This task view on numerical mathematics lists R packages and functions
that are useful for solving numerical problems in linear algebra and
analysis. It shows that R is a viable computing environment for
implementing and applying numerical methods, also outside the realm of
The task view will
cover differential equations,
optimization problems and solvers, or packages and functions operating
on times series, because all these topics are treated extensively in
the corresponding task views
All these task views together will provide a good selection of what is
available in R for the area of numerical mathematics.
task view with its many
links for parallel computing may also be of interest.
The task view has been created to provide an overview of the topic.
If some packages are missing or certain topics in numerical math
should be treated in more detail, please let the maintainer know.
Numerical Linear Algebra
As statistics is based to a large extent on linear algebra, many
numerical linear algebra routines are present in R, and some only
implicitly. Examples of explicitly available functions are vector and
matrix operations, matrix (QR) decompositions, solving linear equations,
eigenvalues/-vectors, singular value decomposition, or least-squares
The recommended package
provides classes and methods
for dense and sparse matrices and operations on them, for example
Cholesky and Schur decomposition, matrix exponential, or norms and
conditional numbers for sparse matrices.
adds generalized (Penrose)
inverses and null spaces of matrices.
computes the exponential, logarithm, and square root
of square matrices, but also powers of matrices or the Frechet
is to be preferred to the function
with the same name in
provides classes and methods for sparse matrices
and for solving linear and least-squares problems in sparse linear
provides R bindings to state-of-the-art implementations
of singular value decomposition (SVD) and eigenvalue/eigenvector
will obtain sparse SVDs using an
iterative thresholding method, while
approximate singular values/vectors of large matrices.
eigenvalues and -vectors for pairs of matrices, and QZ (generalized
generates matrices with a given set of
eigenvalues ('inverse eigenvalue problem').
rARPACK, a wrapper for the ARPACK library, is
typically used to compute only a few eigenvalues/vectors, e.g., a small
number of largest eigenvalues.
uses elementary methods of linear algebra (Gauss, LU,
CGM, Cholesky) to solve linear systems.
contains routines for manipulating
quaternions and octonians (normed division algebras over the real
numbers); quaternions can be useful for handling rotations in
integration of the C++ template libraries 'Armadillo' resp. 'Eigen'
for linear algebra applications written in C++ and integrated in R
for performance and ease of use.
Many special mathematical functions are present in R, especially
logarithms and exponentials, trigonometric and hyperbolic functions, or
Bessel and Gamma functions. Many more special functions are available in
provides an interface to the 'GNU Scientific
Library' that contains implementations of many special functions, for
example the Airy and Bessel functions, elliptic and exponential
integrals, the hypergeometric function, Lambert's W function, and
Airy and Bessel functions, for real and complex numbers, are also
computed in package
Bessel, with approximations for large
includes special functions, such as
error functions and inverses, incomplete and complex gamma function,
exponential and logarithmic integrals, Fresnel integrals, the
polygamma and the Riemann zeta function.
computes Gauss' 2F1 and Appell's F1 hypergeometric
functions for complex parameters and arguments quite accurately.
The hypergeometric (and generalized hypergeometric) function, is
hypergeo, including transformation formulas
and special values of the parameters.
Elliptic and modular functions are available in package
elliptic, including the Weierstrass P function and Jacobi's
There are tools for visualizing complex functions.
Function polyroot() in base R determines all zeros of a polynomial,
based on the Jenkins-Traub algorithm. Linear regression function lm()
can perform polynomial fitting when using
in the model
formula (with option
raw = TRUE).
functionality for manipulating univariate polynomials, like
evaluating polynomials (Horner scheme), differentiating or integrating
them, or solving polynomials, i.e. finding all roots (based on an
fits univariate polynomials to given data,
applying different algorithms.
For multivariate polynomials, package
various tools to manipulate and combine these polynomials of several
facilitates symbolic manipulations on
multivariate polynomials, including basic differential calculus
operations on polynomials, plus some Groebner basis calculations.
consists of a collection of functions
to construct orthogonal polynomials and their recurrence relations,
among them Chebyshev, Hermite, and Legendre polynomials, as well as
spherical and ultraspherical polynomials. There are functions to
operate on these polynomials.
Differentiation and Integration
in base R compute derivatives
of simple expressions symbolically.
implements an approach for numerically
integrating univariate functions in R. It applies adaptive Gauss-Kronrod
quadrature and can handle singularities and unbounded domains to a certain
sets the standard for numerical differentiation
in R, providing numerical gradients, Jacobians, and Hessians, computed
by simple finite differences, Richardson extrapolation, or the highly
accurate complex step approach.
contains functions for computing numerical
derivatives, including Richardson extrapolation or complex step.
computes numerical derivatives of higher orders.
has several routines for numerical integration:
adaptive Lobatto quadrature, Romberg integration, Newton-Cotes
formulas, Clenshaw-Curtis quadrature rules.
integrates functions in two dimensions, also for domains characterized
by polar coordinates or with variable interval limits.
contains a collection of functions to
perform Gaussian quadrature, among them Chebyshev, Hermite, Laguerre,
and Legendre quadrature rules, explicitly returning nodes and weights
in each case. Function
does a similar job.
provides a fast
implementation of (adaptive) Gauss-Hermite quadrature.
