DNNSIM: Single-Index Neural Network for Skewed Heavy-Tailed Data
Provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.
| Version: |
0.1.1 |
| Imports: |
reticulate (≥ 1.37.0), stats (≥ 4.3.0), Rdpack (≥ 2.6) |
| Published: |
2025-01-07 |
| DOI: |
10.32614/CRAN.package.DNNSIM |
| Author: |
Qingyang Liu 
[aut, cre],
Shijie Wang [aut],
Ray Bai [aut],
Dipankar Bandyopadhyay [aut] |
| Maintainer: |
Qingyang Liu <rh8liuqy at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.8.0); PyTorch (https://pytorch.org/);
NumPy (https://numpy.org/); SciPy (https://scipy.org/); sklearn
(https://scikit-learn.org/stable/); |
| Materials: |
NEWS |
| CRAN checks: |
DNNSIM results |
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