Package: spm2
Title: Spatial Predictive Modeling
Version: 1.1.3
Date: 2023-04-05
Authors@R: person("Jin", "Li", email = "jinli68@gmail.com", role = c("aut", "cre"))
Description: An updated and extended version of 'spm' package, by
        introducing some further novel functions for modern statistical
        methods (i.e., generalised linear models, glmnet, generalised
        least squares), thin plate splines, support vector machine,
        kriging methods (i.e., simple kriging, universal kriging, block
        kriging, kriging with an external drift), and novel hybrid
        methods (228 hybrids plus numerous variants) of modern
        statistical methods or machine learning methods with
        mathematical and/or univariate geostatistical methods for
        spatial predictive modelling. For each method, two functions
        are provided, with one function for assessing the predictive
        errors and accuracy of the method based on cross-validation,
        and the other for generating spatial predictions. It also
        contains a couple of functions for data preparation and
        predictive accuracy assessment.
Depends: R (>= 2.10)
Imports: spm, gstat, sp, randomForest, gbm, stats, fields, nlme,
        glmnet, e1071
License: GPL (>= 2)
LazyData: true
RoxygenNote: 7.1.1
Encoding: UTF-8
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2026-07-03 07:25:51 UTC; root
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jinli68@gmail.com>
Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev
        libssl-dev libproj-dev libsqlite3-dev libudunits2-dev
Repository: https://jinli22.r-universe.dev
Date/Publication: 2023-04-06 11:10:02 UTC
RemoteUrl: https://github.com/cran/spm2
RemoteRef: HEAD
RemoteSha: e98b877988ee80fa7d2de3073cc808fea4a90d48