version 0.9.1

DESCRIPTION

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+Package: localboot
+Type: Package
+Title: Local Bootstrap Methods for Various Networks
+Version: 0.9.1
+Date: 2023-12-18
+Authors@R: c(
+    person("Tianhai", "Zu", email = "zuti@mail.uc.edu", role = c("aut", "cre")),
+    person("Yichen", "Qin", email = "qinyn@ucmail.uc.edu", role = c("aut","ctb"))
+  )
+Description: Network analysis usually requires estimating the uncertainty of
+    graph statistics. Through this package, we provide tools to bootstrap 
+    various networks via local bootstrap procedure. Additionally, it includes 
+    functions for generating probability matrices, creating network adjacency 
+    matrices from probability matrices, and plotting network structures. 
+    The reference will be updated soon.
+License: GPL-3
+Depends: R (>= 3.5.0)
+Imports: graphics, viridis, Rcpp (>= 1.0.11)
+Suggests: testthat (>= 3.0.0)
+LinkingTo: Rcpp (>= 1.0.11), RcppEigen
+Encoding: UTF-8
+RoxygenNote: 7.2.3
+NeedsCompilation: yes
+Packaged: 2023-12-21 06:21:26 UTC; tianhaizu
+Author: Tianhai Zu [aut, cre],
+  Yichen Qin [aut, ctb]
+Maintainer: Tianhai Zu <zuti@mail.uc.edu>
+Repository: CRAN
+Date/Publication: 2023-12-21 16:30:02 UTC

MD5

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+b3ff1e6fb2563796e33807e5fb5277e1 *DESCRIPTION
+34b2759e632634bd546f0f996c90462d *NAMESPACE
+2de21dbbe9981853ed8044136e4bfac7 *R/RcppExports.R
+3f12e430a5c85fc62a93fa4a46566c42 *R/generate_network.R
+dbfa3d9aadd2c86f34d882814708dc99 *R/localboot-package.R
+2561a8d9d6edf4d3e1031010b9f1a7a0 *R/localboot.R
+a27164ba5703eb3ac36f0d4ff2fb8673 *R/plot_utils.R
+7db4f5555abf5afff49ac74d6b1b7aee *inst/sim/Fit_local_boot.R
+3297eecf83620025ab244fc3042be725 *inst/sim/Generate_various_networks.R
+cee90d493ca0b4fef38062105123af52 *inst/sim/Optimal_local_size.R
+4c91a053d9937e9a6d05a82697b114ac *inst/sim/Sim1_estimate_others.R
+ecbd16a807885cfd9f774444c9333efc *inst/sim/Sim1_estimates_local.R
+6c19806713660c3fae228f51e2d72e2c *inst/sim/Sim1_truese.R
+7aeb7398c9f8ef60a0931772f320086d *inst/sim/test_localcpp.R
+c6bd637748ae88df554ced68e6771e5f *man/generate_graphon.Rd
+d4a8f4bc1d08552a82ce03853a80b001 *man/generate_network_P.Rd
+29713984eab34fbaf668b7aeb31a0947 *man/localboot-package.Rd
+8e4dfea53e03f3df16393c8debc1214e *man/localboot.Rd
+5a4251717a7faa71fcf4313592d694d2 *man/plot_P.Rd
+2bb486ba071dbebebab7a32ea52a6e73 *man/plot_adj.Rd
+b4f0b014ec53404e843dbc718fabf57e *src/Makevars
+b4f0b014ec53404e843dbc718fabf57e *src/Makevars.win
+60ca2ca032d1a414ce63a92635ac1453 *src/RcppExports.cpp
+7fb7f7de7cc0aa9792b3ae46a8f87cde *src/localboot.cpp

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(generate_graphon)
+export(generate_network_P)
+export(localboot)
+export(plot_P)
+export(plot_adj)
+importFrom(Rcpp,evalCpp)
+importFrom(graphics,filled.contour)
+importFrom(viridis,viridis)
+useDynLib(localboot)