Adaptive multivariate integration over hyper-rectangles in
n-dimensional space is available in package
adaptIntegrate(), based on a C library of the
same name. The integrand functions can even be multi-valued.
Multi-dimensional numerical integration is also covered in package
R2Cuba, a wrapper around the C library Cuba.
it includes an approach to Monte Carlo
integration based on importance sampling.
provides another approach to
multivariate integration in high-dimensional spaces. It creates sparse
n-dimensional grids that can be used as with quadrature rules.
integrate functions over unit spheres and balls in n-dimensional
provides methods to integrate
functions over m-dimensional simplices in n-dimensional space.
Both packages comprise exact methods for polynomials.
holds some routines for numerical
integration over polygonal domains in two dimensions.
extracts features from functional data, such as
first and second derivatives, or curvature at critical points, while
finds roots, extrema and inflection
points of curves defined by discrete points.
Interpolation and Approximation
Base R provides functions
for constant and linear
for cubic (Hermite) spline
performs cubic spline
approximation. Base package splines creates periodic interpolation
splines in function
Interpolation of irregularly spaced data is possible with the
for univariate data,
for data on a 2D
rectangular domain. (This package is distributed under ACM license and
not available for commercial use.)
discrete data, notably
for linear, spline, and
for piecewise cubic Hermite
provides barycentric Lagrange interpolation
(in 1 and 2 dimensions) in
barylag2d(), 1-dim. akima in
and interpolation and approximation of data with rational functions,
i.e. in the presence of singularities, in
interpolation based on piecewise rational functions by applying
Stineman's algorithm. The interpolating function will be monoton in
regions where the specified points change monotonically.
uniroot(), implementing the Brent-Decker algorithm, is the
basic routine in R to find roots of univariate functions. There are
implementations of the bisection algorithm in several contributed
packages. For root finding with higher precision there is function
in the multi-precision package
And for finding roots of multivariate functions see the following two
For solving nonlinear systems of equations the
provides (non-monotone) Barzilai-Borwein spectral methods in
sane(), including a derivative-free variant in
dfsane(), and multi-start features with sensitivity
solves nonlinear systems of equations
using alternatively the Broyden or Newton method, supported by
strategies such as line searches or trust regions.
defines a common interface for solving a set of
Discrete Mathematics and Number Theory
Not so many functions are available for computational number theory.
Note that integers in double precision can be represented exactly up to
2^53 - 1, above that limit a multi-precision package such as
is needed, see below.
provides functions for factorization, prime
numbers, twin primes, primitive roots, modular inverses, extended GCD,
etc. Included are some number-theoretic functions like divisor
functions or Euler's Phi function.
contains various utilities for evaluating
continued fractions and partial convergents.
package enumerates additive partitions
of integers, including restricted and unequal partitions.
generates all permutations or all
combinations of a certain length of a set of elements (i.e. a vector);
it also computes binomial coefficients.
creates and investigates magical squares.
Multi-Precision Arithmetic and Symbolic Mathematics
Multiple precision arithmetic is available in R through package
that interfaces to the GMP C library. Examples are
factorization of integers, a probabilistic prime number test, or
operations on big rationals -- for which linear systems of equations
can be solved.
Multiple precision floating point operations and functions are
provided through package
using the MPFR and GMP
libraries. Special numbers and some special functions are included,
as well as routines for root finding, integration, and optimization
in arbitrary precision.
accesses the symbolic algebra system 'SymPy'
(written in Python) from R.
It supports arbitrary precision computations, linear algebra and
calculus, solving equations, discrete mathematics, and much more.
interfaces the computer algebra system
'Yacas'. It supports symbolic and arbitrary precision computations
in calculus and linear algebra.
MATLAB, Octave, and Python Interfaces
Interfaces to numerical computation software such as MATLAB
(commercial) or Octave (free) will be important when solving difficult
numerical problems. (Please note that the commercial programs SAS and
Mathematica do have facilities to call R functions.)
emulation package contains about 30 simple
functions, replicating MATLAB functions, using the respective MATLAB
names and being implemented in pure R.
provides tools to read and write MAT
files, which is the MATLAB data format. It also enables a
one-directional interface with a MATLAB process, sending and
retrieving objects through a TCP/IP connection.
provides an interface to Octave (a
MATLAB clone). It enables calling Octave functions, passing variables
between R and Octave, and browsing Octave documentation.
Python, through its modules 'NumPy', 'SciPy', 'Matplotlib', 'SymPy',
and 'pandas', has elaborate and efficient numerical and graphical
tools available. R package
permits calls from R to
(with Python module 'rpy2') interfaces R from Python.
'pyRserve' is a Python module for connecting Python to an R process
as an RPC gateway. This R process can run on
a remote machine, variable access and function calls will be delegated
through the network.