loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)
The blockCV package creates spatially or environmentally separated training and testing folds for cross-validation to provide a robust error estimation in spatially structured environments. See
🤠 📿 The Highly Adaptive Lasso
subsemble R package for ensemble learning on subsets of data
R package cross-validation, bootstrap, permutation, and rolling window resampling techniques for the tidyverse.
A document covering machine learning basics. 🤖📊
R-package: Methods for dividing data into groups. Create balanced partitions and cross-validation folds. Perform time series windowing and general grouping and splitting of data. Balance existing groups with up- and downsampling or collapse them to fewer groups.
Computationally efficient confidence intervals for cross-validated AUC estimates in R
Resampling Tools for Time Series Forecasting with Modeltime
Spatial error estimation and variable importance
Bayesian Multi-Trait Multi-Environment for genomic selection[R package] [Dev version]
Experimenting with various implementations and methods of nested cross-validation in R and Python
Population Assignment using Genetic, Non-genetic or Integrated Data in a Machine-learning Framework. Methods in Ecology and Evolution. 2018;9:439–446.
An R package for nonparametric covariance matrix estimation in high dimensions
2024 BreedWheat Genomic Selection pipeline
Light weight R package to do fast data splitting for cross-validation or train/valid/test splits
R scripts for predicting soil organic carbon using soil spectral library from visible, near-infrared and shortwave-infrared (VNIR) and middle-infrared (MIR) using LASSO and PLS regression methods and the target-oriented cross-validation strategy.
CoMoMo combines multiple mortality forecasts using different model combinations. See more from the paper here https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3823511
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