A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Repository Index
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R interface for Apache Spark
Google Earth Engine for R
- earth-engine
- googleearthengine
- google-earth-engine
- r
R frontend for Spark
R package for fast web gl rendering for leaflet
Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome
- cancer-genomics
- cnv-detection
- single-cell
- phylogeny
Umbrella package of the 'spatstat' family................
- r
- spatial-analysis
- spatial-data
- spatstat
Convert GTFS data into a data.table with GPS-like records in R
- gtfs
- transport
- public-transport
- rspatial
Classes and methods for spatio-temporal data
R package to estimate public transport emissions based on GTFS data
- emissions
- environmental-modelling
- gtfs
- public-transport