<?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>itsmdivakaran.r-universe.dev</title><link>https://itsmdivakaran.r-universe.dev</link><description>Recent package updates in itsmdivakaran</description><generator>R-universe</generator><image><url>https://github.com/itsmdivakaran.png</url><title>R packages by itsmdivakaran</title><link>https://itsmdivakaran.r-universe.dev</link></image><lastBuildDate>Wed, 03 Jun 2026 17:34:49 GMT</lastBuildDate><item><title>[itsmdivakaran] ViewR 2.0.0</title><author>imaheshdivakaran@gmail.com (Mahesh Divakaran)</author><description>An advanced, interactive data table and data explorer for
R, delivered as a modern, self-contained 'htmlwidget' with a
high-performance virtualized grid. ViewR renders 'Kaggle'-style
micro-dashboard column headers complete with data-type badges,
mini distribution spark-histograms, and data-completeness
(missingness) bars. It provides hover metadata cards, a sliding
Data Insights drawer with interactive histograms and 'Pareto'
category charts, a multi-condition visual query builder
(AND/OR), a column visibility picker, and a reproducible code
generator that emits 'dplyr', base R, and 'SQL' that matches
the active filter and column state. The interface is
implemented entirely in dependency-free vanilla 'JavaScript'
(no 'React' or build toolchain) and works in the
'RStudio'/'Positron' Viewer, inside 'Shiny' apps, in 'R
Markdown'/'Quarto', or as a portable standalone 'HTML' file. A
single call to viewr() opens the explorer; the legacy
'Shiny'-gadget ViewR() editor remains available.</description><link>https://github.com/r-universe/itsmdivakaran/actions/runs/26906432720</link><pubDate>Wed, 03 Jun 2026 17:34:49 GMT</pubDate><r:package>ViewR</r:package><r:version>2.0.0</r:version><r:status>success</r:status><r:repository>https://itsmdivakaran.r-universe.dev</r:repository><r:upstream>https://github.com/itsmdivakaran/viewr</r:upstream><r:article><r:source>viewdt.Rmd</r:source><r:filename>viewdt.html</r:filename><r:title>Get started with viewdt()</r:title><r:created>2026-06-03 15:26:54</r:created><r:modified>2026-06-03 16:10:23</r:modified></r:article><r:article><r:source>ViewR-intro.Rmd</r:source><r:filename>ViewR-intro.html</r:filename><r:title>Getting Started with ViewR</r:title><r:created>2026-04-29 09:31:06</r:created><r:modified>2026-06-03 16:10:23</r:modified></r:article></item><item><title>[itsmdivakaran] BivLaplaceRL 1.0.0</title><author>imaheshdivakaran@gmail.com (Mahesh Divakaran)</author><description>Implements methods for bivariate and univariate Laplace
transforms of residual lives and reversed residual lives,
associated stochastic ordering concepts, and entropy measures
for reliability analysis. The package covers: (1) Bivariate
Laplace transform of residual lives and stochastic comparisons
based on the bivariate Laplace transform order of residual
lives (BLt-rl), including weak bivariate hazard rate, mean
residual life, and relative mean residual life orders,
nonparametric estimation, and NBUHR/NWUHR aging class
characterisation; Jayalekshmi, Rajesh, and Nair (2022)
&quot;Bivariate Laplace Transform of Residual Lives and Their
Properties&quot; &lt;doi:10.1080/03610926.2022.2085874&gt;; (2) Bivariate
Laplace transform order of reversed residual lives (BLt-Rrl),
reversed hazard gradient, reversed mean residual life, and the
associated stochastic orders (weak bivariate reversed hazard
rate, weak bivariate reversed mean residual life); Jayalekshmi,
Rajesh, and Nair (2022) &quot;Bivariate Laplace Transform Order and
Ordering of Reversed Residual Lives&quot;
&lt;doi:10.1142/S0218539322500061&gt;; (3) Univariate Laplace
transform of residual life, hazard rate, mean residual life,
and the corresponding stochastic orders (Lt-rl order, hazard
rate order, MRL order), together with a nonparametric
