deeptrafo: Fitting Deep Conditional Transformation Models
Allows for the specification of deep conditional transformation
models (DCTMs) and ordinal neural network transformation models, as
described in Baumann et al (2021) <doi:10.1007/978-3-030-86523-8_1> and
Kook et al (2022) <doi:10.1016/j.patcog.2021.108263>. Extensions such as
autoregressive DCTMs (Ruegamer et al, 2022, <doi:10.48550/arXiv.2110.08248>)
and transformation ensembles (Kook et al, 2022, <doi:10.48550/arXiv.2205.12729>)
are implemented.
| Version: |
0.1-1 |
| Depends: |
R (≥ 4.0.0), Formula, tensorflow (≥ 2.2.0), keras (≥
2.2.0), tfprobability (≥ 0.15), deepregression |
| Imports: |
mlt, variables, stats, purrr, survival, R6, reticulate |
| Suggests: |
testthat, knitr, ordinal, tram, cotram, covr |
| Published: |
2022-11-22 |
| DOI: |
10.32614/CRAN.package.deeptrafo |
| Author: |
Lucas Kook [aut, cre],
Philipp Baumann [aut],
David Ruegamer [aut] |
| Maintainer: |
Lucas Kook <lucasheinrich.kook at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
deeptrafo results |
Documentation:
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