R/RcppExports.R

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+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+get_dist_default_eigen <- function(A) {
+    .Call('_localboot_get_dist_default_eigen', PACKAGE = 'localboot', A)
+}
+
+calculate_p_hat_matrix <- function(A, neibors_matrix) {
+    .Call('_localboot_calculate_p_hat_matrix', PACKAGE = 'localboot', A, neibors_matrix)
+}
+
+sample_from_p_cpp <- function(p_hat_matrix, blist, random_matrix, no_loop) {
+    .Call('_localboot_sample_from_p_cpp', PACKAGE = 'localboot', p_hat_matrix, blist, random_matrix, no_loop)
+}
+

R/generate_network.R

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+# Define the formula to generate p matrix of graphon1
+graphon1 <- function(u, size){
+  p_matrix = matrix(0,nrow=size,ncol=size)
+  for(i in 1:size){
+    for(j in 1:size){
+      p <- cos(pi*(u[i]-u[j]))/(2^3) + 0.5
+      p_matrix[i,j] <- p    
+    }
+  }
+  p_matrix
+}
+
+# Define the formula to generate p matrix of graphon2
+graphon2 <- function(u, size){
+  p_matrix = matrix(0,nrow=size,ncol=size)
+  for(i in 1:size){
+    for(j in 1:size){
+      p <- cos(pi*(u[i]-u[j]))/(2^3) + 0.5
+      p_matrix[i,j] <- p    
+    }
+  }
+  p_matrix
+}
+
+# Define the formula to generate p matrix of graphon3
+graphon3 <- function(u, size){
+  p_matrix = matrix(0,nrow=size,ncol=size)
+  for(i in 1:size){
+    for(j in 1:size){
+      p <- cos(pi*(u[i]-u[j]))/(2^3) + 0.5
+      p_matrix[i,j] <- p    
+    }
+  }
+  p_matrix
+}
+# Define the formula to generate p matrix of graphon4
+graphon4 <- function(u, size){
+  p_matrix = matrix(0,nrow=size,ncol=size)
+  for(i in 1:size){
+    for(j in 1:size){
+      p <- cos(pi*(u[i]-u[j]))/(2^3) + 0.5
+      p_matrix[i,j] <- p    
+    }
+  }
+  p_matrix
+}
+
+# Define the formula to generate p matrix of graphon5
+graphon5 <- function(u, size){
+  p_matrix = matrix(0,nrow=size,ncol=size)
+  for(i in 1:size){
+    for(j in 1:size){
+      p <- cos(pi*(u[i]-u[j]))/(2^3) + 0.5
+      p_matrix[i,j] <- p    
+    }
+  }
+  p_matrix
+}
+
+# Define the formula to generate p matrix of graphon6
+graphon6 <- function(u, size){
+  p_matrix = matrix(0,nrow=size,ncol=size)
+  for(i in 1:size){
+    for(j in 1:size){
+      p <- cos(pi*(u[i]-u[j]))/(2^3) + 0.5
+      p_matrix[i,j] <- p    
+    }
+  }
+  p_matrix
+}
+
+# Function to generate sub-network from real network, labeled as 7
+graphon7 <- function(u,size){
+}
+
+
+# Function to generate sub-network from real network, labeled as 8
+graphon8 <- function(u,size){
+}
+
+
+#' Generate a Graphon Probability Matrix
+#'
+#' This function generates a graphon probability matrix based on a specified graphon type. 
+#' Users can control the generation process through various parameters.
+#'
+#' @param size An integer specifying the size of the network.
+#' @param graph_num An integer (default is 1) indicating the graphon type to use. 
+#'        Acceptable values are from 1 to 6.
+#' @param sampling_on_u A logical value determining if uniform sampling should be used for 'u'. 
+#'        Defaults to TRUE. If FALSE, a regular sequence from 0 to 1 is used.
+#' @param u_input An optional numeric vector that provides specific values for 'u'.
+#'        If NULL (default), 'u' is generated based on 'sampling_on_u'.
+#'
+#' @return A matrix of probabilities is returned. 
+#'
+#' @examples
+#' # Generate a graphon probability matrix of size 100 using graphon setting 1
+#' P = generate_graphon(100, 1)
+#'
+#' @export
