No big changes

DESCRIPTION

@@ -9,7 +9,7 @@ Authors@R: c(
   )
 Description: An implementation of Karaboga (2005) Artificial Bee Colony
     Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>.
-    This was developed upon the basic version programmed in C and distributed
+    This was developed upon the basic version programmed in C and available
     at the algorithm's official website.
 Classification/ACM: G.1.6
 Classification/JEL: C61
@@ -26,3 +26,4 @@ Imports:
 RoxygenNote: 6.0.1.9000
 Suggests: testthat,
     covr
+Roxygen: list(markdown = TRUE)

R/ABCoptim-package.R

@@ -3,33 +3,29 @@
 #' An implementation of the Artificial Bee Colony (ABC) Algorithm
 #' 
 #' This is an implementation of Karaboga (2005) ABC optimization algorithm. It
-#' was developed upon the basic version programmed in \code{C} and distributed
+#' was developed upon the basic version programmed in `C` and distributed
 #' at the algorithm's official website (see the references).
 #' 
-#' Please consider that this version is in alpha state of development, thus any
-#' evident (precision) error should be blaimed to the package author (not to
-#' the algorithm itself)
+#' Any evident (precision) error should be blaimed to the package author (not to
+#' the algorithm itself).
 #' 
 #' Please visit the project home for more information:
-#' \url{https://github.com/gvegayon/ABCoptim}.
+#' https://github.com/gvegayon/ABCoptim.
 #' 
 #' @name ABCoptim-package
 #' @aliases ABCoptim-package ABCoptim abc
 #' @docType package
-#' @author George Vega Yon \email{g.vegayon@@gmail.com} [aut],
-#' 
-#' Enyelbert Muñoz \email{enyeldoc2011@@gmail.com} [cnt)
-#' @references D. Karaboga, \emph{An Idea based on Honey Bee Swarm for
-#' Numerical Optimization}, tech. report TR06,Erciyes University, Engineering
+#' @references D. Karaboga, *An Idea based on Honey Bee Swarm for
+#' Numerical Optimization*, tech. report TR06,Erciyes University, Engineering
 #' Faculty, Computer Engineering Department, 2005
-#' \url{http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf}
+#' http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf
 #' 
 #' 
 #' Artificial Bee Colony (ABC) Algorithm (website)
-#' \url{http://mf.erciyes.edu.tr/abc/index.htm}
+#' http://mf.erciyes.edu.tr/abc/index.htm
 #' 
-#' Basic version of the algorithm implemented in \code{C} (ABC's official
-#' website) \url{http://mf.erciyes.edu.tr/abc/form.aspx}
+#' Basic version of the algorithm implemented in `C` (ABC's official
+#' website) http://mf.erciyes.edu.tr/abc/form.aspx
 #' @keywords package
 #' @examples
 #' 

R/RcppExports.R

@@ -1,7 +1,7 @@
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
-abc_cpp_ <- function(par, fn, lb, ub, FoodNumber = 20L, limit = 100L, maxCycle = 1000L, criter = 50L) {
-    .Call(`_ABCoptim_abc_cpp_`, par, fn, lb, ub, FoodNumber, limit, maxCycle, criter)
+.abc_cpp <- function(par, fn, lb, ub, FoodNumber = 20L, limit = 100L, maxCycle = 1000L, criter = 50L) {
+    .Call(`_ABCoptim_abc_cpp`, par, fn, lb, ub, FoodNumber, limit, maxCycle, criter)
 }
 

