AgriFusionR
An Integration Framework for Agricultural Analytics
Assembles agricultural analyses around a single unit of observation, the management unit within a season, and keeps climate, soil and remote-sensing covariates aligned to it. Covariates are aggregated over phenological windows derived from accumulated growing degree days rather than calendar months, following McMaster and Wilhelm (1997) <doi:10.1016/S0168-1923(97)00027-0>. Models are validated with spatial resampling by default, since random cross-validation inflates apparent skill when observations are spatially autocorrelated, as shown by Roberts and others (2017) <doi:10.1111/ecog.02881>. Prediction intervals use split conformal inference after Lei and others (2018) <doi:10.1080/01621459.2017.1307116>. Data sources and learning algorithms are supplied through registries so that new providers and methods can be added without modifying the package.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | AgriFusionR_0.1.0.tar.gz |
231.7 KiB |
0.1.0 |
rolling linux/noble R-4.5 | AgriFusionR_0.1.0.tar.gz |
231.7 KiB |
0.1.0 |
rolling source/ R- | AgriFusionR_0.1.0.tar.gz |
67.6 KiB |
0.1.0 |
latest linux/jammy R-4.5 | AgriFusionR_0.1.0.tar.gz |
231.7 KiB |
0.1.0 |
latest linux/noble R-4.5 | AgriFusionR_0.1.0.tar.gz |
231.7 KiB |
0.1.0 |
latest source/ R- | AgriFusionR_0.1.0.tar.gz |
67.6 KiB |
0.1.0 |
2026-04-23 source/ R- | AgriFusionR_0.1.0.tar.gz |
0 B |