Package {AgriDataTools}

Type:	Package
Title:	Automated Statistical Analysis and Tools for Agricultural Research
Version:	0.2.1
Description:	A comprehensive suite of statistical tools tailored for agricultural and plant breeding research. Provides automated pipelines for analysis of variance and covariance under randomized complete block designs and completely randomized designs, descriptive summary statistics, and post-hoc multiple range tests including Least Significant Difference, Tukey, and Scheffe based on Steel et al. (1997) <isbn:978-0070610286>. Quantitative genetic parameters including genotypic, phenotypic, and environmental variance components and broad-sense heritability follow Burton and Devane (1953) <doi:10.2134/agronj1953.00021962004500100005x>. Genetic advance and genetic advance as percentage of mean estimation follow Johnson et al. (1955) <doi:10.2134/agronj1955.00021962004700070009x>. Genotypic, phenotypic, and environmental correlations follow Miller et al. (1958) <doi:10.2134/agronj1958.00021962005000100020x>. Genotypic and phenotypic path coefficient analysis direct and indirect effects decomposition follows Dewey and Lu (1959) <doi:10.2134/agronj1959.00021962005100090002x>. Principal component analysis follows Jolliffe (2002) <isbn:978-0387954424> and hierarchical clustering follows Sneath and Sokal (1973) <isbn:978-0716706977>.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.0.0)
Imports:	ggplot2, reshape2, factoextra, dendextend, circlize, ggrepel, stats, graphics, dplyr, utils
Config/roxygen2/version:	8.0.0
VignetteBuilder:	knitr
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
URL:	https://faheemkhan15326.github.io/AgriDataTools/
BugReports:	https://github.com/faheemkhan15326/AgriDataTools/issues
NeedsCompilation:	no
Packaged:	2026-09-02 11:42:29 UTC; Faheem khan
Author:	Faheem Khan [aut, cre]
Maintainer:	Faheem Khan <2022ag94@uaf.edu.pk>
Repository:	CRAN
Date/Publication:	2026-09-02 12:20:33 UTC

AgriDataTools: Automated Statistical Analysis and Tools for Agricultural Research

Description

A comprehensive, high-precision biometrical computing toolkit engineered specifically for plant breeding, agronomic trial evaluations, and quantitative genetic research. Provides end-to-end processing pipelines for completely randomized designs (CRD), randomized complete block designs (RCBD), and analysis of covariance (ANCOVA), including ANOVA, variance component partitioning (Vg, Vp, Ve), broad-sense heritability (H2), genetic advance (GA), and post-hoc pairwise mean separation tests (Tukey's HSD, LSD).

The package provides advanced quantitative tools including:

• Descriptive Statistical Summaries: Automated calculation of overall grand mean, range (minimum to maximum limits), standard deviation, standard error, and phenotypic Coefficient of Variation (CV

• Analysis of Covariance (ANCOVA): Advanced experimental error control and adjusted mean estimations across treatment layouts.

• Genetic Variability Analysis: Comprehensive estimation of Phenotypic Coefficient of Variation (PCV), Genotypic Coefficient of Variation (GCV), Heritability (H2), and Genetic Advance (GA).

• Multi-Level Correlation Analysis: Partitioning of association matrices into Genotypic, Phenotypic, and Environmental correlation components.

• Path Coefficient Analysis: Dissection of simple correlation vectors into direct and indirect contribution effects across both Genotypic and Phenotypic levels.

• Principal Component Analysis (PCA): Multivariate dimensionality reduction, eigenvalue extraction, and variance proportion profiling across phenotypic traits.

• Cluster Analysis: Hierarchical agglomerative and k-means clustering strategies for genetic diversity pattern classification.

Author(s)

Faheem Khan (2022ag94@uaf.edu.pk)

See Also

Useful links:

• https://faheemkhan15326.github.io/AgriDataTools/

• Report bugs at https://github.com/faheemkhan15326/AgriDataTools/issues

Hierarchical Cluster Analysis and Phenotypic Diversity Engine

Description

The analyze_clustering function executes an agglomerative hierarchical clustering routine over multi-trait breeding datasets. It automatically computes cluster assignments, genotype groupings, trait cluster means, intra-cluster average distances, and inter-cluster centroid distances.

