Implements the dynamic logistic state space model for binary outcome data proposed by Jiang et al. (2021) <doi:10.1111/biom.13593>. It provides a computationally efficient way to update the prediction whenever new data becomes available. It allows for both time-varying and time-invariant coefficients, and use cubic smoothing splines to model varying coefficients. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at pre-specified time intervals.
Version: 1.1.1 Depends: R (≥ 3.10) Imports: Matrix Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), withr Published: 2025-05-22 DOI: 10.32614/CRAN.package.DLSSM Author: Jiakun Jiang [aut, cre], Wei Yang [aut], Wensheng Guo [aut] Maintainer: Jiakun Jiang <jiakunj at bnu.edu.cn> License: GPL-3 NeedsCompilation: no SystemRequirements: Intel MKL (optional for enhanced performance) CRAN checks: DLSSM results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=DLSSM to link to this page.
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