besselK_boost | R Documentation |
Leapfrog routine
Leapfrog routine
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)
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 |
A numeric indicating the density of the t-distribution
dmvtCpp(diag(1,1,1), 1, diag(1,1,1), 1, TRUE, TRUE)
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