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Type: Package
Title: Kernel Density Estimation for Random Symmetric Positive Definite Matrices
Version: 1.0
Description: Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) <doi:10.48550/arXiv.2506.08816>, Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection.
BugReports: https://github.com/lbelzile/ksm/issues
Imports: Rcpp (≥ 1.0.12)
Suggests: cubature, tinytest
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
License: MIT + file LICENSE
RoxygenNote: 7.3.3
LazyData: true
Depends: R (≥ 2.10)
NeedsCompilation: yes
Packaged: 2025-10-23 15:15:27 UTC; lbelzile
Author: Leo Belzile ORCID iD [aut, cre], Frederic Ouimet ORCID iD [aut]
Maintainer: Leo Belzile <belzilel@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-28 12:30:02 UTC

Solver for Riccati equation

Description

Given two matrices M and S, solve Riccati equation by iterative updating to find the solution \mathbf{R}, where the latter satisfies

\mathbf{R}=\mathbf{M}\mathbf{R}\mathbf{M}^\top + \mathbf{S}

until convergence (i.e., when the Frobenius norm is less than tol, or the maximum number of iterations maxiter is reached.

Usage

Riccati(M, S, tol = 1e-08, maxiter = 10000L)

Arguments

M

matrix

S

matrix

tol

double for tolerance

maxiter

integer, the maximum number of iterations

Value

a list containing

Bandwidth optimization for symmetric matrix kernels

Description

Given a sample of positive definite matrices, perform numerical maximization of the h-block least square (lscv) or leave-one-out likelihood (lcv) cross-validation criteria using a root search.

Usage

bandwidth_optim(
  x,
  criterion = c("lscv", "lcv"),
  kernel = c("Wishart", "smlnorm", "smnorm"),
  tol = 1e-04,
  h = 1L
)

Arguments

x

sample of symmetric matrix observations from which to build the kernel density kernel

criterion

optimization criterion, one of lscv for least square cross-validation at lag h or lcv for leave-one-out cross-validation.

kernel

string, one of Wishart, smlnorm (log-Gaussian) or smnorm (Gaussian).

tol

double, tolerance of optimization (root search)

h

lag step for consideration of observations, for the case criterion=lscv

Value

double, the optimal bandwidth up to tol

Density of Wishart random matrix

Description

Density of Wishart random matrix

Usage

dWishart(x, df, S, log = FALSE)

Arguments

x

array of dimension d by d by n

df

degrees of freedom

S

symmetric positive definite matrix of dimension d by d

log

logical; if TRUE, returns the log density

Value

a vector of length n containing the log-density of the Wishart.

Density of inverse Wishart random matrix

Description

Density of inverse Wishart random matrix

Usage

dinvWishart(x, df, S, log = FALSE)

Arguments

x

array of dimension d by d by n

df

degrees of freedom

S

symmetric positive definite matrix of dimension d by d

log

logical; if TRUE, returns the log density

Value

a vector of length n containing the log-density of the inverse Wishart.

Matrix beta type II density function

Description

Given a random matrix x, compute the density for arguments shape1 and shape2

Usage

dmbeta2(x, shape1, shape2, log = TRUE)

Arguments

x

cube of dimension d by d by n containing the random matrix samples

shape1

positive shape parameter, strictly larger than (d-1)/2.

shape2

positive shape parameter, strictly larger than (d-1)/2.

log

[logical] if TRUE (default), returns the log density.

Value

a vector of length n

Symmetric matrix-variate lognormal density

Description

Density of the lognormal matrix-variate density, defined through the matrix logarithm, with the Jacobian resulting from the transformation

Usage

dsmlnorm(x, b, M, log = TRUE)

Arguments

x

[cube] array of dimension d by d by n

b

[numeric] scale parameter, strictly positive

M

[matrix] location matrix, positive definite

log

[logical] if TRUE (default), returns the log density

Value

a vector of length n

Symmetric matrix-variate normal density

Description

Symmetric matrix-variate normal density

Usage

dsmnorm(x, b, M, log = TRUE)

Arguments

x

[cube] array of dimension d by d by n

b

[numeric] scale parameter, strictly positive

M

[matrix] location matrix, positive definite

log

[logical] if TRUE (default), returns the log density

Value

a vector of length n

Integration with respect to symmetric positive definite matrices

Description

Given a function f defined over the space of symmetric positive definite matrices, compute an integral via numerical integration using the routine cubintegrate.

