tramvs - Optimal Subset Selection for Transformation Models

Greedy optimal subset selection for transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to models from packages 'tram' and 'cotram'.

Last updated 2 days ago

5.15 score 5 scripts 185 downloads

comets - Covariance Measure Tests for Conditional Independence

Covariance measure tests for conditional independence testing against conditional covariance and nonlinear conditional mean alternatives. Contains versions of the generalised covariance measure test (Shah and Peters, 2020, <doi:10.1214/19-aos1857>) and projected covariance measure test (Lundborg et al., 2023, <doi:10.48550/arXiv.2211.02039>). Applications can be found in Kook and Lundborg (2024, <doi:10.1093/bib/bbae475>).

Last updated 7 hours ago

4.73 score 6 stars 2 scripts 212 downloads

tramicp - Model-Based Causal Feature Selection for General Response Types

Extends invariant causal prediction (Peters et al., 2016, <doi:10.1111/rssb.12167>) to generalized linear and transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291>). The methodology is described in Kook et al. (2023, <doi:10.48550/arXiv.2309.12833>).

Last updated 4 months ago

4.18 score 6 stars 132 downloads

tramnet - Penalized Transformation Models

Partially penalized versions of specific transformation models implemented in package 'mlt'. Available models include a fully parametric version of the Cox model, other parametric survival models (Weibull, etc.), models for binary and ordered categorical variables, normal and transformed-normal (Box-Cox type) linear models, and continuous outcome logistic regression. Hyperparameter tuning is facilitated through model-based optimization functionalities from package 'mlr3MBO'. The methodology is described in Kook et al. (2021) <doi:10.32614/RJ-2021-054>. Transformation models and model-based optimization are described in Hothorn et al. (2019) <doi:10.1111/sjos.12291> and Bischl et al. (2016) <arxiv:1703.03373>, respectively.

Last updated 2 days ago

4.15 score 2 scripts 216 downloads

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.

Last updated 2 years ago

1.04 score 11 scripts 216 downloads