An R package implementing the UMAP dimensionality reduction method.

Uniform Manifold Approximation and Projection - R package

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets

R package for single cell and other data analysis using diffusion maps

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A Framework for Dimensionality Reduction in R

R package for dimensionality reduction of small datasets

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R package for Distance Metric Learning

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

R package for Tools for Handling Extraction of Features from Time series (theft)

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

A tidyverse suite for (pre-) machine-learning: cluster, PCA, permute, impute, rotate, redundancy, triangular, smart-subset, abundant and variable features.

Comparison of dimensionality reduction methods

Algorithmic framework for measuring feature importance, outlier detection, model applicability evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.

R package for single-cell RNA-sequencing analysis

Stochastic Neighbor Embedding Experiments in R

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A toolbox for sparse contrastive principal component analysis