Episode 30: The GitHub Secret Sauce

In this episode, I spilled the secret sauce behind my research methods, predicted extreme events, pursued plots off the beaten ggplot path, and added another simulation-based power analysis tool to my belt. References: Statistics Weekly Research GitHub Repository Flexible time-to-event models for double-interval-censored infectious disease data with clearance of the infection as a competing risk Predicting the data structure prior to extreme events from passive observables using echo state network SCpubr: Generate high quality, publication-ready plots of single-cell transcriptomics data Concordances the difference between chi square tests of independence and homogeneity How to create a ggalluvial plot in R? How to create a Sankey plot in R? Understanding the Basics of Package Writing in R R-packages: VIGoR: Variational Bayesian Inference for Genome-Wide Regression ggstats: Extension to 'ggplot2' for Plotting Stats mlpwr: A Power Analysis Toolbox to Find Cost-Efficient Study Designs ARIMAANN: Time Series Forecasting using ARIMA-ANN Hybrid Model makeunique: Make Character Strings Unique