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dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

esmucler.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

gdpc

This package provides functions for computing the Generalized Dynamic Principal Components proposed in Peña and Yohai (2016). It also provides a function to automatically choose the number of components and lags, using the criteria described in Peña, Smucler and Yohai (2020).

mmlasso

R package to compute the MM-Lasso and adaptive MM-Lasso estimators proposed in Smucler and Yohai (2017). A more efficient implementation, with a lot more options and functionalities, is available as part of the pense package https://cran.r-project.org/web/packages/pense/index.html.

odpc

This package provides functions for computing One-Sided Dynamic Principal Components, a novel multivariate time series dimension reduction technique proposed in Peña, Smucler and Yohai (2019) (https://doi.org/10.1080/01621459.2018.1520117).

optimal_adjustment

Scripts to reproduce examples in Rotnitzky and Smucler (2020).

pygdpc

splitreg

This repository contains an R package to perform split regularized regression as defined in Christidis, Lakshmanan, Smucler and Zamar (2019) (https://arxiv.org/abs/1712.03561).

statsmodels

Statsmodels: statistical modeling and econometrics in Python