<?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>mqfarooqi1.r-universe.dev</title><link>https://mqfarooqi1.r-universe.dev</link><description>Recent package updates in mqfarooqi1</description><generator>R-universe</generator><image><url>https://github.com/mqfarooqi1.png</url><title>R packages by mqfarooqi1</title><link>https://mqfarooqi1.r-universe.dev</link></image><lastBuildDate>Thu, 25 Jun 2026 06:18:23 GMT</lastBuildDate><item><title>[mqfarooqi1] GSbench 0.1.0</title><author>mqfarooqi@gmail.com (Muhammad Farooqi)</author><description>A unified interface to fit, cross-validate and benchmark
genomic prediction models from SNP marker data. It implements
genomic best linear unbiased prediction (GBLUP) and
ridge-regression BLUP in base R, and offers a common interface
to machine-learning predictors (elastic net, random forest and
gradient boosting) through optional packages, together with a
stacked ensemble. Cross-validation uses breeding-relevant
schemes and reports prediction accuracy honestly, so models can
be compared fairly. The genomic relationship matrix follows
VanRaden (2008) &lt;doi:10.3168/jds.2007-0980&gt;; the mixed-model
solver follows Endelman (2011)
&lt;doi:10.3835/plantgenome2011.08.0024&gt;; the genomic-selection
framework follows Meuwissen, Hayes and Goddard (2001)
&lt;doi:10.1093/genetics/157.4.1819&gt;.</description><link>https://github.com/r-universe/mqfarooqi1/actions/runs/28157067844</link><pubDate>Thu, 25 Jun 2026 06:18:23 GMT</pubDate><r:package>GSbench</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://mqfarooqi1.r-universe.dev</r:repository><r:upstream>https://github.com/mqfarooqi1/GSbench</r:upstream><r:article><r:source>GSbench.Rmd</r:source><r:filename>GSbench.html</r:filename><r:title>Benchmarking genomic prediction models with GSbench</r:title><r:created>2026-06-24 12:43:15</r:created><r:modified>2026-06-24 12:43:15</r:modified></r:article></item><item><title>[mqfarooqi1] dataProfilerR 0.2.1</title><author>mqfarooqi@gmail.com (Muhammad Farooqi)</author><description>Profiles a data frame with minimal input: column type
inference, missing-value analysis, distributional summary
statistics (including skewness and kurtosis), normality tests,
outlier detection, correlation and categorical-association
analysis, date-column profiling, grouped comparisons and an
overall data-quality score, alongside a set of 'ggplot2'
visualisations. A single entry point, profile_data(), returns a
structured S3 object holding metadata, statistics, diagnostics
and plots, with print(), summary() and plot() methods, and
report() renders the whole profile to a self-contained HTML
file. Statistical methods include the Shapiro-Wilk normality
test as implemented by Royston (1995) &lt;doi:10.2307/2986146&gt; and
the Anderson-Darling test following Stephens (1974)
&lt;doi:10.1080/01621459.1974.10480196&gt;, with power comparisons of
these tests in Yap and Sim (2011)
&lt;doi:10.1080/00949655.2010.520163&gt;, and the categorical
association measure of Cramer (1946, ISBN:9780691080048).</description><link>https://github.com/r-universe/mqfarooqi1/actions/runs/28157067070</link><pubDate>Thu, 25 Jun 2026 06:14:31 GMT</pubDate><r:package>dataProfilerR</r:package><r:version>0.2.1</r:version><r:status>success</r:status><r:repository>https://mqfarooqi1.r-universe.dev</r:repository><r:upstream>https://github.com/mqfarooqi1/dataProfilerR</r:upstream><r:article><r:source>dataProfilerR.Rmd</r:source><r:filename>dataProfilerR.html</r:filename><r:title>Profiling a dataset with dataProfilerR</r:title><r:created>2026-06-11 14:43:14</r:created><r:modified>2026-06-11 15:17:21</r:modified></r:article></item></channel></rss>