Package: autoFRK
Type: Package
Title: Automatic Fixed Rank Kriging
Description: 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.
License: GPL (>= 2)
Version: 1.3.1
Date: 2020-08-21
Depends: R (>= 3.1), spam
Imports: fields (>= 6.9.1), filehashSQLite, filehash, MASS, mgcv,
LatticeKrig (>= 5.4), FNN, filematrix, Rcpp, methods
LinkingTo: Rcpp, RSpectra, RcppEigen, RcppParallel
Author: ShengLi Tzeng [aut, cre], Hsin-Cheng Huang [aut], Wen-Ting Wang [ctb], Douglas Nychka [ctb], Colin Gillespie [ctb]
Maintainer: ShengLi Tzeng <slt.cmu@gmail.com>
NeedsCompilation: yes
Date/Publication: 2020-08-26 23:40:02 UTC
Packaged: 2020-08-22 07:35:38 UTC; h0h1
Repository: CRAN

Package: autoFRK
Type: Package
Title: Automatic Fixed Rank Kriging
Description: 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.
License: GPL (>= 2)
Version: 1.3.2
Date: 2020-09-08
Depends: R (>= 3.1), spam
Imports: fields (>= 6.9.1), filehashSQLite, filehash, MASS, mgcv,
LatticeKrig (>= 5.4), FNN, filematrix, Rcpp, methods
LinkingTo: Rcpp, RSpectra, RcppEigen, RcppParallel
Author: ShengLi Tzeng [aut, cre], Hsin-Cheng Huang [aut], Wen-Ting Wang [ctb], Douglas Nychka [ctb], Colin Gillespie [ctb]
Maintainer: ShengLi Tzeng <slt.cmu@gmail.com>
NeedsCompilation: yes
Date/Publication: 2020-09-09 12:10:03 UTC
Packaged: 2020-09-09 11:44:31 UTC; h0h1
Repository: CRAN