Example usage:
# Simulate epidemic under exponential growth
set.seed( 1111 )
library(phydynR)
births <- c(I = 'parms$beta * I' )
deaths <- c(I = 'parms$gamma * I' )
dm <- build.demographic.process(births=births
, deaths = deaths
, parameterNames=c('beta', 'gamma')
, rcpp=FALSE
, sde = TRUE)
tfgy = dm( theta = list( beta = 1.5, gamma = 1 ), x0 = c(I = 1 ) , t0 = 0, t1 = 10 )
# Simulate transmission tree
library(scott)
n <- 75 # sample size
s <- rep(10, n ) # sample times
X <- matrix( c(rep(1, n), rep(0,n)), byrow=FALSE , ncol = 2) # sample states
min_dt = 10 / 1e4 # controls precision of simulation
o = scott(
tfgy
, s
, X
, min_dt
, event_tol = .1
)
# Here is the table of transmission events within the sample
(w <- o$waifw)
- Convenient subroutines for faster analysis
- scott which takes Ne(t) instead of tfgy
- scott which takes independent estimate of incidence and prevalence
- Improve documentation and examples
- Fast Rcpp version