-François Grolleau-
Anesthesiology and Critical Care physician
Member of Technical Staff at
GUIDE-AI, Stanford Division of Computational Medicine
Anesthesiology and Critical Care physician
Member of Technical Staff at
GUIDE-AI, Stanford Division of Computational Medicine
This R package provides a flexible framework for estimating the variance-covariance matrix of estimated parameters. Estimation relies on providing unbiased estimating functions to compute the empirical sandwich variance.
Provided with covariates and predicted individualized treatment effects (ITEs), this Python library estimates the function mapping observations to their probabilities of belonging to clusters of individuals with identical ITEs.
This Python library lets you boostrap vector-valued statistics fast as it uses parallel processing. Ploting as well as computation of bias-corrected and accelerated confidence intervals are available.