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konfound
Quantify the Robustness of Causal Inferences
View on CRAN: Click
here
Download and install konfound package within the R console
Install from CRAN:
install.packages("konfound")
Install from Github:
library("remotes")
install_github("cran/konfound") Install by package version:
library("remotes")
install_version("konfound", "1.0.2") Attach the package and use:
library("konfound")
Maintained by
Joshua M Rosenberg
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.konfound . https://github.com/konfound-project/konfound/issues . https://github.com/konfound-project/konfound . https://konfound-it.org/konfound/ . konfound results . konfound.pdf . Introduction to konfound . konfound_1.0.2.tar.gz . konfound_1.0.2.zip . konfound_1.0.2.zip . konfound_1.0.2.zip . konfound_1.0.2.tgz . konfound_1.0.2.tgz . konfound_1.0.2.tgz . konfound_1.0.2.tgz . konfound_1.0.2.tgz . konfound_1.0.2.tgz . konfound archive . https://CRAN.R-project.org/package=konfound .
First Published: 2018-04-06
Latest Update: 2021-06-01
Description:
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)
and Frank et al. (2013)
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.
How to cite:
Joshua M Rosenberg (2018). konfound: Quantify the Robustness of Causal Inferences. R package version 1.0.2, https://cran.r-project.org/web/packages/konfound. Accessed 29 Mar. 2025.
Previous versions and publish date:
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Complete documentation for konfound
Functions, R codes and Examples using
the konfound R package
Some associated functions: binary_dummy_data . concord1 . konfound . launch_shiny . mkonfound . mkonfound_ex . pkonfound . summary.konfound .
Some associated R codes: binary_dummy_data.R . concord1.R . helper_output_dataframe.R . helper_output_print.R . helper_output_table.R . helper_plot_correlation.R . helper_plot_threshold.R . konfound-glm-dichotomous.R . konfound-glm.R . konfound-lm.R . konfound-lmer.R . konfound.R . mkonfound-data.R . mkonfound.R . nonlinear_auxiliary.R . pkonfound.R . test_sensitivity.R . test_sensitivity_ln.R . tkonfound.R . zzz.R . Full konfound package functions and examples
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