Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
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Updated
Jul 19, 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)
Comparative Social Research with Multi-level Modelling in R
Copula Based Bivariate Beta-Binomial Model for Diagnostic Test Accuracy Studies
Using Fixed Effect, Random Effect and Hausman Taylor IV to estimate the impacts on wage
An R package for I-prior regression
Stata and R programs to automatically quasi-demean regressors following FGLS-RE or MLE-RE 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.
Monte Carlo Simulation comparing the performance of various estimators for panel data with binary dependent variable models
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