In this episode I thought outside the Box-Pierce; fuzzed, sweated, and flustered my way through basic stats; encountered functions and AI for babies; and chronicled the R-package ‘chronicler’.
References:

NeuralSens: Sensitivity Analysis of Neural Networks
econet: An R Package for Parameter-Dependent Network Centrality Measures
Modified Quantile Regression For Modeling the Low Birth Rate
A Novel Correction for the Adjusted Box-Pierce Test
Comments on identifying causal relationships in nonlinear dynamical systems via empirical mode decomposition
Adaptive numerical simulations with Trixi.jl: A case study of Julia for scientific computing
The balanced bootstrap in SAS
Game-changer AI tool will save mothers and babies
D. Mayo & D. Hand: “Statistical significance and its critics: practicing damaging science, or damaging scientific practice?”
Fuzzy Clustering: Definition
How to Find the P value: Process and Calculations
The Difference Between an Odds Ratio and a Predicted Odds
‘Data analysis with tidyverse’ workshop
Think like a programmeR: the workshop
How to add labels at the end of each line in ggplot2?
Subsetting with multiple conditions in R
Hierarchical data visualization with Shiny and D3
chronicler: Add Logging to Functions

R-packages:

Rwclust: Random Walk Clustering on Weighted Graphs
ScaleSpikeSlab: Scalable Spike-and-Slab
webshot2: Take Screenshots of Web Pages
mverse: Tidy Multiverse Analysis Made Simple
chronicler: Adding Logging to Functions

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