From 995484a9774331f23ddac53b62c70997768a0c28 Mon Sep 17 00:00:00 2001
From: fradenti <francescodenti.personal@gmail.com>
Date: Fri, 6 Oct 2023 13:50:05 +0200
Subject: [PATCH] added donttest and @return

---
 R/estimate_clusters.R         | 3 ++-
 R/mcmc_CAM.R                  | 3 ++-
 R/mcmc_fSAN.R                 | 3 ++-
 R/mcmc_fSAN_sparsemix.R       | 2 +-
 R/mcmc_fiSAN.R                | 2 ++
 R/mcmc_fiSAN_sparsemix.R      | 2 +-
 R/plot.R                      | 7 ++++++-
 R/print.R                     | 2 ++
 R/print_clusters.R            | 2 ++
 R/traceplot.R                 | 4 ++++
 man/estimate_clusters.Rd      | 3 ++-
 man/plot.SANmcmc.Rd           | 5 +++++
 man/print.SANclusters.Rd      | 3 +++
 man/print.SANmcmc.Rd          | 3 +++
 man/sample_CAM.Rd             | 3 ++-
 man/sample_fSAN.Rd            | 3 ++-
 man/sample_fSAN_sparsemix.Rd  | 2 +-
 man/sample_fiSAN.Rd           | 2 ++
 man/sample_fiSAN_sparsemix.Rd | 2 +-
 man/traceplot.Rd              | 5 +++++
 20 files changed, 50 insertions(+), 11 deletions(-)

