Man pages for rmorsomme/PDSIR
Data-augmentation Marko chain Monte Carlo for Fitting the Stochastic SIR Model to Incidence Counts
| add_beta | Include beta to the parameter vector theta |
| add_gamma | Include beta to the parameter vector theta |
| complete_theta | Include beta to the parameter vector theta |
| compute_Ik | Discrete incidence counts for infections |
| compute_Ik_MH_fast | Discrete Incidence Data for Infections (Fast) |
| compute_pi | Probability of being removed before next observation time |
| contribution_observed_removal | Log proposal density of observed removals |
| dexp_trunc_log | Log density of the truncated exponential distribution |
| dprop_x | Log proposal density for the latent data |
| draw_trajectories | Draw trajectories of a SEM |
| dweibull2 | Density of weibull distribution |
| dweibull2_trunc_log | Log density of the truncated Weibull distribution |
| ebola_gueckedou | Weekly infection counts in Gueckedou |
| ebola_guinea_raw | Weekly infection counts in each prefecture of Guinea |
| f_log | Log likelihood of the stochastic SIR model |
| gibbs_theta | Gibbs sampler for theta |
| observed_data | Compute observed data from trajectories of the stochastic SIR... |
| PDSIR | PDSIR: A package for fitting the stochastic SIR process with... |
| propose_tau_I | Generates infection times |
| propose_tau_R | Generates removal times |
| pweibull2 | CDF of weibull distribution |
| rexp_trunc | Random generator for the truncated exponential distribution |
| rprop_x | Generates PD-SIR process conditionally on the observed data |
| run_DAMCMC | Run the DA-MCMC |
| rweibull2 | Random generator for the truncated weibull distribution |
| rweibull2_trunc | Random generator for the truncated weibull distribution |
| simulate_iota | Simulate infection periods |
| simulate_SEM | Simulate trajectories of the Stochastic SIR and SEIR... |
| suff_stat | Compute the sufficient statistics from the latent space |
