PLS-internals

def_AUGI0_ZX_spprec

def_sXaug_EigenDense_QRP_Chol_scaled

def_sXaug_Matrix_QRP_CHM_scaled

def_sXaug_Matrix_CHM_H_scaled

get_from_MME

get_from_MME.AUGI0_ZX_spprec

get_from_MME.sXaug_EigenDense_QRP_Chol_scaled

get_from_MME.sXaug_Matrix_QRP_CHM_scaled

get_from_MME.sXaug_Matrix_CHM_H_scaled

get_from_MME.default

get_from_MME.sparseMatrix

get_from_MME_default

get_from_MME_default.matrix

get_from_MME_default.Matrix

 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.

Franois Rousset

spaMM

Mixed-Effect Models, with or without Spatial Random Effects

François Rousset

Jean-Baptiste Ferdy

Alexandre Courtiol

PLS-internals function

Internal functions for procedure using the ((I,0),(Z,X)) block-order — PLS-internals

Internal functions for procedure using the ((I,0),(Z,X)) block-order

PLS-internals: Internal functions for procedure using the ((I,0),(Z,X)) block-order

Description

Arguments

Details

References