A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Seamless R and C++ Integration

Read excel files (.xls and .xlsx) into R 🖇

Fast reading of delimited files

Combining tree-boosting with Gaussian process and mixed effects models

R package for spatial data handling https://rspatial.github.io/terra/reference/terra-package.html

R Interface to Torch

Text Extraction, Rendering and Converting of PDF Documents

Fast Best-Subset Selection Library

H2Oai GPU Edition

Connect to ODBC databases (using the DBI interface)

Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms - R package

Fast and portable character string processing in R (with the Unicode ICU)

R package for fast and accurate raster zonal statistics

Computer Vision and Image Recognition algorithms for R users

Read untidy Excel files in R https://nacnudus.github.io/tidyxl/

cpp11 helps you to interact with R objects using C++ code.

R package for Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing Based on the UDPipe Natural Language Processing Toolkit