Showing 3 of total 3 results (show query)
lucaskook
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>).
Maintained by Lucas Kook. Last updated 2 months ago.
7 stars 4.24 score
wwiedermann
dda:Direction Dependence Analysis
A collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see <https://www.ddaproject.com>.
Maintained by Wolfgang Wiedermann. Last updated 22 days ago.
3.81 score
cran
seqICP:Sequential Invariant Causal Prediction
Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.
Maintained by Niklas Pfister. Last updated 8 years ago.
1 stars 1.00 score