A0_estimate: Estimate A0

In markwh/A0est: Estimate A0 using Bayes

Description

Fits model for A0 of one of several variants using Hamiltonian Monte Carlo.

Usage

A0_estimate(A0data, variant = c("withLatent", "noLatent", "flatA0",
  "flatBoth"), cores = getOption("mc.cores", default =
  parallel::detectCores()), chains = 3L, iter = 2000L,
  method = c("sampling", "optimizing"), ...)

Arguments

A0data	A list of data required, including known parameters
variant	Which A0 variant to use: withLatent, noLatent, flatA0, flatBoth
cores	Number of processing cores for running chains in parallel. See ?rstan::sampling. Defaults to parallel::detectCores.
chains	A positive integer specifying the number of Markov chains. The default is 3.
iter	Number of iterations per chain (including warmup). Defaults to 2000.
method	Either "sampling" (default) which runs the MC sampler, or "optimizing" which optimizes to find the maximum a posteriori estimate.
...	Other arguments passed to rstan::sampling() for customizing the Monte Carlo sampler

markwh/A0est documentation built on May 29, 2019, 3:44 a.m.