Package {AgriDataTools}

Type:	Package
Title:	Automated Statistical Analysis and Tools for Agricultural Research
Version:	0.1.2
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, stats, graphics, dplyr, utils
Config/roxygen2/version:	8.0.0
VignetteBuilder:	knitr
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
NeedsCompilation:	no
Packaged:	2026-08-01 08:55:04 UTC; Faheem khan
Author:	Faheem Khan [aut, cre]
Maintainer:	Faheem Khan <2022ag94@uaf.edu.pk>
Repository:	CRAN
Date/Publication:	2026-08-08 11:20:06 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) and randomized complete block designs (RCBD), 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%).

• 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)

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,
  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 benchmark breeding dataset
data(gv_data, package = "AgriDataTools")

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

# Run cluster engine (Modify k as needed, e.g., 3, 4, 5, or 6)
cluster_results <- analyze_clustering(
  data = gv_data,
  traits = my_traits,
  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, and computes modern multivariate metrics including Kaiser-Guttman retention rules, eigenvector loadings, and percentage trait contributions.

Usage

analyze_pca(
  data,
  traits,
  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 your 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),
   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, reporting_level = 2)

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 of class "list" containing 4 computational components:

If reporting_level >= 1, the compiled ANOVA table along with the grand mean and CV% are printed directly to the console before returning the list.

See Also

anova_rcbd

Examples

   # Execute complete CRD partition on your actual target trait (GYPM)
   crd_results <- anova_crd(data = gv_data, trait = "GYPM")
   

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, reporting_level = 2)

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

A structured named list containing 4 computational components:

See Also

compute_lsd

Examples

   # Execute complete RCBD partition on gv_data asset for Plant Height (PH)
   rcbd_results <- anova_rcbd(data = gv_data, trait = "PH")

Analysis of Covariance (ANCOVA) Engine for Plant Breeding Trials

Description

The 'compute_ancova' function performs an exhaustive Analysis of Covariance across agricultural experimental records. It adjusts the treatment (genotypic) means for differences in a concomitant variable (covariate) to improve experimental precision and error control.

Usage

compute_ancova(data, response_trait, covariate_trait, reporting_level = 2)

Arguments

Details

In agricultural experiments, variation in a dependent phenotypic trait can sometimes be partially controlled by measuring a secondary auxiliary property (covariate, e.g., initial stand count or flowering duration). This engine fits a linear model incorporating both the categorical genotypic design structure and the continuous covariate vector, extracting the adjusted sums of squares, F-statistics, and adjusted marginal means.

Value

Invisibly returns a structured named list of class "list" containing 5 computational components:

If reporting_level >= 1, the compiled ANCOVA table is printed directly to the console before returning the output object.

See Also

validate_agri_data, compute_lsd

Examples

data(gv_data, package = "AgriDataTools")

# Perform ANCOVA using Plant Height (PH) as response and Spike Length (SL) as covariate
ancova_res <- compute_ancova(
  data = gv_data,
  response_trait = "PH",
  covariate_trait = "SL",
  reporting_level = 2
)
print(ancova_res$ancova_table)

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, 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 of class "list" containing 9 correlation and significance matrices:

If reporting_level >= 1, formatted correlation matrices with significance flags are printed directly to the console before returning the output object.

Examples

# Load benchmark breeding dataset
data(gv_data, package = "AgriDataTools")

# Define trait columns
my_traits <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW", "GYPM")

# Run multi-level correlation engine
corr_results <- compute_correlation(
  data = gv_data,
  traits = my_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,
  alpha = 0.05,
  reporting_level = 2
)

Arguments

Value

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

Examples

# Load integrated data matrices
data(gv_data, package = "AgriDataTools")

# Total replications matching the experimental design blocks
reps <- length(unique(gv_data$Replication))

# Generating upstream ANOVA structure from gv_data for the target trait (PH)
model_fit <- aov(PH ~ Genotype + Replication, data = gv_data)
anova_summary <- summary(model_fit)[[1]]

mock_anova <- list(
  anova_table = data.frame(
    Source = c("Genotype", "Replication", "Error"),
    Df = anova_summary$Df,
    MS = anova_summary$`Mean Sq`,
    stringsAsFactors = FALSE
  )
)

# Running post-hoc separation sweeps with clean letters group layout
lsd_output <- compute_lsd(
  data = gv_data, 
  trait = "PH", 
  anova_results = mock_anova, 
  total_replications = reps, 
  alpha = 0.05
)
print(lsd_output$ranked_means)

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, compute_summary_stats

Examples

   # Compute multi-level correlation matrices
   corr_payload <- compute_correlation(data = gv_data, reporting_level = 0)
   
   # Define all 6 causal predictor traits driving Grain Yield per Meter
   all_predictors <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW")
   
   # Run path coefficient analysis across all causal traits with full report
   path_out <- compute_path_analysis(
     correlation_payload = corr_payload, 
     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.

