meteo2.0-3 package

RFSI & STRK Interpolation for Meteo and Environmental Variables

acc.metric.fun

Accuracy metrics calculation

cv.rfsi

Nested k-fold cross-validation for Random Forest Spatial Interpolation...

cv.strk

k-fold cross-validation for spatio-temporal regression kriging

data.prepare

Prepare data

get_coordinates

Get lon/lat coordinates for a specific location name.

get_meteo

Get daily, monthly, or annual; aggregated or long-term means meteorolo...

meteo2STFDF

Create an object of STFDF-class class from two data frames (observatio...

near.obs

Finds n nearest observations from given locations.

near.obs.soil

Finds n nearest observations from given locations for soil mapping.

pred.rfsi

Random Forest Spatial Interpolation (RFSI) prediction

pred.strk

Spatio-temporal regression kriging prediction

rfillspgaps

Close gaps of a grid or raster Layer data

rfilltimegaps

Disaggregation in the time dimension through the use of splines for ea...

rfsi

Random Forest Spatial Interpolation (RFSI) model

rm.dupl

Find point pairs with equal spatial coordinates from STFDF-class objec...

temp_geom

Calculate geometrical temperature trend

tgeom2STFDF

Calculate geometrical temperature trend

tiling

Tiling raster or Spatial-class Grid or Pixels object

tune.rfsi

Tuning of Random Forest Spatial Interpolation (RFSI) model

Download source packageRead PDF manual

Random Forest Spatial Interpolation (RFSI, Sekulić et al. (2020) <doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) <doi:10.1002/2013JD020803>, Sekulić et al. (2020) <doi:10.1007/s00704-019-03077-3>) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data.

  • Maintainer: Aleksandar Sekulić
  • License: GPL (>= 2.0) | file LICENCE
  • Last published: 2024-04-18

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