+generate_graphon <- function(size, graph_num = 1,sampling_on_u=TRUE,u_input=NULL){
+
+  #handle user provided u
+  if(!is.null(u_input)){u = u_input}else{
+    if(sampling_on_u){
+      u = stats::runif(n = size)
+    }else{
+      u = seq(from = 0, to = 1,length.out=size)
+    }
+  }
+  u = sort(u)
+  # Use switch to select the graphon
+  p_matrix <- switch(as.character(graph_num),
+                           "1" = graphon1(u, size),
+                           "2" = graphon2(u, size),
+                           "3" = graphon3(u, size),
+                           "4" = graphon4(u, size),
+                           "5" = graphon5(u, size),
+                           "6" = graphon6(u, size),
+                           stop("Invalid graph_num: should be between 1 and 6"))
+  return(p_matrix)
+
+}
+
+#' Generate Network Adjacency Matrix from Probability Matrix
+#'
+#' This function generates a network adjacency matrix from a given probability matrix. 
+#' It checks if the input is a valid probability matrix and can produce either a single 
+#' network or multiple replicates.
+#'
+#' @param P A square matrix representing the probability matrix, where each element 
+#'     is a probability (between 0 and 1) of an edge between nodes.
+#' @param replicate An integer indicating the number of network replicates to generate. 
+#'     Defaults to 1.
+#' @param symmetric.out A logical value indicating whether the output matrix should be 
+#'     symmetric. Defaults to TRUE.
+#' @param noloop A logical value indicating whether to include self-loops in the network. 
+#'     Defaults to FALSE.
+#'
+#' @return If `replicate` is 1, returns a single adjacency matrix. If `replicate` is 
+#'     greater than 1, returns a list of adjacency matrices. Each matrix is a square 
+#'     binary matrix, where 1 indicates the presence of an edge and 0 indicates its absence.
+#'
+#' @examples
+#' P = generate_graphon(100, 1)
+#' network = generate_network_P(P, replicate = 1, symmetric.out = TRUE)
+#'
+#' @export
+generate_network_P = function(P, replicate = 1, symmetric.out=TRUE, noloop = FALSE){
+  ## Check P
+  cond1 = ((all(P>=0))&&(all(P<=1)))
+  cond2 = (nrow(P)==ncol(P))
+  if (!(cond1&&cond2)){
+    stop("* gmodel.P : P is not a valid probability matrix.")
+  }
+
+  ## Parameter
+  n = nrow(P)
+
+  ## replicate 1 case
+  if (replicate==1){
+    tmpmat = matrix(stats::runif(n^2),nrow=n)
+    if (symmetric.out){
+      tmpmat[lower.tri(tmpmat)] <- t(tmpmat)[lower.tri(t(tmpmat))]
+    }
+    G = (tmpmat<P)*1
+  } else {
+    G = list()
+    for (i in 1:replicate){
+      tmpmat = matrix(stats::runif(n^2),nrow=n)
+      if (symmetric.out){
+        tmpmat[lower.tri(tmpmat)] <- t(tmpmat)[lower.tri(t(tmpmat))]
+      }
+      tmpG = 1*(tmpmat<P)
+      if (noloop){
+        diag(tmpG) = 0
+      }
+      G[[i]] = tmpG
+    }
+  }
+  ## return output
+  return(G)
+}

R/localboot-package.R

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+#' localboot: A Package for Local Bootstrap Methods for Various Networks
+#'
+#' Network analysis usually requires estimating the uncertainty of
+#' a graph statistics. Through this package, we provides tools to bootstrap 
+#' various networks via local bootstrap procedure. Additionally, it includes 
+#' functions for generating probability matrices, creating network adjacency 
+#' matrices from probability matrices, and plotting network structures. 
+#'
+#' @name localboot-package
+#' @aliases localboot-package
+#' @docType package
+"_PACKAGE"