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:	1.1.0
Depends:	R (≥ 3.10)
Imports:	Matrix
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), withr
Published:	2025-03-17
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
CRAN checks:	DLSSM results

Documentation:

Reference manual:	DLSSM.pdf
Vignettes:	DLSSM (source, R code)

Downloads:

Package source:	DLSSM_1.1.0.tar.gz
Windows binaries:	r-devel: DLSSM_1.1.0.zip, r-release: DLSSM_1.1.0.zip, r-oldrel: DLSSM_1.1.0.zip
macOS binaries:	r-release (arm64): DLSSM_1.1.0.tgz, r-oldrel (arm64): DLSSM_1.1.0.tgz, r-release (x86_64): DLSSM_1.1.0.tgz, r-oldrel (x86_64): DLSSM_1.1.0.tgz
Old sources:	DLSSM archive

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