Updated docs

.Rbuildignore

@@ -34,3 +34,7 @@ build-pkg*
 .valgrindrc
 ^doc$
 ^Meta$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
+^\.github$

.github/.gitignore

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

.github/workflows/pkgdown.yaml

@@ -0,0 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/[email protected]
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -73,3 +73,4 @@ src/*.dll
 inst/doc
 /doc/
 /Meta/
+docs

DESCRIPTION

@@ -3,9 +3,10 @@ Type: Package
 Title: Network Diffusion Algorithms
 Version: 0.2.1
 Date: 2018-04-20
-Authors@R: person("Simon", "Dirmeier",
+Authors@R: c(person("Simon", "Dirmeier",
                   email = "[email protected]",
-                  role = c("aut", "cre"))
+                  role = c("aut")),
+             person("Yinchun", "Su", email = "[email protected]", role = c("ctb", "cre")))
 Maintainer: Simon Dirmeier <[email protected]>
 Description: Implementation of network diffusion algorithms such as
   heat diffusion or Markov random walks. Network diffusion algorithms generally
@@ -20,6 +21,7 @@ License: GPL (>=3)
 Depends: R (>= 3.4)
 LazyData: TRUE
 LinkingTo: Rcpp, RcppEigen
+biocViews: sparseMatrixStats
 Imports:
     Rcpp,
     igraph,
@@ -34,7 +36,7 @@ Suggests:
     lintr,
     Matrix
 VignetteBuilder: knitr
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.0
 Encoding: UTF-8
 Config/testthat/edition: 3
 SystemRequirements: C++17

R/diffusr-package.R

@@ -25,9 +25,6 @@
 #' @author Simon Dirmeier \email{simon.dirmeier@@gmx.de}
 #' @name diffusr-package
 #'
-#' @docType package
-#' @keywords package
-#'
 #' @references
 #'
 #' Tong, H., Faloutsos, C., & Pan, J. Y. (2006),

R/heat_diffusion.R

@@ -21,15 +21,17 @@
 #' Graph diffusion using a heat diffusion process on a Laplacian matrix.
 #'
 #' @description An amount of starting heat gets distribution using the
-#' Laplacian matrix of a graph. Every iteration (or time interval) \code{t}
-#'heat streams from the starting nodes into surrounding nodes.
+#'   Laplacian matrix of a graph. Every iteration (or time interval) \eqn{t}
+#'   heat streams from the starting nodes into surrounding nodes.
 #'
 #' @export
 #'
-#' @param h0   an \code{n x p}-dimensional numeric non-negative vector/matrix
-#'  of starting temperatures
-#' @param graph  an (\code{n x n})-dimensional numeric non-negative adjacence
-#'  matrix representing the graph
+#' @param h0   an \eqn{n \times p}-dimensional numeric non-negative
+#'   \code{\link[base]{matrix}} (or
+#'   \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}},
+#'   \code{\link[base]{vector}}) of starting temperatures
+#' @param graph  an (\eqn{n \times n})-dimensional numeric non-negative
+#'   adjacence matrix representing the graph
 #' @param t  time point when heat is measured
 #' @param ...  additional parameters
 #' @return  returns the heat on every node as numeric vector
@@ -41,7 +43,9 @@
 #' @importFrom Rcpp sourceCpp
 #'
 #' @references
-#' \url{https://en.wikipedia.org/wiki/Laplacian_matrix} \cr
+#'
+#' \url{https://en.wikipedia.org/wiki/Laplacian_matrix}
+#'
 #' \url{https://en.wikipedia.org/wiki/Heat_equation}
 #'
 #' @examples

R/is.dgCMatrix.R

@@ -1,4 +1,4 @@
-#' Check if a matrix is `dgCMatrix` class
+#' Check if a matrix is \code{dgCMatrix} class
 #'
 #' @param mat The matrix to check
 #'

R/mat_util.R

@@ -23,11 +23,10 @@
 #' @export
 #'
 #' @param obj  \code{\link[base]{matrix}} (or
-#'   \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}}, \link[base]{vector}) that
-#'   is stochstically normalized
+#'   \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}},
+#'   \code{\link[base]{vector}}) that is stochstically normalized
 #' @param ...  additional params
-#' @return  returns the normalized \code{\link[base]{matrix}} (or
-#'   \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}}, \link[base]{vector})
+#' @return  returns the normalized matrix/vector)
 #'
 #' @importFrom checkmate assert check_matrix test_numeric test_atomic_vector
 #'

