In this episode, I pared down three important -omics data considerations, coded my way into wave of new R packages, boasted about the Big Book of R, and havered over the ‘dots’ package.
References:

Editorial: Statistical Data Science - Theory and Applications in Analyzing Omics Data
Introducing riskCommunicator: An R package to obtain interpretable effect estimates for public health
On the mixed Kibria–Lukman estimator for the linear regression model
Rejection Region (Critical Region) for Statistical Tests
Join rstudio::conf(2022) Virtually
ggdensity: A new R package for plotting high-density regions
R Color
Generalized Linear Models, Part I: The Logistic Model
Part 1 of 3: 300+ milestone for Big Book of R
Part 2 of 3: 300+ milestone for Big Book of R
Part 3 of 3: 300+ milestone for Big Book of R

R-packages:

htmcglm:  Hypothesis Testing for McGLMs
nextGenShinyApps:  Advanced Tools for Building the Next Generation of 'Shiny' Applications and Dashboards
robustmeta:  Robust Inference for Meta-Analysis with Influential Outlying Studies
lnmCluster:  Perform Logistic Normal Multinomial Clustering for Microbiome Compositional Data
binaryTimeSeries:  Analyzes a Binary Variable During a Time Series
codebookr:  Create Codebooks from Data Frames
dots:  Dot Density Maps
ggpackets:  Package Plot Layers for Easier Portability and Modularization
trouBBlme4SolveR:  Troubles Solver for 'lme4'

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