Help for Writing Tests Based on Function Examples (exampletestr)
Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the ‘testthat’ package. Documentation must be done with ‘roxygen2’.

Ordinal Forests: Prediction and Class Width Inference with Ordinal Target Variables (ordinalForest)
Ordinal forests (OF) are a method for ordinal regression with high-dimensional and low-dimensional data that is able to predict the values of the ordinal target variable for new observations and at the same time estimate the relative widths of the classes of the ordinal target variable. Using a (permutation-based) variable importance measure it is moreover possible to rank the importances of the covariates. OF will be presented in an upcoming technical report by Hornung et al.. The main functions of the package are: ordfor() (construction of OF), predict.ordfor() (prediction of the target variable values of new observations), and plot.ordfor() (visualization of the estimated relative widths of the classes of the ordinal target variable).

Quantification of Color Pattern Variation (patternize)
Quantification of variation in organismal color patterns as obtained from image data. Patternize defines homology between pattern positions across images either through fixed landmarks or image registration. Pattern identification is performed by categorizing the distribution of colors using either an RGB threshold or unsupervised image segmentation.

Spatial Data Framework for ggplot2 (ggspatial)
Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of Spatial* objects.

Spatial Methods and Indices (spind)
Functions for spatial methods based on generalized estimating equations (GEE) and wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR), conducting multi-model inference, and stepwise model selection. Further, contains functions for spatially corrected model accuracy measures.

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