R/mrw.R

@@ -21,21 +21,21 @@
 #' Graph diffusion using a Markov random walk
 #'
 #' @description
-#' A Markov Random Walk takes an inital distribution \code{p0}
+#' A Markov Random Walk takes an inital distribution \eqn{p_0}
 #' and calculates the stationary distribution of that.
-#' The diffusion process is regulated by a restart probability \code{r} which
+#' The diffusion process is regulated by a restart probability \eqn{r} which
 #' controls how often the MRW jumps back to the initial values.
 #'
-#' @param p0  an \code{n x p}-dimensional numeric non-negative vector/matrix
+#' @param p0  an \eqn{n \times p}-dimensional numeric non-negative vector/matrix
 #'  representing the starting distribution of the Markov chain
 #'  (does not need to sum to one).
 #'
-#' @param graph  an (\code{n x n})-dimensional numeric non-negative adjacence
+#' @param graph  an \eqn{n \times p}-dimensional numeric non-negative adjacence
 #'   \code{\link[base]{matrix}} (or
 #'   \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}}) representing the graph
 #'
-#' @param r  a scalar between (0, 1). restart probability if a Markov random
-#' walk with restart is desired
+#' @param r  a scalar between \eqn{(0, 1)}. restart probability if a Markov
+#'   random walk with restart is desired
 #'
 #' @param thresh  threshold for breaking the iterative computation of the
 #'  stationary distribution. If the absolute difference of the distribution at
@@ -53,7 +53,8 @@
 #' @param correct.for.hubs if \code{TRUE} multiplies a correction factor to the
 #'  nodes, such that the random walk gets not biased to nodes with high
 #'  degree. In that case the original input matrix will be normalized as:
-#'  \deqn{ P(j | i) =  1 /degree(i) *  min(1, degree(j)/degree(j))}
+#'  \deqn{ P(j | i) = \dfrac{1}{\text{degree}(i)} \times
+#'    \min \left(1, \dfrac{\text{degree}(j)}{\text{degree}(i)}\right)}
 #'  \emph{Note that this will not consider edge weights.}
 #'
 #' @param allow.ergodic Allow multiple components in a graph.
@@ -62,9 +63,9 @@
 #'
 #' @return  returns a list with the following elements
 #'  \itemize{
-#'   \item p.inf  the stationary distribution as numeric vector
-#'   \item transition.matrix the column normalized transition matrix used for
-#'         the random walk
+#'   \item \code{p.inf}  the stationary distribution as numeric vector
+#'   \item \code{transition.matrix} the column normalized transition matrix used
+#'         for the random walk
 #'  }
 #'
 #' @references

R/neighbors.R

@@ -23,7 +23,7 @@
 #' @description
 #' For every node in a set of nodes the graph gets traversed along the node's
 #' shortest paths to its neighbors. Nearest neighbors are added until a maximum
-#' depth of \code{k} is reached. For settings where there are more than \code{k}
+#' depth of \eqn{k} is reached. For settings where there are more than \eqn{k}
 #' neighbors having the same distance, all neighbors are returned.
 #'
 #' @export
@@ -33,8 +33,8 @@
 #'
 #' @param graph an (\eqn{n \times n})-dimensional numeric non-negative adjacence
 #'   \code{\link[base]{matrix}} (or
-#'   \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}}, \link[base]{vector})
-#'   representing the graph
+#'   \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}},
+#'   \code{\link[base]{vector}}) representing the graph
 #'
 #' @param k the depth of the nearest neighbor search, e.g. the depth of the
 #'   graph traversal

README.md

@@ -20,13 +20,15 @@ Before installation, we recommended you install Intel oneAPI Math Kernel Library
 Windows users can download oneMKL from [Intel's website](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-download.html) and install it in the default directory. The default directory is: `C:\Program Files (x86)\Intel\oneAPI`.
 
 Debian users can download oneMKL using apt in the Debian non-free repo:
-```{bash}
+
+``` bash
 # Install oneMKL version 2020.4.304-4
 sudo apt install intel-mkl-full
 ```
 
 Or using the Intel repo:
-```{bash}
+
+``` bash
 # Set up the repository and signed the entry
 wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
 | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
@@ -38,7 +40,8 @@ sudo apt install intel-oneapi-mkl
 ```
 
 Fedora users can download oneMKL by using dnf:
-```{bash}
+
+``` bash
 # Create dnf repository file
 tee > /tmp/oneAPI.repo << EOF
 [oneAPI]
@@ -57,12 +60,14 @@ sudo dnf install intel-oneapi-mkl
 ## Installation
 
 Install `diffusr` using:
-```{r}
+
+``` r
 install.packages("diffusr")
 ```
 
 Alternatively use the latest version from github:
-```{r}
+
+``` r
 devtools::install_github("randef1ned/diffusr", build_vignettes = TRUE)
 ```
 

_pkgdown.yml

@@ -0,0 +1,4 @@
+url: https://diffusr.yinchun.su
+template:
+  bootstrap: 5
+