Checking the build in Linux

.Rbuildignore

@@ -6,5 +6,4 @@
 ^\.github$
 ^README\.Rmd$
 ^LICENSE\.md$
-^obsolete$
 ^data-raw$

.github/workflows/pkgdown.yaml

@@ -4,12 +4,11 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
   release:
     types: [published]
   workflow_dispatch:
 
-name: pkgdown
+name: pkgdown.yaml
 
 permissions: read-all
 

.gitignore

@@ -3,3 +3,4 @@
 .RData
 .Ruserdata
 docs
+inst/doc

DESCRIPTION

@@ -39,5 +39,6 @@ Imports:
 Depends: 
     R (>= 2.10)
 Suggests: 
-    knitr
+    knitr,
+    rmarkdown
 BugReports: https://github.com/f8l5h9/spqdep/issues

R/m.surround.R

@@ -125,19 +125,7 @@
 #' plot(msurr_points, type = 1)
 #' plot(msurr_points, type = 2)
 #'
-#' # Example 4: Examples with multipolygons
-#' fname <- system.file("shape/nc.shp", package="sf")
-#' nc <- sf::st_read(fname)
-#' plot(sf::st_geometry(nc))
-#' m <- 3
-#' r <- 1
-#' msurr_polygonsf <- m.surround(x = nc, m = m, r = r,
-#'                    distance = "Great Circle",
-#'                    control=list(dtmaxpc = 0.20))
-#' plot(msurr_polygonsf, type = 1)
-#' plot(msurr_polygonsf, type = 2)
-#'
-#' # Example 5: With regular lattice
+#' # Example 4: With regular lattice
 #' sfc = sf::st_sfc(sf::st_polygon(list(rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0)))))
 #' hexs <- sf::st_make_grid(sfc, cellsize = 0.1, square = FALSE)
 #' hexs.sf <- sf::st_sf(hexs)

spqdep.Rproj

@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: 56260ffc-b211-4479-a592-08103c3b5e3d
 
 RestoreWorkspace: Default
 SaveWorkspace: Default

vignettes/.gitignore

@@ -0,0 +1,2 @@
+*.html
+*.R

vignettes/bibliospq.bib

@@ -119,3 +119,37 @@ @book{Kanaroglou2016
 }
 
 @Comment{jabref-meta: databaseType:bibtex;}
+
+
+
+@article{farber_testing_2015,
+	title = {Testing for {Spatial} {Independence} {Using} {Similarity} {Relations}},
+	volume = {47},
+	issn = {0016-7363},
+	doi = {10.1111/gean.12044},
+	abstract = {In this article, we construct new, simple, and nonparametric tests for spatial independence using symbolic analysis. An important aspect is that the tests are free of a priori assumptions about the functional form of dependence, making them especially suitable in situations where the dependence is nonlinear. We define the concept of a similarity relation, which is used to keep track of similarity between neighboring observations. This similarity count is used to construct new statistical tests based on both random permutation simulations and derived asymptotic distributions. We include a Monte Carlo study to better illustrate the properties and the behavior of the new tests under several synthetically generated processes. Apart from being competitive compared with other nonparametric and parametric tests, results underline the outstanding power of the new tests for nonlinear-dependent spatial processes.},
+	language = {English},
+	number = {2},
+	journal = {Geographical Analysis},
+	author = {Farber, S. and Marin, M. R. and Paez, A.},
+	month = apr,
+	year = {2015},
+	keywords = {Geography, dependency tests, estimation bias, network autoregressive models, topology},
+	pages = {97--120},
+	file = {PDF:/home/antonio-paez/antonio-rogue/Zotero/storage/SRDR385M/Farber et al. - 2015 - Testing for Spatial Independence Using Similarity Relations.pdf:application/pdf},
+}
+
+
+@article{paezSpatioTemporalAnalysisEnvironmental2021,
+	title = {A {Spatio}-{Temporal} {Analysis} of the {Environmental} {Correlates} of {COVID}-19 {Incidence} in {Spain}},
+	volume = {53},
+	issn = {0016-7363},
+	doi = {10.1111/gean.12241},
+	abstract = {The novel SARS-CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS-CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio-temporal analysis in Spain of the incidence of COVID-19, the disease caused by the virus. Use of spatial Seemingly Unrelated Regressions (SUR) allows us to model the incidence of reported cases of the disease per 100,000 population as an interregional contagion process, in addition to a function of temperature, humidity, and sunshine. In the analysis we also control for GDP per capita, percentage of older adults in the population, population density, and presence of mass transit systems. The results support the hypothesis that incidence of the disease is lower at higher temperatures and higher levels of humidity. Sunshine, in contrast, displays a positive association with incidence of the disease. Our control variables also yield interesting insights. Higher incidence is associated with higher GDP per capita and presence of mass transit systems in the province; in contrast, population density and percentage of older adults display negative associations with incidence of COVID-19.},
+	number = {3},
+	journal = {Geographical Analysis},
+	author = {Paez, Antonio and Lopez, Fernando A. and Menezes, Tatiane and Cavalcanti, Renata and Pitta, Maira Galdino da Rocha},
+	year = {2021},
+	pages = {397--421},
+	file = {gean.12241:/home/antonio-paez/antonio-rogue/Zotero/storage/827756QB/gean.12241.pdf:application/pdf},
+}

