<?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>bkpraveenars-del.r-universe.dev</title><link>https://bkpraveenars-del.r-universe.dev</link><description>Recent package updates in bkpraveenars-del</description><generator>R-universe</generator><image><url>https://github.com/bkpraveenars-del.png</url><title>R packages by bkpraveenars-del</title><link>https://bkpraveenars-del.r-universe.dev</link></image><lastBuildDate>Tue, 15 Sep 2026 07:15:27 GMT</lastBuildDate><item><title>[bkpraveenars-del] cwad 0.2.0</title><author>bkpraveenars@gmail.com (Praveen Kumar)</author><description>A reproducible mixed-model toolkit for plant-breeding
trial design. It evaluates any replication allocation under a
known genetic relationship (kinship) matrix using one common
linear-mixed-model engine on genotype means. Crucially,
allocation and analysis model are crossed rather than
confounded: every allocation can be scored both with and
without kinship, so the precision gain attributable to a design
can be separated from the gain attributable to the
kinship-based analysis adopted alongside it. It computes
A-optimal, connectivity-aware allocations via rank-1
Sherman-Morrison updates, and provides Monte-Carlo stress tests
for outlier shrinkage and kinship mis-specification, each with
a matched control arm.</description><link>https://github.com/r-universe/bkpraveenars-del/actions/runs/36326148123</link><pubDate>Tue, 15 Sep 2026 07:15:27 GMT</pubDate><r:package>cwad</r:package><r:version>0.2.0</r:version><r:status>success</r:status><r:repository>https://bkpraveenars-del.r-universe.dev</r:repository><r:upstream>https://github.com/bkpraveenars-del/cwad</r:upstream><r:article><r:source>cwad.Rmd</r:source><r:filename>cwad.html</r:filename><r:title>Separating design gains from analysis-model gains with cwad</r:title><r:created>2026-09-15 07:07:54</r:created><r:modified>2026-09-15 07:07:54</r:modified></r:article></item><item><title>[bkpraveenars-del] BKVerify 0.1.1</title><author>bkpraveenars@gmail.com (Praveen Kumar)</author><description>Audits published or draft plant breeding tables for
internal arithmetic consistency. Analysis of variance tables,
precision statistics and genetic variability parameters are
jointly over-determined by exact algebraic identities;
'BKVerify' recomputes every derivable quantity using
rounding-interval arithmetic and reports a value as
inconsistent only when no combination of values inside the
reported rounding intervals can satisfy the identity. The
package deliberately restricts itself to relationships that
hold irrespective of which variance-component definition an
author adopted, so that flagged results reflect arithmetic
inconsistency rather than methodological disagreement.
Implemented checks cover analysis of variance internal
structure, coefficient of variation, standard error of mean and
critical difference, the genetic advance identity of Johnson,
Robinson and Comstock (1955)
&lt;doi:10.2134/agronj1955.00021962004700070009x&gt;, the relation
between genotypic and phenotypic coefficients of variation and
broad-sense heritability, and admissibility of reported
correlation matrices.</description><link>https://github.com/r-universe/bkpraveenars-del/actions/runs/37282876045</link><pubDate>Sat, 05 Sep 2026 05:27:50 GMT</pubDate><r:package>BKVerify</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://bkpraveenars-del.r-universe.dev</r:repository><r:upstream>https://github.com/bkpraveenars-del/bkverify</r:upstream></item><item><title>[bkpraveenars-del] BKMutate 0.1.0</title><author>bkpraveenars@gmail.com (Praveen Kumar B. K.)</author><description>A colour-first toolkit for the statistical analysis of
induced mutagenesis experiments in crop plants. It fits
dose-response models to physical and chemical mutagen data and
estimates the median lethal and growth-reduction doses (LD50,
GR50) with confidence intervals obtained from Fieller's
theorem; quantifies first-generation biological damage
(lethality, injury and pollen sterility); and estimates
mutagenic effectiveness and mutagenic efficiency. Effectiveness
and efficiency are conventionally reported as point estimates
only; this package treats them as functions of binomial
proportions and supplies interval estimates by the delta method
on the logarithmic scale and by the nonparametric bootstrap. It
further provides chlorophyll mutation spectrum analysis with
tests of homogeneity and diversity, generalised linear models
for second-generation mutant counts with formal assessment of
overdispersion, and formal comparison of mutagens including
relative biological effectiveness. Every analysis returns a
tidy result object and a publication-ready 'ggplot2' figure.
