gridIMRF

projpath

glm.nodev.fit

fitme_body

fitmv_body

HLfit.obj

.HLfit_body_augZXy

.solve_IRLS_as_ZX

.solve_IRLS_as_spprec

.is_spprec

.NB_shapeFn

.NB_shapeInv

.ZWZt

.calc_CARdispGammaGLM

.calc_dispGammaGLM

.crossprod

.dispFn

.dispInv

.evalWrapper

.is_spprec_fit

.modify_list

.nuFn

.nuInv

.rhoFn

.rhoInv

.safe_opt

.setCluster

.stripOffset

.stripRanefs

.tcrossprod

.unloads4spaMM

.wrap_Ltri_t_chol

dimnames.bigq

Initialize

Variogram

coef

coef&lt;-

corFactor

corMatrix

getCovariate

logDet

recalc

spaMM_Gamma

get_HLCorcall

HLCor.obj

corrHLfit_body

corrMM.LRT

getCall.HLfit

model.offset.HLfit

getPar

HLCor_body

HLfit_body

HLfit_body_old

makeTicks

Matern.corr

niceLabels

overcat

crack

recond

 internal

spaMM-internal

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted  likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

spaMM-internal: Internal spaMM Functions

Description

Arguments

Details