vignettes/spq_userguide.Rmd → vignettes/user-guide.Rmd

@@ -1,37 +1,26 @@
 ---
-title: "spqdep user guide"
-subtitle: "The user guide <br>  <br>  <br>"
+title: "User Guide"
 author:
 - Fernando A. López, Technical University of Cartagena (Spain)
 - Román Mínguez, University of Castilla-La Mancha (Spain)
 - Antonio Páez, McMaster University (Canada)
 - Manuel Ruiz, Technical University of Cartagena (Spain) <br> <br> <br>
 date: "`r Sys.Date()` <br>"
-output:
-  bookdown::html_document2:
-    number_sections: yes
-    toc: yes
-    toc_depth: 2
-    toc_float:
-      collapsed: no
-      smooth_scroll: no
-    toc_title: Article Outline
-    self_contained: TRUE
-    linkcolor: red
 link-citations: yes
 bibliography: ["bibliospq.bib"]
-vignette: |
-  %\VignetteIndexEntry{spq_userguide}
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{user-guide}
+  %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
-  %\VignetteEngine{knitr::rmarkdown} 
-editor_options: 
-  chunk_output_type: inline
 ---
 
-<style>
-body {
-text-align: justify}
-</style>
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
@@ -54,7 +43,7 @@ Global and local statistics give information about the spatial structure of the
 
 Three data sets will be used as examples in this guide:
 
-- **provinces_spain**: The division of Spain into provinces. It is a multypolygon geometry with isolated provinces (Canary and Balearic islands without neighbouring provinces). See by example Paez et al. 2021.
+- **provinces_spain**: The division of Spain into provinces. It is a multypolygon geometry with isolated provinces (Canary and Balearic islands without neighbouring provinces). See for example @paezSpatioTemporalAnalysisEnvironmental2021.
 
 - **FastFood.sf**: A simple feature (sf) dataframe containing the locations of a selection of fast food restaurants in the city of Toronto, Canada (data are from 2008). The data set used as example in @ruiz2010. It is a geometry of points.
 
@@ -503,7 +492,7 @@ data(FastFood.sf)
 
 # Similarity test
 
-The Farber et al. (2014) paper develop the similarity test
+@farber_testing_2015 develop the similarity test.
 
 The \code{similarity.test()} function calculates the similarity test for both asymptotic distribution and permutational resampling.
 
@@ -537,7 +526,3 @@ summary(jc1)
 ```
 
 # References
-
-Farber, S., Marin, M. R., & Páez, A. (2015). Testing for spatial independence using similarity relations. Geographical Analysis, 47(2), 97-120.
-
-Paez, A., Lopez, F. A., Menezes, T., Cavalcanti, R., & Pitta, M. G. D. R. (2021). A spatio‐temporal analysis of the environmental correlates of COVID‐19 incidence in Spain. Geographical analysis, 53(3), 397-421.