Usage

analyze_clustering(
  data,
  traits,
  genotype_col = NULL,
  k = 4,
  linkage_method = "ward.D2",
  reporting_level = 1
)

Arguments

Value

Invisibly returns a named list of class "list" containing 9 detailed computational components:

If reporting_level >= 1, comprehensive cluster summary tables and distance matrices are printed to the console prior to returning the list.

Examples

# Load your own dataset
data(gv_data, package = "AgriDataTools")

# Specify trait columns matching your dataset structure
traits <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW", "GYPM")

# Run cluster engine (Modify k as needed, e.g., 3, 4, 5, or 12)
cluster_results <- analyze_clustering(
  data = gv_data,
  traits = traits,
  genotype_col = "Genotype",
  k = 4,
  linkage_method = "ward.D2",
  reporting_level = 1
)

Principal Component Analysis for Agronomic Traits

Description

Performs Principal Component Analysis (PCA) on targeted quantitative agronomic parameters. Supports dynamic trait mapping to convert trait abbreviations into full descriptive names, dynamic genotype column recognition, automatic replication aggregation to genotypic means, and computes modern multivariate metrics including Kaiser-Guttman retention rules, eigenvector loadings, and percentage trait contributions.

Usage

analyze_pca(
  data,
  traits,
  genotype_col = NULL,
  scale = TRUE,
  reporting_level = 2,
  trait_lookup = NULL
)

Arguments

Value

A structured named list containing 6 multivariate components:

Examples

library(AgriDataTools)
data("gv_data", package = "AgriDataTools")

# Define custom trait mapping before running (Edit names as needed)
custom_traits_map <- c(
    "PH"   = "Plant Height",
    "SL"   = "Spike Length",
    "PL"   = "Peduncle Length",
    "NOT"  = "Number of Tillers",
    "NOSS" = "Number of Spikelets per Spike",
    "TGW"  = "Thousand Grain Weight",
    "GYPM" = "Grain Yield per Meter"
)

# Run Modern PCA with full trait names
pca_results <- analyze_pca(
    data = gv_data,
    traits = names(custom_traits_map),
    genotype_col = "Genotype",
    trait_lookup = custom_traits_map
)

Comprehensive Analysis of Variance (ANOVA) Engine for Completely Randomized Design (CRD)

Description

The anova_crd function executes a complete, high-precision linear model analysis for agricultural, laboratory, or greenhouse trials laid out under a Completely Randomized Design (CRD). It computes partition sums of squares, hypothesis testing statistics, treatment variances, significance flags, and the Coefficient of Variation (CV

Usage

anova_crd(data, trait, genotype_col = NULL, reporting_level = 1)

Arguments

Details

In laboratory experiments, growth chamber studies, or field trials with completely homogeneous environments, blocking is unnecessary. This function utilizes standard least-squares projection to build the classic orthogonal CRD ANOVA matrix, modeling the response vector as a function of treatment effects without blocking constraints:

Y_{ij} = \mu + T_i + \varepsilon_{ij}

Where T_i represents the treatment/genotype effect, and \varepsilon_{ij} is the residual experimental error. The function handles both balanced and unbalanced data structures perfectly, ensuring proper adjustments to degrees of freedom if replication numbers vary across lines.

Value

Invisibly returns a structured named list containing 4 computational components:

• anova_table: A data.frame acting as the standard ANOVA source matrix table for CRD.

• cv_percentage: A numeric scalar representing the computed Coefficient of Variation percentage.

• mean_square_error: A numeric scalar representing the isolated Residual Error Mean Square.

• grand_mean: A numeric scalar representing the overall arithmetic mean value.

See Also

anova_rcbd

Examples

# Load your own dataset
data(gv_data, package = "AgriDataTools")
# Execute complete CRD partition on target trait Plant Height(PH)
crd_results <- anova_crd(data = gv_data, trait = "PH", genotype_col = "Genotype")

Comprehensive Analysis of Variance (ANOVA) Engine for Randomized Complete Block Design (RCBD)

Description

The anova_rcbd function executes a complete, high-precision linear model analysis for agricultural trials laid out under an RCBD framework. It computes partition sums of squares, hypothesis testing statistics, significance flags, and the Coefficient of Variation (CV

Usage

anova_rcbd(
  data,
  trait,
  genotype_col = NULL,
  rep_col = NULL,
  reporting_level = 1
)

Arguments

Details

In plant breeding and agronomy trials, isolating block variance from the true experimental error is vital to properly evaluate lines, cultivars, or treatments. This function uses standard least-squares projection to build the classic orthogonal ANOVA matrix:

Y_{ij} = \mu + G_i + R_j + e_{ij}

Where G_i represents the genotype effect, R_j is the replication block effect, and e_{ij} is the residual experimental error.