Usage

integrate_spd(
  f,
  dim,
  tol = 0.001,
  lb = 1e-08,
  ub = Inf,
  neval = 1000000L,
  method = c("suave", "hcubature"),
  ...
)

Arguments

f

function to evaluate that takes as arguments array of size dim by dim by 1.

dim

dimension of integral, only two or three dimensions are supported

tol

double for tolerance of numerical integral

lb

lower bound for integration range of eigenvalues

ub

upper bound for integration range of eigenvalues

neval

maximum number of evaluations

method

string indicating the method from cubature

...

additional arguments for the function f

Value

list returned by the integration routine. See the documentation of cubintegrate for more details.

Examples

integrate_spd(
  dim = 2L,
  neval = 1e4L,
  f = function(x, S){
   dWishart(x, df = 10, S = S, log = FALSE)},
  S = diag(2))

Wishart kernel density

Description

Given a sample of m points xs from an original sample and a set of n new sample matrices x at which to evaluate the Wishart kernel, return the density with bandwidth parameter b.

Usage

kdens_Wishart(x, xs, b, log = TRUE)

Arguments

x

cube of size d by d by n of points at which to evaluate the density

xs

cube of size d by d by m of sample matrices which are used to construct the kernel

b

positive double giving the bandwidth parameter

log

bool; if TRUE, return the log density

Value

a vector of length n containing the (log) density of the sample x

Symmetric matrix log-normal kernel density

Description

Given a sample of m points xs from an original sample and a set of n new sample matrices x at which to evaluate the symmetric matrix normal log kernel, return the density with bandwidth parameter b.

Usage

kdens_smlnorm(x, xs, b, log = TRUE)

Arguments

x

cube of size d by d by n of points at which to evaluate the density

xs

cube of size d by d by m of sample matrices which are used to construct the kernel

b

positive double giving the bandwidth parameter

log

bool; if TRUE, return the log density

Value

a vector of length n containing the (log) density of the sample x

Symmetric matrix normal kernel density

Description

Given a sample of m points xs from an original sample and a set of n new sample matrices x at which to evaluate the symmetric matrix normal kernel, return the density with bandwidth parameter b. Note that this kernel suffers from boundary spillover.

Usage

kdens_smnorm(x, xs, b, log = TRUE)

Arguments

x

cube of size d by d by n of points at which to evaluate the density

xs

cube of size d by d by m of sample matrices which are used to construct the kernel

b

positive double giving the bandwidth parameter

log

bool; if TRUE, return the log density

Value

a vector of length n containing the (log) density of the sample x

Kernel density estimators for symmetric matrices

Description

Given a sample of m points xs from an original sample and a set of n new sample symmetric positive definite matrices x at which to evaluate the kernel, return the density with bandwidth parameter b.

Usage

kdens_symmat(x, xs, kernel = "Wishart", b = 1, log = TRUE)

Arguments

x

cube of size d by d by n of points at which to evaluate the density

xs

cube of size d by d by m of sample matrices which are used to construct the kernel

kernel

string, one of Wishart, smnorm or smlnorm.

b

positive double giving the bandwidth parameter

log

bool; if TRUE, return the log density

Value

a vector of length n containing the (log) density of the sample x

Likelihood cross-validation for symmetric positive definite matrix kernels

Description

Given a cube of sample observations (consisting of random symmetric positive definite matrices), and a vector of candidate bandwidth parameters b, compute the leave-one-out likelihood cross-validation criterion and return the bandwidth among the choices that minimizes the criterion.

Usage

lcv_kdens_symmat(x, b, kernel = "Wishart")

Arguments

x

array of dimension d by d by n

b

vector of candidate bandwidth, strictly positive

kernel

string indicating the kernel, one of Wishart or smlnorm.

Value

a list with arguments

Likelihood cross validation criterion for Wishart kernel

Description

Given a cube x and a bandwidth b, compute the leave-one-out cross validation criterion by taking out a slice and evaluating the kernel at the holdout value.

Usage

lcv_kern_Wishart(x, b)

Arguments

x

[cube] array of dimension d by d by n

b

[numeric] scale parameter, strictly positive

Value

the value of the log objective function

Likelihood cross validation criterion for symmetric matrix lognormal kernel

Description

Given a cube x and a bandwidth b, compute the leave-one-out cross validation criterion by taking out a slice and evaluating the kernel at the holdout value.

Usage

lcv_kern_smlnorm(x, b)

Arguments

x

[cube] array of dimension d by d by n

b

[numeric] scale parameter, strictly positive

Value

the value of the log objective function

Likelihood cross validation criterion for symmetric matrix normal kernel

Description

Given a cube x and a bandwidth b, compute the leave-one-out cross validation criterion by taking out a slice and evaluating the kernel at the holdout value.

