<?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>vinodhpmd.r-universe.dev</title><link>https://vinodhpmd.r-universe.dev</link><description>Recent package updates in vinodhpmd</description><generator>R-universe</generator><image><url>https://github.com/vinodhpmd.png</url><title>R packages by vinodhpmd</title><link>https://vinodhpmd.r-universe.dev</link></image><lastBuildDate>Thu, 24 Sep 2026 15:16:31 GMT</lastBuildDate><item><title>[cran] ZooRisk 1.0.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Provides quantitative tools for assessing zoonotic disease
risk across animal, human, environmental, and transmission
interfaces. The package supports exposure and transmission risk
estimation, spillover risk assessment, risk scoring,
cross-species comparison, transmission-network analysis, Monte
Carlo uncertainty simulation, and sensitivity analysis. The One
Health framework underlying these assessments is described by
World Health Organization, Food and Agriculture Organization of
the United Nations, United Nations Environment Programme, and
World Organisation for Animal Health (2022)
&lt;doi:10.4060/cc2289en&gt;.</description><link>https://github.com/r-universe/cran/actions/runs/36019779777</link><pubDate>Thu, 24 Sep 2026 15:16:31 GMT</pubDate><r:package>ZooRisk</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://cran.r-universe.dev</r:repository><r:upstream>https://github.com/cran/ZooRisk</r:upstream><r:article><r:source>zoorisk-introduction.Rmd</r:source><r:filename>zoorisk-introduction.html</r:filename><r:title>ZooRisk: Quantitative Assessment of Zoonotic Disease Risk</r:title><r:created>2026-09-24 15:16:31</r:created><r:modified>2026-09-24 15:16:31</r:modified></r:article></item><item><title>[cran] CausalLoopAnalytics 1.0.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Provides tools for constructing signed causal-loop models,
discovering directed causal relationships from time-series data
using Granger-style tests, identifying and classifying
reinforcing and balancing feedback loops, quantifying loop
strength, assessing loop stability by bootstrap resampling,
calculating network centrality and leverage-point scores,
comparing causal-loop models, and producing publication-ready
base R visualizations and summaries. The package is
domain-agnostic and can be used in human medicine, veterinary
medicine, agriculture, epidemiology, ecology, public health,
and One Health. Methods are based on Granger (1969)
&lt;doi:10.2307/1912791&gt; and Efron (1979)
&lt;doi:10.1214/aos/1176344552&gt;.</description><link>https://github.com/r-universe/cran/actions/runs/35671261850</link><pubDate>Mon, 21 Sep 2026 23:22:50 GMT</pubDate><r:package>CausalLoopAnalytics</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://cran.r-universe.dev</r:repository><r:upstream>https://github.com/cran/CausalLoopAnalytics</r:upstream></item><item><title>[cran] TrialFlowR 1.0.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Summarizes participant flow and disposition in clinical
trials, including CONSORT-style randomized controlled trials,
parallel-group, crossover, cluster randomized, and multi-arm
trials. Provides functions for screening failures, exclusions
and reasons, allocation, follow-up, loss to follow-up,
withdrawals, intention-to-treat and per-protocol populations,
and participant-disposition summaries. The methods are based on
established principles for reporting participant flow and
disposition in randomized trials; see Schulz et al. (2010)
&lt;doi:10.1136/bmj.c332&gt;.</description><link>https://github.com/r-universe/cran/actions/runs/35370275522</link><pubDate>Fri, 18 Sep 2026 11:40:07 GMT</pubDate><r:package>TrialFlowR</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://cran.r-universe.dev</r:repository><r:upstream>https://github.com/cran/TrialFlowR</r:upstream></item><item><title>[cran] AutoViz 1.0.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Provides automated exploratory data analysis,
visualization recommendation, summary statistics, missing-value
assessment, outlier screening, and HTML dashboard generation
for rectangular data. The package automatically identifies
variable types and common analytical relationships and
recommends appropriate visualization methods based on data
structure, cardinality, and analytical objectives. It provides
a transparent, reproducible workflow for data profiling and
visualization that can be used independently or as a component
within interactive applications.</description><link>https://github.com/r-universe/cran/actions/runs/34887777874</link><pubDate>Mon, 14 Sep 2026 19:29:05 GMT</pubDate><r:package>AutoViz</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://cran.r-universe.dev</r:repository><r:upstream>https://github.com/cran/AutoViz</r:upstream><r:article><r:source>AutoViz-introduction.Rmd</r:source><r:filename>AutoViz-introduction.html</r:filename><r:title>AutoViz: Automated Exploratory Data Visualization</r:title><r:created>2026-09-14 19:29:05</r:created><r:modified>2026-09-14 19:29:05</r:modified></r:article></item><item><title>[vinodhpmd] BioMixModel 1.0.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Tools for fitting and interpreting mixed-effects models
for clustered, longitudinal and heterogeneous biological data.
Provides variance partitioning, intraclass correlation,
penalized likelihood summaries, a heterogeneous-data
information criterion, model comparison, diagnostics, and
ensemble-style summaries for multilevel data. The package is
designed as a complementary, interpretable workflow around
established mixed-model methods.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/35715100351</link><pubDate>Wed, 02 Sep 2026 09:29:11 GMT</pubDate><r:package>BioMixModel</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/vinodhpmd/biomixmodel</r:upstream></item><item><title>[vinodhpmd] OutbreakR 0.1.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Provides tools for epidemiological analysis of disease
outbreaks, including measures of disease frequency,
association, impact, transmission, and vaccine effectiveness.