diff --git a/R/estimate_clusters.R b/R/estimate_clusters.R
index 5c24ac0..fee6b27 100644
--- a/R/estimate_clusters.R
+++ b/R/estimate_clusters.R
@@ -16,13 +16,14 @@
 #' @seealso \code{\link[salso:salso]{salso::salso()}}, \code{\link{print.SANmcmc}}, \code{\link{plot.SANmcmc}}, \code{\link{print.SANclusters}}
 #'
 #' @examples 
+#' \donttest{
 #' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
 #' out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 #' estcl <- estimate_clusters(out)
 #' estcl
-#'
+#' }
 #'
 #' @export
 #' @importFrom salso salso
diff --git a/R/mcmc_CAM.R b/R/mcmc_CAM.R
index 7354391..b5c6495 100644
--- a/R/mcmc_CAM.R
+++ b/R/mcmc_CAM.R
@@ -122,6 +122,7 @@
 #' 
 #' 
 #' @examples 
+#' \donttest{
 #' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
@@ -130,7 +131,7 @@
 #' out <- sample_CAM(nrep = 3000, y = y, group = g,
 #'                   maxL = 50, maxK = 50)
 #' out 
-#'
+#' }
 #' 
 #' @references Denti, F., Camerlenghi, F., Guindani, M., and Mira, A. (2023). A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data. 
 #' \emph{Journal of the American Statistical Association}, 118(541), 405-416. DOI: 10.1080/01621459.2021.1933499
diff --git a/R/mcmc_fSAN.R b/R/mcmc_fSAN.R
index 706ef81..e4ec9ec 100644
--- a/R/mcmc_fSAN.R
+++ b/R/mcmc_fSAN.R
@@ -125,6 +125,7 @@
 #'
 #'
 #' @examples 
+#' \donttest{
 #' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
@@ -133,7 +134,7 @@
 #' out <- sample_fSAN(nrep = 3000, y = y, group = g, 
 #'                    alpha = 1, beta = 1)
 #' out 
-#'
+#' }
 #' 
 #' @references D’Angelo, L., Canale, A., Yu, Z., and Guindani, M. (2023). 
 #' Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data. \emph{Biometrics}, 79(2), 1370–1382. DOI: 10.1111/biom.13626
diff --git a/R/mcmc_fSAN_sparsemix.R b/R/mcmc_fSAN_sparsemix.R
index 8b84af1..f2ed59c 100644
--- a/R/mcmc_fSAN_sparsemix.R
+++ b/R/mcmc_fSAN_sparsemix.R
@@ -120,7 +120,7 @@
 #' }
 #'
 #' @examples 
-#' \dontrun{
+#' \donttest{
 #' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
diff --git a/R/mcmc_fiSAN.R b/R/mcmc_fiSAN.R
index e8c6a2a..2fbf8be 100644
--- a/R/mcmc_fiSAN.R
+++ b/R/mcmc_fiSAN.R
@@ -125,6 +125,7 @@
 #' }
 #'
 #' @examples 
+#' \donttest{
 #' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
@@ -132,6 +133,7 @@
 #' lines(density(y[g==2]), col = 2)
 #' out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 #' out 
+#' }
 #' 
 #' @references 
 #' Frühwirth-Schnatter, S., Malsiner-Walli, G. and Grün, B. (2021).
diff --git a/R/mcmc_fiSAN_sparsemix.R b/R/mcmc_fiSAN_sparsemix.R
index d0c7902..37abbb5 100644
--- a/R/mcmc_fiSAN_sparsemix.R
+++ b/R/mcmc_fiSAN_sparsemix.R
@@ -124,7 +124,7 @@
 #' }
 #'
 #' @examples 
-#' \dontrun{
+#' \donttest{
 #' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
diff --git a/R/plot.R b/R/plot.R
index 82cd71e..c19db53 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -13,13 +13,18 @@
 #' @param ncores if the partition is computed, the number of CPU cores to use to estimate the clusters, i.e., the number of simultaneous runs at any given time. A value of zero indicates to use all cores on the system.
 #' @param ... additional graphical parameters to be passed when \code{type = "scatter"} is used.
 #'
+#' @return The function plots a summary of the fitted model.
+#'
 #' @seealso \code{\link{print.SANmcmc}}, \code{\link{estimate_clusters}}
 #'
-#' @examples set.seed(123)
+#' @examples 
+#' \donttest{
+#' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
 #' out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 #' plot(out, type = "ecdf", palette_brewed = TRUE)
+#' }
 #'
 #' @importFrom graphics abline lines points boxplot par
 #' @importFrom grDevices colorRampPalette
diff --git a/R/print.R b/R/print.R
index 33295b4..2166ea3 100644
--- a/R/print.R
+++ b/R/print.R
@@ -6,6 +6,8 @@
 #' \code{\link{sample_fSAN}}, \code{\link{sample_fSAN_sparsemix}}, or \code{\link{sample_CAM}}).
 #' @param ... ignored.
 #'
+#' @return The function prints a summary of the fitted model.
+#' 
 #' @seealso \code{\link{estimate_clusters}}, \code{\link{plot.SANmcmc}}
 #' 
 #' @export
diff --git a/R/print_clusters.R b/R/print_clusters.R
index b41afea..57be7e7 100644
--- a/R/print_clusters.R
+++ b/R/print_clusters.R
@@ -4,6 +4,8 @@
 #' @param x object of class \code{SANclusters} (the result of a call to \code{\link{estimate_clusters}})
 #' @param ... ignored.
 #' 
+#' @return The function prints a summary of the estimated clusters.
+#' 
 #' @export
 print.SANclusters <- function(x, ...)
 {
diff --git a/R/traceplot.R b/R/traceplot.R
index 1eb615a..b50fc3e 100644
--- a/R/traceplot.R
+++ b/R/traceplot.R
@@ -21,12 +21,16 @@
 #' 
 #' @note The function is not available for the observational weights \eqn{\omega}.
 #' 
+#' @return The function displays the traceplots of the MCMC algorithm.
+#' 
 #' @examples 
+#' \donttest{
 #' set.seed(123)
 #' y <- c(rnorm(170),rnorm(70,5))
 #' g <- c(rep(1,100), rep(2, 140))
 #' out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 #' traceplot(out, params = c("mu", "sigma2"), trunc_plot = 2)
+#' }
 #' 
 #' @importFrom graphics par
 #' @importFrom grDevices devAskNewPage
diff --git a/man/estimate_clusters.Rd b/man/estimate_clusters.Rd
index 6efc6f3..55c7f60 100644
--- a/man/estimate_clusters.Rd
+++ b/man/estimate_clusters.Rd
@@ -29,13 +29,14 @@ Object of class \code{SANclusters}. The object contains:
 Given the MCMC output, estimate the observational and distributional partitions using \code{\link[salso:salso]{salso::salso()}}.
 }
 \examples{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
 out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 estcl <- estimate_clusters(out)
 estcl
-
+}
 
 }
 \seealso{
diff --git a/man/plot.SANmcmc.Rd b/man/plot.SANmcmc.Rd
index 40ddcf0..44f29b3 100644
--- a/man/plot.SANmcmc.Rd
+++ b/man/plot.SANmcmc.Rd
@@ -31,16 +31,21 @@ If \code{estimated_clusters = NULL}, the displayed partition is computed using \
 
 \item{...}{additional graphical parameters to be passed when \code{type = "scatter"} is used.}
 }
+\value{
+The function plots a summary of the fitted model.
+}
 \description{
 Plot method for objects of class \code{SANmcmc}. 
 The function displays two graphs, meant to analyze the estimated distributional and observational clusters.
 }
 \examples{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
 out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 plot(out, type = "ecdf", palette_brewed = TRUE)
+}
 
 }
 \seealso{
diff --git a/man/print.SANclusters.Rd b/man/print.SANclusters.Rd
index c2f0925..27a6204 100644
--- a/man/print.SANclusters.Rd
+++ b/man/print.SANclusters.Rd
@@ -11,6 +11,9 @@
 
 \item{...}{ignored.}
 }
+\value{
+The function prints a summary of the estimated clusters.
+}
 \description{
 Print the cluster-specific sample means and variances of the estimated observational and distributional partition.
 }
diff --git a/man/print.SANmcmc.Rd b/man/print.SANmcmc.Rd
index 5e29fa1..58b85c0 100644
--- a/man/print.SANmcmc.Rd
+++ b/man/print.SANmcmc.Rd
@@ -12,6 +12,9 @@
 
