Use rlang::is_installed() instead of requireNamespace for conditional…

… examples and vignette code chunks
poissonconsulting · Sep 17, 2024 · 227c7a6 · 227c7a6
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diff --git a/R/dev.R b/R/dev.R
@@ -288,7 +288,7 @@ dev_pois_zi <- function(x, lambda, prob = 0, res = FALSE) {
 #' @family dev_dist
 #' @export
 #'
-#' @examplesIf requireNamespace("sn")
+#' @examplesIf rlang::is_installed("sn")
 #' dev_skewnorm(c(-2:2))
 #' dev_skewnorm(-2:2, 0, 1, 5)
 #' dev_skewnorm(-2:2, 0, 1, 5, res = TRUE)

diff --git a/R/log-lik.R b/R/log-lik.R
@@ -214,7 +214,7 @@ log_lik_pois_zi <- function(x, lambda = 1, prob = 0) {
 #' @family log_lik_dist
 #' @export
 #'
-#' @examplesIf requireNamespace("sn")
+#' @examplesIf rlang::is_installed("sn")
 #' log_lik_skewnorm(c(-2:2))
 #' log_lik_skewnorm(c(-2:2), shape = -2)
 #' log_lik_skewnorm(c(-2:2), shape = 2)

diff --git a/R/numericise.R b/R/numericise.R
@@ -77,7 +77,7 @@ numericise.POSIXct <- function(x, ...) {
 #' @examples
 #'
 #' # hms
-#' @examplesIf requireNamespace("hms")
+#' @examplesIf rlang::is_installed("hms")
 #' numericise(hms::as_hms("00:01:03"))
 numericise.hms <- function(x, ...) {
   rlang::check_installed("hms")

diff --git a/R/ran.R b/R/ran.R
@@ -180,7 +180,7 @@ ran_pois_zi <- function(n = 1, lambda = 1, prob = 0) {
 #' @family ran_dist
 #' @export
 #'
-#' @examplesIf requireNamespace("sn")
+#' @examplesIf rlang::is_installed("sn")
 #' ran_skewnorm(10, shape = -1)
 #' ran_skewnorm(10, shape = 0)
 #' ran_skewnorm(10, shape = 1)

diff --git a/R/res.R b/R/res.R
@@ -335,7 +335,7 @@ res_student_standardized <- function(x, mean, sd, theta) {
 #' @family res_dist
 #' @export
 #'
-#' @examplesIf requireNamespace("sn")
+#' @examplesIf rlang::is_installed("sn")
 #' res_skewnorm(c(-2:2))
 res_skewnorm <- function(x, mean = 0, sd = 1, shape = 0, type = "dev", simulate = FALSE) {
   rlang::check_installed("sn")

diff --git a/R/sens.R b/R/sens.R
@@ -62,7 +62,7 @@ sens_student <- function(mean, sd, theta, sd_mult = 2) {
 #' @family sens_dist
 #' @export
 #'
-#' @examplesIf requireNamespace("sn")
+#' @examplesIf rlang::is_installed("sn")
 #' sens_skewnorm(10, 3, -1, 2)
 #' sens_skewnorm(10, 3, 3, 0.8)
 sens_skewnorm <- function(mean, sd, shape, sd_mult = 2) {

diff --git a/R/skewnorm.R b/R/skewnorm.R
@@ -10,7 +10,7 @@
 #' @rdname skewnorm
 #' @export
 #'
-#' @examplesIf requireNamespace("sn")
+#' @examplesIf rlang::is_installed("sn")
 #' dskewnorm(x = -2:2, mean = 0, sd = 1, shape = 0.1)
 #' dskewnorm(x = -2:2, mean = 0, sd = 1, shape = -1)
 #' qskewnorm(p = c(0.1, 0.4), mean = 0, sd = 1, shape = 0.1)

