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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-devel (arm64): DLSSM_1.1.0.tgz, r-release (arm64): DLSSM_1.1.0.tgz, r-oldrel (arm64): DLSSM_1.1.0.tgz, r-devel (x86_64): 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

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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