Description

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'