R/abc_optim.R

@@ -6,62 +6,62 @@
 #' @param fn A function to be minimized, with first argument of the vector of
 #' parameters over which minimization is to take place. It should return a
 #' scalar result.
-#' @param ... In the case of \code{abc_*}, further arguments to be passed to 'fn',
+#' @param ... In the case of `abc_*`, further arguments to be passed to 'fn',
 #' otherwise, further arguments passed to the method.
 #' @param FoodNumber Number of food sources to exploit. Notice that the param
-#' \code{NP} has been deprecated.
+#' `NP` has been deprecated.
 #' @param lb,ub Numeric vectors or scalars. Upper and lower bounds of the
 #' parameters to be optimized.
 #' @param limit Integer scalar. Limit of a food source.
 #' @param maxCycle Integer scalar. Maximum number of iterations.
 #' @param optiinteger Logical scalar. Whether to optimize binary parameters or not.
 #' @param criter Integer scalar. Stop criteria (numer of unchanged results) until stopping
-#' @param parscale Numeric vector of length \code{length(par)}. Scale applied
-#' to the parameters (see \code{\link[stats:optim]{optim}}).
-#' @param fnscale Numeric scalar. Scale applied function. If \code{fnscale < 0},
-#' then the problem becomes a maximization problem (see \code{\link[stats:optim]{optim}}).
+#' @param parscale Numeric vector of length `length(par)`. Scale applied
+#' to the parameters (see [stats:optim::optim()]).
+#' @param fnscale Numeric scalar. Scale applied function. If `fnscale < 0`,
+#' then the problem becomes a maximization problem (see [stats:optim::optim()]).
 #'
 #' @details 
 #' 
 #' This implementation of the ABC algorithm was developed based on the basic
-#' version written in \code{C} and published at the algorithm's official
+#' version written in `C` and published at the algorithm's official
 #' website (see references).
 #' 
-#' \code{abc_optim} and \code{abc_cpp} are two different implementations of the
-#' algorithm, the former using pure \code{R} code, and the later using \code{C++},
+#' `abc_optim` and `abc_cpp` are two different implementations of the
+#' algorithm, the former using pure `R` code, and the later using `C++`,
 #' via the \pkg{Rcpp} package. Besides of the output, another important
-#' difference between the two implementations is speed, with \code{abc_cpp}
+#' difference between the two implementations is speed, with `abc_cpp`
 #' showing between 50\% and 100\% faster performance.
 #' 
-#' Upper and Lower bounds (\code{ub}, \code{lb}) equal to infinite will be replaced
-#' by either \code{.Machine$double.xmax} or \code{-.Machine$double.xmax}.
+#' Upper and Lower bounds (`ub`, `lb`) equal to infinite will be replaced
+#' by either `.Machine$double.xmax` or `-.Machine$double.xmax`.
 #' 
-#' If \code{D} (the number of parameters to be optimzed) is greater than one,
-#' then \code{lb} and \code{ub} can be either scalars (assuming that all the
+#' If `D` (the number of parameters to be optimzed) is greater than one,
+#' then `lb` and `ub` can be either scalars (assuming that all the
 #' parameters share the same boundaries) or vectors (the parameters have
 #' different boundaries each other).
 #' 
-#' @return An list of class \code{abc_answer}, holding the following elements:
+#' @return An list of class `abc_answer`, holding the following elements:
 #' \item{Foods}{Numeric matrix. Last position of the bees.}
-#' \item{f}{Numeric vector. Value of the function evaluated at each set of \code{Foods}.}
-#' \item{fitness}{Numeric vector. Fitness of each \code{Foods}.}
-#' \item{trial}{Integer vector. Number of trials at each \code{Foods}.}
+#' \item{f}{Numeric vector. Value of the function evaluated at each set of `Foods`.}
+#' \item{fitness}{Numeric vector. Fitness of each `Foods`.}
+#' \item{trial}{Integer vector. Number of trials at each `Foods`.}
 #' \item{value}{Numeric scalar. Value of the function evaluated at the optimum.}
 #' \item{par}{Numeric vector. Optimum found.}
 #' \item{counts}{Integer scalar. Number of cycles.}
 #' \item{hist}{Numeric matrix. Trace of the global optimums.}
 #' 
 #' @author George Vega Yon \email{g.vegayon@@gmail.com}
-#' @references D. Karaboga, \emph{An Idea based on Honey Bee Swarm for
-#' Numerical Optimization}, tech. report TR06,Erciyes University, Engineering
+#' @references D. Karaboga, *An Idea based on Honey Bee Swarm for
+#' Numerical Optimization*, tech. report TR06,Erciyes University, Engineering
 #' Faculty, Computer Engineering Department, 2005
-#' \url{http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf}
+#' http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf
 #' 
 #' Artificial Bee Colony (ABC) Algorithm (website)
-#' \url{http://mf.erciyes.edu.tr/abc/index.htm}
+#' http://mf.erciyes.edu.tr/abc/index.htm
 #' 
-#' Basic version of the algorithm implemented in \code{C} (ABC's official
-#' website) \url{http://mf.erciyes.edu.tr/abc/form.aspx}
+#' Basic version of the algorithm implemented in `C` (ABC's official
+#' website) http://mf.erciyes.edu.tr/abc/form.aspx
 #' @keywords optimization
 #' @examples
 #' 
@@ -428,7 +428,7 @@ abc_optim <- function(
 }
 
