DATES — Analysis Modules Documentation

Getting Started & General Workflow

Essential

Getting Started

General workflow guide covering dataset upload options, variable selection, compute engine, and plot customization.

Core Workflow

Data Preparation

Pre-processing

Reshape Data (Wide ↔ Long)

Transform experimental matrices between Wide replicate columns and Long tidy rows with automated auto-detection.

Data Prep

Data Replicator

Synthesize multi-replicate trial datasets from treatment means while preserving exact mean and error bounds.

Data Prep

Data Transformer & Normalization

Apply 15 variance-stabilizing and normalizing mathematical transformations with real-time domain validation.

Data Prep

Normality & Distribution Analysis

Evaluate Gaussian normal distribution using 10 statistical significance tests, skewness, kurtosis, and diagnostic plots.

Data Prep

Basic Statistics

Foundational

Descriptive Statistics

Central tendency, dispersion, skewness, kurtosis, and distributional summaries with publication-ready tables.

Basic Stats

Student's t-Test Analysis

Compare means across quantitative datasets using One-Sample, Independent Two-Sample, and Paired t-tests.

Basic Stats

F-Test for Equality of Variances

Evaluate homoscedasticity and variance ratios between two independent measurement groups.

Basic Stats

Chi-Square Test Analysis (χ²)

Perform Goodness-of-Fit and Test of Independence cross-tabulations on categorical variables.

Basic Stats

General Suits

Core Utilities

Mean Comparison & Groupings

Group descriptive metrics, multiple post-hoc mean separation tests (Tukey, LSD, Dunnett, Duncan), and compact letter displays.

General Suits

Plot Arrangement (Figure Layout Studio)

Publication-ready multi-panel figure editor with physical millimetre geometry, journal presets (Nature, Cell, Science), and high-DPI exports.

General Suits

Experimental Designs (ANOVA)

Design of Experiments

Basic Designs

Completely Randomized Design (CRD)

ANOVA for 1-Factor, 2-Factor, 3-Factor, and Nested completely randomized experimental designs.

Basic Design

Randomized Complete Block Design (RCBD)

ANOVA for 1-Factor, 2-Factor, 3-Factor, and Nested randomized complete block experimental designs.

Basic Design

Advanced Block Designs

Augmented RCBD

Screening trial ANOVA for unreplicated candidate test entries evaluated alongside replicated benchmark control checks.

Advanced Block

Alpha Lattice Design

Incomplete block experimental design ANOVA with intra-block error partitioning, adjusted treatment means, and relative efficiency.

Advanced Block

Row–Column Designs

Latin Square Design (LSD)

ANOVA with simultaneous two-way spatial or temporal environmental blocking (Rows and Columns).

Row-Column

Repeated-Measures Designs

Repeated Measures 2-Way ANOVA

Longitudinal time-series ANOVA with Mauchly sphericity test, Greenhouse-Geisser and Huynh-Feldt p-value adjustments.

Repeated Measures

Multi-Factor Designs

Split-Plot Design

Two-tier error partitioning (Main-Plot Error A and Sub-Plot Error B) across 1x1, 2x1, 1x2, and 2x2 factorials.

Multi-Factor

Split-Split Plot Design

Three-tier error partitioning (Error A, Error B, Error C) across 8 factorial combinations.

Multi-Factor

Strip-Plot Design

Three-way error partitioning (Vertical Error A, Horizontal Error B, Interaction Error C).

Multi-Factor

Multi-Environment / Pooled Designs

Pooled CRD

Multi-environment combined CRD ANOVA evaluating treatment main effects and treatment-by-environment interactions.

Pooled Design

Pooled RCBD

Multi-environment combined RCBD ANOVA evaluating block-in-environment variation and treatment stability across locations.

Pooled Design

Pooled Aug. RCBD (PL-Aug-RBD)

Multi-environment augmented screening ANOVA with pooled control check residuals and entry-by-environment stability analysis.

Pooled Design

Pooled Split-Plot

Multi-environment split-plot factorial ANOVA with pooled Error A and Error B partitioning across testing sites.

Pooled Design

Pooled Strip-Plot

Multi-environment strip-plot ANOVA with three-way error partitioning (Error A, Error B, Error C) and environmental interactions.

Pooled Design

Covariate Analysis

ANCOVA

ANCOVA in CRD

Analysis of Covariance in CRD adjusting group means for continuous baseline covariates, slope homogeneity testing, and LS-Means contrasts.

Covariate

ANCOVA in RCBD

Analysis of Covariance in RCBD controlling spatial block variability while removing continuous baseline covariate bias from treatment means.

Covariate

Plant Breeding

Genetic Analytics

Variance Components & Heritability

Partition total trait variation into factor, block, interaction, and residual error components with OLS EMS & REML Mixed Models.

Breeding

Line × Tester

Factorial cross design analysis evaluating General Combining Ability (GCA), Specific Combining Ability (SCA), heterosis, and genetic variances.

Breeding

Diallel Analysis (Griffing)

Griffing Methods 1, 2, 3, & 4 (Fixed & Random Models) evaluating General Combining Ability (GCA), Specific Combining Ability (SCA), and Reciprocal Effects.

Breeding

Diallel Analysis (Hayman)

Hayman graphical & component diallel analysis evaluating additive vs dominance components, Vr-Wr regression, and narrow heritability.

Breeding

Mahalanobis D² Diversity

Mahalanobis D-squared distance estimation, covariance shrinkage regularization, Tocher optimization clustering, and trait contribution analysis.

Breeding

Multi-Environment (MET) Analysis

Stability & Biplots

AMMI Analysis

Additive Main Effects and Multiplicative Interaction (AMMI) analysis, IPCA decomposition, AMMI1/AMMI2 biplots, and AMMI Stability Values.

MET Analysis

GGE Biplot

GGE biplots, Which-Won-Where polygon mega-environment partitioning, Mean vs Stability coordinates, and environment evaluation.

MET Analysis

Stability Models

Parametric stability models including Eberhart & Russell regression (bi & S2di), Wricke Ecovalence, Shukla Variance, Francis CV, and Lin & Binns Superiority.

MET Analysis

Multivariate & Relationship Analysis

Multivariate

Correlation (P-S-K) & Genetic Correlation

Pearson, Spearman, Kendall, Phenotypic (rp), Genotypic (rg), and Environmental (re) correlation matrix decomposition.

Relationship

Regression

Simple and Multiple Linear Regression modeling, R-squared goodness-of-fit, t-test coefficient significance, VIF multicollinearity, and diagnostic plots.

Relationship

Path Analysis

Dewey & Lu cause-and-effect path coefficient analysis partitioning correlation coefficients into direct effects, indirect effects, and residual variation.

Relationship

PCA (Principal Component Analysis)

Dimensionality reduction, Eigenvalues, Scree plots, Varimax/Promax factor rotations, and interactive 2D/3D PCA Biplots.

Multivariate

Cluster Analysis

Hierarchical and K-Means clustering with Euclidean, Manhattan, and Mahalanobis distance metrics and interactive dendrogram plots.

Multivariate