Functions support prevalence, incidence, attack rates,
mortality, case fatality, risk ratios, odds ratios, rate
ratios, attributable measures, reproduction numbers, herd
immunity thresholds, contingency tables, grouped analyses, and
outbreak line-list validation.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/37192970609</link><pubDate>Tue, 04 Aug 2026 14:10:31 GMT</pubDate><r:package>OutbreakR</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/cran/OutbreakR</r:upstream></item><item><title>[vinodhpmd] inferstat 0.1.1</title><author>vinodhkumar.rajendran@gmail.com (Vinodh Kumar Obli Rajendran)</author><description>Provides publication-ready tools for inferential
statistical analyses, assumption checking, effect size
estimation, statistical visualizations, and standardized
reporting. Methods for assessing normality follow Shapiro and
Wilk (1965) &lt;doi:10.1093/biomet/52.3-4.591&gt;, and guidance for
standardized effect sizes is informed by Cohen (1988)
&lt;doi:10.4324/9780203771587&gt;.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/37193171561</link><pubDate>Tue, 04 Aug 2026 09:40:14 GMT</pubDate><r:package>inferstat</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/cran/inferstat</r:upstream></item><item><title>[vinodhpmd] ExpDesignR 0.1.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Provides functions for generating randomized experimental
designs for biomedical, veterinary, agricultural, and clinical
research. The package includes functions for simple
randomization, block randomization, stratified randomization,
cluster randomization, Latin square designs, crossover designs,
allocation summaries, schedule export, and visualization of
treatment allocations.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/36234196527</link><pubDate>Mon, 03 Aug 2026 01:33:33 GMT</pubDate><r:package>ExpDesignR</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/vinodhpmd/expdesignr</r:upstream></item><item><title>[vinodhpmd] EconEvalR 0.1.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Provides functions for economic evaluation, including
Cost-Benefit Analysis (CBA), Benefit-Cost Ratio (BCR), Net
Present Value (NPV), Internal Rate of Return (IRR), Partial
Budgeting, Budget Impact Analysis (BIA), Cost-Effectiveness
Analysis (CEA), Decision Tree Analysis, Sensitivity Analysis,
and Probabilistic Sensitivity Analysis. The package produces
summaries, graphical displays, and reproducible workflows for
applications in veterinary science, agriculture, public health,
epidemiology, health economics, and related fields.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/35715108682</link><pubDate>Sun, 02 Aug 2026 17:49:09 GMT</pubDate><r:package>EconEvalR</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/vinodhpmd/econevalr</r:upstream></item><item><title>[vinodhpmd] EpiLossR 0.1.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar Obli Rajendran)</author><description>Provides standardized tools for estimating direct economic
losses associated with mortality and morbidity in animal
diseases. The package implements reproducible methods for
calculating mortality- and morbidity-related losses using a
common S3 object framework with methods for summarization,
visualization, and data export. It is intended for veterinary
epidemiologists, animal health economists, veterinarians,
researchers, and students.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/37194366134</link><pubDate>Sat, 01 Aug 2026 11:57:53 GMT</pubDate><r:package>EpiLossR</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/vinodhpmd/epilossr</r:upstream><r:article><r:source>getting-started.Rmd</r:source><r:filename>getting-started.html</r:filename><r:title>Getting Started with EpiLossR</r:title><r:created>2026-08-01 11:04:07</r:created><r:modified>2026-08-01 11:04:07</r:modified></r:article></item><item><title>[vinodhpmd] AutoEDA 0.0.1</title><author>vinodhkumar.rajendran@gmail.com (Vinodhkumar)</author><description>Automatically performs exploratory data analysis,
including data summaries, missing value analysis, descriptive
statistics, visualizations, correlation analysis, outlier
detection, and report generation.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/38389977033</link><pubDate>Tue, 28 Jul 2026 12:38:22 GMT</pubDate><r:package>AutoEDA</r:package><r:version>0.0.1</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/vinodhpmd/autoeda</r:upstream></item><item><title>[vinodhpmd] DataAudit 0.1.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodh Kumar Obli Rajendran)</author><description>Provides tools for systematic assessment, validation, and
reporting of data quality. The package detects common
data-quality problems including missing or blank values,
duplicate observations, infinite values, constant and near-zero
variance variables, outliers, invalid ranges and categories,
type and pattern violations, problematic dates and identifiers,
sequence errors, dependency violations, and cross-variable
inconsistencies. It also supports reusable validation rules,
integrated audit reports, and standardized data-quality scoring
for reproducible data-quality assessment workflows.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/35594294500</link><pubDate>Tue, 28 Jul 2026 12:01:41 GMT</pubDate><r:package>DataAudit</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/vinodhpmd/dataaudit</r:upstream><r:article><r:source>getting-started-with-dataaudit.Rmd</r:source><r:filename>getting-started-with-dataaudit.html</r:filename><r:title>Getting Started with DataAudit</r:title><r:created>2026-07-28 08:20:33</r:created><r:modified>2026-07-28 08:20:33</r:modified></r:article></item><item><title>[vinodhpmd] EpiQuestionR 0.1.0</title><author>vinodhkumar.rajendran@gmail.com (Vinodh Kumar Obli Rajendran)</author><description>Comprehensive toolkit for questionnaire scoring,
psychometric validation, reliability assessment,
epidemiological survey analysis, KAP studies, One Health
research, structural equation modelling, item response theory,
GIS visualization, dashboards and automated reporting.</description><link>https://github.com/r-universe/vinodhpmd/actions/runs/38131572121</link><pubDate>Fri, 17 Jul 2026 08:05:26 GMT</pubDate><r:package>EpiQuestionR</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://vinodhpmd.r-universe.dev</r:repository><r:upstream>https://github.com/vinodhpmd/epiquestionr</r:upstream></item></channel></rss>