bandle-cpp: bessel function of the second kind from boost library

In ococrook/bandle: An R package for the Bayesian analysis of differential subcellular localisation experiments

bessel function of the second kind from boost library

Description

Leapfrog routine

Leapfrog routine

Usage

besselK_boost(x, v)

besselK(x, v)

matern(nu, a, rho, tau, D)

trenchDetcpp(c)

trenchInvcpp(v)

loglikeGPcpp(Y, Z, A, logcovDet, sigmak, nk, D, Y2)

likelihoodGPcpp(Xk, tau, h, nk, D, materncov = 0L, nu = 2)

gradientrhomatern(Y, drvrhomatern, nk, D, Z, A, sigmak)

gradientamatern(Y, amatern, nk, D, Z, A, sigmak)

gradientGPcppmatern(Xk, tau, h, nk, D, nu)

LeapfrogGPcppPC(Xk, lambda, tau, p, x, m, nk, D, L, delta, nu)

sampleGPmeanmaterncpp(Xk, tau, h, nk, D, nu)

makeComponent(X, BX, Y, BY, j)

sampleGPmeancpp(Xk, tau, h, nk, D)

normalisedData(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, j)

normalisedDatamatern(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, j, nu)

centeredDatamatern(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, K, nu)

componentloglike(centereddata, sigmak)

comploglike(centereddata, sigmak)

comploglikelist(centereddata, sigmak)

sampleDirichlet(numSamples, alpha)

sampleOutliercpp(allocoutlierprob)

sampleAlloccpp(allocprob)

centeredData(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, K)

mahaInt(X, mu, sigma, isChol = FALSE)

dmvtInt(X, mu, cholDec, log, df)

dmvtCpp(X_, mu_, sigma_, df_, log_, isChol_)

gradientGPcpp(Xk, tau, h, nk, D)

LeapfrogGPcpp(Xk, tau, p, x, m, nk, D, L, delta)

rcpp_pgdraw(b, c)

Arguments

x	position
v	argument of trench algorithm
nu	smoothness parameter of matern covariance
a	amplitude
rho	length-scale
tau	indexing term
D	number of samples
c	parameter of PG distribution
Y	pointer to data to be subset. X and Y will be joined
Z	special matrix from trench algorithm (see Crook et al arxiv 2019)
A	special matrix from trench algorithm (see Crook et al arxiv 2019)
logcovDet	log determine of the covariancematrix
sigmak	variance term
nk	number of observations
Y2	vectorised data (see Crook et al arxiv 2019)
Xk	The data
h	vector of hyperparamters
materncov	logical indicating whether to use matern or gaussian covariance. Defaults to Guassian covariance
drvrhomatern	deterivate of matern covariance wrt to rho
amatern	deterivate of matern covariance wrt to amplitude
lambda	parameters of penalised complexity prior
p	momentum
m	mass
L	iterations
delta	stepsize
X	data
BX	indexing set to make component
BY	pointer to subsetting matrix
j	indicator of localisations i.e. niche j
Xknown	data with known localisations
Xunknown	data with unknown localisations
BXun	indexing set for unknown localisations
hypers	vector of hyperparameters
K	number of components
centereddata	pointer to centered data
numSamples	The number of samples desired
alpha	The concentration parameter
allocoutlierprob	The probabilities of being allocated to the outlier component
allocprob	probability of being allocated to particular component
mu	mean
sigma	variance matrix
isChol	boolen indicated whether sigma is cholesky decomposition
cholDec	Cholesky decomposition of variance matrix
log	boolen of log density
df	degrees of freedom for t distribution
X_	the data
mu_	the mean
sigma_	the variance matrix
df_	the degrees of freedom
log_	return log density (boolean).
isChol_	is variance matrix in cholesky decomposition
b	parameter of PG distribution

Value

A numeric indicating the density of the t-distribution

Examples

dmvtCpp(diag(1,1,1), 1, diag(1,1,1), 1, TRUE, TRUE)

ococrook/bandle documentation built on Nov. 4, 2024, 12:27 a.m.