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seqICP  

Sequential Invariant Causal Prediction
View on CRAN: Click here


Download and install seqICP package within the R console
Install from CRAN:
install.packages("seqICP")

Install from Github:
library("remotes")
install_github("cran/seqICP")

Install by package version:
library("remotes")
install_version("seqICP", "1.1")



Attach the package and use:
library("seqICP")
Maintained by
Niklas Pfister
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-06-27
Latest Update: 2017-07-25
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.
How to cite:
Niklas Pfister (2017). seqICP: Sequential Invariant Causal Prediction. R package version 1.1, https://cran.r-project.org/web/packages/seqICP. Accessed 05 Mar. 2026.
Previous versions and publish date:
1.0 (2017-06-27 08:40)
Other packages that cited seqICP R package
View seqICP citation profile
Other R packages that seqICP depends, imports, suggests or enhances
Complete documentation for seqICP
Functions, R codes and Examples using the seqICP R package
Some associated functions: seqICP . seqICP.s . seqICP_package . seqICPnl . seqICPnl.s . summary.seqICP . summary.seqICPnl . 
Some associated R codes: seqICP.R . seqICP.s.R . seqICPnl.R . seqICPnl.s.R . summary.seqICP.R . summary.seqICPnl.R .  Full seqICP package functions and examples
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