Automatic fixed rank kriging for (irregularly located)
spatial data using a class of basis functions with multi-resolution features
and ordered in terms of their resolutions. The model parameters are estimated
by maximum likelihood (ML) and the number of basis functions is determined
by Akaike's information criterion (AIC). For spatial data with either one
realization or independent replicates, the ML estimates and AIC are efficiently
computed using their closed-form expressions when no missing value occurs. Details
regarding the basis function construction, parameter estimation, and AIC calculation
can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For
data with missing values, the ML estimates are obtained using the expectation-
maximization algorithm. Apart from the number of basis functions, there are
no other tuning parameters, making the method fully automatic. Users can also
include a stationary structure in the spatial covariance, which utilizes
'LatticeKrig' package.
fields (>= 6.9.1), filehashSQLite, filehash, MASS, LatticeKrig
(>= 5.4), FNN, filematrix, Rcpp
ShengLi Tzeng [aut, cre], Hsin-Cheng Huang [aut], Wen-Ting Wang [ctb], Douglas Nychka [ctb], Colin Gillespie [ctb]

fields (>= 6.9.1), filehashSQLite, filehash, MASS, mgcv,
LatticeKrig (>= 5.4), FNN, filematrix, Rcpp

fields (>= 6.9.1), filehashSQLite, filehash, MASS, mgcv,
LatticeKrig (>= 5.4), FNN, filematrix, Rcpp, methods

Algorithms for Automatically Fitting MFA Models
c(
person(given = "John",
family = "Davey",
role = c("aut", "cre"),
email = "john.c.m.davey@gmail.com"),
person(given = "Sharon",
family = "Lee",
role = "ctb"),
person(given = "Garique",
family = "Glonek",
role = "ctb"),
person(given = "Suren",
family = "Rathnayake",
role = "ctb"),
person(given = "Geoff",
family = "McLachlan",
role = "ctb"),
person(given = "Albert Ali",
family = "Salah",
role = "ctb"),
person(given = "Heysem",
family = "Kaya",
role = "ctb"))
Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically.
The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible.
abind, MASS, Matrix, Rfast, expm, stats, utils, Rdpack,
pracma, usethis
John Davey [aut, cre],
Sharon Lee [ctb],
Garique Glonek [ctb],
Suren Rathnayake [ctb],
Geoff McLachlan [ctb],
Albert Ali Salah [ctb],
Heysem Kaya [ctb]