Joint Marginal-Conditional Model (MargCond)
Fits joint marginal conditional models for multivariate longitudinal data, as in Proudfoot, Faig, Natarajan, and Xu (2018) <doi:10.1002/sim.7552>. Development of this package was supported by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Client for ‘AWS Transcribe’ (aws.transcribe)
Client for ‘AWS Transcribe’ <https://…/transcribe>, a cloud transcription service that can convert an audio media file in English and other languages into a text transcript.

Elicitation of Independent Conditional Means Priors for Generalised Linear Models (indirect)
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.