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 |
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