estimator. Shannon entropy and Golomb's (1966) information
generating function are also provided. Parametric families
supported include the Gumbel bivariate exponential,
Farlie-Gumbel-Morgenstern (FGM), bivariate power, and
Schur-constant distributions. Plotting utilities and a
simulation framework for evaluating estimator performance are
also provided.</description><link>https://github.com/r-universe/itsmdivakaran/actions/runs/26220842837</link><pubDate>Thu, 30 Apr 2026 12:49:50 GMT</pubDate><r:package>BivLaplaceRL</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://itsmdivakaran.r-universe.dev</r:repository><r:upstream>https://github.com/itsmdivakaran/bivlaplacerl</r:upstream><r:article><r:source>introduction.Rmd</r:source><r:filename>introduction.html</r:filename><r:title>Introduction to BivLaplaceRL</r:title><r:created>2026-03-12 06:33:09</r:created><r:modified>2026-04-30 07:54:20</r:modified></r:article></item><item><title>[itsmdivakaran] EasyStat 2.0.0</title><author>imaheshdivakaran@gmail.com (Mahesh Divakaran)</author><description>Provides automated statistical analysis, rich
visualization, and multi-format narrative reporting through a
unified pipeline. Descriptive statistics are available via
easy_describe() and easy_group_summary(). Inferential tests
with plain-language narratives are provided by
easy_regression(), easy_logistic_regression(), easy_ttest(),
easy_anova(), easy_chisq(), easy_ztest(), easy_ftest(),
easy_correlation(), easy_wilcox(), and easy_kruskal().
Publication-ready 'ggplot2' visualizations are produced by
easy_histogram(), easy_boxplot(), easy_scatter(),
easy_barplot(), easy_qqplot(), easy_density(),
easy_correlation_heatmap(), easy_regression_diagnostics(), and
easy_odds_ratio_plot(). The core Narrative Generator Module
applies conditional logic to extracted p-values, effect sizes,
and model-fit metrics to produce statistically sound,
human-readable explanations automatically. Results render in
the 'RStudio' Viewer (HTML), the console (ASCII), or export
directly to Microsoft Word via 'flextable' and 'officer'.</description><link>https://github.com/r-universe/itsmdivakaran/actions/runs/26387052570</link><pubDate>Thu, 30 Apr 2026 08:26:18 GMT</pubDate><r:package>EasyStat</r:package><r:version>2.0.0</r:version><r:status>success</r:status><r:repository>https://itsmdivakaran.r-universe.dev</r:repository><r:upstream>https://github.com/itsmdivakaran/easystat</r:upstream><r:article><r:source>EasyStat.Rmd</r:source><r:filename>EasyStat.html</r:filename><r:title>Getting Started with EasyStat</r:title><r:created>2026-03-13 07:28:43</r:created><r:modified>2026-03-13 07:28:43</r:modified></r:article></item><item><title>[itsmdivakaran] RenyiExtropy 0.4.0</title><author>itsmdivakaran@gmail.com (Divakaran Mahesh)</author><description>Provides functions to compute Shannon entropy, Renyi
entropy, Tsallis entropy, and related extropy measures for
discrete probability distributions. Includes joint and
conditional entropy, KL divergence, Jensen-Shannon divergence,
cross-entropy, normalized entropy, and Renyi extropy (including
the conditional and maximum forms). All measures use the
natural logarithm (nats). Useful for information theory,
statistics, and machine learning applications.</description><link>https://github.com/r-universe/itsmdivakaran/actions/runs/25982135830</link><pubDate>Tue, 17 Mar 2026 06:29:09 GMT</pubDate><r:package>RenyiExtropy</r:package><r:version>0.4.0</r:version><r:status>success</r:status><r:repository>https://itsmdivakaran.r-universe.dev</r:repository><r:upstream>https://github.com/itsmdivakaran/renyiextropy</r:upstream><r:article><r:source>RenyiExtropy.Rmd</r:source><r:filename>RenyiExtropy.html</r:filename><r:title>Introduction to RenyiExtropy</r:title><r:created>2026-03-17 06:29:09</r:created><r:modified>2026-03-17 06:29:09</r:modified></r:article></item></channel></rss>