updates

DESCRIPTION

@@ -3,7 +3,8 @@ Package: BioGA
 Title: Bioinformatics Genetic Algorithm (BioGA)
 Version: 0.99.0
 Authors@R: 
-    person("Dany", "Mukesha", , "[email protected]", role = c("aut", "cre"),
+    person("Dany", "Mukesha", , "[email protected]", 
+            role = c("aut", "cre"),
            comment = c(ORCID = "0009-0001-9514-751X"))
 Description: Genetic algorithm are a class of optimization algorithms
     inspired by the process of natural selection and genetics. This
@@ -17,7 +18,8 @@ BugReports: https://github.com/danymukesha/BioGA/issues
 Imports:
     ggplot2,
     graphics,
-    Rcpp
+    Rcpp,
+    sessioninfo
 Suggests: 
     knitr,
     rmarkdown

NAMESPACE

@@ -3,5 +3,6 @@ importFrom(Rcpp,
            evalCpp,
            sourceCpp)
 import(ggplot2,
-       graphics)
+       graphics,
+       sessioninfo)
 useDynLib(BioGA, .registration = TRUE)

NEWS.md

@@ -0,0 +1,3 @@
+# BioGA 0.99.0
+
+* Added a `NEWS.md` file to track changes to the package.

R/BioGA-package.R

@@ -4,5 +4,6 @@
 ## usethis namespace: start
 #' @import biocViews
 #' @importFrom ggplot2 ggplot aes geom_line coord_flip
+#' @import sessioninfo
 ## usethis namespace: end
 NULL

R/RcppExports.R

@@ -3,15 +3,18 @@
 
 #' Function to perform crossover between selected individuals
 #'
-#' @param selected_parents Numeric matrix representing the selected individuals.
+#' @param selected_parents Numeric matrix representing the selected
+#' individuals.
 #' @param offspring_size Number of offspring to generate.
 #' @return Numeric matrix representing the offspring.
 #' @examples
 #' # example of usage
 #' genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10)
-#' population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5)
+#' population <- BioGA::initialize_population_cpp(genomic_data,
+#'                 population_size = 5)
 #' fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population)
-#' selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2)
+#' selected_parents <- BioGA::selection_cpp(population, fitness,
+#'                 num_parents = 2)
 #' BioGA::crossover_cpp(selected_parents, offspring_size = 2)
 #' @export
 crossover_cpp <- function(selected_parents, offspring_size) {
@@ -20,13 +23,16 @@ crossover_cpp <- function(selected_parents, offspring_size) {
 
 #' Function to evaluate fitness using genomic data
 #'
-#' @param genomic_data Numeric matrix of genomic data where rows represent genes/features and columns represent samples.
-#' @param population Numeric matrix representing the population of individuals.
+#' @param genomic_data Numeric matrix of genomic data where rows represent
+#' genes/features and columns represent samples.
+#' @param population Numeric matrix representing the population of
+#' individuals.
 #' @return Numeric vector of fitness scores for each individual.
 #' @examples
 #' # example of usage
 #' genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10)
-#' population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5)
+#' population <- BioGA::initialize_population_cpp(genomic_data,
+#'                 population_size = 5)
 #' BioGA::evaluate_fitness_cpp(genomic_data, population)
 #' @export
 evaluate_fitness_cpp <- function(genomic_data, population) {
@@ -35,7 +41,8 @@ evaluate_fitness_cpp <- function(genomic_data, population) {
 
