{ "_id": "6abf930299f7aa8c6aa59000", "Package": "dataprep", "Type": "Package", "Title": "Fast, Efficient, and Versatile Data Preprocessing and Reshaping\nwith 'C++', 'OpenMP' & 'SIMD'", "Version": "0.1.8", "Authors@R": "c(\nperson(\"Chun-Sheng\", \"Liang\", role = c(\"aut\", \"cre\"),\nemail = \"chun-shengliang@qq.com\",\ncomment = c(ORCID = \"0000-0002-7386-1958\")),\nperson(\"Hao\", \"Wu\", role = \"aut\"),\nperson(\"Hai-Yan\", \"Li\", role = \"aut\"),\nperson(\"Qiang\", \"Zhang\", role = \"aut\"),\nperson(\"Zhanqing\", \"Li\", role = \"aut\"),\nperson(\"Ke-Bin\", \"He\", role = \"aut\")\n)", "Description": "Fast, efficient, and versatile preprocessing and reshaping\nof tabular and time-series data. Most heavy routines are\nimplemented in 'C++' via 'Rcpp', with optional 'OpenMP'\nparallelization and 'SIMD' acceleration ('AVX2' / 'AVX-512') on\nsupported hardware. The 0.1.8 release rewrites the cleaning\nroutines in 'C++' and delivers a 1.1–1146× speedup over 0.1.5.\nThe 'melt()' and 'dcast()' reshaping functions achieve a\n0.6×–1628.9× speedup for 'melt()' and a 1.9×–799.8× speedup for\n'dcast()' relative to every one of the seven major alternatives\nin the R and Python ecosystems, at every tested scale (from\n1,000 to 100,000,000 rows), and produce output identical to\n'reshape2', 'data.table', 'tidyr', 'pandas', 'polars', 'dask',\nand 'duckdb'. Core preprocessing steps include variable\ndeletion by missing fraction, observation deletion by\nconsecutive missing runs, point-by-point weighted outlier\nremoval via conditional extremum, traditional percentile-based\noutlier removal, and linear interpolation within short time\nperiods. The package also provides fast reshaping, descriptive\nstatistics, missing-value diagnosis, multiple imputation\nstrategies, winsorization, several outlier detection methods\n(IQR, MAD, percentile), data transformation and\nstandardization, categorical encoding, duplicate removal, data\nvalidation, data quality reporting, and stratified sampling.\nFeature-engineering helpers cover binning, high-correlation and\nlow-variance filtering, and string cleaning. Time-series tools\ncover detrending, diurnal-cycle removal, rolling statistics,\nlag creation, resampling, simple decomposition, day/night and\nseason flags, log returns, drift detection, and panel\nbalancing. Fit/transform-style machine-learning interfaces\nprevent data leakage during preprocessing. Methods are based\non, and improved from: Liang, C.-S., Wu, H., Li, H.-Y., Zhang,\nQ., Li, Z. & He, K.-B. (2020)\n. This work was supported\nby the National Natural Science Foundation of China (No.\n12301674).", "ByteCompile": "TRUE", "VignetteBuilder": "knitr, rmarkdown", "SystemRequirements": "C++17, optional OpenMP", "License": "GPL (>= 2)", "Encoding": "UTF-8", "NeedsCompilation": "yes", "URL": "https://github.com/chunshengliang/dataprep,\nhttps://chunshengliang.github.io/dataprep/", "BugReports": "https://github.com/chunshengliang/dataprep/issues", "Config/testthat/edition": "3", "LazyData": "true", "Config/roxygen2/version": "8.1.0", "Repository": "https://chunshengliang.r-universe.dev", "Date/Publication": "2026-10-01 09:46:00 UTC", "RemoteUrl": "https://github.com/chunshengliang/dataprep", "RemoteRef": "HEAD", "RemoteSha": "a38d35d1ae567dec2b795a76e3532d64370fb665", "Packaged": { "Date": "2026-10-02 11:09:53 UTC", "User": "root" }, "Author": "Chun-Sheng Liang [aut, cre] (ORCID:\n),\nHao Wu [aut],\nHai-Yan Li [aut],\nQiang Zhang [aut],\nZhanqing Li [aut],\nKe-Bin He [aut]", "Maintainer": 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