List registered sources and learners
Examples
list_sources()
#> name kind provides network
#> 1 demo series tmin, tmax, prcp FALSE
#> 2 demo_soil static clay, sand FALSE
#> 3 power series tmin, tmax, prcp TRUE
#> 4 chirps series prcp TRUE
#> 5 daymet series tmin, tmax, prcp, srad TRUE
#> 6 worldclim static wc_* TRUE
#> 7 soilgrids static clay, sand, soc, phh2o TRUE
#> 8 elevation static elevation, slope, aspect TRUE
#> description
#> 1 Deterministic synthetic daily weather, offline
#> 2 Deterministic synthetic soil texture, offline
#> 3 NASA POWER daily agroclimatology via 'nasapower'
#> 4 CHIRPS daily rainfall via the 'chirps' package
#> 5 Daymet daily weather, North America, via 'daymetr'
#> 6 WorldClim climate normals via 'geodata'
#> 7 SoilGrids soil properties via 'geodata'
#> 8 SRTM elevation and terrain derivatives via 'geodata'
list_learners()
#> name requires
#> 1 lm
#> 2 glm
#> 3 knn
#> 4 ranger ranger
#> 5 xgboost xgboost
#> 6 cubist Cubist
#> 7 enet glmnet
#> 8 svm kernlab
#> 9 gam mgcv
#> 10 stack
#> description
#> 1 Ordinary least squares; always available
#> 2 Generalised linear model; pass `family` to change it
#> 3 k nearest neighbours on standardised predictors
#> 4 Random forest via 'ranger'
#> 5 Gradient boosting via 'xgboost'
#> 6 Rule-based model trees via 'Cubist'
#> 7 Elastic net with lambda by cross-validation ('glmnet')
#> 8 Support vector regression via 'kernlab'
#> 9 Generalised additive model via 'mgcv'
#> 10 Stacked ensemble of the available base learners, weighted by non-negative least squares