 #' Function to initialize the population from genomic data
 #'
-#' @param genomic_data Numeric matrix of genomic data where rows represent genes/features and columns represent samples.
+#' @param genomic_data Numeric matrix of genomic data where rows represent
+#' genes/features and columns represent samples.
 #' @param population_size Number of individuals in the population.
 #' @return Numeric matrix representing the initialized population.
 #' @examples
@@ -55,9 +62,11 @@ initialize_population_cpp <- function(genomic_data, population_size) {
 #' @examples
 #' # example of usage
 #' genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10)
-#' population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5)
+#' population <- BioGA::initialize_population_cpp(genomic_data,
+#'                 population_size = 5)
 #' fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population)
-#' selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2)
+#' selected_parents <- BioGA::selection_cpp(population,
+#'                 fitness, num_parents = 2)
 #' offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2)
 #' BioGA::mutation_cpp(offspring, mutation_rate = 0)
 #' @export
@@ -68,16 +77,19 @@ mutation_cpp <- function(offspring, mutation_rate) {
 #' Function to replace non-selected individuals in the population
 #'
 #' Replace non-selected individuals in the population
-#' @param population Numeric matrix representing the population of individuals.
+#' @param population Numeric matrix representing the population of
+#' individuals.
 #' @param offspring Numeric matrix representing the offspring.
 #' @param num_to_replace Number of individuals to replace.
 #' @return Numeric matrix representing the updated population.
 #' @examples
 #' # example of usage
 #' genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10)
-#' population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5)
+#' population <- BioGA::initialize_population_cpp(genomic_data,
+#'                 population_size = 5)
 #' fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population)
-#' selected_parents <- BioGA::selection_cpp(population, fitness, num_parents = 2)
+#' selected_parents <- BioGA::selection_cpp(population, fitness,
+#'                       num_parents = 2)
 #' offspring <- BioGA::crossover_cpp(selected_parents, offspring_size = 2)
 #' mutated_offspring <- BioGA::mutation_cpp(offspring, mutation_rate = 0)
 #' BioGA::replacement_cpp(population, mutated_offspring, num_to_replace = 1)
@@ -88,14 +100,16 @@ replacement_cpp <- function(population, offspring, num_to_replace) {
 
 #' Function to select individuals based on fitness scores
 #'
-#' @param population Numeric matrix representing the population of individuals.
+#' @param population Numeric matrix representing the population of
+#' individuals.
 #' @param fitness Numeric vector of fitness scores for each individual.
 #' @param num_parents Number of individuals to select.
 #' @return Numeric matrix representing the selected individuals.
 #' @examples
 #' # example of usage
 #' genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10)
-#' population <- BioGA::initialize_population_cpp(genomic_data, population_size = 5)
+#' population <- BioGA::initialize_population_cpp(genomic_data,
+#'                 population_size = 5)
 #' fitness <- BioGA::evaluate_fitness_cpp(genomic_data, population)
 #' BioGA::selection_cpp(population, fitness, num_parents = 2)
 #' @export

R/plot_fitness_history.R

@@ -17,7 +17,9 @@ plot_fitness_history <- function(fitness_history) {
   fitness_values <- unlist(fitness_history)
 
   # Create generation index
-  generations <- rep(seq_along(fitness_history), vapply(fitness_history, length, integer(1)))
+  generations <-
+    rep(seq_along(fitness_history),
+        vapply(fitness_history, length, integer(1)))
 
   # Create data frame
   df <- data.frame(Generation = generations, Fitness = fitness_values)

README.Rmd

@@ -13,9 +13,14 @@ knitr::opts_chunk$set(
 )
 ```
 
-# BioGA <a href="https://danymukesha.github.io/BioGA/"><img src="man/figures/logo.png" align="right" height="139" alt="BioGA website" /></a>
-
-The `BioGA` package provides a set of functions for genetic algorithm optimization tailored for analyzing high throughput genomic data. These functions are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R.
+# BioGA 
+<a href="https://danymukesha.github.io/BioGA/"><img src="man/figures/logo.png"
+align="right" height="139" alt="BioGA website" /></a>
+
+The `BioGA` package provides a set of functions for genetic algorithm 
+optimization tailored for analyzing high throughput genomic data. 
+These functions are implemented in C++ for improved speed and efficiency, 
+with an easy-to-use interface for use within R.
 
 ## Installation