Description

In this episode, I kept it short and sweet with a new statistical journal reference, data science job scraping, and random learning for R-packages.

References:

• Generalized resilience and robust statistics
• Stochastic continuum-armed bandits with additive models: Minimax regrets and adaptive algorithm
• Generate random ID values for subjects in SAS
• Linear Regression Equation Explained
• An Example of Specifying Within-Subjects Factors in Repeated Measures
• Designing Online Data Science Training for the Modern Age
• Optimizing my search for Data scientist jobs by scraping Indeed with R
• Seeking Inspiration from Random Learning

R-packages:

1. BiOFI: Feature Identification Between Metabolome and Microbiome in Disease and Health
2. glmmrBase: Specification of Generalised Linear Mixed Models
3. REDCapTidieR: Extract 'REDCap' Databases into Tidy 'Tibble's
4. verbaliseR: Make your Text Mighty Fine
5. betaSandwich: Robust Confidence Intervals for Standardized Regression Coefficients
6. DynForest: Random Forest with Multivariate Longitudinal Predictors
7. roBTT: Robust Bayesian T-Test
8. alqrfe: Adaptive Lasso Quantile Regression with Fixed Effects
9. recurrentpseudo: Creates Pseudo-Observations and Analysis for Recurrent Event Data
10. alkahest: Pre-Processing XY Data from Experimental Methods