Usage

lcv_kern_smnorm(x, b)

Arguments

x

[cube] array of dimension d by d by n

b

[numeric] scale parameter, strictly positive

Value

the value of the log objective function

Least square cross validation criterion for Wishart kernel

Description

Finite sample h-block leave-one-out approximation to the least square criterion, omitting constant term.

Usage

lscv_kern_Wishart(x, b, h = 1L)

Arguments

x

[cube] array of dimension d by d by n

b

[numeric] scale parameter, strictly positive

h

separation vector; only pairs that are |i-j| \leq h apart are considered

Value

a vector of length two containing the log of the summands

Least square cross validation criterion for log symmetric matrix normal kernel

Description

Finite sample h-block leave-one-out approximation to the least square criterion, omitting constant term. Only pairs that are |i-j| \leq h apart are considered.

Usage

lscv_kern_smlnorm(x, b, h = 1L)

Arguments

x

[cube] array of dimension d by d by n

b

[numeric] scale parameter, strictly positive

h

[int] integer indicating the separation lag

Value

a vector of length two containing the log of the summands

Log of mean with precision

Description

Log of mean with precision

Usage

meanlog(x)

Arguments

x

vector of log components to add

Value

double with the log mean of elements

Multivariate gamma function

Description

Given a vector of points x and an order p, compute the multivariate gamma function. The function is defined as

\gamma_p(x) = \pi^{p(p-1)/4}\prod_{i=1}^p \Gamma\{x + (1-i)/2\}.

Usage

mgamma(x, p, log = FALSE)

Arguments

x

[vector] of points at which to evaluate the function

p

[int] dimension of the multivariate gamma function, strictly positive.

log

[logical] if TRUE, returns the log multivariate gamma function.

Value

a matrix with one column of the same length as x

Random generation from first-order vector autoregressive model

Description

Given a matrix of coefficients M and a covariance matrix Sigma, simulate K vectors from a first-order autoregressive process.

Usage

rVAR(n, M, Sigma, K = 1L, order = 1L, burnin = 25L)

Arguments

n

sample size

M

matrix of autoregressive coefficients

Sigma

covariance matrix

K

integer, degrees of freedom

order

order of autoregressive process, only 1 is supported at current.

burnin

number of iterations discarded

Value

a list of length n containing matrices of size K by d

Examples

M <- matrix(c(0.3, -0.3, -0.3, 0.3), nrow = 2)
Sigma <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
rVAR(n = 100, M = M, Sigma = Sigma, K = 10)

Random matrix generation from first-order autoregressive Wishart process

Description

Given a matrix of coefficients M and a covariance matrix Sigma, simulate n random matrices from a first-order autoregressive Wishart process by simulating from cross-products of vector autoregressions

Usage

rWAR(n, M, Sigma, K = 1L, order = 1L, burnin = 25L)

Arguments

n

sample size

M

matrix of autoregressive coefficients

Sigma

covariance matrix

K

integer, degrees of freedom

order

order of autoregressive process, only 1 is supported at current.

burnin

number of iterations discarded

Value

an array of size d by d by n containing the samples

References

C. Gourieroux, J. Jasiak, and R. Sufana (2009). The Wishart Autoregressive process of multivariate stochastic volatility, Journal of Econometrics, 150(2), 167-181, <doi:10.1016/j.jeconom.2008.12.016>.

Examples

M <- matrix(c(0.3, -0.3, -0.3, 0.3), nrow = 2)
Sigma <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
rWAR(n = 10, M = M, Sigma = Sigma, K = 5)

Random matrix generation from Wishart distribution

Description

Random matrix generation from Wishart distribution

Usage

rWishart(n, df, S)

Arguments

n

[integer] sample size

df

[double] degrees of freedom, positive

S

[matrix] a d by d positive definite scale matrix

Value

an array of dimension d by d by n containing the samples

Realized variance of Amazon and SPY

Description

Intraday realized covariances of the returns between the Amazon stock (rvarAMZN) and the SPDR S&P 500 ETF (rvarSPY) using five minutes data, for the period of September 13th, 2023 to September 12, 2024.

Usage

realvar

Format

A 2 by 2 by 250 array

Source

Anne MacKay

Examples

data(realvar, package = "ksm")
bopt <- bandwidth_optim(
 x = realvar,
 criterion = "lscv",
 kernel = "Wishart",
 h = 4L
 )

Random matrix generation from the inverse Wishart distribution

Description

Random matrix generation from the inverse Wishart distribution

Usage

rinvWishart(n, df, S)

Arguments

n

[integer] sample size

df

[double] degrees of freedom, positive

S

[matrix] a d by d positive definite scale matrix

Value

an array of dimension d by d by n containing the samples

Random matrix generation from matrix beta type II distribution

Description

This function only supports the case of diagonal matrices

Usage

rmbeta2(n, d, shape1, shape2)

Arguments

n

sample size

d

dimension of the matrix

shape1

positive shape parameter, strictly larger than (d-1)/2.

shape2

positive shape parameter, strictly larger than (d-1)/2.

