8 of 8 plots · 3 blocks · 03 Oct 2026
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Plot A01 · Block A ·
Sam Rogers · Adelaide University
Active · last commit today
The speed package optimises spatial experimental designs by rearranging treatments to improve statistical efficiency while maintaining statistical validity. It uses simulated annealing to: Minimise treatment adjacency (reducing neighbour effects) Maintain spatial balance across rows and columns Respect blocking constraints if specified Provide visualisation tools for design evaluation
Data visualiation·Design of experiments
Experimental design
Plot A02 · Block A ·
Sam Rogers · Adelaide University
Active · last commit yesterday
The goal of biometryassist is to provide functions to aid in the Design and Analysis of Agronomic-style experiments through easy access to documentation and helper functions, especially while teaching these concepts.
Data analysis·Data visualiation·Model diagnostics
Experimental design·Multi-environment trials
Plot A03 · Block A ·
Julian Taylor · Adelaide University
Active · last commit yesterday
The biomAid package has been specifically built to provide biometricians with flexible functions for interpreting and further modelling of fixed and random effects from complex linear mixed models fitted with software such as ASReml-R V4. There are tools for summarising and interpreting effects from factor analytic models, conducting bespoke multiple comparisons, conducting random and fixed regressions of correlated effects, calculating accuracy and generalised heritability, simulating multi-environment multi-treatment data and padding out data from irregular trial layouts. See the GitHub repository for more information.
Data analysis·Data validation·Data visualiation·Model post-processing
Multi-environment trials·Multiple comparisons·accuracy·simulation·random regression·factor analytic models·FAST·iClass·wald tests
Plot A04 · Block A ·
Max Moldovan · Adelaide University
Active · last commit 2 days ago
nert is an R package that gives an analyst direct access to gridded environmental data from Australia's Terrestrial Ecosystem Research Network (TERN): soil moisture, actual evapotranspiration, soil properties and classification, soil biodiversity, canopy height and vegetation phenology. Each dataset has its own reader function, and a batch function, collect_tern_data(), takes many locations and dates and returns the extracted values as a single data.table, ready for analysis. The data located on TERN's server as Cloud-Optimised GeoTIFFs and virtual rasters. nert reads them through GDAL's /vsicurl driver, so HTTP range requests fetch only the tiles a query needs and never the whole file. Each dataset has its own reader function, and all of them share one dispatcher that builds the URL, checks dates and parameters, authenticates with a TERN API key and returns a terra::SpatRaster. Retries use GDAL's own HTTP retry settings. The package is MIT-licensed and has about 950 unit tests, with CI on Linux, macOS and Windows across three R versions. It is currently in rOpenSci software peer review.
Data processing·Data visualiation·Data collection
Experimental design·Multi-environment trials·Environmental data collection
Plot A05 · Block A ·
Julian Taylor · Adelaide University
Active · last commit 59 days ago
wgAim (whole genome Analysis using integrated modelling) is a unified whole-genome analysis toolkit for plant breeding populations, built entirely around the ASReml-R linear mixed modelling engine. It capable of readying genetic marker data for analysis as well as conducting univariate and multi-xx QTL, GWAS and genomic prediction analyses. Additional to the genomic analyses, it has a suite of visualisations to help intepret the results of the analyses.
Data analysis·Data visualiation
multivariate·multi-environment·quantitative trait loci·genome wide association analysis·genomic prediction·line seelction·gene-trait analyses
Plot B01 · Block B ·
Patrick Li · Australian National University
Active · last commit yesterday
`torch-openreml` is a PyTorch-based framework for fitting flexible linear mixed-effects models using AI-REML optimisation. It represents covariance structures as differentiable computation graphs, allowing users to combine standard covariance components with custom, parameterised structures. The framework uses automatic differentiation and numerical optimisation to estimate variance and covariance parameters, with computations supported on CPUs and compatible hardware accelerators through PyTorch. It is designed for applications such as multi-environment trials, genotype-by-environment interaction, spatial modelling, and genomic prediction, while also providing a flexible framework for incorporating machine-learning-based covariance structures.
Data analysis
Linear mixed models·Numerical optimisation
Plot B02 · Block B ·
Emi Tanaka · Australian National University
Active · last commit 36 days ago
Reporting heritability estimates is an important to quantitative genetics studies and breeding experiments. Here we provide functions to calculate various broad-sense heritabilities from 'asreml' and 'lme4' model objects. All methods we have implemented in this package have extensively discussed in the article by Schmidt et al. (2019)
Data analysis
Multi-environment trials·Single environment trials
Plot C01 · Block C ·
Adam Sparks · Curtin University
Maintained · last commit 4 months ago
A collection of helper functions for small-plot trial analysis using ASReml-R
Data analysis·Model diagnostics
Data analysis·ASREML·Small plot trial