Package: dataprep
Type: Package
Title: Fast, Efficient, and Versatile Data Preprocessing and Reshaping
        with 'C++', 'OpenMP' & 'SIMD'
Version: 0.1.8
Authors@R: c(
    person("Chun-Sheng", "Liang", role = c("aut", "cre"),
           email = "chun-shengliang@qq.com",
           comment = c(ORCID = "0000-0002-7386-1958")),
    person("Hao", "Wu", role = "aut"),
    person("Hai-Yan", "Li", role = "aut"),
    person("Qiang", "Zhang", role = "aut"),
    person("Zhanqing", "Li", role = "aut"),
    person("Ke-Bin", "He", role = "aut")
  )
Description: Fast, efficient, and versatile preprocessing and reshaping
        of tabular and time-series data. Most heavy routines are
        implemented in 'C++' via 'Rcpp', with optional 'OpenMP'
        parallelization and 'SIMD' acceleration ('AVX2' / 'AVX-512') on
        supported hardware. The 0.1.8 release rewrites the cleaning
        routines in 'C++' and delivers a 1.1–1146× speedup over 0.1.5.
        The 'melt()' and 'dcast()' reshaping functions achieve a
        0.6×–1628.9× speedup for 'melt()' and a 1.9×–799.8× speedup for
        'dcast()' relative to every one of the seven major alternatives
        in the R and Python ecosystems, at every tested scale (from
        1,000 to 100,000,000 rows), and produce output identical to
        'reshape2', 'data.table', 'tidyr', 'pandas', 'polars', 'dask',
        and 'duckdb'. Core preprocessing steps include variable
        deletion by missing fraction, observation deletion by
        consecutive missing runs, point-by-point weighted outlier
        removal via conditional extremum, traditional percentile-based
        outlier removal, and linear interpolation within short time
        periods. The package also provides fast reshaping, descriptive
        statistics, missing-value diagnosis, multiple imputation
        strategies, winsorization, several outlier detection methods
        (IQR, MAD, percentile), data transformation and
        standardization, categorical encoding, duplicate removal, data
        validation, data quality reporting, and stratified sampling.
        Feature-engineering helpers cover binning, high-correlation and
        low-variance filtering, and string cleaning. Time-series tools
        cover detrending, diurnal-cycle removal, rolling statistics,
        lag creation, resampling, simple decomposition, day/night and
        season flags, log returns, drift detection, and panel
        balancing. Fit/transform-style machine-learning interfaces
        prevent data leakage during preprocessing. Methods are based
        on, and improved from: Liang, C.-S., Wu, H., Li, H.-Y., Zhang,
        Q., Li, Z. & He, K.-B. (2020)
        <doi:10.1016/j.scitotenv.2020.140923>. This work was supported
        by the National Natural Science Foundation of China (No.
        12301674).
Depends: R (>= 4.0.0)
ByteCompile: TRUE
Imports: Rcpp (>= 1.0.10), ggplot2, stats, parallel
LinkingTo: Rcpp (>= 1.0.10)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), data.table, reshape2,
        tidyr, dplyr, microbenchmark, reticulate
VignetteBuilder: knitr, rmarkdown
SystemRequirements: C++17, optional OpenMP
License: GPL (>= 2)
Encoding: UTF-8
NeedsCompilation: yes
URL: https://github.com/chunshengliang/dataprep,
        https://chunshengliang.github.io/dataprep/
BugReports: https://github.com/chunshengliang/dataprep/issues
Config/testthat/edition: 3
LazyData: true
Config/roxygen2/version: 8.1.0
Packaged: 2026-10-01 19:54:44 UTC; root
Author: Chun-Sheng Liang [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-7386-1958>), Hao Wu [aut], Hai-Yan
        Li [aut], Qiang Zhang [aut], Zhanqing Li [aut], Ke-Bin He [aut]
Maintainer: Chun-Sheng Liang <chun-shengliang@qq.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-10-01 19:38:41 UTC
RemoteUrl: https://github.com/cran/dataprep
RemoteRef: HEAD
RemoteSha: fb21cd919e01d2a7973783ba4d6c83e245c4a7c9
Built: R 4.6.0; wasm32-unknown-emscripten; 2026-10-01 19:57:56 UTC; unix
