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

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