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borrowr

0.2.0Package record

Estimate Causal Effects with Borrowing Between Data Sources

0packages depend
3.6Kdownloads / year
–test coverage
11/13flavors OK

Overview

About

Estimate population average treatment effects from a primary data source with borrowing from supplemental sources. Causal estimation is done with either a Bayesian linear model or with Bayesian additive regression trees (BART) to adjust for confounding. Borrowing is done with multisource exchangeability models (MEMs). For information on BART, see Chipman, George, & McCulloch (2010) doi:10.1214/09-AOAS285. For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) doi:10.1093/biostatistics/kxx031.

Install

Health

CRAN checks
2NOTE11OK
Check messages
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Jeffrey A. Boatman <jeffrey.boatman@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = c("Jeffrey", "A."),
                      family = "Boatman",
                      role = c("aut", "cre"),
                      email = "jeffrey.boatman@gmail.com"),
               person(given = c("David", "M."),
                      family = "Vock",
                      role = "aut"),
               person(given = c("Joseph", "S."),
                      family = "Koopmeiners",
                      role = "aut"))
as necessary.

CRAN check page ↗

Slowest check: 4.1 min · r-devel-windows-x86_64
Status changes
  • NOTE11 OK, 2 NOTENow
  • NOTE12 OK, 2 NOTE2026-04-22
  • ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
  • NOTE12 OK, 2 NOTE2026-03-10
Advisories

none known

Documentation

In release 0.2.0
READMEYes · 62 wordsVignettesYes · 1 · dynamicNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
3
Examples that run
100%3 run in a plain check
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
50%
Help pages citing references
67%

Downloads

3.6K
CRAN downloads in the past year
Rank #9,321 · ~10/day · ~299/mo
Daily download trend is not available in this view yet.
36330 days
1K90 days
3.6K1 year
Compare downloads with other packages →
Also on381 r2u20 autocran79 c2d4u

Dependencies

Declared dependencies
7 external dependencies (excludes base and recommended)

Installing borrowr adds 3 packages (3 with compiled code, 6.4 MB of source tarballs). Heaviest: BART 3.6 MB, Rcpp 1.8 MB, mvtnorm 1.0 MB.

Depends (1)
R >= 3.5.0
Imports (3)
LinkingTo (1)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (from the free-text Author field) (3)
Author, Maintainer since 0.1.0 (2019)
Releases (3)

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
  • 0.2.0Latest
    record
    2020-12-08 · current release · diff ↗ ·
    added 1 export
    credint
  • R
    R 4.0.0 released · 2020-04-24
  • 0.1.1
    record
    2019-10-30 · diff ↗
  • 0.1.0
    record
    2019-07-19
  • R
    R 3.6.0 released · 2019-04-26

Package metadata

First published
2019-07-19
Total releases
3 / 7 yrs
License
GPL (>= 3) OSI
Minimum R
≥ 3.5.0
Bundled data
3.1 KB / 1 file
Download size
94 KB
Appears in task views

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("borrowr")

Cite the R Observatory

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

APA

Balamuta, J. J. (2026). R Observatory: Metrics for borrowr version 0.2.0 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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