Version 0.1.2

DESCRIPTION

@@ -20,7 +20,6 @@ Imports:
  cli,
  ggplot2,
  glue,
- lbfgs,
  numDeriv,
  purrr,
  Rcpp,

NAMESPACE

@@ -37,7 +37,6 @@ importFrom(ggplot2,scale_y_continuous)
 importFrom(ggplot2,theme)
 importFrom(glue,glue)
 importFrom(graphics,hist)
-importFrom(lbfgs,lbfgs)
 importFrom(numDeriv,grad)
 importFrom(parallel,detectCores)
 importFrom(parallel,mclapply)

R/plot.r

@@ -53,7 +53,7 @@
 #' n = 1000L,
 #' f = dbinom,
 #' continuous = FALSE,
-#' args_f = list(size = 5, prob = 0.5),
+#' args_f = list(size = 10, prob = 0.5),
 #' xlim = c(0, 10)
 #' )
 #' plot(x)

R/print.r

@@ -35,7 +35,7 @@
 #' n = 2000L,
 #' f = dbinom,
 #' continuous = FALSE,
-#' args_f = list(size = 5, prob = 0.5),
+#' args_f = list(size = 10, prob = 0.5),
 #' xlim = c(0, 10)
 #' )
 #' print(x)

R/qqplot.R

@@ -60,7 +60,7 @@ qqplot <- function(x, ...) {
 #' )
 #' qqplot(y)
 #'
-#' @seealso [accept_reject()], [plot.accept_reject()], [inspect()] and
+#' @seealso [qqplot.accept_reject()], [accept_reject()], [plot.accept_reject()], [inspect()] and
 #' [qqplot()].
 #'
 #' @importFrom Rcpp evalCpp
@@ -82,9 +82,7 @@ qqplot.accept_reject <-
 
  continuous <- attr(x, "continuous")
  sample_quantiles <- sort(x)
-
- n <- length(x)
- p <- seq(1L, n) / (n + 1L)
+ p <- seq(0, 1, length.out = length(x))
  theoretical_quantiles <- quantile(x, probs = p, ...)
 
  df <- data.frame(Theoretical = theoretical_quantiles, Sample = sample_quantiles)

README.Rmd

@@ -63,7 +63,7 @@ In the examples, we use well-known distributions, but it's important to note tha
 
 ### Generating discrete observations
 
-As an example, let $X \sim Poisson(\lambda = 0.7)$. We will generate $n = 1000$ observations of $X$ using the acceptance-rejection method, using the `AcceptReject::accept_reject()` function. Note that it is necessary to provide the `xlim` argument. Try to set an upper limit value for which the probability of $X$ assuming that value is zero or very close to zero. In this case, we choose `xlim = c(0, 6)`, where `dpois(x = 6, lambda = 0.7)` is very close to zero (`r dpois(x = 6, lambda = 0.7)`).
+As an example, let $X \sim Poisson(\lambda = 0.7)$. We will generate $n = 1000$ observations of $X$ using the acceptance-rejection method, using the `accept_reject()` function. Note that it is necessary to provide the `xlim` argument. Try to set an upper limit value for which the probability of $X$ assuming that value is zero or very close to zero. In this case, we choose `xlim = c(0, 6)`, where `dpois(x = 6, lambda = 0.7)` is very close to zero (`r dpois(x = 6, lambda = 0.7)`).
 
 ```{r}
 library(AcceptReject)

README.md

@@ -143,10 +143,10 @@ package might be what you need. 🎉
 
 As an example, let $X \sim Poisson(\lambda = 0.7)$. We will generate
 $n = 1000$ observations of $X$ using the acceptance-rejection method,
-using the `AcceptReject::accept_reject()` function. Note that it is
-necessary to provide the `xlim` argument. Try to set an upper limit
-value for which the probability of $X$ assuming that value is zero or
-very close to zero. In this case, we choose `xlim = c(0, 6)`, where
+using the `accept_reject()` function. Note that it is necessary to
+provide the `xlim` argument. Try to set an upper limit value for which
+the probability of $X$ assuming that value is zero or very close to
+zero. In this case, we choose `xlim = c(0, 6)`, where
 `dpois(x = 6, lambda = 0.7)` is very close to zero (8.1142728^{-5}).
 
 ``` r
@@ -361,7 +361,7 @@ case_1 <- accept_reject(
  xlim = c(0, 6)
 )
 toc()
-#> 0.008 sec elapsed
+#> 0.005 sec elapsed
 
 # Specifying the base probability density function
 tic()
@@ -377,7 +377,7 @@ case_2 <- accept_reject(
  c = 1.2
 )
 toc()
-#> 0.005 sec elapsed
+#> 0.004 sec elapsed
 
 # Visualizing the results
 p1 <- plot(case_1)

docs/pkgdown.yml

@@ -4,5 +4,5 @@ pkgdown_sha: ~
 articles:
  accept_reject: accept_reject.html
  inspect: inspect.html
-last_built: 2024-05-02T11:05Z
+last_built: 2024-05-06T18:42Z