diff --git a/man/dev_skewnorm.Rd b/man/dev_skewnorm.Rd
diff --git a/man/log_lik_skewnorm.Rd b/man/log_lik_skewnorm.Rd
diff --git a/man/numericise.Rd b/man/numericise.Rd
diff --git a/man/ran_skewnorm.Rd b/man/ran_skewnorm.Rd
diff --git a/man/res_skewnorm.Rd b/man/res_skewnorm.Rd
diff --git a/man/sens_skewnorm.Rd b/man/sens_skewnorm.Rd
diff --git a/man/skewnorm.Rd b/man/skewnorm.Rd
diff --git a/vignettes/beta-binomial-deviance-residuals.Rmd b/vignettes/beta-binomial-deviance-residuals.Rmd
@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
 library(extras)
 ```
 
-```{r eval = requireNamespace("tidyr") & requireNamespace("ggplot2") & requireNamespace("viridis") & requireNamespace("scales"), include = FALSE}
+```{r eval = rlang::is_installed("tidyr") & rlang::is_installed("ggplot2") & rlang::is_installed("viridis") & rlang::is_installed("scales"), include = FALSE}
 library(tidyr)
 library(ggplot2)
 library(viridis)
@@ -77,7 +77,7 @@ The problem with the first approach is that likelihood profile of the beta-binom
 Consequently, increasing $\theta$ increases the deviance even if the expected value of the fitted model is the same as the saturated binomial model. 
 
 
-```{r eval = requireNamespace("tidyr") & requireNamespace("ggplot2") & requireNamespace("viridis") & requireNamespace("scales"), echo = FALSE, fig.width = 7, fig.height = 4, fig.cap = "Fig. 1: Beta-binomial likelihood profile for $x = 1$ and $n = 5$, for different values of $\\theta$. $\\theta = 0$ corresponds to the binomial case."}
+```{r eval = rlang::is_installed("tidyr") & rlang::is_installed("ggplot2") & rlang::is_installed("viridis") & rlang::is_installed("scales"), echo = FALSE, fig.width = 7, fig.height = 4, fig.cap = "Fig. 1: Beta-binomial likelihood profile for $x = 1$ and $n = 5$, for different values of $\\theta$. $\\theta = 0$ corresponds to the binomial case."}
 n_samp <- 1000
 x <- 1
 size <- 5
@@ -144,7 +144,7 @@ We will refer to the optimized $p$ value for the $i^{th}$ data point as $p_i^*$,
 We use $p^*$ to calculate the saturated log-likelihood that produces deviances that are (a) strictly positive, and (b) relative to the value of $\theta$.
 The value of $p_i^*$ for the example case with $x = 1$, $n = 5$, and $\theta = 0.5$ is shown in Figure 2.
 
-```{r eval = requireNamespace("tidyr") & requireNamespace("ggplot2") & requireNamespace("viridis") & requireNamespace("scales"), echo = FALSE, fig.width = 7, fig.height = 4, fig.cap = "Fig. 2: Beta-binomial likelihood profile for $x = 1$, $n = 5$, and $\\theta = 0.5$. The dashed vertical line shows the likelihood at the expected value of the beta-binomial distribution ($n \\cdot p$) where $p = \\frac{x}{n} = \\frac{1}{5} = 0.2$. As you can see, this is not the $p$ for which the likelihood is maximized. The solid vertical line shows the likelihood at its maximum point. In this case, $p^* = 0.26$."}
+```{r eval = rlang::is_installed("tidyr") & rlang::is_installed("ggplot2") & rlang::is_installed("viridis") & rlang::is_installed("scales"), echo = FALSE, fig.width = 7, fig.height = 4, fig.cap = "Fig. 2: Beta-binomial likelihood profile for $x = 1$, $n = 5$, and $\\theta = 0.5$. The dashed vertical line shows the likelihood at the expected value of the beta-binomial distribution ($n \\cdot p$) where $p = \\frac{x}{n} = \\frac{1}{5} = 0.2$. As you can see, this is not the $p$ for which the likelihood is maximized. The solid vertical line shows the likelihood at its maximum point. In this case, $p^* = 0.26$."}
 n_samp <- 1000
 x <- 1
 size <- 5
@@ -187,7 +187,7 @@ $$r_i = \text{sign}(x_i - (n p_i^*)) \sqrt{d_i}.$$
 The following plot shows histograms of deviance residuals for 10,000 randomly generated data points with $n = 50$ and various values of $p$ and $\theta$.
 
 
-```{r eval = requireNamespace("tidyr") & requireNamespace("ggplot2") & requireNamespace("viridis") & requireNamespace("scales"), echo = FALSE, fig.height = 8, fig.width = 7, fig.cap = "Fig. 4: Histograms of deviance residuals for 10,000 beta-binomial data points simulated with $n = 50$, $p = \\{0.3, 0.5, 0.9 \\}$, and $\\theta = \\{0.0, 0.1, 0.5, 1.0 \\}$. The percentages within each panel (i.e., each combination of $p$ and $\\theta$) sum to 100%."}
+```{r eval = rlang::is_installed("tidyr") & rlang::is_installed("ggplot2") & rlang::is_installed("viridis") & rlang::is_installed("scales"), echo = FALSE, fig.height = 8, fig.width = 7, fig.cap = "Fig. 4: Histograms of deviance residuals for 10,000 beta-binomial data points simulated with $n = 50$, $p = \\{0.3, 0.5, 0.9 \\}$, and $\\theta = \\{0.0, 0.1, 0.5, 1.0 \\}$. The percentages within each panel (i.e., each combination of $p$ and $\\theta$) sum to 100%."}
 n_samp <- 10000
 size <- 50
 prob <- c(0.3, 0.5, 0.9)