Author: | 1.Overview | 1.1 What is Geographically Weighted Panel Regression (GWPR) | 1.2 Why we need to use GWPR | 1.3 How we can perform GWPR | 2 Functions | 2.1 bw.GWPR(): who is your neighborhoods? | 2.1.1 Choosing $h$: the kernel bandwidth | 2.1.2 Adaptive and Fixed Distance Bandwidth | 2.1.3 "bigdata" Setting | 2.1.4 "human.set.range" Setting | 2.1.5 "gradientIncrement" Setting | 2.1.6 "model" Setting | 2.2 GWPR.moran.test(): are the residuals really clustering | 2.3 Model Selection: which model is better? | 2.3.1 GWPR.pFtest(): Locally F test | 2.3.2 GWPR.plmtest(): Locally Breusch-Pagan Lagrange Multiplier test | 2.3.3 GWPR.phtest(): Locally Hausman test | 2.4 GWPR(): the regression process | END | Archive in GitHub: | BugReports: | Reference

Overview | Minimal linear GWPR example | Accessing results | Bandwidth selection (grid search) | Full pipeline with gwpr() | Diagnostics | Long-running examples