Value

Invisibly returns a structured named list of class "list" containing 4 computational components:

• anova_table: A data.frame acting as the standard ANOVA source matrix table containing Df, SS, MS, F_value, p_value, and significance flags.

• cv_percentage: The computed Coefficient of Variation percentage scalar (CV

• mean_square_error: The isolated Residual Error Mean Square (EMS), ready for genetic parameter engines.

• grand_mean: The general mean arithmetic value of the evaluated trait.

See Also

anova_crd

Examples

# Load your own dataset
data(gv_data, package = "AgriDataTools")
# Execute complete RCBD partition on target trait Plant Height(PH)
rcbd_results <- anova_rcbd(data = gv_data, trait = "PH")

Compact Analysis of Covariance (ANCOVA) Table Engine for Plant Breeding

Description

The compute_ancova function evaluates an Analysis of Covariance (ANCOVA) and compiles a streamlined, standard biometrical table featuring Degrees of Freedom (Df), Sum of Products (SP_XY), Mean Products (MP_XY), exact Biometrical F-values, p-values, significance flags, and rigorous Covariance components (Cov_e, Cov_g, Cov_p).

Usage

compute_ancova(
  data,
  response_trait,
  covariate_trait,
  genotype_col = NULL,
  rep_col = NULL,
  reporting_level = 1
)

Arguments

Details

The mathematical partitioning under RCBD framework is computed using joint reduction methods:

SP_{Total} = SP_{Replications} + SP_{Genotypes} + SP_{Residual}

MP_{XY} = \frac{SP_{XY}}{Df}

Cov_e = MP_{Error}

Cov_g = \frac{MP_{Genotypes} - MP_{Error}}{r}

Cov_p = Cov_g + \frac{Cov_e}{r}

Value

Invisibly returns a structured list containing:

Examples

library(AgriDataTools)
data("gv_data", package = "AgriDataTools")

# Run Analysis of Covariance between Grain Yield per Meter and Plant Height
ancova_results <- compute_ancova(
    data = gv_data,
    response_trait = "GYPM",
    covariate_trait = "PH"
)

Multi-Level Genetic, Phenotypic, and Environmental Correlation Engine

Description

The compute_correlation function calculates genotypic (r_g), phenotypic (r_p), and environmental (r_e) correlation coefficient matrices across quantitative traits using analysis of variance (ANOVA) and covariance (ANCOVA) partitions, with integrated significance flags.

Usage

compute_correlation(
  data,
  traits = NULL,
  genotype_col = NULL,
  rep_col = NULL,
  reporting_level = 1
)

Arguments

Details

The engine partitions variance and covariance components using mean squares (MS) and mean cross-products (MCP):

r_g = \frac{Cov_g}{\sqrt{\sigma^2_{g1} \cdot \sigma^2_{g2}}}

r_p = \frac{Cov_p}{\sqrt{\sigma^2_{p1} \cdot \sigma^2_{p2}}}

r_e = \frac{Cov_e}{\sqrt{\sigma^2_{e1} \cdot \sigma^2_{e2}}}

Value

Invisibly returns a structured named list containing 9 correlation and significance matrices:

Examples

# Load your own dataset
data(gv_data, package = "AgriDataTools")

# Specify trait columns matching your dataset structure
traits <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW", "GYPM")

# Run correlation engine
corr_results <- compute_correlation(
  data = gv_data,
  traits = traits,
  reporting_level = 1
)

Fisher's Least Significant Difference (LSD) Post-Hoc Mean Comparison Engine

Description

The compute_lsd function executes a rigorous, high-precision pairwise post-hoc mean separation analysis using Fisher's Least Significant Difference protocol. It isolates critical differences, evaluates pairwise significance metrics, and outputs comprehensive ranking tables with group letters.

