<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>chunshengliang.r-universe.dev</title><link>https://chunshengliang.r-universe.dev</link><description>Recent package updates in chunshengliang</description><generator>R-universe</generator><image><url>https://github.com/chunshengliang.png</url><title>R packages by chunshengliang</title><link>https://chunshengliang.r-universe.dev</link></image><lastBuildDate>Thu, 01 Oct 2026 09:46:00 GMT</lastBuildDate><item><title>[chunshengliang] dataprep 0.1.8</title><author>chun-shengliang@qq.com (Chun-Sheng Liang)</author><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. &amp; He, K.-B. (2020)
&lt;doi:10.1016/j.scitotenv.2020.140923&gt;. This work was supported
by the National Natural Science Foundation of China (No.
12301674).</description><link>https://github.com/r-universe/chunshengliang/actions/runs/36998998748</link><pubDate>Thu, 01 Oct 2026 09:46:00 GMT</pubDate><r:package>dataprep</r:package><r:version>0.1.8</r:version><r:status>success</r:status><r:repository>https://chunshengliang.r-universe.dev</r:repository><r:upstream>https://github.com/chunshengliang/dataprep</r:upstream><r:article><r:source>dataprep-workflow.Rmd</r:source><r:filename>dataprep-workflow.html</r:filename><r:title>dataprep: a leakage-free preprocessing workflow</r:title><r:created>2026-09-26 17:01:22</r:created><r:modified>2026-10-01 09:46:00</r:modified></r:article><r:article><r:source>dataprep-cleaning.Rmd</r:source><r:filename>dataprep-cleaning.html</r:filename><r:title>dataprep: cleaning pipeline</r:title><r:created>2026-09-26 17:01:22</r:created><r:modified>2026-10-01 09:46:00</r:modified></r:article><r:article><r:source>dataprep-plots.Rmd</r:source><r:filename>dataprep-plots.html</r:filename><r:title>dataprep: descriptive statistics and diagnostic plots</r:title><r:created>2026-09-26 17:01:22</r:created><r:modified>2026-10-01 09:46:00</r:modified></r:article><r:article><r:source>dataprep-philosophy.Rmd</r:source><r:filename>dataprep-philosophy.html</r:filename><r:title>dataprep: design philosophy and preprocessing methodology</r:title><r:created>2026-09-26 17:01:22</r:created><r:modified>2026-10-01 09:46:00</r:modified></r:article><r:article><r:source>dataprep-melt-dcast.Rmd</r:source><r:filename>dataprep-melt-dcast.html</r:filename><r:title>dataprep: fast reshaping with melt() and dcast()</r:title><r:created>2026-09-26 17:01:22</r:created><r:modified>2026-10-01 09:46:00</r:modified></r:article><r:article><r:source>dataprep-performance.Rmd</r:source><r:filename>dataprep-performance.html</r:filename><r:title>dataprep: performance and cross-engine consistency</r:title><r:created>2026-09-26 17:01:22</r:created><r:modified>2026-10-01 09:46:00</r:modified></r:article><r:article><r:source>dataprep-migration.Rmd</r:source><r:filename>dataprep-migration.html</r:filename><r:title>dataprep: upgrading from 0.1.5 to 0.1.8</r:title><r:created>2026-09-26 17:01:22</r:created><r:modified>2026-10-01 09:46:00</r:modified></r:article></item></channel></rss>