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.

Version: 1.1
Depends: R (≥ 3.2.3)
Imports: dHSIC, mgcv, stats
Published: 2017-07-25
DOI: 10.32614/CRAN.package.seqICP
Author: Niklas Pfister and Jonas Peters
Maintainer: Niklas Pfister <pfister at>
License: GPL-3
NeedsCompilation: no
CRAN checks: seqICP results


Reference manual: seqICP.pdf


Package source: seqICP_1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): seqICP_1.1.tgz, r-oldrel (arm64): seqICP_1.1.tgz, r-release (x86_64): seqICP_1.1.tgz, r-oldrel (x86_64): seqICP_1.1.tgz
Old sources: seqICP archive


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