Browse Items (8208 total)

One of the most attractive features of R is its linear modeling capabilities. We describe
a Python package, salmon, that brings the best of R’s linear modeling functionality to
Python in a Pythonic way – by providing composable objects for…

Holistic linear regression extends the classical best subset selection problem by adding
additional constraints designed to improve the model quality. These constraints include
sparsity-inducing constraints, sign-coherence constraints and linear…

For randomized controlled trials (RCTs) with a single intervention’s impact being measured on multiple outcomes, researchers often apply a multiple testing procedure (such
as Bonferroni or Benjamini-Hochberg) to adjust p values. Such an adjustment…

Empirical likelihood enables a nonparametric, likelihood-driven style of inference without relying on assumptions frequently made in parametric models. Empirical likelihoodbased tests are asymptotically pivotal and thus avoid explicit studentization.…

This article introduces the Python package gcimpute for missing data imputation.
Package gcimpute can impute missing data with many different variable types, including
continuous, binary, ordinal, count, and truncated values, by modeling data as…

The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018).
It provides functionalities to estimate parameters in causal models based on machine…

Market models constitute a significant cornerstone of empirical applications in business, industrial organization, and policymaking macroeconomics. The econometric literature proposes various estimation methods for markets in equilibrium, which…

The tipsae package implements a set of small area estimation tools for mapping proportions and indicators defined on the unit interval. It provides for small area models
defined at area level, including the classical beta regression, zero- and/or…

The existence of latent clusters with different responses to a treatment is a major
concern in scientific research, as latent effect heterogeneity often emerges due to latent
or unobserved features – e.g., genetic characteristics, personality…

This paper introduces software packages for efficiently imputing missing data using
deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement
a recently developed approach to multiple imputation known as MIDAS, which…