Update Docs and Source

R/ising-glauber.R

@@ -1,6 +1,6 @@
-#' Implementation to simulate a Kuramoto model of oscillators
+#' Implementation to simulate time series on a network from the Ising-Glauber model 
 #'
-#' Simulate Kuramoto model on a ground truth network
+#' Simulate Ising-Glauber model on a ground truth network
 #'
 #' @param input_matrix The input (ground-truth) adjacency matrix of a graph with N nodes. Must be valid N*N square adjacency matrix.
 #' @param L Integer length of the desired time series.

R/kuramoto.R

@@ -8,7 +8,7 @@
 #' @param strength Float coupling strength (prefactor for interaction terms).
 #' @param phases Vector of of initial phases.
 #' @param freqs Vector of internal frequencies.
-#' @return An N * L array of synthetic time series data.
+#' @return List with TS matrix containing an N*L array of synthetic time series data.
 #' @export
 simulate_kuramoto <- function(input_matrix, L, dt = 0.01, strength = 0, phases = NULL, freqs = NULL) {
   # create return list

R/lotka-volterra.R

@@ -11,7 +11,7 @@
 #' @param dt Float or vector of sizes of time steps when simulating the continuous-time dynamics.
 #' @param stochastic Boolean determining whether to simulate the stochastic or deterministic dynamics.
 #' @param pertb Vector of perturbation magnitude of nodes' growth. If not specified, default to 0.01 for all nodes.
-#' @return An N * L array of synthetic time series data.
+#' @return List with TS matrix containing an N*L array of synthetic time series data.
 #' @export
 simulate_lotka <- function(input_matrix, L, init = NULL, gr = NULL, cap = NULL, inter = NULL, dt = 1e-2, stochastic = TRUE, pertb = NULL) {
   # get num of nodes in adj matrix

R/sherrington-kirkpatrick.R

@@ -5,7 +5,7 @@
 #' @param input_matrix The input (ground-truth) adjacency matrix of a graph with `N` nodes. Must be valid square adjacency matrix.
 #' @param L The length of the desired time series.
 #' @param noise True or false value to generate noise
-#' @return An N * L array of synthetic time series data
+#' @return List with TS matrix containing an N*L array of synthetic time series data.
 #' @export
 simulate_sherrington <- function(input_matrix, L, noise = FALSE) {
   # create return list

R/single-unbiased-random-walker.R

@@ -10,7 +10,7 @@
 #' @param input_matrix the input (ground-truth) graph with N nodes. Must be valid square adjacency matrix.
 #' @param L the length of the desired time series.
 #' @param initial_node starting node for walk
-#' @return results a list with TS matrix an N*L array of synthetic time series data.
+#' @return List with TS matrix containing an N*L array of synthetic time series data.
 #' @export
 single_unbiased_random_walker <- function(input_matrix, L, initial_node = NULL) {
   # get adj matrix and set up vector of indices

R/voter.R

@@ -11,7 +11,7 @@
 #' @param L the length of the desired time series.
 #' @param noise if noise is present, with this probability a node's state will be randomly redrawn from (-1,1) \cr
 #' independent of its neighbors' states. If 'automatic', set noise to 1/N.
-#' @return results a list with TS matrix an N*L array of synthetic time series data.
+#' @return List with TS matrix containing an N*L array of synthetic time series data.
 #' @export
 voter <- function(input_matrix, L, noise = NULL) {
   # get adj matrix