Provides functions to compute agricultural crop diversification indices for crop data across zones and years. The package implements widely used diversification and concentration measures including Herfindahl Index,Simpson Index, Entropy Index, Ogive Index, and Maximum Proportion Index.

Provides a comprehensive pipeline for data quality checks and
    statistical assumption diagnostics in agricultural experimental data.
    Functions cover outlier detection using Interquartile Range (IQR) fence,
    Z-score, modified Z-score (Hampel identifier), Grubbs test and Dixon
    Q-test with consensus flagging; missing data pattern analysis and
    mechanism classification (Missing Completely At Random/Missing At
    Random/Missing Not At Random (MCAR/MAR/MNAR)) via Little's test;
    normality testing using Shapiro-Wilk, Anderson-Darling,
    Kolmogorov-Smirnov, Lilliefors, Pearson chi-square and Jarque-Bera tests;
    homogeneity of variance via Bartlett, Levene and Fligner-Killeen tests;
    independence of errors via Durbin-Watson, Breusch-Godfrey and
    Wald-Wolfowitz runs tests; experimental design validation for Completely
    Randomised Design (CRD), Randomised Complete Block Design (RCBD), Latin
    Square Design (LSD) and factorial designs; qualitative variable consistency
    checks; and automated HyperText Markup Language (HTML) report generation.
    Designed to align with Findable, Accessible, Interoperable and Reusable
    (FAIR) data principles. Methods follow Gomez and Gomez (1984,
    ISBN:978-0471870920) and Montgomery (2017, ISBN:978-1119492443).

Functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) <doi:10.1109/TSMC.1973.4309314>.

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>.