The performance of the one_step() function, an internal function used in the implementation of C++ using Rcpp, has been improved;
The method qqplot.accept_reject() has been added, which constructs the QQ-Plot of an object of class accept_reject returned by the function accept_reject();
The qqplot.accept_reject() function utilizes the scattermore package if the point density is high, i.e., above 10 thousand observations;
The function accept_reject() now has the argument cores, which allows the user to control the number of cores that will be used if parallel = TRUE. The default, cores = NULL, means that all processor cores will be used. If parallel = FALSE, the cores argument is ignored;
The DESCRIPTION file was edited;
Another bibliographic reference was added to the accept_reject() function;
The dependency on the lbfgs package has been removed;
New unit tests have been introduced;
Bug fix.
Improved performance in serial and parallel processing with Rcpp and RcppArmadillo;
Now it is possible to specify a different base density/probability mass function than the uniform one. If none is specified, the uniform density (either discrete or continuous) is assumed for the case of discrete or continuous random variables, respectively;
Now the function inspect() is available, allowing you to compare the base probability density function with the theoretical density function. The inspect() function is useful for finding a reasonable base density function. It returns an object of the classes gg and ggplot with the density curves, the intersection area, and the value of the intersection. Users are not obligated to use the inspect() function since the accept_reject() function already takes care of a lot. However, for the continuous case, providing the f_base argument to the accept_reject() function with a good candidate base density function can be a good idea;
In generating observations of continuous random variables, using histogram with the same breaks as the R graphics hist() function, in the histogram created by ggplot2;
Providing alerts regarding the limits passed to the xlim argument of the accept_reject() function. If a significant density/probability mass is present, a warning will be issued. The alert can be omitted by setting warning = FALSE;
In the plot.accept_reject() function, there's an additional argument hist = TRUE (default). If hist = TRUE, a histogram is plotted along with the base density, in the case of generating pseudo-random observations of a continuous random variable. If hist = FALSE, the theoretical density is plotted alongside the observed density;
The print.accept_reject() function now informs whether the case is discrete or continuous and the xlim;
Putting the order of the specifications of the arguments of the exported functions in the order of the arguments of the functions;
The warning messages have been improved;
Bug fix.