'R' Bindings for the 'Boost' Math Functions.

boostmath

Providing simple access to Boost’s Math functions in R, no compilation required.

Installation

You can install the development version of boostmath from GitHub with:

# install.packages("remotes")
remotes::install_github("andrjohns/boostmath")

Or you can install pre-built binaries from R-Universe:

install.packages("boostmath", repos = c("https://andrjohns.r-universe.dev",
                                        "https://cran.r-project.org"))

Usage

Functions can be used directly after loading the package:

library(boostmath)
#> 
#> Attaching package: 'boostmath'
#> The following object is masked from 'package:grDevices':
#> 
#>     pdf
#> The following object is masked from 'package:base':
#> 
#>     mode

hypergeometric_pFq(c(1, 2.5), c(0.5, 2), 1)
#> [1] 6.675991
ibeta_inv(2.1, 5.2, 0.7)
#> [1] 0.361431
owens_t(2.1, 4.2)
#> [1] 0.00893221

Any Boost Math functions that share the same name as R functions are sufffixed with _boost to avoid conflicts:

beta_boost(3, 2)
#> [1] 0.08333333
lgamma_boost(5)
#> [1] 3.178054

Quadrature and Differentiation

Boost’s integration routines are also available for use with R functions:

trapezoidal(function(x) { 1/(5 - 4*cos(x)) }, a = 0, b = 2*pi)
#> [1] 2.094395

gauss_legendre(function(x) { x * x * atan(x) }, a = 0, b = 1, points = 20)
#> [1] 0.2106573

gauss_kronrod(function(x) { exp(-x * x / 2) }, a = 0, b = Inf, points = 15)
#> [1] 1.253314

As well as numerical differentiation by finite-differencing or the complex-step method:

finite_difference_derivative(exp, 1.7)
#> [1] 5.473947

complex_step_derivative(exp, 1.7)
#> [1] 5.473947

Distribution Functions

boostmath implements Boost’s approach of creating a distribution ‘object’ which the various distribution functions (e.g., pdf, quantile) can be applied:

# Normal distribution with mean = 0, sd = 1
dist <- normal_distribution(0, 1)
# Apply generic functions
cdf(dist, 0.5)
#> [1] 0.6914625
logcdf(dist, 0.5)
#> [1] -0.3689464
pdf(dist, 0.5)
#> [1] 0.3520653
logpdf(dist, 0.5)
#> [1] -1.043939
hazard(dist, 0.5)
#> [1] 1.141078
chf(dist, 0.5)
#> [1] 1.175912
mean(dist)
#> [1] 0
median(dist)
#> [1] 0
mode(dist)
#> [1] 0
range(dist)
#> [1] -Inf  Inf
quantile(dist, 0.2)
#> [1] -0.8416212
standard_deviation(dist)
#> [1] 1
support(dist)
#> [1] -Inf  Inf
variance(dist)
#> [1] 1
skewness(dist)
#> [1] 0
kurtosis(dist)
#> [1] 3
kurtosis_excess(dist)
#> [1] 0

Alternatively, the PDF, CDF, log-PDF, log-CDF, and quantile functions for statistical distributions can just be called directly:

beta_pdf(0.1, 1.2, 2.1)
#> [1] 1.569287

beta_lpdf(0.1, 1.2, 2.1)
#> [1] 0.4506213

beta_cdf(0.1, 1.2, 2.1)
#> [1] 0.1380638

beta_lcdf(0.1, 1.2, 2.1)
#> [1] -1.98004

beta_quantile(0.5, 1.2, 2.1)
#> [1] 0.3335097