Add synthetic maize data, docs and data-raw

Add synthetic phenotype and genotype example data for the selection.index package: maize_pheno and maize_geno (.rda) plus a data-raw/maize.R script that generates them (uses usethis::use_data). Consolidate dataset documentation into R/data.R (roxygen) and add generated man pages for maize_pheno and maize_geno. Remove legacy R/seldata.R and R/weight.R (docs for seldata and weight updated to point to R/data.R). Also include compiled native build artifacts in src (RcppExports.o, math_primitives.o, selection.index.dll). These changes provide reproducible example datasets and updated documentation for examples/tests.

Author: zankrut20

R/data.R

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+# R/data.R
+
+#' Synthetic Maize Phenotypic Data
+#'
+#' A synthetic dataset containing multi-environment phenotypic records for 100
+#' maize genotypes. Designed to demonstrate phenotypic selection index
+#' calculations and variance-covariance matrix estimations.
+#'
+#' @name maize_pheno
+#' @docType data
+#' @format A data frame with 600 rows and 6 variables:
+#' \describe{
+#'   \item{Genotype}{Factor representing 100 unique maize genotypes.}
+#'   \item{Environment}{Factor representing 2 distinct growing environments.}
+#'   \item{Block}{Factor representing 3 replicate blocks within each environment.}
+#'   \item{Yield}{Numeric vector of grain yield in kg/ha.}
+#'   \item{PlantHeight}{Numeric vector of plant height in centimeters.}
+#'   \item{DaysToMaturity}{Numeric vector of days to physiological maturity.}
+#' }
+#' @source Simulated for the selection.index package to provide reproducible examples.
+#' @usage data(maize_pheno)
+#' @keywords datasets
+#' @examples
+#' data(maize_pheno)
+#' head(maize_pheno)
+NULL
+
+#' Synthetic Maize Genomic Data
+#'
+#' A synthetic dataset containing Single Nucleotide Polymorphism (SNP) marker
+#' data for the 100 maize genotypes found in maize_pheno. Designed for
+#' testing genomic selection indices (GESIM/RGESIM) and relationship matrices.
+#'
+#' @name maize_geno
+#' @docType data
+#' @format A numeric matrix with 100 rows (genotypes) and 500 columns (SNP markers):
+#' \describe{
+#'   \item{Rows}{100 genotypes, matching the Genotype column in maize_pheno.}
+#'   \item{Columns}{500 simulated SNP markers, coded as 0, 1, or 2.}
+#' }
+#' @source Simulated for the selection.index package to provide reproducible examples.
+#' @usage data(maize_geno)
+#' @keywords datasets
+#' @examples
+#' data(maize_geno)
+#' dim(maize_geno)
+NULL
+
+#' Selection Index DataSet
+#'
+#' A dataset containing the data of three replications and 48 progenies
+#' with 7 different traits.
+#'
+#' \itemize{
+#' \item rep. Replications
+#' \item treat. Treatments/Genotypes
+#' \item sypp. Seed Yield per Plant
+#' \item dtf. Days to 50% Flowering
+#' \item rpp. Racemes per Plant
+#' \item ppr. Pods per Raceme
+#' \item ppp. Pods per Plant
+#' \item spp. Seeds per Pod
+#' \item pw. Pods Weight
+#' }
+#' @docType data
+#' @name seldata
+#' @usage data(seldata)
+#' @format A data frame with 75 rows and 9 columns
+NULL
+
+#' Weight dataset
+#'
+#' A dataset containing the data of 2 different weights namely euqal weight and
+#' broad sense heritability
+#'
+#' \itemize{
+#' \item EW. Equal Weight
+#' \item h2. Broad Sense Heritability
+#' }
+#' @docType data
+#' @name weight
+#' @usage data(weight)
+#' @format A data frame with 7 rows and 3 columns
+NULL

R/seldata.R

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+# data-raw/maize.R
+# Generate synthetic maize data for the selection.index package
+
+set.seed(2026)
+
+# 1. Phenotypic Data (maize_pheno)
+# Simulating 100 genotypes across 2 environments and 3 blocks for 3 traits
+n_genotypes <- 100
+n_env <- 2
+n_blocks <- 3
+
+maize_pheno <- expand.grid(
+  Genotype = paste0("G", sprintf("%03d", 1:n_genotypes)),
+  Environment = paste0("Env", 1:n_env),
+  Block = paste0("B", 1:n_blocks)
+)
+
+# Simulate traits with some correlation
+base_yield <- rnorm(nrow(maize_pheno), mean = 8000, sd = 500)
+base_height <- base_yield * 0.02 + rnorm(nrow(maize_pheno), mean = 50, sd = 10)
+base_maturity <- 120 - (base_yield * 0.001) + rnorm(nrow(maize_pheno), mean = 5, sd = 2)
+
+maize_pheno$Yield <- round(base_yield, 2)
+maize_pheno$PlantHeight <- round(base_height, 2)
+maize_pheno$DaysToMaturity <- round(base_maturity, 0)
+
+# 2. Genomic Data (maize_geno)
+# Simulating a matrix of 100 genotypes x 500 SNP markers (coded 0, 1, 2)
+n_markers <- 500
+maize_geno <- matrix(
+  sample(0:2, n_genotypes * n_markers, replace = TRUE, prob = c(0.25, 0.5, 0.25)),
+  nrow = n_genotypes,
+  ncol = n_markers
+)
+rownames(maize_geno) <- paste0("G", sprintf("%03d", 1:n_genotypes))
+colnames(maize_geno) <- paste0("SNP_", sprintf("%03d", 1:n_markers))
+
+# Save the generated data to the data/ directory
+usethis::use_data(maize_pheno, maize_geno, overwrite = TRUE)