DLSSM: Dynamic Logistic State Space Prediction Model

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: 0.1.0
Depends: R (≥ 3.10)
Imports: Matrix
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), withr
Published: 2022-12-13
Author: Jiakun Jiang [aut, cre], Wei Yang [aut], Wensheng Guo [aut]
Maintainer: Jiakun Jiang <jiakunj at bnu.edu.cn>
License: GPL-3
NeedsCompilation: no
CRAN checks: DLSSM results


Reference manual: DLSSM.pdf
Vignettes: DLSSM


Package source: DLSSM_0.1.0.tar.gz
Windows binaries: r-devel: DLSSM_0.1.0.zip, r-release: DLSSM_0.1.0.zip, r-oldrel: DLSSM_0.1.0.zip
macOS binaries: r-release (arm64): DLSSM_0.1.0.tgz, r-oldrel (arm64): DLSSM_0.1.0.tgz, r-release (x86_64): DLSSM_0.1.0.tgz, r-oldrel (x86_64): DLSSM_0.1.0.tgz


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