Science Research Weekly

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 Py...

In this episode, I didn’t get a lot of sleep. But I did get a lot of quirky Python, classy SAS, and ridiculous R. References: A systematic review of statistical methodology used to evaluate progression of chronic kidney disease using electronic healthcare records Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverte...

In this episode, RStudio gets a Posit-tively new name, SAS estimates area with  Monte Carlo simulation, and I take in the view of package ‘P2C2M.Skyline.’ References: Progressive censoring schemes for marshall-olkin pareto distribution with applications Assessing model adequacy for Bayesian Skyline plots using posterior predictive simulation Testing for Benford’s Law in very small samples: Si...

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 K...

In this episode, I clued in on the claims that Linear Programming Discriminant analysis is effective, mused on the methods of PROC SQL in SAS, and fired up a virtual machine to use for package ‘vmr’. References: moreThanANOVA: A user-friendly Shiny/R application for exploring and comparing data with interactive visualization LPDA: A new classification method based on linear programming Speedups in...

In this episode, I assessed applications for AI-generated synthetic data, read up on the radical R tutorial rflow , and stepped into the ‘multiverse’ package to manage reporting on many different statistical analyses. References: Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India The risk of bias in denoising methods: Examples from neuroimaging Baye...

In this episode, I explored model selection methods for fending off invading   outliers, marveled at May’s Top 40 CRAN packages, refreshed my suppositions on samples space, and rallied up to replace words in text using package ‘rflashtext’. References: Robust model selection using the out-of-bag bootstrap in linear regression Data-informed deep optimization Compute the multivariate t den...

In this episode, I broadened up my bioinformatics background, found that the latest R-release was a Funny-Looking Kid, and extracted lobster species using package ‘OpenTreeChronograms’.  References: A New Tobit Ridge-Type Estimator of the Censored Regression Model with Multicollinearity Problem Selecting predictive biomarkers from genomic data COBREXA.jl: constraint-based reconstruction and e...

In this episode, I tackled ‘omics’ data with a Bayesian fire engine, soared over a timeline of statistical ideas, and decreased statistical bias by increasing bias understanding. References: A Survey of Statistical Methods for Microbiome Data Analysis PyCon US: Successful Return to In-Person in 2022 PUSH, POP, and reset options for ODS graphics Using Bayesian Additive Regression Trees for Flexible...

In this episode, I learned there are both good and bad R books, umbrella reviews are meta on top of meta, flowcharts can be fashioned in ggplot2, and package ‘ggpie’ is more than mere fluff. References: Monotone Regression: A Simple and Fast O(n) PAVA Implementation Pro Data Visualization Using R and JavaScript: Analyze and Visualize Key Data on the Web Doing Meta-Analysis with R - A Hands-On Guid...

In this episode, I chained together multiple scientific last names, found heaps of PLOS ONE journal articles, and learned how to grow a backbone in R using package ‘backbone’. References: Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R More on Multidimensional Scaling and Unfolding in R: smacof Version 2 A Practical Guide to the Numerical Impl...

In this episode I thought outside the Box-Pierce; fuzzed, sweated, and flustered my way through basic stats; encountered functions and AI for babies; and chronicled the R-package ‘chronicler’. References: NeuralSens: Sensitivity Analysis of Neural Networks econet: An R Package for Parameter-Dependent Network Centrality Measures Modified Quantile Regression For Modeling the Low Birth Rate A Novel C...

In this episode, I got lost in among the statistical trees, flipped a Bernoulli trial coin, and wrapped myself up in package ‘ggblanket’. References: sensobol: An R Package to Compute Variance-Based Sensitivity Indices The R Package stagedtrees for Structural Learning of Stratified Staged Trees Wave Effects of the Fractional Shallow Water Equation and the Fractional Optical Fiber Equation Two Doma...

In this episode, I dived into non-normal distributions, went from rags to ridges, rode the Python Language Summit lightning, and looked at the ‘GenericML’ package. References: Covsim: An R package for Simulating Non-Normal Data for Structural Equation Models Using Copulas Rags2ridges: A One-stop-L2-shop for Graphical Modeling of High-Dimensional Precision Matrices Transmissibility in Interactive N...

In this episode, I dig into the first articles of a new Journal of Statistical Software volume, try not to mix up package ‘nvmix’ with ‘nmix’, scratch my head over Navier-Stokes equations, and take the random R-package section in a metabolomics direction. References: Journal of Statistical Software Volume 102 Convolutional Neural Networks for Very Low-Dimensional LPV Approximations of Incompressib...

In this episode, I stretch before vigorous calisthenics, give a thumbs up to O’Reilly coding books, get to the bottom of R vectorization, try not to panic during   debugging, and showcase the R-package ‘fauxnaif’. References: A NSFD Discretization of Two-Dimensional Singularly Perturbed Semilinear Convection-Diffusion Problems Misnomers regarding outliers and their usefulness in statisti...

In this episode, I jump into Julia packages, estimate how to pronounce the generalized Kibria-Lukman estimator, commiserate with Python, and showcase the R-package ‘rsconnect’. References: Generalized Kibria-Lukman Estimator: Method, Simulation, and Application Trending Julia Packages Use a heat map to visualize an ordinal response in longitudinal data The McNemar test in SAS Happy Birthday Neyman...

In this episode, I play with jackknives, chuckle over Python, list upcoming R conferences, and showcase the R-package ‘RcppBDT’. References: Additive Noise-Induced System Evolution (ANISE) Face Recognition in Single Sample Per Person Fusing Multi-Scale Features Extraction and Virtual Sample Generation Methods An Epidemiological Compartmental Model With Automated Parameter Estimation and Forecastin...

In this episode, I drink from the firehose of CRAN packages, learn a use for ‘two truths and a lie’ in the statistics classroom, and showcase the R-package ‘pipeliner’. References: https://www.frontiersin.org/articles/10.3389/fams.2022.848898/abstract https://www.frontiersin.org/articles/10.3389/fams.2022.852314/full https://stat.ethz.ch/pipermail/r-announce/2022/000681.html https://cran.r-project...

In this episode, I brace myself for the R release of Vigorous Calisthenics, scratch my head at Lyapunov Exponents, survey the brewing battlefield in the AI/ML Wars, and showcase the R-package 'numform'. References: https://journal.r-project.org/archive/2021/RJ-2021-109/RJ-2021-109.pdf https://journal.r-project.org/archive/2021/RJ-2021-080/RJ-2021-080.pdf https://www.jstatsoft.org/article/view/v101...

Trailer for the podcast Statistics Weekly.