mcmctsir: mcmctsir
In adbecker/tsiR: An Implementation of the TSIR Model

This function runs the TSIR model using a MCMC estimation. The susceptibles are still reconstructed in the same way as the regular tsir model, however beta, alpha, and sbar (or whatever combination you enter) are estimated using rjargs.

mcmctsir(data, xreg = "cumcases", IP = 2, nsim = 100,
  regtype = "gaussian", sigmamax = 3, userYhat = numeric(),
  update.iter = 10000, n.iter = 30000, n.chains = 3,
  n.adapt = 1000, burn.in = 100, method = "deterministic",
  epidemics = "cont", pred = "forward", seasonality = "standard",
  inits.fit = FALSE, threshold = 1, sbar = NULL, alpha = NULL,
  add.noise.sd = 0, mul.noise.sd = 0, printon = F)

`data`	The data frame containing cases and interpolated births and populations.
`xreg`	The x-axis for the regression. Options are 'cumcases' and 'cumbirths'. Defaults to 'cumcases'.
`IP`	The infectious period in weeks. Defaults to 2 weeks.
`nsim`	The number of simulations to do. Defaults to 100.
`regtype`	The type of regression used in susceptible reconstruction. Options are 'gaussian', 'lm' (linear model), 'spline' (smooth.spline with 2.5 degrees freedom), 'lowess' (with f = 2/3, iter = 1), 'loess' (degree 1), and 'user' which is just a user inputed vector. Defaults to 'gaussian' and if that fails then defaults to loess.
`sigmamax`	The inverse kernal width for the gaussian regression. Default is 3. Smaller, stochastic outbreaks tend to need a lower sigma.
`userYhat`	The inputed regression vector if regtype='user'. Defaults to NULL.
`update.iter`	Number of MCMC iterations to use in the update aspect. Default is 10000.
`n.iter`	Number of MCMC iterations to use. Default is 30000.
`n.chains`	Number of MCMC chains to use. Default is 3.
`n.adapt`	Adaptive number for MCMC. Default is 1000.
`burn.in`	Burn in number. Default is 100.
`method`	The type of next step prediction used. Options are 'negbin' for negative binomial, 'pois' for poisson distribution, and 'deterministic'. Defaults to 'deterministic'.
`epidemics`	The type of data splitting. Options are 'cont' which doesn't split the data up at all, and 'break' which breaks the epidemics up if there are a lot of zeros. Defaults to 'cont'.
`pred`	The type of prediction used. Options are 'forward' and 'step-ahead'. Defaults to 'forward'.
`seasonality`	The type of contact to use. Options are standard for 52/IP point contact or schoolterm for just a two point on off contact or none for a single contact parameter. Defaults to standard.
`inits.fit`	Whether or not to fit initial conditions using simple least squares as well. Defaults to FALSE. This parameter is more necessary in more chaotic locations.
`threshold`	The cut off for a new epidemic if epidemics = 'break'. Defaults to 1.
`sbar`	The mean number of susceptibles. Defaults to NULL, i.e. the function estimates sbar.
`alpha`	The mixing parameter. Defaults to NULL, i.e. the function estimates alpha.
`add.noise.sd`	The sd for additive noise, defaults to zero.
`mul.noise.sd`	The sd for multiplicative noise, defaults to zero.
`printon`	Whether to show diagnostic prints or not, defaults to FALSE.