Description

In this episode, I browsed yet more Monte Carlo simulation papers, introduced myself to various structural equation models, championed base R, and went neural to neural with the R package ‘cito’.

References:

• A kernel mixing strategy for use in adaptive Markov chain Monte Carlo and stochastic optimization contexts
• Bootstrap-based inferential improvements to the simplex nonlinear regression model
• Python 3.10.6 is available
• Stanine Score: Definition, Examples, How to Convert
• The Four Models You Meet in Structural Equation Modeling
• Downstream Bioinformatics Analysis of Omics Data with edgeR
• Simulating data from a non-linear function by specifying a handful of points
• RObservations #36: Opinions on RStudio’s name change. A Bayesian approach with Stan
• Base-R Is Alive and Well

R-packages:

• cito Building and Training Neural Networks
• cbioportalR Browse and Query Clinical and Genomic Data from cBioPortal
• gtreg Regulatory Tables for Clinical Research
• diffdfs Compute the Difference Between Data Frames
• fake Flexible Data Simulation Using the Multivariate Normal Distribution
• seeker Simplified Fetching and Processing of Microarray and RNA-Seq Data
• ympes Collection of Helper Functions