Value

a cube of dimension d by d by n

Random vector generation from the multivariate normal distribution

Description

Sampler derived using the eigendecomposition of the covariance matrix vcov.

Usage

rmnorm(n, mean, vcov)

Arguments

n

sample size

mean

mean vector of length d

vcov

a square positive definite covariance matrix, of the same dimension as mean.

Value

an n by d matrix of samples

Examples

rmnorm(n = 10, mean = c(0, 2), vcov = diag(2))

Rotation matrix for 2 dimensional problems

Description

Rotation matrix for 2 dimensional problems

Usage

rotation2d(par)

Arguments

par

double for angle in [0,2\pi).

Value

a 2 by 2 rotation matrix

Rotation matrix for 3 dimensional problems

Description

Rotation matrix for 3 dimensional problems

Usage

rotation3d(par)

Arguments

par

vector of length 3 containing elements \phi \in [0, 2*\pi), \theta \in [0, \pi] and \psi \in [0, 2\pi), in that order.

Value

a 3 by 3 rotation matrix

Rotation matrix with scaling for Monte Carlo integration

Description

Rotation matrix with scaling for Monte Carlo integration

Usage

rotation_scaling(ang, scale)

Arguments

ang

vector of length 1 containing \theta \in [0, 2\pi] for d=2, or of 3 containing elements \phi \in [0, 2*\pi), \theta \in [0, \pi] and \psi \in [0, 2\pi) for d=3.

scale

vector of length 2 (d=2) or 3 (d=3), strictly positive

Value

a 2 by 2, or 3 by 3 scaling matrix, depending on inputs

Target densities for simulation study

Description

Target densities for simulation study

Usage

simu_fdens(x, model, d)

Arguments

x

cube of dimension d by d by n containing the sample matrices

model

integer between 1 and 6 indicating the simulation scenario

d

dimension of the problem, an integer between 2 and 10

Value

a vector of length n containing the density

Integrated squared error via Monte Carlo

Description

Given a target density and a kernel estimator, evaluate the integrated squared error by Monte Carlo integration by simulating from uniform variates on the hypercube.

Usage

simu_ise_montecarlo(x, b, kernel, model, B = 10000L, delta = 0.001)

Arguments

x

a cube of dimension d by d by n containing the sample matrices which define the kernel matrix estimator

b

positive double, bandwidth parameter

model

integer between 1 and 6 indicating the simulation scenario

B

number of Monte Carlo replications, default to 10K

delta

double less than 1; the integrals on [0, \infty) are truncated to [\delta, 1/\delta].

Value

a vector of length 2 containing the mean and the standard deviation of the estimator.

Kullback-Leibler divergence via Monte Carlo

Description

Given a target density and a kernel estimator, evaluate the Kullback-Leibler divergence by Monte Carlo integration by simulating draws from the corresponding model.

Usage

simu_kldiv(x, b, kernel, model, B = 10000L, nrep = 10L)

Arguments

x

a cube of dimension d by d by n containing the sample matrices which define the kernel matrix estimator

b

positive double, bandwidth parameter

model

integer between 1 and 6 indicating the simulation scenario

B

number of Monte Carlo replications, default to 10K

Value

a vector of length 2 containing the mean and the standard deviation of the estimator.

Target densities for simulation study

Description

Target densities for simulation study

Usage

simu_rdens(n, model, d)

Arguments

n

sample size

model

integer between 1 and 6 indicating the simulation scenario

d

dimension of the matrix, an integer between 2 and 10

Value

a cube of dimension d by d by n containing the sample matrices

Log of sum with precision

Description

Log of sum with precision

Usage

sumlog(x)

Arguments

x

vector of log components to add

Value

double with the log sum of elements

Signed sum with precision

Description

Given a vector of signs and log arguments, return their sum with precision avoiding numerical underflow assuming that the sum is strictly positive.

Usage

sumsignedlog(x, sgn)

Arguments

x

vector of log components

sgn

sign of the operator

Value

log sum of elements

Symmetrize matrix

Description

Given an input matrix, symmetrize by taking average of lower and upper triangular components as A + A^\top.

Usage

symmetrize(A)

Arguments

A

square matrix

Value

symmetrized version of A

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