R Observatory: Metrics for InvariantCausalPrediction version 0.8

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

InvariantCausalPrediction

0.8Package record

Invariant Causal Prediction

0packages depend

4Kdownloads / year

–test coverage

11/13flavors OK

Overview

About

Maintained by Nicolai MeinshausenCRAN page ↗

Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.

Install

R (CRAN)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("InvariantCausalPrediction", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-invariantcausalprediction

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-InvariantCausalPrediction

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-InvariantCausalPrediction

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

2NOTE11OK

Check messages

NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Maintainer: ‘Nicolai Meinshausen <meinshausen@stat.math.ethz.ch>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Nicolai",
                    family = "Meinshausen",
                    role = c("aut", "cre"),
                    email = "meinshausen@stat.math.ethz.ch")
as necessary.

CRAN check page ↗

Slowest check: 1.8 min · r-oldrel-windows-x86_64

Status changes

NOTE11 OK, 2 NOTENow
NOTE12 OK, 2 NOTE2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.8

READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION

Help pages: 5includes a package overview page
Examples that run: 100%2 run in a plain check
Exported functions with examples: 100%
Documented parameters: 93%
Return-value docs: 100%
Help pages citing references: 80%

How these are measured

Downloads

4K

CRAN downloads in the past year

Rank #8,550 · ~11/day · ~331/mo

Releases R versions

Daily download trend is not available in this view yet.

41830 days

1.1K90 days

4K1 year

Compare downloads with other packages →

Also on99 r2u2 autocran1K conda_forge44 c2d4u

How downloads are counted

Dependencies

Declared dependencies

2 external dependencies (excludes base and recommended)

Installing InvariantCausalPrediction adds 18 packages (11 with compiled code, 16 MB of source tarballs). Heaviest: glmnet 2.7 MB, partykit 2.1 MB, mboost 2.0 MB.

Depends (2)

glmnet mboost

Imports (0)

none

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (from the free-text Author field) (1)

Nicolai Meinshausen

Maintainer since 0.1-4 (2014)

Listed in earlier versions (1)

Dominik Rothenhaeusler

no longer listed · 0.7-1

Releases (14)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
R
R 4.2.0 released · 2022-04-22
R
R 4.1.0 released · 2021-05-18
R
R 4.0.0 released · 2020-04-24
0.8Latest
record
2019-11-10 · current release · diff ↗
R
R 3.6.0 released · 2019-04-26
0.7-2
record
2018-10-08 · diff ↗ ·
removed 1 export
causalDantzig
R
R 3.5.0 released · 2018-04-23
0.7-1
record
2017-10-03 · diff ↗ ·
added 1 export
causalDantzig
R
R 3.4.0 released · 2017-04-21
R
R 3.3.0 released · 2016-05-03
0.6-1
record
2016-05-02 · diff ↗
0.6-0
record
2016-04-12 · diff ↗

Show 11 earlier eventsHide 11 earlier events

0.5-0
record
2016-04-05 · diff ↗
0.4-1
record
2015-10-07 · diff ↗
0.4-0
record
2015-10-01 · diff ↗
0.3-4
record
2015-09-17 · diff ↗
0.3-3
record
2015-06-10 · diff ↗ ·
removed 1 export
hiddenICE
0.3-2
record
2015-05-04 · diff ↗
R
R 3.2.0 released · 2015-04-16
0.3-1
record
2015-04-14 · diff ↗ ·
removed 1 export
feedbackNetwork

added 1 export
hiddenICE
0.3
record
2015-04-13 · diff ↗ ·
added 2 exports
hiddenICP, feedbackNetwork
0.1-4
record
2014-12-12
R
R 3.1.0 released · 2014-04-10

Package metadata

First published: 2014-12-12
Total releases: 14 / 12 yrs
License: GPL
Download size: 15 KB

Appears in task views

Causal Inference

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("InvariantCausalPrediction")

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for InvariantCausalPrediction version 0.8 [Data set]. HJJB, LLC. Data release v2026-10-26. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-26, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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