# Automated Spatial Prediction using Ensemble Machine Learning

## Documentation for package ‘landmap’ version 0.0.9

- DESCRIPTION file.

## Help Pages

buffer.dist | Derive buffer distances for a list of points
buffer.dist-method | Derive buffer distances for a list of points
cookfarm | The Cook Agronomy Farm data set
download.landgis | Access and download layers from OpenLandMap.org (LandGIS data service)
edgeroi | The Edgeroi Data Set
edgeroi.grids | The Edgeroi Data Set
edgeroi.grids100 | The Edgeroi Data Set
fit.vgmModel | Fit variogram using point data
fit.vgmModel-method | Fit variogram using point data
getSpatialTiles | Split a Spatial object into tiles
getSpatialTiles-method | Estimate a tiling system
getSpatialTiles-method | Split a Spatial object into tiles
isis | ISRIC Soil Information System
landgis.tables | OpenLandMap list of GeoTIFFs global mosaics
landmap | landmap: Automated Spatial Prediction using Ensemble Machine Learning
makeTiles | Make a tiling system from a bounding box
model.data | Overlay points and grids and prepare regression matrix
munsell | Standard color palettes for soil properties and classes
predict.spLearner | Predict using spLearner at new locations
print.spLearner | Print object of type 'spLearner'
sample.grid | Sample spatial points by grids
sample.grid-method | Sample spatial points by grids
search.landgis | Search for available landgis layers
sic1997 | The SIC 1997 Data Set
soil.classes | Soil classification tables
soil.legends | Standard color palettes for soil properties and classes
soil.vars | Standard color palettes for soil properties and classes
SpatialComponents-class | A class for gridded components derived using the 'spc' method
SpatialMemberships-class | A class for membership maps derived using the 'fkmeans' classification
spc | Generate Principal Components using SpatialPixelsDataFrame object
spc-method | Generate Principal Components using SpatialPixelsDataFrame object
spfkm | Fit a supervised fuzzy kmeans model and predict memberships
spfkm-method | Fit a supervised fuzzy kmeans model and predict memberships
spmultinom | Fits a multinomial logistic regression to spatial data
spmultinom-method | Fits a multinomial logistic regression to spatial data
spsample.prob | Estimate occurrence probabilities of a sampling plan (points)
spsample.prob-method | Estimate occurrence probabilities of a sampling plan (points)
tile | Tile spatial layers
tile-method | Tile spatial layers
train.spLearner | Train a spatial prediction and/or interpolation model using Ensemble Machine Learning
train.spLearner-method | Train a spatial prediction and/or interpolation model using Ensemble Machine Learning
train.spLearner.matrix | Train a spatial prediction and/or interpolation model using Ensemble Machine Learning from a regression/classification matrix
TT2tri | Soil texture class to texture fractions conversion
tune.spLearner | Optimize spLearner by fine-tuning parameters and running feature selection
tune.spLearner-method | Optimize spLearner by fine-tuning parameters and running feature selection
USDA.TT.im | Probability density for texture triangle