1)
Rfrechet.mat(nrow, ncol, lambda = 1, mu = 0, sigma = 1)
Rlaplace.mat(nrow, ncol, mu = 0, sigma = 1)
Rgumbel.mat(nrow, ncol, mu = 0, sigma = 1)
Rarcsine.mat(nrow, ncol, min = 0, max = 1)
Rnorm.mat(nrow, ncol, mean = 0, sd = 1)
colRunif(nrow, ncol, min = rep_len(0, ncol), max = rep_len(1, ncol))
colRbeta(nrow, ncol, alpha, beta)
colRexp(nrow, ncol, rate = rep_len(1, ncol))
colRchisq(nrow, ncol, df)
colRgamma(nrow, ncol, shape, rate = rep_len(1, ncol))
colRgeom(nrow, ncol, prob)
colRcauchy(nrow, ncol, location = rep_len(0, ncol), scale = rep_len(1, ncol))
colRt(nrow, ncol, df, ncp)
colRpareto(nrow, ncol, shape = rep_len(1, ncol), scale = rep_len(1, ncol))
colRfrechet(nrow, ncol, lambda = rep_len(1, ncol), 
mu = rep_len(0, ncol), sigma = rep_len(1, ncol))
colRlaplace(nrow, ncol, mu = rep_len(0, ncol), sigma = rep_len(1, ncol))
colRgumbel(nrow, ncol, mu = rep_len(0, ncol), sigma = rep_len(1, ncol))
colRarcsine(nrow, ncol, min = rep_len(0, ncol), max = rep_len(1, ncol))
colRnorm(nrow, ncol, mean = rep_len(0, ncol), sd = rep_len(1, ncol))
setSeed(seed = nanoTime())

Arguments

n	The number of values to generate.
nrow	The number of rows.
ncol	The number of columns.
min	The lower value.
max	The upper value.
alpha	The shape parameter alpha.
beta	The shape parameter beta.
rate	Rgamma: The rate parameter.
df	Rt: The degrees of freedom.
lambda, shape	The shape parameter.
location, mu	The location parameter.
scale, sigma	The scale parameter.
ncp	The non-centrality parameter.
prob	The probability of success on each trial.
seed	A single value, interpreted as an integer.
mean	Vector of means.
sd	Vector of standard deviations.

Details

• Runif, Runif.mat, colRunif: generates random values from the uniform distribution, similar to R's built-in runif function. The type used is $min + (max - min) \cdot U$, where U is a uniform random variable in the interval (0, 1).

• Rbeta, Rbeta.mat, colRbeta: generates random values from the beta distribution with parameters alpha and beta. The type used involves generating two gamma-distributed variables \( X_1 \) and \( X_2 \), and returning $\frac{X_1}{X_1 + X_2}$, where \( X_1 \) and \( X_2 \) have the shape parameters alpha and beta, respectively.

• Rexp, Rexp.mat, colRexp: generates random values from the exponential distribution with the specified rate parameter. The type used is $-\frac{\log(U)}{\text{rate}}$, where U is a uniform random variable in the interval (0, 1).

• Rchisq, Rchisq.mat, colRchisq: generates random values from the chi-squared distribution with df degrees of freedom. The type used is the sum of the squares of df independent standard normal random variables, i.e., $Y_1^2 + Y_2^2 + \dots + Y_{df}^2$, where each \( Y_i \) is a standard normal random variable.

• Rgamma, Rgamma.mat, colRgamma: generates random values from the gamma distribution with shape and rate parameters. The type used for shape greater than or equal to 1 is the Marsaglia and Tsang method, which involves generating a variable \( V \) and returning $d \cdot V \cdot \text{rate}$, where \( d \) is a function of the shape and \( V \) is derived from a normal random variable. For shape less than 1, a combination of the uniform and exponential distributions is used, involving an acceptance-rejection method.

• Rcauchy, Rcauchy.mat, colRcauchy: generates random values from the Cauchy distribution with specified location and scale parameters. The type used for this is $\text{location} + \text{scale} \cdot \tan(\pi \cdot (U - 0.5))$, where U is a uniform random variable.

• Rt, Rt.mat, colRt: generates random values from the t-distribution with specified df degrees of freedom and an optional non-centrality parameter ncp. The type used is $\frac{Z}{\sqrt{\frac{Y}{df}}}$, where Z is a standard normal random variable and Y is a chi-squared random variable. If ncp is provided, the type used is $\frac{Z + ncp}{\sqrt{\frac{Y}{df}}}$.

• Rgeom, Rgeom.mat, colRgeom: generates random values from the geometric distribution with the specified probability prob. The type used is $\left\lfloor \frac{\log(U)}{\log(1 - prob)} \right\rfloor$, where U is a uniform random variable.

• Rpareto, Rpareto.mat, colRpareto: generates random values from the Pareto distribution with parameters shape and scale. The type used is $\text{scale} \cdot (1 - U)^{-\frac{1}{\text{shape}}}$, where U is a uniform random variable in the interval (0, 1).

• Rfrechet, Rfrechet.mat, colRfrechet: generates random values from the Frechet distribution with parameters shape, mu, and scale. The type used is $\mu + \text{scale} \cdot (-\log U)^{1/\text{shape}}$, where U is a uniform random variable in (0, 1).