Methods follow Konzak et al. (1965, ISBN:9789201150653),
Fieller (1954) &lt;doi:10.1111/j.2517-6161.1954.tb00159.x&gt; and
Katz et al. (1978) &lt;doi:10.2307/2530610&gt;.</description><link>https://github.com/r-universe/bkpraveenars-del/actions/runs/37283044848</link><pubDate>Sat, 05 Sep 2026 05:26:52 GMT</pubDate><r:package>BKMutate</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://bkpraveenars-del.r-universe.dev</r:repository><r:upstream>https://github.com/bkpraveenars-del/bkmutate</r:upstream></item><item><title>[bkpraveenars-del] BKQualit 0.1.0</title><author>bkpraveenars@gmail.com (Praveen Kumar B. K.)</author><description>A colour-first toolkit for the analysis of qualitative
(categorical) traits in plant breeding and genetics. It tests
observed segregation against the classical Mendelian
expectations, fitting every standard mono-, di- and trihybrid
ratio automatically and ranking them by goodness of fit, with
Yates continuity correction and Monte Carlo exact tests for
sparse tables. It partitions chi-square across families into
pooled and heterogeneity components, so that a poor overall fit
can be attributed either to the hypothesised ratio or to
variation between families. Genetic linkage is estimated by
maximum likelihood from two-locus second filial generation and
backcross data, with logarithm of odds scores and
likelihood-ratio confidence intervals for the recombination
fraction. The package further computes Shannon-Weaver and
Simpson diversity for descriptor states used in distinctness,
uniformity and stability testing, and performs multiple
correspondence analysis and Gower-distance clustering of mixed
categorical and quantitative descriptors. Every analysis
returns a tidy result object and a publication-ready 'ggplot2'
figure. Methods follow Mather (1951, &lt;ISBN:9780416470406&gt;),
Allard (1956) &lt;doi:10.3733/hilg.v24n10p235&gt; and Gower (1971)
&lt;doi:10.2307/2528823&gt;.</description><link>https://github.com/r-universe/bkpraveenars-del/actions/runs/37283118987</link><pubDate>Sat, 05 Sep 2026 05:26:29 GMT</pubDate><r:package>BKQualit</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://bkpraveenars-del.r-universe.dev</r:repository><r:upstream>https://github.com/bkpraveenars-del/bkqualit</r:upstream></item><item><title>[bkpraveenars-del] BKBreed 0.3.2</title><author>bkpraveenars@gmail.com (Praveen Kumar B. K.)</author><description>A compact, colour-first toolkit for the analysis of plant
breeding and genetics field experiments. It provides analysis
of variance for the randomised block design (RBD) and factorial
RBD, a check-anchored intra-block analysis for augmented
alpha-lattice designs, and the core biometrical-genetics
workflow used in crop improvement: estimation of genetic
variability (genotypic and phenotypic coefficients of
variation, broad-sense heritability, expected genetic advance),
genotypic and phenotypic correlation, path-coefficient
analysis, line x tester and Griffing diallel combining-ability
analysis (general combining ability and specific combining
ability), Mahalanobis D-square genetic-divergence analysis with
Tocher and hierarchical clustering, and genotype-by-environment
stability analysis (Eberhart-Russell regression and the
additive main effects and multiplicative interaction (AMMI)
model). Methods follow Griffing (1956) &lt;doi:10.1071/BI9560463&gt;
and Eberhart and Russell (1966)
&lt;doi:10.2135/cropsci1966.0011183X000600010011x&gt;. Every analysis
returns a tidy result object and a publication-ready 'ggplot2'
figure using a bespoke high-contrast colour system.</description><link>https://github.com/r-universe/bkpraveenars-del/actions/runs/36316830523</link><pubDate>Fri, 31 Jul 2026 07:53:40 GMT</pubDate><r:package>BKBreed</r:package><r:version>0.3.2</r:version><r:status>success</r:status><r:repository>https://bkpraveenars-del.r-universe.dev</r:repository><r:upstream>https://github.com/bkpraveenars-del/bkbreed</r:upstream></item></channel></rss>