Episode 20: The Count of Monte Carlo

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