spell checks + fixes for cran

DESCRIPTION

@@ -9,7 +9,7 @@ Authors@R: c(
     person("Vahan", "Nanumyan", email = "[email protected]",
             role = c('aut'), comment = c(ORCID = "0000-0001-9054-3217")),
     person("Laurence", "Brandenberger", email = "[email protected]",
-            role = c('aut')),
+            role = c('ctb')),
     person("Giacomo", "Vaccario", email = "[email protected]",
             role = c('ctb'))
             )
@@ -21,7 +21,7 @@ Description: Provides functions for model fitting and selection of generalised h
     The package is based on the research developed at the Chair of Systems Design, ETH Zurich.
     Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <arXiv:1607.02441>.
     Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>.
-    Casiraghi, G., (2017) <arxiv:1702.02048>
+    Casiraghi, G., (2017) <arXiv:1702.02048>
     Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>
     Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>.
     Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>.

R/data.R

@@ -103,7 +103,7 @@
 #' Highschool contact network multiplex representation
 #'
 #' **highschool.multiplex**: list containing the adjacency matrix of 327 x 327
-#' highschool students, and the adjacency matrices correspodning to the 5
+#' highschool students, and the adjacency matrices corresponding to the 5
 #' predictors used in Casiraghi2017.
 #'
 #' @docType data
@@ -168,6 +168,6 @@
 #' Mastrandrea, R., Fournet, J. & Barrat, A. Contact patterns in a high school:
 #' A comparison between data collected using wearable sensors, contact diaries
 #' and friendship surveys. PLoS One 10, 1–26 (2015).
-#' @source \href{http://www.sociopatterns.org}{http://www.sociopatterns.orgg}
+#' @source \href{http://www.sociopatterns.org}{http://www.sociopatterns.org}
 #'   
 "contacts.adj"

R/endogenousstats.R

@@ -47,7 +47,7 @@ checkGraphtype <- function(graph) {
 #'
 #' @param graph A graph adjacency matrix or an edgelist.  The edgelist needs to
 #'   have 3 columns: a sender vector, a target vector and an edgecount vector.
-#' @param nodes optional character/factor vector. If an edgelist is provied,
+#' @param nodes optional character/factor vector. If an edgelist is provided,
 #'   you have to provide a list of unique identifiers of your nodes in the graph.
 #'   This is because in the edgelist, isolates are usually not recorded.
 #'   If you do not specify isolates in your nodes object, they are excluded
@@ -109,7 +109,7 @@ reciprocity_stat <- function(graph, nodes = NULL, zero_values = NULL){
 #' dyad in the graph. Shared partner statistics count for each dyad involving
 #' nodes i and j in the graph, how many nodes k these two nodes have in common
 #' (or share). The shared partner $k$ counts are weighted by their
-#' interactions with the focal nodes $i$ and $j$. This is neccessary in
+#' interactions with the focal nodes $i$ and $j$. This is necessary in
 #' dense multi-edge graphs to ensure that meaningful triadic closure is
 #' detected. The statistic can be calculated in 3 different forms: undirected,
 #' incoming shared partners (where shared partner k: k->i and k->j) and outgoing
@@ -119,9 +119,9 @@ reciprocity_stat <- function(graph, nodes = NULL, zero_values = NULL){
 #'   have 3 columns: a sender vector, a target vector and an edgecount vector.
 #' @param weighted set to TRUE.
 #' @param triad.type set to \code{undirected}. Can be set to \code{incoming}
-#'   or \code{outgoing} instead. This then corresponds to directd triadic closure
+#'   or \code{outgoing} instead. This then corresponds to directed triadic closure
 #'   in the multi-edge graph.
-#' @param nodes optional character/factor vector. If an edgelist is provied,
+#' @param nodes optional character/factor vector. If an edgelist is provided,
 #'   you have to provide a list of unique identifiers of your nodes in the graph.
 #'   This is because in the edgelist, isolates are usually not recorded.
 #'   If you do not specify isolates in your nodes object, they are excluded
@@ -263,7 +263,7 @@ findPartners_all <- function(node, el){
 #'   attribute match between two nodes (=10) and attribute mismatch (=1)). If
 #'   set to \code{absdiff} it calculates the difference in values from variable
 #'   for each dyad in the graph.
-#' @param nodes optional character/factor vector. If an edgelist is provied, you
+#' @param nodes optional character/factor vector. If an edgelist is provided, you
 #'   have to provide a list of unique identifiers of your nodes in the graph.
 #'   This is because in the edgelist, isolates are usually not recorded. If you
 #'   do not specify isolates in your nodes object, they are excluded from the

R/logl.R

@@ -29,7 +29,7 @@ logLik.ghype <- function(object, ...){
 #' @param selfloops boolean, has ghype model selfloops? considered only if
 #'   object is an adjacency matrix
 #' @param adj optional matrix, adjacency matrix of which to compute
-#'   log-likelihood, cconsidered only if object is ghype model If adj is not
+#'   log-likelihood, considered only if object is ghype model If adj is not
 #'   passed, and object is a ghype model, the log-likelihood is computed for the
 #'   original adjacency matrix stored in object.
 #' @param multinomial  optional boolean. Force multinomial approximation? If not

R/residuals.nrm.R

@@ -1,7 +1,7 @@
 #' Method to compute residuals of nrm models
 #'
 #' @param object nrm object
-#' @param adj odjacency against which to compute residuals
+#' @param adj adjacency against which to compute residuals
 #' @param RMSLE logical, return log residuals? default FALSE
 #' @param null logical. use null model?
 #' @param ... additional parameters to be passed to and from internal functions.

R/stats.R

@@ -54,7 +54,7 @@ Jn <- function(beta=NULL, w=NULL, xi=NULL, adj=NULL,
 #' 
 #' Pass either the models or the model parameters as arguments
 #' 
-#' @param adj optinal adjacency matrix
+#' @param adj optimal adjacency matrix
 #' @param xi  optional xi matrix
 #' @param omega0  optional propensity matrix of null model
 #' @param omega1  optional propensity matrix of alternative model

README.md

@@ -28,7 +28,7 @@ devtools::install_github("gi0na/r-ghypernet")
 
 # Dependencies
 The package uses the library `BiasedUrn` to work with Wallenius' non-central hypergeometric distribution.
-Although this is not required, it is recommended to install the `BiasedUrn` R package, increasing the number of 'colors', i.e., the number of dimensions of the distribution.
+Although this is not required, it is recommended to install the `BiasedUrn` R package, increasing the number of 'colours', i.e., the number of dimensions of the distribution.
 It can be easily done modifying the _makevar_ file.
 In case the `BiasedUrn` library cannot be found, all computations will be performed using the multinomial approximation.
 

cran-comments.md

@@ -1,7 +1,9 @@
 ## Test environments
 * local OS X install, R 4.0.2
 * win-builder (devel, release and oldrelease)
-* rhub
+* rhub (Fedora Linux, R-devel, clang, gfortran,
+        Windows Server 2008 R2 SP1, R-devel, 32/64 bit,
+        Ubuntu Linux 20.04.1 LTS, R-release, GCC)
 
 ## R CMD check results
-There were no ERRORs, WARNINGs, nor NOTEs.
+0 errors ✓ | 0 warnings ✓ | 0 notes ✓