Description

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