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MultiBD

MultiBD is anR package for directlikelihood-based inference of multivariate birth-death processes.

Installation

1. InstallCRAN release version:

install.packages("MultiBD")

2. Install the bleeding-edge version ofMultiBD fromgithub:

devtools::install_github("msuchard/MultiBD")

Short example

library(MultiBD)data(Eyam)loglik_sir <- function(param, data) {  alpha <- exp(param[1]) # Rates must be non-negative  beta  <- exp(param[2])    # Set-up SIR model  drates1 <- function(a, b) { 0 }  brates2 <- function(a, b) { 0 }  drates2 <- function(a, b) { alpha * b     }  trans12 <- function(a, b) { beta  * a * b }    sum(sapply(1:(nrow(data) - 1), # Sum across all time steps k             function(k) {               log(                 dbd_prob(  # Compute the transition probability matrix                   t  = data$time[k + 1] - data$time[k], # Time increment                   a0 = data$S[k], b0 = data$I[k],       # From: S(t_k), I(t_k)                                                         drates1, brates2, drates2, trans12,                   a = data$S[k + 1], B = data$S[k] + data$I[k] - data$S[k + 1],                   computeMode = 4, nblocks = 80         # Compute using 4 threads                 )[1, data$I[k + 1] + 1]                 # To: S(t_(k+1)), I(t_(k+1))               )             }))}loglik_sir(log(c(3.204, 0.019)), Eyam) # Evaluate at mode

Vignettes

1. SimpleMCMC under SIR
2. SIRmodel and proposed branching approximation

License

MultiBD is licensed under Apache License 2.0

Development status

Beta

Acknowledgements

• This project is supported in part through the National ScienceFoundation grant DMS 1264153 and National Institutes of Health grant R01AI107034.

References

1. Ho LST, Xu J, Crawford FW, Minin VN, Suchard MA (2018).Birth/birth-deathprocesses and their computable transition probabilities with biologicalapplications. Journal of Mathematical Biology 76(4) 911-944.
2. Ho LST, Crawford FW, Suchard MA (2018).Direct likelihood-basedinference for discretely observed stochastic compartmental models ofinfectious disease. Annals of Applied Statistics. In press.