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comets - Covariance Measure Tests for Conditional Independence

Covariance measure tests for conditional independence testing against conditional covariance and nonlinear conditional mean alternatives. The package implements 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:10.1214/24-AOS2447>). The tram-GCM test, for censored responses, is implemented including the Cox model and survival forests (Kook et al., 2024, <doi:10.1080/01621459.2024.2395588>). Application examples to variable significance testing and modality selection can be found in Kook and Lundborg (2024, <doi:10.1093/bib/bbae475>).

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conditional-independence-testmachine-learningnonparametriccpp

4.08 score 10 stars 12 scripts 378 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:10.1080/01621459.2024.2395588>).

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3.74 score 11 stars 275 downloads

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'. Application to shift-scale transformation models are described in Siegfried et al. (2024, <doi:10.1080/00031305.2023.2203177>).

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3.30 score 9 scripts 725 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.), normal and transformed-normal (Box-Cox type) linear models, and continuous outcome logistic regression. Models can be specified via a formula interface. Hyperparameter tuning is facilitated through model-based optimization functionalities from package 'mlr3mbo'. The accompanying vignette describes the methodology used in 'tramnet' in detail. Transformation models and model-based optimization are described in Hothorn et al. (2019) <doi:10.1111/sjos.12291> and Bischl et al. (2017) <doi:10.48550/arXiv.1703.03373>, respectively.

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3.30 score 2 scripts 956 downloads