v0.7.1

This version:

• Adds rstan functionality to the package. Previous versions did not include rstan because it did not produce reasonable results. The issue appeared to be in the implementation of reduce_sum so within-chain parallelisation is removed for rstan sampling but available with the cmdstanr sampling. The user can select the sampler with the argument mcmc.pkg.
• Adds stochastic approximation expectation maximisation algorithm to the MCML sampler. This algorithm uses a Robbins-Munro approach to estimating the log-likelihood and so requires far fewer MCMC samples per iteration, as all MCMC samples are retained an used on each iteration. This algorithm can be used with or without Ruppert-Polyak averaging.
• Adaptive sample sizes are included for MCMC-ML.
• New convergence criteria are included based on the marginal improvement in the log-likelihood. At convergence the log-likelihood will fail to improve. To account for the stochastic nature of the algorithm, an upper bound is used based on the estimated variance of the log-likelihood differences.
• Some small bugs and errors are fixed.

What's Changed

• v0.6.1 fixed LA in optim by @samuel-watson in #31
• Add DIRECT and incorporate BOBYQA natively by @samuel-watson in #32
• Update README.md by @samuel-watson in #33
• Newuoa & L-BFGS by @samuel-watson in #35

Full Changelog: v0.6.1...v0.7.1