Data & Analytics

R survival analysis. Use for Kaplan-Meier, Cox regression, survival curves with survival and survminer.

R glmnet package for regularized regression. Use for lasso, ridge, and elastic-net regularization.

R regularized regression. Use for lasso, ridge, elastic-net with glmnet, and penalized regression.

R vip package for variable importance. Use for computing and visualizing variable importance scores.

R lime package for local explanations. Use for explaining individual predictions with local interpretable models.

R iml package for interpretable ML. Use for model-agnostic interpretability methods.

R packages for ML interpretability. Use for explaining and interpreting machine learning models.

R DALEX package for model explanations. Use for explaining complex machine learning models.

R tidymodels package for machine learning. Use for modeling workflows with recipes, parsnip, tune, and yardstick.

R rpart package for decision trees. Use for recursive partitioning classification and regression trees.

R randomForest package for random forest models. Use for classification and regression with ensemble of decision trees.

R nlme package for mixed-effects models. Use for linear and nonlinear mixed-effects models with correlation structures.

R mlr3 package for machine learning. Use for modern ML framework with pipelines, tuning, and benchmarking.

R lme4 package for mixed-effects models. Use for fitting linear and generalized linear mixed-effects models.

R kernlab package for kernel methods. Use for support vector machines and kernel-based learning.

R h2o package for scalable ML. Use for distributed machine learning with AutoML and deep learning.

R gbm package for gradient boosting. Use for gradient boosted regression and classification models.

R e1071 package for SVM and misc functions. Use for support vector machines, naive Bayes, and clustering.

R caret package for machine learning. Use for training, tuning, and evaluating classification and regression models.

R arules package for association rules. Use for mining frequent itemsets and association rules.

R machine learning frameworks. Use for unified ML workflows with tidymodels, caret, mlr3, and h2o.

R Boruta package for feature selection. Use for all-relevant feature selection using random forest.

R umap package for UMAP. Use for Uniform Manifold Approximation and Projection visualization.

R irlba package for fast SVD/PCA. Use for truncated SVD and PCA on large matrices.