Episode 9: Boxes and Babies

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