ExtremaRank v0.4.0: native R package

ExtremaRank now runs directly in R through a standalone native R/C++ package. No Python runtime, reticulate or command-line bridge is required. Existing Python algorithms remain available.

install.packages(c("Rcpp", "BH", "Matrix", "digest"))
install.packages("https://github.com/heise3/ExtremaRank/releases/download/v0.4.0/extremarank_0.4.0.tar.gz", repos = NULL, type = "source")
library(extremarank)

The R source archive is about 41 KiB and installs without the large benchmark dataset archive. Source installation requires a C++17 compiler.

• Native paired and independent Welch audits, individual membership/sign certificates, conservative minimum deletion intervals, conditional exact extrema and one-based R witnesses.
• Dense matrices, sparse Matrix and SummarizedExperiment input, sparse donor pseudobulk and SingleCellExperiment aggregation.
• Direct R limma, limma-voom, edgeR and DESeq2 refits with frozen covariates/universes, streamed scenario caps and preserved failed fits.
• RDS and usable TSV exports, including exact strings, metadata and raw invalid scores.

Validation: local R CMD check Status OK (zero errors/warnings/notes); 1,004 cases and 439,536 independent exact checks; 183 real complete-feature ranks matched; 4,347,470 donor-pseudobulk sums matched. Standard R-only tests cover frozen Fraction golden cases, complete subsets, search limits, extreme floats, experiment objects and model integration.

Native certificates concern ranking membership/effect signs on frozen inputs. R model refits are observed sensitivity and do not certify unseen deletions. Real failed Kang edgeR and DESeq2 scenarios remain recorded without result-based repairs. CPTAC deletion examples concern technical measurement runs.

Native R guide · 中文原生 R 指南 · Validation

SHA256SUMS records the R source package, full repository source archive and Python wheel. Third-party example data retain their source terms; see DATA_LICENSE.md.

Final commit 1ed6a311b748545872558e9fafebebca074a9b5c: all eight GitHub jobs passed, including native R builds/checks on Windows, macOS and Linux and direct Bioconductor model/experiment tests. Native R CI · Regression CI.