Usage

compute_lsd(
  data,
  trait,
  anova_results,
  total_replications,
  geno_col = NULL,
  alpha = 0.05,
  reporting_level = 2
)

Arguments

Value

A structured named list of class "list" containing 4 computational components:

Examples

data(gv_data, package = "AgriDataTools")
reps <- length(unique(gv_data$Replication))
rcbd_results <- anova_rcbd(data = gv_data, trait = "PH", reporting_level = 0)

lsd_output <- compute_lsd(
  data = gv_data, 
  trait = "PH", 
  anova_results = rcbd_results, 
  total_replications = reps, 
  alpha = 0.05
)

High-Precision Multi-Trait Cause-and-Effect Path Coefficient Analysis Engine

Description

The compute_path_analysis function executes phenotypic and genotypic path coefficient analysis based on the classic standard methodology of Dewey and Lu (1959). It partitions correlation coefficients between causal developmental traits and a target response variable into direct influence coefficients and indirect pathways acting through interconnected traits.

Usage

compute_path_analysis(
  correlation_payload,
  response_trait,
  predictor_traits = NULL,
  reporting_level = 2
)

Arguments

Details

Path coefficient analysis provides a matrix-based decomposition of direct and indirect components. For a target response variable, let R denote the correlation matrix among causal predictor traits, and r denote the vector of correlations between predictors and the response variable. Standardized direct path coefficients (\beta) are calculated via matrix inversion:

\beta = R^{-1} r

Indirect effects are cross-products between inter-trait correlations and direct path coefficients. The unexplained residual effect (R_X) is derived as:

R_X = \sqrt{1 - \sum (\beta_i \times r_i)}

Value

A structured named list containing path partition analyses for available correlation levels (Genotypic and/or Phenotypic):

See Also

compute_correlation

Examples

data(gv_data, package = "AgriDataTools")
corr_results <- compute_correlation(data = gv_data, reporting_level = 0)
all_predictors <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW")
path_results <- compute_path_analysis(
  correlation_payload = corr_results, 
  response_trait = "GYPM",
  predictor_traits = all_predictors,
  reporting_level = 2
)

Scheffe's Post-Hoc Mean Comparison and Contrast Evaluation Engine

Description

The compute_scheffe function executes a rigorous, high-precision post-hoc mean separation analysis using Scheffe's method. It evaluates critical differences, pairwise contrasts, and outputs comprehensive ranking tables with statistical group letters.

Usage

compute_scheffe(
  data,
  trait,
  anova_results,
  total_replications,
  geno_col = NULL,
  alpha = 0.05,
  reporting_level = 2
)

Arguments

Value

A structured named list of class "list" containing 4 computational components:

Examples

data(gv_data, package = "AgriDataTools")
reps <- length(unique(gv_data$Replication))
rcbd_results <- anova_rcbd(data = gv_data, trait = "PH", reporting_level = 0)

scheffe_output <- compute_scheffe(
  data = gv_data,
  trait = "PH",
  anova_results = rcbd_results,
  total_replications = reps
)

Comprehensive Descriptive and Summary Statistics Engine for Phenotypic Traits

Description

The compute_summary_stats function performs an exhaustive descriptive statistical sweep across multiple numeric traits in an agricultural dataset. It calculates central tendency, dispersion, and distribution shape metrics (skewness and kurtosis) for line screening.

Usage

compute_summary_stats(data, traits = NULL, reporting_level = 1)

Arguments

Details

Before executing hypothesis testing models like ANOVA, establishing dataset distribution profiles is critical. This engine parses target numerical vectors to extract metrics: Standard Error of the Mean is calculated as SE = \frac{SD}{\sqrt{n}}, Skewness measures distribution asymmetry, and Kurtosis indicates tail weight relative to a normal curve. It dynamically filters out environmental factors like 'Genotype' or 'Replication' and targets purely phenotypic observations.

Value

A detailed structured data.frame where rows represent traits and columns contain calculated metrics.

See Also

validate_agri_data

Examples

# Generate standard summary profiles across all phenotypic traits
descriptive_grid <- compute_summary_stats(data = gv_data)

Tukey's Honestly Significant Difference (HSD) Post-Hoc Mean Comparison Engine

Description

The compute_tukey function executes a high-precision pairwise post-hoc mean separation analysis using Tukey's Honestly Significant Difference framework. It calculates family-wise error control thresholds, pairwise differences, and outputs comprehensive ranking tables with group letters.

