<?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>gcostaneto.r-universe.dev</title><link>https://gcostaneto.r-universe.dev</link><description>Recent package updates in gcostaneto</description><generator>R-universe</generator><image><url>https://github.com/gcostaneto.png</url><title>R packages by gcostaneto</title><link>https://gcostaneto.r-universe.dev</link></image><lastBuildDate>Thu, 22 Oct 2026 18:20:18 GMT</lastBuildDate><item><title>[gcostaneto] EnvRtype 0.1.3</title><author>germano.cneto@gmail.com (Germano Costa-Neto)</author><description>An enviromics toolkit for genotype-by-environment analysis
and envirotype-informed genomic prediction. Collects worldwide
daily and hourly weather from 'NASA POWER', soil profiles from
'SoilGrids', elevation and bioclimatic layers from 'WorldClim',
agro-ecological zones from 'FAO GAEZ', and downscaled 'CMIP6'
climate scenarios, with checkpointed retrieval that can resume
after an interrupted run. Derives agro-meteorological
parameters (solar radiation, evapotranspiration, thermal time,
atmospheric demand), runs a FAO-56 single crop coefficient soil
water balance, and maps daily weather onto crop developmental
stages. Builds environmental covariable matrices, envirotype
typologies and frequency matrices, mega-environment clusters,
historical risk profiles and target population of environments
weights. Reduces interval-resolved envirotypes by principal
component analysis, delineates soil zones with Gaussian mixture
models, and constructs linear, Gaussian and arc-cosine
environmental relatedness kernels. Fits Bayesian reaction-norm
kernel models and predicts trained genotypes at untested
locations and planting dates, with coverage diagnostics,
spatial mapping and planting-window optimisation. Simulation
functions generate multi-environment trials and enviromes with
known architecture for power analysis. Methods are described in
Costa-Neto, Galli, Carvalho, Crossa and Fritsche-Neto (2021)
&lt;doi:10.1093/g3journal/jkab040&gt;, and build on the Bayesian
genotype-by-environment kernel sampler of Granato, Cuevas,
Luna-Vazquez, Crossa, Montesinos-Lopez, Burgueno and
Fritsche-Neto (2018) &lt;doi:10.1534/g3.118.200435&gt;.</description><link>https://github.com/r-universe/gcostaneto/actions/runs/39935356497</link><pubDate>Thu, 22 Oct 2026 18:20:18 GMT</pubDate><r:package>EnvRtype</r:package><r:version>0.1.3</r:version><r:status>success</r:status><r:repository>https://gcostaneto.r-universe.dev</r:repository><r:upstream>https://github.com/gcostaneto/envrtype</r:upstream><r:article><r:source>EnvRtype.Rmd</r:source><r:filename>EnvRtype.html</r:filename><r:title>EnvRtype: Envirotyping for Quantitative Genetics and Plant Breeding</r:title><r:created>2026-09-30 16:32:31</r:created><r:modified>2026-10-01 17:32:39</r:modified></r:article></item></channel></rss>