Epidemiological metrics for netdiffuseR

DESCRIPTION

@@ -94,3 +94,4 @@ Collate:
     'struct_equiv.R'
     'struct_test.R'
     'survey_to_diffnet.R'
+    'transmission.R'

NAMESPACE

@@ -91,6 +91,7 @@ export(as.dgCMatrix)
 export(as_dgCMatrix)
 export(as_diffnet)
 export(as_spmat)
+export(as_transmission_tree)
 export(bass_F)
 export(bass_dF)
 export(bass_f)
@@ -186,6 +187,7 @@ export(threshold)
 export(toa_diff)
 export(toa_mat)
 export(transformGraphBy)
+export(transmission_tree)
 export(vertex_covariate_compare)
 export(vertex_covariate_dist)
 export(vertex_mahalanobis_dist)

NEWS.md

@@ -11,6 +11,9 @@
 * New dataset `epigames` and `epigamesDiffNet`: a simulated epidemic game
   network with 594 nodes and 15 time periods from the WKU Epi Games study.
 
+* `exposure()` and `rdiffnet()` now support `mode = "stochastic"`, allowing
+  for probabilistic interpretation of edge weights in exposure calculations.
+
 ## Internal changes
 
 * Fixed CRAN example error in `round_to_seq()`: `plot(w, x)` replaced with
@@ -19,7 +22,6 @@
 
 * Removed `configure` framework. R already provides paths and configuration for OpenMP.
 
-
 # Changes in netdiffuseR version 1.24.0 (2025-12-09)
 
 * New function `degree_adoption_diagnostic()` analyzes the correlation between network 

R/adjmat.r

@@ -537,6 +537,29 @@ toa_mat.default <- function(per, t0, t1) {
   )
 }
 
+# Build (adopt, cumadopt) from (toa, tod) intervals for a single behavior.
+# Each node i is considered adopted on periods [toa[i], tod[i] - 1]; when
+# tod[i] is NA the adoption is absorbing and the interval runs through t1.
+cumadopt_from_intervals <- function(toa, tod, t0, t1, labels = NULL) {
+  n <- length(toa)
+  T <- t1 - t0 + 1L
+  adopt    <- matrix(0L, nrow = n, ncol = T)
+  cumadopt <- matrix(0L, nrow = n, ncol = T)
+  for (i in seq_len(n)) {
+    s_val <- toa[i]
+    if (is.na(s_val)) next
+    s <- as.integer(s_val) - t0 + 1L
+    e_val <- tod[i]
+    e <- if (is.na(e_val)) T else (as.integer(e_val) - 1L - t0 + 1L)
+    if (s >= 1L && s <= T) adopt[i, s] <- 1L
+    if (s <= e && s >= 1L && e <= T && e >= 1L) cumadopt[i, s:e] <- 1L
+  }
+  rn <- if (length(labels)) labels else seq_len(n)
+  dimnames(adopt)    <- list(rn, t0:t1)
+  dimnames(cumadopt) <- list(rn, t0:t1)
+  list(adopt = adopt, cumadopt = cumadopt)
+}
+
 # @rdname toa_mat
 # @export
 toa_mat.numeric <- function(times, labels=NULL,

R/data.r

@@ -980,6 +980,21 @@ NULL # "epigames"
 #' A directed dynamic graph with 594 vertices and 15 time periods. The attributes
 #' in the graph are described in \code{\link{epigames}}.
 #'
+#' By default, this \code{diffnet} object is **non-cumulative** (each slice represents 
+#' ephemeral daily contacts) and **valued** (edge weights represent contact duration in seconds). 
+#' 
+#' To reconstruct the classic cumulative/binarized network, you can run:
+#' 
+#' \preformatted{
+#' epigames_cumul <- epigamesDiffNet
+#' 
+#' # 1. Accumulate the history across time periods
+#' epigames_cumul$graph <- Reduce("+", epigames_cumul$graph, accumulate = TRUE)
+#' 
+#' # 2. Apply a logical cut-off to binarize the network
+#' epigames_cumul$graph <- lapply(epigames_cumul$graph, function(m) { m@x[] <- 1; m })
+#' }
+#'
 #' Non-adopters have \code{toa = NA}.
 #'
 #' @format A \code{\link{diffnet}} class object.