Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
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Updated
Apr 16, 2024 - Python
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
ggplot-based graphics and useful functions for GAMs fitted using the mgcv package
Covers the basics of mixed models, mostly using @lme4
Testing differences in cell type proportions from single-cell data.
A document introducing generalized additive models.📈
An R package for extracting results from mixed models that are easy to use and viable for presentation.
👓 Functions related to R visualizations
Mixed models @lme4 + custom covariances + parameter constraints
Workshop on using Mixed Models with R
Demonstration of alternatives to lme4
Functions for using mgcv for mixed models. 📈
Illustrate CR models with individual heterogeneity (multistate, random-effect, finite-mixture)
Copula Based Bivariate Beta-Binomial Model for Diagnostic Test Accuracy Studies
Comparative Social Research with Multi-level Modelling in R
Using Fixed Effect, Random Effect and Hausman Taylor IV to estimate the impacts on wage
An R package for I-prior regression
a meta-analysis on the effect of intravenous magnesium on myocardial infarction
Raw files for a document providing an overview of mixed models from varying perspectives.
Cluster-specific logistic regression models for whether an NBA team will make the playoffs given the current statistics of that team. Specifically uses population averaged models (PA) based on generalized estimating equations (GEE); Also, uses cluster-specific (each team) random effects models
Monte Carlo Simulation comparing the performance of various estimators for panel data with binary dependent variable models
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