Package: agriReg 0.1.0

agriReg: Linear and Nonlinear Regression for Agricultural Data

Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via 'lme4'), nonlinear growth curves (logistic, 'Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via 'drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and 'ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) <doi:10.1371/journal.pone.0146021>, and Bates and others (2015) <doi:10.18637/jss.v067.i01>.

Authors:Sadikul Islam [aut, cre]

agriReg_0.1.0.tar.gz
agriReg_0.1.0.zip(r-4.7-any)agriReg_0.1.0.zip(r-4.6-any)agriReg_0.1.0.zip(r-4.5-any)
agriReg_0.1.0.tgz(r-4.6-any)agriReg_0.1.0.tgz(r-4.5-any)
agriReg_0.1.0.tar.gz(r-4.7-any)agriReg_0.1.0.tar.gz(r-4.6-any)
agriReg_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
agriReg/json (API)

# Install 'agriReg' in R:
install.packages('agriReg', repos = c('https://sadikulislamiasri-hub.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.30 score 305 downloads 15 exports 77 dependencies

Last updated from:d86450b33c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK137
source / vignettesOK456
linux-releaseOK148
macos-releaseOK119
macos-oldrelOK101
windows-develOK79
windows-releaseOK112
windows-oldrelOK85
wasm-releaseOK135

Exports:clean_agri_datacompare_modelsed_estimatesfit_dose_responsefit_linearfit_nonlinearfit_polynomialgof_statsload_example_datamodel_summaryoptimum_doseoutlier_flagplot_fitresidual_checkyield_normalize

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecowplotcpp11DerivdoBydplyrdrcfarverforecastFormulafracdiffgenericsggplot2gluegtablegtoolsisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvminqamodelrmultcompmvtnormnlmenloptrnnetnumDerivpatchworkpbkrtestpillarpkgconfigplotrixpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7sandwichscalesSparseMstringistringrsurvivalTH.datatibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Advanced: Mixed models and multi-environment trials
Multi-environment mixed model | Fixed genotype, random environment | Post-hoc comparisons with emmeans | Fitting multiple growth curves by group | Overlay curves on a single plot | Comparing dose-response curves across species | Exporting a comparison table to Word / Excel
Rendered fromadvanced-mixed-models.Rmdusingknitr::rmarkdown

Last update: 2026-03-31
Started: 2026-03-31

Getting started with agriReg
Overview | 1 · Data preparation | Cleaning and outlier detection | Normalisation | Advanced outlier flagging | 2 · Linear models | Simple OLS | Diagnostic plots | Full residual panel | Mixed-effects model | Polynomial selection | 3 · Nonlinear models | Logistic growth | Gompertz curve | Asymptotic (Mitscherlich) model | Quadratic and optimum dose | Linear-plateau model | 4 · Dose-response models | Fit a 4-parameter log-logistic curve | Effective dose estimates | 5 · Model comparison | 6 · Extracting results for reports | Saving plots | Session information
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-03-31
Started: 2026-03-31