 #' @export
-#' @param x An object of class \code{abc_answer}.
+#' @param x An object of class `abc_answer`.
 #' @rdname abc_optim
 print.abc_answer <- function(x, ...) {
   cat("\n")
@@ -545,7 +545,7 @@ abc_cpp <- function(
 
   fun <- function(par) fn(par/parscale, ...)/fnscale
 
-  ans <- abc_cpp_(par, fun, lb, ub, FoodNumber, limit, maxCycle, criter)
+  ans <- .abc_cpp(par, fun, lb, ub, FoodNumber, limit, maxCycle, criter)
   ans[["fn"]] <- fn
 
   structure(
@@ -557,18 +557,18 @@ abc_cpp <- function(
 }
 
 #' @export
-#' @details The \code{plot} method shows the trace of the objective function
+#' @details The `plot` method shows the trace of the objective function
 #' as the algorithm unfolds. The line is merely the result of the objective
-#' function evaluated at each point (row) of the \code{hist} matrix return by
-#' \code{abc_optim}/\code{abc_cpp}.
+#' function evaluated at each point (row) of the `hist` matrix return by
+#' `abc_optim`/`abc_cpp`.
 #' 
-#' For now, the function will return with error if \code{...} was passed to
-#' \code{abc_optim}/\code{abc_cpp}, since those argumens are not stored with the
+#' For now, the function will return with error if `...` was passed to
+#' `abc_optim`/`abc_cpp`, since those argumens are not stored with the
 #' result.
 #' 
 #' @rdname abc_optim
 #' @param y Ignored
-#' @param main,xlab,ylab,type Passed to \code{\link[graphics:plot.default]{plot}}.
+#' @param main,xlab,ylab,type Passed to [graphics:plot.default::plot()].
 plot.abc_answer <- function(
   x,
   y = NULL,

src/RcppExports.cpp

@@ -5,9 +5,9 @@
 
 using namespace Rcpp;
 
-// abc_cpp_
-List abc_cpp_(NumericVector& par, Function& fn, const NumericVector& lb, const NumericVector& ub, int FoodNumber, int limit, int maxCycle, int criter);
-RcppExport SEXP _ABCoptim_abc_cpp_(SEXP parSEXP, SEXP fnSEXP, SEXP lbSEXP, SEXP ubSEXP, SEXP FoodNumberSEXP, SEXP limitSEXP, SEXP maxCycleSEXP, SEXP criterSEXP) {
+// abc_cpp
+List abc_cpp(NumericVector& par, Function& fn, const NumericVector& lb, const NumericVector& ub, int FoodNumber, int limit, int maxCycle, int criter);
+RcppExport SEXP _ABCoptim_abc_cpp(SEXP parSEXP, SEXP fnSEXP, SEXP lbSEXP, SEXP ubSEXP, SEXP FoodNumberSEXP, SEXP limitSEXP, SEXP maxCycleSEXP, SEXP criterSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
@@ -19,13 +19,13 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< int >::type limit(limitSEXP);
     Rcpp::traits::input_parameter< int >::type maxCycle(maxCycleSEXP);
     Rcpp::traits::input_parameter< int >::type criter(criterSEXP);
-    rcpp_result_gen = Rcpp::wrap(abc_cpp_(par, fn, lb, ub, FoodNumber, limit, maxCycle, criter));
+    rcpp_result_gen = Rcpp::wrap(abc_cpp(par, fn, lb, ub, FoodNumber, limit, maxCycle, criter));
     return rcpp_result_gen;
 END_RCPP
 }
 
 static const R_CallMethodDef CallEntries[] = {
-    {"_ABCoptim_abc_cpp_", (DL_FUNC) &_ABCoptim_abc_cpp_, 8},
+    {"_ABCoptim_abc_cpp", (DL_FUNC) &_ABCoptim_abc_cpp, 8},
     {NULL, NULL, 0}
 };
 

src/abc.cpp

@@ -207,8 +207,8 @@ void SendScoutBees(
   return;
 }
 
-// [[Rcpp::export]]
-List abc_cpp_(
+// [[Rcpp::export(name=".abc_cpp")]]
+List abc_cpp(
     NumericVector & par, 
     Function & fn,
     const NumericVector & lb,