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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 stat.math.ethz.ch> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | seqICP results |
| Reference manual: | seqICP.pdf |
| Package source: | seqICP_1.1.tar.gz |
| Windows binaries: | r-devel: seqICP_1.1.zip, r-release: seqICP_1.1.zip, r-oldrel: seqICP_1.1.zip |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.