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’.
scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility DataspNNGP R Package for Nearest Neighbor Gaussian Process ModelsFeller-Pareto and Related Distributions: Numerical Implementation and Actuarial ApplicationsA new outlier detection method for spherical dataConfidence bands in survival analysisHow to stagger labels on an axis in PROC SGPLOTExperimental Design: Definition and TypesThe Difference Between R-squared and Adjusted R-squaredTop 5 Python Libraries for Data Science (2023) EditionBase-R and Tidyverse Code, Side-by-SideR Design Patterns, Base-R vs. Tidyverse With a view toward the teaching of R beginnersHello Shiny PythonWhich programming language should I learn?R Markdown Tips: Code, Images, Comments, Tables, and moreIs Data Science a Dying Profession?evalR Evaluation of Unverified Codegoldfish Statistical Network Models for Dynamic Network Databaffle Make Waffle Plots with Base GraphicsggDoE Modern Graphs for Design of Experiments with 'ggplot2'jjAnno An Annotation Package for 'ggplot2' OutputmultinomialLogitMix Clustering Multinomial Count Data under the Presence of CovariatesIntLIM Integration of Omics Data Using Linear Modelingscrutiny Error Detection in ScienceshinyHugePlot Efficient Plotting of Large-Sized Datactmva Continuous-Time Multivariate Analysisggcoverage Visualize Genome Coverage with Various Annotations