Claude Skills by LeoLin990405

R cluster package for clustering algorithms. Use for PAM, CLARA, AGNES, DIANA, and other clustering methods.

R dbscan package for density-based clustering. Use for DBSCAN, OPTICS, and HDBSCAN clustering.

R factoextra package for cluster visualization. Use for visualizing clustering results and PCA.

R mclust package for model-based clustering. Use for Gaussian mixture models and model-based clustering.

R deep learning with torch, keras, tensorflow. Use for neural networks, CNNs, RNNs, and GPU acceleration.

R keras package for deep learning. Use for neural networks with TensorFlow backend.

R torch package for deep learning. Use for PyTorch-style neural networks in R.

R Rtsne package for t-SNE. Use for t-distributed stochastic neighbor embedding visualization.

R packages for dimensionality reduction. Use for PCA, t-SNE, UMAP, and other dimension reduction methods.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

R survival package for survival analysis. Use for Kaplan-Meier curves, Cox regression, and time-to-event analysis.

R survminer package for survival visualization. Use for publication-ready Kaplan-Meier plots and forest plots.

R time series forecasting. Use for prophet, forecast, fable, ARIMA, and exponential smoothing.

R fable package for tidy time series forecasting. Use for modern forecasting with tsibble integration.

R forecast package for time series forecasting. Use for ARIMA, ETS, and automatic forecasting.

R prophet package for time series forecasting. Use for forecasting with seasonality, holidays, and trend changes.

R tsibble package for tidy time series. Use for temporal data structures with tidyverse integration.

R tree-based models. Use for random forests, decision trees, and ensemble methods with ranger, randomForest, rpart.

R ranger package for random forests. Use for fast implementation of random forests for classification and regression.

R network analysis packages. Use for graph analysis, social network analysis, network visualization, and community detection.

R network analysis with igraph, sna. Use for centrality, community detection, and network metrics.

R igraph package for network analysis. Use for graph creation, analysis, centrality, community detection, and visualization.

R network package for network data. Use for creating and manipulating network objects.

R sna package for social network analysis. Use for network statistics and visualization with statnet.

R statnet suite for network analysis. Use for statistical modeling of network data including ERGM.

R tidygraph package for tidy graph manipulation. Use for dplyr-style operations on network data.

R dynamic/temporal networks with ndtv, networkDynamic, tsna. Use for time-varying networks and network evolution.