Usage

compute_tukey(
  data,
  trait,
  anova_results,
  total_replications,
  geno_col = NULL,
  alpha = 0.05,
  reporting_level = 2
)

Arguments

Value

A structured named list of class "list" containing 4 computational components:

Examples

data(gv_data, package = "AgriDataTools")
reps <- length(unique(gv_data$Replication))
rcbd_results <- anova_rcbd(data = gv_data, trait = "PH", reporting_level = 0)

tukey_output <- compute_tukey(
  data = gv_data,
  trait = "PH",
  anova_results = rcbd_results,
  total_replications = reps
)

Genetic Variability and Quantitative Inheritance Parameters Estimation Engine

Description

The estimate_variability function calculates comprehensive biometric genetic profiles from replication-based agricultural trial datasets. It partitions phenotypic variance into Genotypic Variance (Vg), Phenotypic Variance (Vp), and Environmental Variance (Ve), and computes critical breeding metrics including Genotypic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), Broad-Sense Heritability (H2), Genetic Advance (GA), and Genetic Advance as

Usage

estimate_variability(anova_results, total_replications, reporting_level = 1)

Arguments

Details

In quantitative genetics, phenotypic variance must be dissected into its components to determine the role of genetic factors versus environmental noise. This function extracts the Error Mean Square (EMS) and Genotypic Mean Square (GMS) directly from completed ANOVA matrices:

V_g = \frac{GMS - EMS}{r}

V_p = V_g + EMS

H^2 = \frac{V_g}{V_p}

Where r represents the total absolute replication count.

If high environmental variations cause the computed Genotypic Variance to become negative, the system automatically applies a mathematical lower boundary floor at 0.0001 to preserve downstream pipeline integrity and issues a detailed structural warning message.

Value

A structured named list containing calculated genetic variability components:

See Also

anova_rcbd, anova_crd

Examples

data(gv_data, package = "AgriDataTools")
reps <- length(unique(gv_data$Replication))
rcbd_results <- anova_rcbd(data = gv_data, trait = "PH", reporting_level = 0)
var_metrics <- estimate_variability(anova_results = rcbd_results, total_replications = reps)

Genotypic Variability and Agricultural Research Dataset

Description

Evaluated performance parameters across multiple line and cultivar iterations under a randomized complete block layout.

Usage

data(gv_data)

Format

A data.frame containing phenotypic records with wheat morphological traits:

Genotype

Factor or character vector identifying evaluated breeding germplasm lines or cultivars.

Replication

Factor or integer vector indicating experimental replications or blocks within the layout.

PH

Plant Height measured in centimeters (cm).

PL

Peduncle Length measured in centimeters (cm).

SL

Spike Length measured in centimeters (cm).

NOT

Number of Tillers per plant.

NOSS

Number of Spikelets Per Spike.

TGW

Thousand Grain Weight measured in grams (g).

GYPM

Grain Yield Per Meter measured in grams (g).

Source

Orignal experimentSal data from a wheat field trial conducted under Randomized Complete Block Design (RCBD) involving multiple genotypes and phenotypic traits.

Examples

data(gv_data, package = "AgriDataTools")
head(gv_data)

Advanced High-Precision Publication-Ready Graphics Suite

Description

The plot_agri_graphics function serves as the unified visualization hub for the AgriDataTools package. It handles basic statistical diagnostics (residuals, correlations) alongside modern publication-grade graphical representations for mean performance, PCA space, hierarchical dendrograms, and path analysis direct effects.

Usage

plot_agri_graphics(
  type,
  payload,
  trait_name = "Target Character Matrix",
  reporting_level = 0,
  num_clusters = 4
)

Arguments

Details

Visualizing high-dimensional screening metrics across diverse lines or cultivars requires balancing diagnostic model validation checks with advanced multivariate aesthetics. This engine supports base diagnostic rendering as well as optimized ggplot2 geometries featuring dynamic color palettes, non-overlapping labels, and geometric layout vector mapping fields.

Value

Invisibly returns a logical scalar TRUE upon successful execution. This function is primarily invoked for its side effect of rendering publication-grade graphical plots (e.g., residual diagnostic plots, correlation heatmaps, mean performance barcharts, PCA biplots, circular dendrograms, or path analysis plots) to the active graphics device.

