A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.

We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) <doi:10.1007/s11634-021-00457-4>. This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences.

The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.

Joint models have been widely used to study the associations between longitudinal biomarkers and a survival outcome. However, existing joint models only consider one or a few longitudinal 
    biomarkers and cannot deal with high-dimensional longitudinal biomarkers. This package can be used to fit our recently developed penalized joint model that can handle high-dimensional longitudinal biomarkers. 
    Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the longitudinal biomarkers on the survival outcome, which allows for variable selection. 
    Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.