Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) <doi:10.1007/s11336-008-9089-5>.

A wrapper around the 'Blat' command line SMTP mailer for Windows.
   'Blat' is public domain software, but be sure to read the license before use.
    It can be found at the Blat website http://www.blat.net.

Fit a variety of Bayesian latent variable models, including confirmatory
   factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.

Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'.

Create a blended curve from two survival curves, which is particularly useful
          for survival extrapolation in health technology assessment. The main idea is to
          mix a flexible model that fits the observed data well with a parametric model that
          encodes assumptions about long-term survival. The two curves are blended into a
          single survival curve that is identical to the first model over the range of 
          observed times and gradually approaches the parametric model over the extrapolation
          period based on a given weight function. This approach allows for the inclusion of
          external information, such as data from registries or expert opinion, to guide
          long-term extrapolations, especially when dealing with immature trial data.
          See Che et al. (2022) <doi:10.1177/0272989X221134545>.