spelling correction

NEWS.md

@@ -6,5 +6,5 @@
 * Better input checking for mvmreg and gmm
 * Better input checking for greed_cond
 * Correction of compilation problems on solaris
-* Correction of pointer problem comming from shed_row/shed_col
+* Correction of pointer problem coming from shed_row/shed_col
 * Added a `NEWS.md` file to track changes to the package.

R/diaggmm.R

@@ -57,7 +57,7 @@ setClass("diaggmm_fit",slots = list(model="diaggmm"),contains="icl_fit")
 #' @slot name generative model name
 #' @slot icl icl value of the fitted model
 #' @slot K number of extracted clusters over row and columns
-#' @slot cl a numeric vector with row and clolumns cluster indexes
+#' @slot cl a numeric vector with row and columns cluster indexes
 #' @slot obs_stats a list with the following elements:
 #' \itemize{
 #' \item counts: numeric vector of size K with number of elements in each clusters

R/misssbm.R

@@ -158,8 +158,8 @@ setMethod(f = "plot",
 #' @return a list with the model parameters estimates (MAP), the fields are:
 #' \itemize{
 #' \item \code{'pi'}: cluster proportions 
-#' \item \code{'thetakl'}: between clusters connection probabilites (array of size K x K),
-#' \item \code{'epsilonkl'}: between clusters dyad observation probabilites (array of size K x K) for block-dyad sampling and double for dyad sampling,
+#' \item \code{'thetakl'}: between clusters connection probabilities (array of size K x K),
+#' \item \code{'epsilonkl'}: between clusters dyad observation probabilities (array of size K x K) for block-dyad sampling and double for dyad sampling,
 #' }
 #' @export 
 setMethod(f = "coef", 

R/sbm.R

@@ -16,7 +16,7 @@ NULL
 #' @slot alpha Dirichlet over cluster proportions prior parameter (default to 1)
 #' @slot a0 Beta prior parameter over links (default to 1)
 #' @slot b0 Beta prior parameter over no-links (default to 1)
-#' @slot type define the type of networks (either "directed" or "undirected", default to "directed"), for undirected graphs the adjacency matrix is supposed to be symetric.
+#' @slot type define the type of networks (either "directed" or "undirected", default to "directed"), for undirected graphs the adjacency matrix is supposed to be symmetric.
 #' @seealso \code{\link{sbm_fit-class}},\code{\link{sbm_path-class}}
 #' @seealso \code{\link{greed}}
 #' @examples 

README.Rmd

@@ -41,7 +41,7 @@ Eventually, the whole path of solutions from $K^*$ to 1 cluster is extracted. Th
 
 ## Installation
 
-You can install the developpement version of greed from [GitHub](https://github.com/) with:
+You can install the development version of greed from [GitHub](https://github.com/) with:
 
 ``` r
 #GitHub

cran-comments.md

@@ -27,7 +27,7 @@ Duration: 6m 58.3s
 ### macOS-release
 
 ── R CMD check results ──────────────────────────────────────── greed 0.5.1 ────
-Duration: 7m 9.9s
+Duration: 8m 5.1s
 
 0 errors ✔ | 0 warnings ✔ | 0 notes ✔
 
@@ -44,26 +44,26 @@ Duration: 13m 24.6s
 
 ### Ubuntu 20.04-release
 
+
 ── R CMD check results ──────────────────────────────────────── greed 0.5.1 ────
-Duration: 6m 23.5s
+Duration: 9m 33.5s
 
 ❯ checking installed package size ... NOTE
-    installed size is 43.5Mb
+    installed size is 48.1Mb
     sub-directories of 1Mb or more:
-      libs  42.5Mb
+      libs  45.8Mb
 
 0 errors ✔ | 0 warnings ✔ | 1 note ✖
 
 ### Ubuntu 20.04-devel
 
-
-── R CMD check results ──────────────────────────────────────── greed 0.5.1 ────
-Duration: 6m 58.3s
+ ── R CMD check results ──────────────────────────────────────── greed 0.5.1 ────
+Duration: 6m 59.9s
 
 ❯ checking installed package size ... NOTE
-    installed size is 43.5Mb
+    installed size is 48.1Mb
     sub-directories of 1Mb or more:
-      libs  42.5Mb
+      libs  45.8Mb
 
 0 errors ✔ | 0 warnings ✔ | 1 note ✖
 

vignettes/GMM.Rmd

@@ -104,7 +104,7 @@ params$Sigmak[[2]]
 ```
 
 
-Such simpler diagonal model may be of interest in particular for high dimensional settings for two reasons. First, the number of parameters (even if they are integrated out in the clustering phase) is reduced and this can be interesting when $d$ is important, but also because the prior maybe defined such that it will be less informative. You may try with a subset of the fashion mnist data provided with the package which contains 784 dimensionals vectors (28x28 flattened images). In such settings, you may also want to switch the optimization algorithm to `` ?`seed-class` ``, this algorithm is less efficient than the hybrid algorithm used by default by greed. But, since it relied on a seeded initialization it is also a little bit less costly. In this case, you may increase the initial value for $K$ since, this algorithm is not able to find an clustering with a number of cluster bigger than the value of $K$ provided by the user. Still, it may simplify the clustering and return an optimal clustering with less clusters.    
+Such simpler diagonal model may be of interest in particular for high dimensional settings for two reasons. First, the number of parameters (even if they are integrated out in the clustering phase) is reduced and this can be interesting when $d$ is important, but also because the prior maybe defined such that it will be less informative. You may try with a subset of the fashion mnist data provided with the package which contains 784 dimensional vectors (28x28 flattened images). In such settings, you may also want to switch the optimization algorithm to `` ?`seed-class` ``, this algorithm is less efficient than the hybrid algorithm used by default by greed. But, since it relied on a seeded initialization it is also a little bit less costly. In this case, you may increase the initial value for $K$ since, this algorithm is not able to find an clustering with a number of cluster bigger than the value of $K$ provided by the user. Still, it may simplify the clustering and return an optimal clustering with less clusters.    
 
 ```{r fashion-diaggmm}
 data("fashion")