Blend
Bayesian Longitudinal Regularized Semiparametric Quantile Mixed Models
Our recently developed fully Bayesian semiparametric quantile 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++'.
- Version0.1.0
- R versionunknown
- LicenseGPL-2
- Needs compilation?Yes
- Last release11/25/2024
Documentation
Team
Kun Fan
MaintainerShow author detailsCen Wu
Show author detailsRolesAuthor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports2 packages
- Linking To2 packages