In this episode, I found that Bambi (Bayesian Model Building Interface) is more than a little deer, rooted for the round-robin operator for genetic algorithms, and had my convictions that machine learning is not magic reinforced.
References:

HighFrequencyCovariance:  A Julia Package for Estimating Covariance Matrices Using High Frequency Financial Data
Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python
Spbsampling: An R Package for Spatially Balanced Sampling
plot3logit: Ternary Plots for Interpreting Trinomial Regression Models
Learning Base R (2nd Edition)
Python and R for the Modern Data Scientist
Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis
Healthcare researchers must be wary of misusing AI
New method to identify symmetries in data using Bayesian statistics
Python 3.11.0rc2 is now available
Complex Layouts using the SG Procedures
Factor Analysis Guide with an Example
How to Choose Appropriate Clustering Method for Your Dataset
How to Apply AI to Small Data Sets?
The R Consortium Needs Your Help with satRdays
Visualizing OLS Linear Regression Assumptions in R

R-packages:

TPCselect:  Variable Selection via Threshold Partial Correlation
historicalborrow:  Non-Longitudinal Bayesian Historical Borrowing Models
historicalborrowlong:  Longitudinal Bayesian Historical Borrowing Models
kgp:  1000 Genomes Project Metadata
DBIsqldf:  Manipulate R Data Frames Using SQL
latentFactoR:  Data Simulation Based on Latent Factors
TSdeeplearning:  Deep Learning Model for Time Series Forecasting

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