A comprehensive suite of statistical and analytical tools for 
    agricultural research. Includes complete analysis of variance (ANOVA) 
    functions for all experimental designs: Completely Randomized Design (CRD), 
    Randomized Block Design (RBD), Pooled RBD, Split Plot with all variations, 
    Split-Split Plot, Strip Plot, Latin Square, Factorial, Augmented, and Alpha 
    Lattice, with proper error terms and comprehensive Standard Error (SE) and 
    Critical Difference (CD) calculations. Features multiple post-hoc tests: 
    Least Significant Difference (LSD), Duncan Multiple Range Test (DMRT), 
    Tukey Honestly Significant Difference (HSD), Student-Newman-Keuls (SNK), 
    Scheffe, Bonferroni, and Dunnett, along with assumption checking and 
    publication-ready output. Advanced methods include stability analysis using 
    Eberhart-Russell regression, Additive Main Effects and Multiplicative 
    Interaction (AMMI), Finlay-Wilkinson regression, Shukla stability variance, 
    Wricke ecovalence, Coefficient of Variation (CV), and Cultivar Superiority 
    Index as described in Eberhart and Russell (1966) 
    <doi:10.2135/cropsci1966.0011183X000600010011x>. Thermal indices include 
    Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units 
    (PTU), and Heat Use Efficiency (HUE). Crop growth analysis covers Crop 
    Growth Rate (CGR), Relative Growth Rate (RGR), Net Assimilation Rate (NAR), 
    and Leaf Area Index (LAI). Also provides harvest index, yield gap analysis, 
    economic efficiency indices (Benefit-Cost ratio), nutrient use efficiency 
    calculations, correlation matrix, Principal Component Analysis (PCA), path 
    analysis, and Structural Equation Modeling (SEM). Statistical methods follow 
    Gomez and Gomez (1984, ISBN:0471870927) and Panse and Sukhatme (1985, 
    ISBN:8170271169).

Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.