seqICP-package {seqICP} | R Documentation |
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.
The DESCRIPTION file:
Package: | seqICP |
Title: | Sequential Invariant Causal Prediction |
Version: | 1.1 |
Author: | Niklas Pfister and Jonas Peters |
Maintainer: | Niklas Pfister <pfister@stat.math.ethz.ch> |
Description: | 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. |
Depends: | R (>= 3.2.3) |
License: | GPL-3 |
Encoding: | UTF-8 |
LazyData: | true |
Imports: | dHSIC, mgcv, stats |
RoxygenNote: | 6.0.1 |
Index of help topics:
seqICP Sequential Invariant Causal Prediction seqICP-package Sequential Invariant Causal Prediction seqICP.s Sequential Invariant Causal Prediction for an individual set S seqICPnl Non-linear Invariant Causal Prediction seqICPnl.s Non-linear Invariant Causal Prediction for an individual set S summary.seqICP summary function summary.seqICPnl summary function
Niklas Pfister and Jonas Peters
Maintainer: Niklas Pfister <pfister@stat.math.ethz.ch>
Pfister, N., P. Bühlmann and J. Peters (2017). Invariant Causal Prediction for Sequential Data. ArXiv e-prints (1706.08058).
Peters, J., P. Bühlmann, and N. Meinshausen (2016). Causal inference using invariant prediction: identification and confidence intervals. Journal of the Royal Statistical Society, Series B (with discussion) 78 (5), 947–1012.