v0.99 (v1.0 beta release)

This release is a beta for v1.0. The package's new features & functionality are documented in the two vignettes, Package_Introduction and Time_Series (see README file in the repository for more information). The only feature that is not implemented as of yet are ideal point marginal effects.

Because the new version relies on cmdstanr, which is not on CRAN, idealstan will remain only on Github until cmdstanr can be put on CRAN as well.

Some of the new features include:

1. Mixed outcomes -- both discrete and continuous distributions can be used in the same model for different items (continuous, ordinal, binary). You need to pass a column model_id to id_make to make this work, as well as specify discrete outcome/response as outcome_disc and any continuous outcomes/responses as outcome_cont.
2. Within-chain parallelization -- you can now specify the number of cores ncores as a multiple of nchains to use multiple cores per chain and speed up processing.
3. A variety of new defaults/priors/processes to improve & speed up dynamic ideal point estimation.