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brian-j-smith
MachineShop:Machine Learning Models and Tools
Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.
Maintained by Brian J Smith. Last updated 7 months ago.
classification-modelsmachine-learningpredictive-modelingregression-modelssurvival-models
62 stars 7.95 score 121 scripts
cran
irr:Various Coefficients of Interrater Reliability and Agreement
Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson's A, Kendall's W, Cohen's Kappa, ...
Maintained by Matthias Gamer. Last updated 6 years ago.
3 stars 5.25 score 20 dependents
lenz99
kappaGold:Agreement of Nominal Scale Raters (with a Gold Standard)
Estimate agreement of a group of raters with a gold standard rating on a nominal scale. For a single gold standard rater the average pairwise agreement of raters with this gold standard is provided. For a group of (gold standard) raters the approach of S. Vanbelle, A. Albert (2009) <doi:10.1007/s11336-009-9116-1> is implemented. Bias and standard error are estimated via delete-1 jackknife.
Maintained by Matthias Kuhn. Last updated 4 months ago.
2.00 score