Science Research Weekly

Get ready for the top 10 errors in R, self-assembling peptides, microbiome mishaps, a theory-defying dwarf planet ring system, and a chance to save tiny hearts.  Science On. References The virtual microbiome: A computational framework to evaluate microbiome analyses A dense ring of the trans-Neptunian object Quaoar outside its Roche limit 3D Printing of Self-Assembling Nanofibrous Multidomain...

Get ready for heart-attack healing hydrogels, super rare supernovas, volumes of viroid sequences, stealth oceans, finding hex logos and packages by topic in CRAN, and NASA funding for exobiology.  Science On. References This groundbreaking biomaterial heals tissues from the inside out A high-mass X-ray binary descended from an ultra-stripped supernova Mining metatranscriptomes reveals a vast...

Get ready for new deep sea bacterial phyla, extra ancient caecilian fossils, R Journal Volume 14/3 JuliaHub 6.0, visualizing star maps in R, and an IceCube neutrino competition!  Science Research Weekly covers 3 topic areas: Research Articles, Research Tools, and Research Funding.  Science On. References Global patterns of diversity and metabolism of microbial communities in deep-sea hyd...

Welcome to Science Research Weekly, the successor to my first podcast, Statistics Weekly.  This abbreviated trial run of an episode includes: a research article on microbes from a volcanic island, the largest stellar catalogue to date, Arduino project ideas for 2023, and NSF Research and Development grants. References: The Early Microbial Colonizers of a Short-Lived Volcanic Island in the Kin...

Welcome to Science Research Weekly.  This is the successor to my first podcast, Statistics Weekly.  Every week, I will cover 3 topic areas: Research Articles, Research Tools, and Research Funding.  Check out the accompanying blog at: https://scienceresearchweekly.wordpress.com/

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In this episode, I pondered the problem of false positives in publications, rotated around the JuliaHub, machinated over machine learning projects, and put R package ‘drawCell’ to the test. References: Are most published research findings false in a continuous universe? Principal component analysis A no-free-lunch theorem for multitask learning JuliaHub Signal-to-noise ratio and statistical tests...

In this episode, I marched through machine learning abstractions, brought home the BQN, discovered statistics projects to work on over Christmas, and found the Gosling that lays the golden egg, References: Quantifying uncertainty of machine learning methods for loss given default Evaluating the performance of memory type logarithmic estimators using simple random sampling A fast kernel independenc...

In this episode, I got into the spirit of Sinterklaas with Python, jumpstarted Julia learning, cleared up confusion between ANOVA and regression results, and color paletted the night away. References: Estimation of population variance under ranked set sampling method by using the ratio of supplementary information with study variable 10 Simple Reasons to Learn the Julia Programming Language (in 20...

In this episode, I picked the choicest R journal articles, served up a digital poker hand in SAS, and tried not to let a defective R-package get me down. References: brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R shinybrms: Fitting Bayesian Regression Models Using a Graphical User Interface for the R Package brms R-miss-tastic: a unified platform for missi...

In this episode, I skimmed over a dimension estimator, skipped past a piecewise linear curve, and slipped by the launch of a simple data science ebook. References: The generalized ratios intrinsic dimension estimator The area under a piecewise linear curve Adapted Randomization confusing statistical term #13: missing at random and missing completely at random When to Use plotly? PCA for Categorica...

In this episode, I dusted off my genomic gloves with the HCLC-FC and CLIMB methods, waxed poetic with Python’s latest alpha release, and solved plotting mysteries with R-package ‘sherlock’. References: Regression discontinuity threshold optimization Explaining predictive factors in patient pathways using autoencoders HCLC-FC: A novel statistical method for phenome-wide association studies Uncertai...

In this episode, I took advantage of more than one free data science course, mused over mappings for R, and helped myself to a heap of helper functions with R package ‘mlmhelpr’. References: The Hybrid Model of Autoregressive Integrated Moving Average and Fuzzy Time Series Markov Chain on Long Memory Data Predicting prediction: A systematic workflow to analyze factors affecting the classification...

In this episode, I waved my hand across several financial data journal articles,  took advantage of free data science tutorials, learned how to run a dev   environment from a flash drive, and did a double take at a seeming CRAN package duplication. References: Analyzing Intraday Financial Data in R: The highfrequency Package A Practitioner's Guide and MATLAB Toolbox for Mixed Freque...

In this episode, I tried to keep my acronyms straight, dealt with big and tall (data), poked the ribs of SPSS, and contrasted the PIC with the AIC (more acronyms on the march!) References: Fast Penalized Regression and Cross Validation for Tall Data with the oem Package synthACS: Spatial Microsimulation Modeling with Synthetic American Community Survey Data BGVAR: Bayesian Global Vector Autoregres...

In this episode, I spilled the secret sauce behind my research methods, predicted extreme events, pursued plots off the beaten ggplot path, and added another simulation-based power analysis tool to my belt. References: Statistics Weekly Research GitHub Repository Flexible time-to-event models for double-interval-censored infectious disease data with clearance of the infection as a competing risk P...

In this episode, I contributed to the Python Developers Survey, wiped a sweaty brow over the Isoband Incident, and booted up package 'bupaverse' for business process analytics. References: The R Journal Volume 14/1 gsDesign 3.4.0 is released Improvements in handling bytes encoding Join the Python Developers Survey 2022: Share and learn about the community Who rotated my cookie?  CRAN and the...

In this episode, I netted new ways to knock neural networks out of the park, let R do my calculus calculations for me, garnered gentle overviews to many   machine learning topics, and made sure my phylogenic trees were publication ready with the R package ‘CancerEvolutionVisualization'. References: Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia calculus: High-Di...

In this episode, I kept it quick with appropriate microbial resistance data procedures, using a TI 83 calculator for statistics, and hoping for an easy entry to NEON data wrangling with package ‘neonOS’. References: Statistical Network Analysis with Bergm ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models AMR: An R Package for Working with Antimicrobial Resistance Data new...

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 Sp...

In this episode, I found that Bambi (Bayesian Model Building Interface) is more than a little deer, rooted for the round-robin operator for genetic algorithms, and had my convictions that machine learning is not magic reinforced. References: HighFrequencyCovariance:  A Julia Package for Estimating Covariance Matrices Using High Frequency Financial Data Bambi: A Simple Interface for Fitting Ba...

In this episode, I chose mediation analysis over a hospital heist, gained additional sample size calculation insights, memorized machine learning methods, and battled my way up a text-based tower using package ‘rwarrior’. References: Hierarchical Clustering with Contiguity Constraint in R Blang: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribu...

In this episode, I bite off more journal articles than I could chew, bookmarked how to reorder SGPLOT bars, was underwhelmed by R surprises, and muddled through the meta-package ‘easystats’. References: On the Programmatic Generation of Reproducible Documents Automatic Identification and Forecasting of Structural Unobserved Components Models with UComp exuber: Recursive Right-Tailed Unit Root Test...

In this episode, I grabbed the tools to conduct mobility data analysis and interactive plots in Python, detected outliers in spherical data, considered the next programing language to learn, and readied the statistical syrup for waffle plots using package ‘baffle’. References: scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data spNNGP R Package for...

In this episode, I played catch-up with the Journal of Statistical Software, schemed how to snag a top spot in the XP Learning Challenge, and roared alongside the R-package ‘TRexSelector’. References: modelsummary: Data and Model Summaries in R stringi: Fast and Portable Character String Processing in R evgam: An R Package for Generalized Additive Extreme Value Models Julia 1.8 Highlights The Box-...