Usage

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

Arguments

Value

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

Author(s)

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

Examples

data(gv_data, package = "AgriDataTools")
reps <- length(unique(gv_data$Replication))

model_fit <- aov(PH ~ Genotype + Replication, data = gv_data)
anova_summary <- summary(model_fit)[[1]]

mock_anova <- list(
  anova_table = data.frame(
    Source = c("Genotype", "Replication", "Error"),
    Df = anova_summary$Df,
    MS = anova_summary$`Mean Sq`,
    stringsAsFactors = FALSE
  )
)

scheffe_output <- compute_scheffe(
  data = gv_data,
  trait = "PH",
  anova_results = mock_anova,
  total_replications = reps
)
print(scheffe_output$ranked_means)

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 = 2)

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

if (interactive()) {
   # Generate standard summary profiles across all phenotypic traits
   descriptive_grid <- compute_summary_stats(data = gv_data)
   print(descriptive_grid)
}

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.

Usage

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

Arguments

Value

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

Author(s)

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

Examples

data(gv_data, package = "AgriDataTools")
reps <- length(unique(gv_data$Replication))

model_fit <- aov(PH ~ Genotype + Replication, data = gv_data)
anova_summary <- summary(model_fit)[[1]]

mock_anova <- list(
  anova_table = data.frame(
    Source = c("Genotype", "Replication", "Error"),
    Df = anova_summary$Df,
    MS = anova_summary$`Mean Sq`,
    stringsAsFactors = FALSE
  )
)

tukey_output <- compute_tukey(
  data = gv_data,
  trait = "PH",
  anova_results = mock_anova,
  total_replications = reps
)
print(tukey_output$ranked_means)

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 = 2)

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 or block 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

if (interactive()) {
   # Execute complete genetic variability partitioning
   rcbd_out <- anova_rcbd(data = gv_data, trait = "PH", reporting_level = 0)
   var_metrics <- estimate_variability(anova_results = rcbd_out, total_replications = 3)
   print(var_metrics$heritability_percentage)
}

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

Experimental plant breeding field trial records.

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 = 2,
  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
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_res <- compute_lsd(gv_data, "PH", m_anova, reps)
plot_agri_graphics(type = "mean", payload = lsd_res,
                   trait_name = "Plant Height")

# 2. PCA: Multivariate variation
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_res <- analyze_pca(data = gv_data, traits = traits, trait_lookup = custom_traits_map)
plot_agri_graphics(type = "pca", payload = pca_res,
                   trait_name = "PCA Plot")

# 3. Clustering: Dendrogram with flexible cluster parameter option
cl_res <- analyze_clustering(data = gv_data, traits = traits, k = k_groups)
plot_agri_graphics(type = "cluster", payload = cl_res,
                   trait_name = "Clustering", num_clusters = k_groups)

# 4. Residuals: Diagnostic plots
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
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)
corr_res <- compute_correlation(data = gv_data, traits = traits, reporting_level = 0)
all_predictors <- c("PH", "SL", "PL", "NOT", "NOSS", "TGW")
path_res <- compute_path_analysis(
   correlation_payload = corr_res, 
   response_trait = "GYPM", 
   predictor_traits = all_predictors,
   reporting_level = 0
)
plot_agri_graphics(type = "path", payload = path_res, 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 is engineered to perform exhaustive, multi-dimensional quality control, type alignment checks, and semantic structural audits on user-provided datasets before they are passed to sensitive breeding and quantitative genetic workflows.

Usage

validate_agri_data(data, strict_mode = TRUE, reporting_level = 2)

Arguments

Details

In biometric computing, agricultural research, and plant breeding quantitative data analysis, downstream models like ANOVA, Heritability estimations, and Path analysis are highly vulnerable to layout irregularities. Silent formatting issues inside spreadsheets can bias variance components or trigger generic engine failures.

To solve this, 'validate_agri_data' runs an automated, multi-tiered defensive pipeline:

1. Object and Class Matrix Verification: Ensures the input object is a true dataframe.

2. Dimension Capability Evaluation: Checks for absolute row/column structural thresholds.

3. Strict Column Header Matching: Verifies the existence of structural keys and core traits.

4. Categorical Factor Integrity Audits: Verifies grouping layouts for Genotypes and Replications.

5. Quantitative Type Compliance Testing: Validates phenotypic trait vectors for numeric compliance.

6. Biological Range and Bound Inspections: Screens for mathematical anomalies like negative values.

7. Missing Value (NA) Variance Profiling: Analyzes missing data distribution across blocks.

Value

A logical scalar TRUE if the dataset completely satisfies the operational constraints of the biometrical pipeline. If any structural non-compliance is detected, it throws a highly structured, informative exception detailing the exact coordinates of the failure.

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

# Assuming package environments are active and datasets are loaded via data(gv_data)
if (interactive()) {
    # Trigger standard validation sweep
    validation_status <- validate_agri_data(data = gv_data, strict_mode = TRUE)
    message("Is dataset operational? ", validation_status)
}