• Rlaplace, Rlaplace.mat, colRlaplace: generates random values from the Laplace distribution with location parameter mu and scale parameter sigma. The type used is $\mu - \sigma \cdot \text{sign}(U) \cdot \log(1 - 2 \cdot |U|)$, where U is a uniform random variable in (0, 1).

• Rgumbel, Rgumbel.mat, colRgumbel: generates random values from the Gumbel (type I extreme value) distribution with parameters mu and sigma. The type used is $\mu - \sigma \cdot \log(-\log U)$, where U is a uniform random variable in (0, 1).

• Rarcsine, Rarcsine.mat, colRarcsine: generates random values from the arcsine distribution over the interval [min, max]. The type used is $\text{min} + (\text{max} - \text{min}) \cdot \sin^2(\pi U / 2)$, where U is a uniform random variable in (0, 1).

• setSeed: Set the seed for the Rngs. Not working with parallel version of column - row sample.

Value

Each function, for example Runif, returns a vector with simulated values from the respective distribution. Each function.mat, for example Runif.mat, returns a matrix with simulated values from the respective distribution. Each colFunction, for example colRunif, returns a matrix with column major simulated values from the respective distribution.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

runif, rbeta, rexp, rchisq, rgamma, rcauchy, rt, rgeom

Examples

# Scalar draws
x_unif     <- Runif(5, 0, 1)
x_beta     <- Rbeta(5, 2, 5)
x_exp      <- Rexp(5, 1.5)
x_chisq    <- Rchisq(5, 4)
x_gamma    <- Rgamma(5, 2, 2)
x_cauchy   <- Rcauchy(5, 0, 1)
x_t        <- Rt(5, df = 5, ncp = 2)
x_geom     <- Rgeom(5, 0.5)
x_pareto   <- Rpareto(5, shape = 2, scale = 1)
x_frechet  <- Rfrechet(5, lambda = 1, mu = 0, sigma = 1)
x_laplace  <- Rlaplace(5, mu = 0, sigma = 1)
x_gumbelt  <- Rgumbel(5, mu = 0, sigma = 1)
x_arcsine  <- Rarcsine(5, min = 0, max = 1)
x_norm     <- Rnorm(5)

#matrices
x_unif     <- Runif.mat(5,2, 0, 1)
x_beta     <- Rbeta.mat(5,2, 2, 5)
x_exp      <- Rexp.mat(5,2, 1.5)
x_chisq    <- Rchisq.mat(5,2, 4)
x_gamma    <- Rgamma.mat(5,2, 2, 2)
x_cauchy   <- Rcauchy.mat(5,2, 0, 1)
x_t        <- Rt.mat(5,2, df = 5, ncp = 2)
x_geom     <- Rgeom.mat(5,2, 0.5)
x_pareto   <- Rpareto.mat(5,2, shape = 2, scale = 1)
x_frechet  <- Rfrechet.mat(5,2, lambda = 1, mu = 0, sigma = 1)
x_laplace  <- Rlaplace.mat(5,2, mu = 0, sigma = 1)
x_gumbelt  <- Rgumbel.mat(5,2, mu = 0, sigma = 1)
x_arcsine  <- Rarcsine.mat(5,2, min = 0, max = 1)
x_norm     <- Rnorm.mat(5,2)

# Column-wise (vectorized by column) draws
x_col_unif    <- colRunif(5, 2, min = c(0, 1), max = c(1, 2))
x_col_beta    <- colRbeta(5, 2, alpha = c(2, 5), beta = c(5, 2))
x_col_exp     <- colRexp(5, 2, rate = c(1.5, 2.0))
x_col_chisq   <- colRchisq(5, 2, df = c(4, 5))
x_col_gamma   <- colRgamma(5, 2, shape = c(2, 3), rate = c(2, 1))
x_col_cauchy  <- colRcauchy(5, 2, location = c(0, 1), scale = c(1, 2))
x_col_t       <- colRt(5, 2, df = c(5, 6), ncp = c(2, 1))
x_col_geom    <- colRgeom(5, 2, prob = c(0.5, 0.3))
x_col_pareto  <- colRpareto(5, 2, shape = c(2, 3), scale = c(1, 1))
x_col_frechet <- colRfrechet(5, 2, lambda = c(1, 2), mu = c(0, 0), sigma = c(1, 1))
x_col_laplace <- colRlaplace(5, 2, mu = c(0, 1), sigma = c(1, 2))
x_col_gumbel  <- colRgumbel(5, 2, mu = c(0, 1), sigma = c(1, 2))
x_col_arcsine <- colRarcsine(5, 2, min = c(0, 0.5), max = c(1, 1.5))
x_col_norm    <- colRnorm(5, 2)

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Time functions

Description

Functions to get the current time in different units.

Usage

nanoTime()

Details

• nanoTime: The current nanoseconds of the system.

Value

Each function returns a numeric value.

Author(s)

Manos Papadakis <[email protected]>.

See Also

runif, rbeta, rexp, rchisq, rgamma, rcauchy, rt, rgeom

Examples

x <- nanoTime()

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