Version 0.1.2

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 0.1.1
-Date: 2024-04-24 16:27:00 UTC
-SHA: 64032aec8d4d6aef2e7e10002c67c883b4dd6086
+Version: 0.1.2
+Date: 2024-05-06 19:46:15 UTC
+SHA: 6f70ae08bd81bb87481903682b06ebcd8b8141b7

NEWS.md

@@ -36,4 +36,10 @@
 
 * The [DESCRIPTION](https://raw.githubusercontent.com/prdm0/AcceptReject/main/DESCRIPTION) file was edited;
 
-* Another bibliographic reference was added to the `accept_reject()` function.
+* 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.

README.Rmd

@@ -68,6 +68,7 @@ As an example, let $X \sim Poisson(\lambda = 0.7)$. We will generate $n = 1000$
 ```{r}
 library(AcceptReject)
 library(cowplot) # install.packages("cowplot")
+
 # Ensuring Reproducibility
 set.seed(0) 
 
@@ -162,6 +163,42 @@ For the discrete case, if the user mistakenly specifies any of these arguments,
 
 If you choose to specify a base density, it's convenient to inspect it by comparing the base density function with the theoretical probability density function. The [`inspect()`](https://prdm0.github.io/AcceptReject/reference/accept_reject.html) function facilitates this task. The [`inspect()`](https://prdm0.github.io/AcceptReject/reference/accept_reject.html) function will plot the base probability density function and the theoretical probability density function, find the intersection between the densities, and display the value of the intersection area on the plot. These are important pieces of information to decide if the base probability density function specified in the `args_f_base` argument and the value of `c` (default is 1) are appropriate.
 
+Another example, considering $X \sim Beta(\alpha = 2, \beta = 2)$:
+
+```{r}
+library(AcceptReject)
+library(cowplot)
+
+# Ensuring reproducibility
+set.seed(0)
+
+x <- accept_reject(
+  n = 100000L,
+  f = dbeta,
+  continuous = TRUE,
+  args_f = list(shape1 = 2, shape2 = 2),
+  xlim = c(0, 1)
+)
+print(x)
+a <- plot(x)
+b <- qqplot(x)
+plot_grid(a, b, nrow = 2L, labels = c("a", "b"))
+
+# Inspecting the estimated value of c by accept_reject()
+# Note that c = 1.5 indeed causes the base density,
+# which in this case was the default (uniform), to overlap the # density of the beta:
+inspect(
+  f = dbeta,
+  args_f = list(shape1 = 2, shape2 = 2),
+  f_base = dunif,
+  args_f_base = c(min = 0, max = 1),
+  xlim = c(0, 1),
+  c = attr(x, "c")
+)
+```
+
+Note that `c` is an attribute of `x`. To access all attributes, you can use `attributes(x)`. Most likely, you won't need to access these attributes. They are useful for plotting methods.
+
 ## 🕵️‍♀️ Example of inspection
 
 ```{r}
@@ -207,8 +244,7 @@ library(tictoc) # install.packages("tictoc")
 # Ensuring reproducibility
 set.seed(0)
 
-# Não especificando a função densidade de probabilidade base
-
+# Not specifying the base probability density function
 tic()
 case_1 <- accept_reject(
   n = 2000,

README.md

@@ -157,6 +157,7 @@ library(AcceptReject)
 #> 
 #>     qqplot
 library(cowplot) # install.packages("cowplot")
+
 # Ensuring Reproducibility
 set.seed(0) 
 
@@ -293,6 +294,62 @@ plot. These are important pieces of information to decide if the base
 probability density function specified in the `args_f_base` argument and
 the value of `c` (default is 1) are appropriate.
 
+Another example, considering $X \sim Beta(\alpha = 2, \beta = 2)$:
+
+``` r
+library(AcceptReject)
+library(cowplot)
+
+# Ensuring reproducibility
+set.seed(0)
+
+x <- accept_reject(
+  n = 100000L,
+  f = dbeta,
+  continuous = TRUE,
+  args_f = list(shape1 = 2, shape2 = 2),
+  xlim = c(0, 1)
+)
+print(x)
+#> 
+#> ── Accept-Reject Samples ───────────────────────────────────────────────────────
+#> ℹ It's not necessary, but if you want to extract the observations, use as.vector().
+#> ✔ Case: continuous
+#> ✔ Number of observations: 100000
+#> ✔ c: 1.5
+#> ✔ Probability of acceptance (1/c): 0.6667
+#> ✔ Observations: 0.8967 0.2655 0.3721 0.5729 0.6608 0.6291 0.206 0.1766 0.687 0.3841...
+#> ✔ xlim = 0 1
+#> 
+#> ────────────────────────────────────────────────────────────────────────────────
+a <- plot(x)
+b <- qqplot(x)
+plot_grid(a, b, nrow = 2L, labels = c("a", "b"))
+```
+
+<img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
+
+``` r
+
+# Inspecting the estimated value of c by accept_reject()
+# Note that c = 1.5 indeed causes the base density,
+# which in this case was the default (uniform), to overlap the # density of the beta:
+inspect(
+  f = dbeta,
+  args_f = list(shape1 = 2, shape2 = 2),
+  f_base = dunif,
+  args_f_base = c(min = 0, max = 1),
+  xlim = c(0, 1),
+  c = attr(x, "c")
+)
+```
+
+<img src="man/figures/README-unnamed-chunk-5-2.png" width="100%" />
+
+Note that `c` is an attribute of `x`. To access all attributes, you can
+use `attributes(x)`. Most likely, you won’t need to access these
+attributes. They are useful for plotting methods.
+
 ## 🕵️‍♀️ Example of inspection
 
 ``` r
@@ -327,7 +384,7 @@ b <- inspect(
 plot_grid(a, b, nrow = 2L, labels = c("a", "b"))
 ```
 
-<img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-6-1.png" width="100%" />
 
 Notice that considering the distribution in scenario “a” in the code
 above is more convenient. Note that the area is approximately 1, the
@@ -350,8 +407,7 @@ library(tictoc) # install.packages("tictoc")
 # Ensuring reproducibility
 set.seed(0)
 
-# Não especificando a função densidade de probabilidade base
-
+# Not specifying the base probability density function
 tic()
 case_1 <- accept_reject(
   n = 2000,
@@ -361,7 +417,7 @@ case_1 <- accept_reject(
   xlim = c(0, 6)
 )
 toc()
-#> 0.005 sec elapsed
+#> 0.003 sec elapsed
 
 # Specifying the base probability density function
 tic()
@@ -386,7 +442,7 @@ p2 <- plot(case_2)
 plot_grid(p1, p2, nrow = 2L)
 ```
 
-<img src="man/figures/README-unnamed-chunk-6-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-7-1.png" width="100%" />
 
 ``` r
 
@@ -396,7 +452,7 @@ q2 <- qqplot(case_2)
 plot_grid(q1, q2, nrow = 1L)
 ```
 
-<img src="man/figures/README-unnamed-chunk-6-2.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-7-2.png" width="100%" />
 
 Notice that the results were very close in a graphical analysis.
 However, the execution time specifying a convenient base density was