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Blend
View on CRAN: Click
here
Download and install Blend package within the R console
Install from CRAN:
install.packages("Blend")
Install from Github:
library("remotes")
install_github("cran/Blend")
Install by package version:
library("remotes")
install_version("Blend", "0.1.1.1")
Attach the package and use:
library("Blend")
Maintained by
Kun Fan
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.Blend . https://github.com/kunfa/Blend . Blend results . Blend.pdf . Blend_0.1.1.1.tar.gz . Blend_0.1.1.1.zip . Blend_0.1.1.1.zip . Blend_0.1.1.1.zip . Blend_0.1.1.1.tgz . Blend_0.1.1.1.tgz . Blend_0.1.1.1.tgz . Blend_0.1.1.1.tgz . Blend_0.1.1.1.tgz . Blend_0.1.1.1.tgz . Blend archive . https://CRAN.R-project.org/package=Blend .
First Published: 2024-11-25
Latest Update: 2024-11-25
Description:
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++'.
How to cite:
Kun Fan (2024). Blend: Robust Bayesian Longitudinal Regularized Semiparametric Mixed Models. R package version 0.1.1.1, https://cran.r-project.org/web/packages/Blend. Accessed 17 Feb. 2025.
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