Package: matrixStats 1.5.0
matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors)
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
Authors:
matrixStats_1.5.0.tar.gz
matrixStats_1.5.0.tar.gz(r-4.5-noble)matrixStats_1.5.0.tar.gz(r-4.4-noble)
matrixStats_1.5.0.tgz(r-4.4-emscripten)matrixStats_1.5.0.tgz(r-4.3-emscripten)
matrixStats.pdf |matrixStats.html✨
matrixStats/json (API)
NEWS
# Install 'matrixStats' in R: |
install.packages('matrixStats', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/henrikbengtsson/matrixstats/issues
Last updated 8 days agofrom:d229f6365e. Checks:2 OK. Indexed: no.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Jan 07 2025 |
R-4.5-linux-x86_64 | OK | Jan 07 2025 |
Exports:allocArrayallocMatrixallocVectorallValueanyMissinganyValuebinCountsbinMeanscolAllscolAnyMissingscolAnyNAscolAnyscolAvgsPerRowSetcolCollapsecolCountscolCummaxscolCumminscolCumprodscolCumsumscolDiffscolIQRDiffscolIQRscolLogSumExpscolMadDiffscolMadscolMaxscolMeans2colMedianscolMinscolOrderStatscolProdscolQuantilescolRangescolRankscolSdDiffscolSdscolSums2colTabulatescolVarDiffscolVarscolWeightedMadscolWeightedMeanscolWeightedMedianscolWeightedSdscolWeightedVarscountdiff2indexByRowiqriqrDifflogSumExpmadDiffmean2productrowAllsrowAnyMissingsrowAnyNAsrowAnysrowAvgsPerColSetrowCollapserowCountsrowCummaxsrowCumminsrowCumprodsrowCumsumsrowDiffsrowIQRDiffsrowIQRsrowLogSumExpsrowMadDiffsrowMadsrowMaxsrowMeans2rowMediansrowMinsrowOrderStatsrowProdsrowQuantilesrowRangesrowRanksrowSdDiffsrowSdsrowSums2rowTabulatesrowVarDiffsrowVarsrowWeightedMadsrowWeightedMeansrowWeightedMediansrowWeightedSdsrowWeightedVarssdDiffsignTabulatesum2t_tx_OP_yvarDiffweightedMadweightedMeanweightedMedianweightedSdweightedVarx_OP_y
Dependencies: