Quantify the Robustness of Causal Inferences
Calculate delta star for sensitivity analysis
Calculate rxy based on ryxGz, rxz, and ryz
Calculate R2xz based on variances and standard error
Calculate R2yz based on ryxGz and R2
Perform a Chi-Square Test
Extract Degrees of Freedom for Fixed Effects in a Linear Mixed-Effects...
Konfound Analysis for Generalized Linear Models with Dichotomous Outco...
Konfound Analysis for Generalized Linear Models
Konfound Analysis for Linear Models
Konfound Analysis for Linear Mixed-Effects Models
Konfound Analysis for Various Model Types
Meta-Analysis and Sensitivity Analysis for Multiple Studies
Output data frame based on model estimates and thresholds
Output printed text with formatting
Output a Tidy Table from a Model Object
Perform sensitivity analysis for published studies
Plot Correlation Diagram
Plot Effect Threshold Diagram
Draw Figures for Change in Effect Size in 2x2 Tables
Perform Sensitivity Analysis on 2x2 Tables
Verify regression model with control variable Z
Verify unconditional regression model
Package Initialization Functions and Utilities
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., the treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with nor which the treatment had no effect 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.
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