Main function and methods (sensemakr)Here you will find the main functions of the 


Sensemakr: extending omitted variable bias 

Sensitivity analysis to unobserved confounders 

Sensitivity analysis plots for 


Sensitivity analysis print and summary methods for 
Sensitivity statisticsThese functions compute sensitivity statistics suited for routine reporting in regression table. For example, the 

Computes the robustness value 

Computes the partial R2 and partial (Cohen's) f2 

Partial R2 of groups of covariates in a linear regression model 

Sensitivity statistics for regression coefficients 

Sensitivity plotsThese functions provide direct access to sensitivity contour plots and extreme sensitivity plots for customization. 

Contour plots of omitted variable bias 

Extreme scenarios plots of omitted variable bias 

Add bounds to contour plot of omitted variable bias 

Bias, adjusted estimates and standard errorsGiven a pair of partial R2 values that describes unobsverd confounders, these functions compute the bias, adjusted estimate, adjusted standard errors, and other statistics that one would have obtained in the regression that includes a confounder with such stregth. 


Biasadjusted estimates, standarderrors and tvalues 
Bounds on confoundingFunctions for computing bounds on the maximum strength of unobserved confounding by means of comparison with the explanatory power of observed covariates. 

Bounds on the strength of unobserved confounders using observed covariates 

DatasetsDatasets with applied examples. 

Data from the 2016 referendum for peace with the FARC in Colombia. 

Data from survey of Darfurian refugees in eastern Chad. 