Examples

data(gv_data, package = "AgriDataTools")
traits <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW", "GYPM")

# Define custom mapping or number of clusters beforehand
k_groups <- 4

# 1. Mean performance: Genotypic performance with LSD
if (interactive()) {
  reps <- length(unique(gv_data$Replication))
  fit <- aov(PH ~ Genotype + Replication, data = gv_data)
  m_anova <- list(anova_table = data.frame(
    Source = c("Genotype", "Replication", "Error"),
    Df = summary(fit)[[1]]$Df,
    MS = summary(fit)[[1]][[3]]
  ))
  lsd_output <- compute_lsd(gv_data, "PH", m_anova, reps, reporting_level = 0)
  plot_agri_graphics(type = "mean", payload = lsd_output,
                     trait_name = "Plant Height")
}

# 2. PCA: Multivariate variation with unique actual genotype names
if (interactive()) {
   custom_traits_map <- c(
       "PH"    = "Plant Height",
       "SL"    = "Spike Length",
       "PL"    = "Peduncle Length",
       "NOT"   = "Number of Tillers",
       "NOSS"  = "Number of Spikelets per Spike",
       "TGW"   = "Thousand Grain Weight",
       "GYPM"  = "Grain Yield per Meter"
   )
   pca_results <- analyze_pca(data = gv_data, traits = traits, 
                              trait_lookup = custom_traits_map, 
                              reporting_level = 0)
   plot_agri_graphics(type = "pca", payload = pca_results,
                      trait_name = "PCA Plot")
}

# 3. Clustering: Dendrogram with flexible cluster parameter option
if (interactive()) {
   cluster_results <- analyze_clustering(data = gv_data, traits = traits,
    k = k_groups, reporting_level = 0)
   plot_agri_graphics(type = "cluster", payload = cluster_results,
                      trait_name = "Clustering", num_clusters = k_groups)
}

# 4. Residuals: Diagnostic plots
if (interactive()) {
  fit <- lm(PH ~ Genotype, data = gv_data)
  res_pl <- list(residuals = residuals(fit),
                 fitted_values = fitted(fit))
  plot_agri_graphics(type = "residual", payload = res_pl,
                     trait_name = "Residuals")
}

# 5. Correlations: Phenotypic matrix
if (interactive()) {
  cor_m <- cor(gv_data[, traits], use = "pairwise.complete.obs")
  plot_agri_graphics(type = "correlation",
                     payload = list(correlation_matrix = cor_m),
                     trait_name = "Correlation")
}

# 6. Path Analysis: Direct Effects Plot for Grain Yield per Meter (GYPM)
if (interactive()) {
  corr_results <- compute_correlation(data = gv_data, traits = traits, reporting_level = 0)
  all_predictors <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW")
  path_results <- compute_path_analysis(
    correlation_payload = corr_results, 
    response_trait = "GYPM", 
    predictor_traits = all_predictors,
    reporting_level = 0
  )
  plot_agri_graphics(type = "path", payload = path_results, 
                     trait_name = "Grain Yield per Meter (GYPM)")
}

Rigid Multi-Layered Structural Data Validation and Matrix Integrity Engine

Description

The validate_agri_data function serves as the primary data defense matrix of the AgriDataTools package. It performs automated multi-dimensional quality control, type alignment checks, and semantic structural audits on agricultural research datasets before passing them to downstream quantitative genetic workflows.

Usage

validate_agri_data(data, reporting_level = 1)

Arguments

Details

In quantitative genetics, downstream models like ANOVA, Heritability estimations, and Path analysis are highly vulnerable to layout irregularities. Silent formatting issues can bias variance components or trigger execution failures.

validate_agri_data runs a dynamic defensive pipeline:

1. Object Matrix Verification: Ensures input is a valid data.frame with observations.

2. Dynamic Header Discovery: Auto-detects Genotype, Replication, and Trait columns without rigid hardcoding.

3. Factor Balance Audits: Verifies minimum levels for lines and blocks.

4. Numeric Type & Range Compliance: Validates numeric integrity and flags biological anomalies (e.g., negative values).

5. Orthogonality Sweep: Evaluates design layout cell frequencies for balance.

Value

A logical scalar TRUE if the dataset completely satisfies the operational constraints of the biometrical pipeline. Throws informative errors if critical structural faults are detected.

References

• Cochran, W.G. and Cox, G.M. (1957). Experimental Designs. 2nd Edition, John Wiley & Sons, New York.

• Falconer, D.S. and Mackay, T.F.C. (1996). Introduction to Quantitative Genetics. 4th Edition, Longman, Essex.

Examples

# Trigger dynamic validation sweep
validation_status <- validate_agri_data(data = gv_data)