Spatiotemporal Resampling Methods for 'mlr3'
c(person(given = "Patrick",
family = "Schratz",
role = c("aut", "cre"),
email = "patrick.schratz@gmail.com",
comment = c(ORCID = "0000-0003-0748-6624")),
person(given = "Marc",
family = "Becker",
role = "aut",
email = "marcbecker@posteo.de",
comment = c(ORCID = "0000-0002-8115-0400")),
person(given = "Jannes",
family = "Muenchow",
role = "ctb",
email = "jannes.muenchow@uni-jena.de",
comment = c(ORCID = "0000-0001-7834-4717")),
person(given = "Michel",
family = "Lang",
role = "ctb",
email = "michellang@gmail.com",
comment = c(ORCID = "0000-0001-9754-0393")))
Extends the mlr3 ML framework with spatio-temporal resampling
methods to account for the presence of spatiotemporal autocorrelation
(STAC) in predictor variables. STAC may cause highly biased
performance estimates in cross-validation if ignored.
checkmate, data.table, ggplot2, mlr3 (>= 0.7.0), mlr3misc (>=
0.1.7), paradox, R6, testthat (>= 3.0.0), utils
bbotk, blockCV (>= 2.1.1), CAST, ggsci, ggtext, GSIF, knitr,
lgr, mlr3filters, mlr3pipelines, mlr3tuning, patchwork, plotly,
rmarkdown, rpart, sf, skmeans, vdiffr, withr

checkmate, data.table, ggplot2, mlr3 (>= 0.7.0), mlr3misc (>=
0.7.0), paradox, R6, utils
bbotk, blockCV (>= 2.1.1), CAST, ggsci, ggtext, GSIF, knitr,
lgr, mlr3filters, mlr3pipelines, mlr3tuning, patchwork, plotly,
rmarkdown, rpart, sf, skmeans, testthat (>= 3.0.0), vdiffr,
withr

bbotk, blockCV (>= 2.1.1), CAST, ggsci, ggtext, GSIF, knitr,
lgr, mlr3filters, mlr3pipelines, mlr3tuning, patchwork, plotly,
raster, rmarkdown, rpart, sf, skmeans, testthat (>= 3.0.0),
vdiffr, withr

bbotk, blockCV (>= 2.1.1), CAST, ggsci, ggtext, knitr, lgr,
mlr3filters, mlr3pipelines, mlr3tuning, patchwork, plotly,
raster, rmarkdown, rpart, sf, skmeans, testthat (>= 3.0.0),
vdiffr, withr

bbotk, blockCV (>= 2.1.1), caret, CAST, ggsci, ggtext, knitr,
lgr, mlr3filters, mlr3pipelines, mlr3tuning, patchwork, plotly,
raster, rmarkdown, rpart, sf, skmeans, sperrorest, testthat (>=
3.0.0), vdiffr, withr