 \item{...}{ignored.}
 }
+\value{
+The function prints a summary of the fitted model.
+}
 \description{
 Print method for objects of class \code{SANmcmc}.
 }
diff --git a/man/sample_CAM.Rd b/man/sample_CAM.Rd
index 0918daa..e5ff133 100644
--- a/man/sample_CAM.Rd
+++ b/man/sample_CAM.Rd
@@ -138,6 +138,7 @@ The clustering of observations (observational clustering) is provided by the all
 The distribution of the probabilities is \eqn{ \{\omega_{l,k}\}_{l=1}^{\infty} \sim GEM(\beta)} for all \eqn{k = 1,2,\dots}
 }
 \examples{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
@@ -146,7 +147,7 @@ lines(density(y[g==2]), col = 2)
 out <- sample_CAM(nrep = 3000, y = y, group = g,
                   maxL = 50, maxK = 50)
 out 
-
+}
 
 }
 \references{
diff --git a/man/sample_fSAN.Rd b/man/sample_fSAN.Rd
index f1bb39f..6149fa7 100644
--- a/man/sample_fSAN.Rd
+++ b/man/sample_fSAN.Rd
@@ -139,6 +139,7 @@ The distribution of the probabilities is \eqn{(\omega_{1,k},\dots,\omega_{L,k})\
 Moreover, the dimension \eqn{L} is random (see Frühwirth-Schnatter et al., 2021).
 }
 \examples{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
@@ -147,7 +148,7 @@ lines(density(y[g==2]), col = 2)
 out <- sample_fSAN(nrep = 3000, y = y, group = g, 
                    alpha = 1, beta = 1)
 out 
-
+}
 
 }
 \references{
diff --git a/man/sample_fSAN_sparsemix.Rd b/man/sample_fSAN_sparsemix.Rd
index 26b46f3..456d112 100644
--- a/man/sample_fSAN_sparsemix.Rd
+++ b/man/sample_fSAN_sparsemix.Rd
@@ -134,7 +134,7 @@ The clustering of observations (observational clustering) is provided by the all
 The distribution of the probabilities is \eqn{(\omega_{1,k},\dots,\omega_{L,k})\sim Dirichlet_L(\beta,\dots,\beta)} for all \eqn{k = 1,\dots,K}.
 }
 \examples{
-\dontrun{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
diff --git a/man/sample_fiSAN.Rd b/man/sample_fiSAN.Rd
index af4aaeb..e3aa185 100644
--- a/man/sample_fiSAN.Rd
+++ b/man/sample_fiSAN.Rd
@@ -141,6 +141,7 @@ The distribution of the probabilities is \eqn{(\omega_{1,k},\dots,\omega_{L,k})\
 Moreover, the dimension \eqn{L} is random (see Frühwirth-Schnatter et al., 2021).
 }
 \examples{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
@@ -148,6 +149,7 @@ plot(density(y[g==1]), xlim = c(-5,10))
 lines(density(y[g==2]), col = 2)
 out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 out 
+}
 
 }
 \references{
diff --git a/man/sample_fiSAN_sparsemix.Rd b/man/sample_fiSAN_sparsemix.Rd
index 57c00b6..3616535 100644
--- a/man/sample_fiSAN_sparsemix.Rd
+++ b/man/sample_fiSAN_sparsemix.Rd
@@ -139,7 +139,7 @@ The clustering of observations (observational clustering) is provided by the all
 The distribution of the probabilities is \eqn{(\omega_{1,k},\dots,\omega_{L,k})\sim Dirichlet_L(\beta,\dots,\beta)} for all \eqn{k = 1,2,\dots}.
 }
 \examples{
-\dontrun{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
diff --git a/man/traceplot.Rd b/man/traceplot.Rd
index 911b353..f2989c0 100644
--- a/man/traceplot.Rd
+++ b/man/traceplot.Rd
@@ -27,6 +27,9 @@ traceplot(object, params,
 
 \item{trunc_plot}{integer (default = 10). For multidimensional parameters, the maximum number of components to be plotted.}
 }
+\value{
+The function displays the traceplots of the MCMC algorithm.
+}
 \description{
 Check the convergence of the MCMC through visual inspection of the chains.
 }
@@ -34,10 +37,12 @@ Check the convergence of the MCMC through visual inspection of the chains.
 The function is not available for the observational weights \eqn{\omega}.
 }
 \examples{
+\donttest{
 set.seed(123)
 y <- c(rnorm(170),rnorm(70,5))
 g <- c(rep(1,100), rep(2, 140))
 out <- sample_fiSAN(nrep = 3000, y = y, group = g, beta = 1)
 traceplot(out, params = c("mu", "sigma2"), trunc_plot = 2)
+}
 
 }