seqICP-package {seqICP}

Sequential Invariant Causal Prediction

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.

Details

The DESCRIPTION file:

Package:	seqICP
Title:	Sequential Invariant Causal Prediction
Version:	1.1
Author:	Niklas Pfister and Jonas Peters
Maintainer:	Niklas Pfister <[e-mail]>
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

Author(s)

Niklas Pfister and Jonas Peters

Maintainer: Niklas Pfister <[e-mail]>

References

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.