konfound: Quantify the Robustness of Causal Inferences
Statistical methods that quantify the conditions necessary to alter
inferences, also known as sensitivity analysis, are becoming increasingly
important to a variety of quantitative sciences. A series of recent works,
including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al.
(2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses
by considering the characteristics of omitted variables or unobserved cases
that would change an inference if such variables or cases were observed. These
analyses generate statements such as "an omitted variable would have to be
correlated at xx with the predictor of interest (e.g., treatment) and outcome
to invalidate an inference of a treatment effect". Or "one would have to replace
pp percent of the observed data with null hypothesis cases to invalidate the
inference". We implement these recent developments of sensitivity analysis and
provide modules to calculate these two robustness indices and generate such
statements in R. In particular, the functions konfound(), pkonfound() and
mkonfound() allow users to calculate the robustness of inferences for a user's
own model, a single published study and multiple studies respectively.
| Version: |
0.4.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
broom, broom.mixed, crayon, dplyr, ggplot2, mice, purrr, rlang, tidyr, tibble |
| Suggests: |
margins, pbkrtest, devtools, forcats, knitr, lme4, rmarkdown, roxygen2, testthat, ggrepel, covr |
| Published: |
2021-06-01 |
| Author: |
Joshua M Rosenberg [aut, cre],
Ran Xu [ctb],
Qinyun Lin [ctb],
Spiro Maroulis [ctb],
Kenneth A Frank [ctb] |
| Maintainer: |
Joshua M Rosenberg <jmichaelrosenberg at gmail.com> |
| BugReports: |
https://github.com/jrosen48/konfound/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/jrosen48/konfound |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
konfound results |
Documentation:
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