The Big Book of R adds five new free, open-source books covering R programming for production, survey analysis, causal inference, biodiversity data, and natural resources.
Compares Satterthwaite, Liu, and leading-term approximations for tail probabilities of weighted sums of chi-squared variables in high-dimensional genomic data.
Explores missing likelihood-ratio tests in survey regression models, comparing Wald, score, and Rao-Scott tests with sample vs. population scaling.
Explores challenges and algorithms for weighted sampling without replacement in R, focusing on achieving specified marginal probabilities.
Analyzing the probability of covering all birthdays in a group and the expected number of people needed, framed as the Coupon Collector's Problem.
Explores automatic delta-method transformations for variance estimates in R's survey package, enabling correct standard errors after mathematical operations.
Explains a crucial flaw in using boxplots for data visualization and suggests better alternatives.
An update on the polymath research project about non-transitive dice and its statistical implications for the Wilcoxon/Mann-Whitney test.
Discusses the nuanced role of assumptions in statistics, distinguishing between necessary and sufficient conditions, and their impact on interpreting models like linear regression.
Announces the addition of 6 new R programming books to the Big Book of R collection, covering statistics, machine learning, and data science.
Explains key causal inference estimands (ATE, ATT, ATU) and how to calculate them using observational data, with a focus on R and the potential outcomes framework.
![Big Book of R at 400 [New milestone!]](https://alldevblogs.blob.core.windows.net/thumbs/article-12e01710b8e8-full-a2e2e955.webp)
The Big Book of R, a curated collection of free R programming books, celebrates a milestone of over 400 entries and requests community support for hosting costs.
Explores the asymptotic behavior of parameter estimates in linear mixed models, focusing on the loglikelihood as a quadratic form in Gaussian variables.
Analyzes the accuracy of a leading eigenvalue approximation for quadratic forms in Gaussian variables, comparing it to traditional methods.
Explains why the svylme package uses maximum likelihood instead of REML for survey-weighted linear mixed models, focusing on design and sampling constraints.
Explores sparse correlation structures in statistical models and the conditions under which the Central Limit Theorem holds for dependent data.
Announcing a preprint for the svylme package, introducing the svy2lme function for fitting linear mixed models to complex survey data.
A guide to manually generating predicted values for logistic regression using matrix multiplication in R, as an alternative to the predict() function.
A technical guide to performing multilevel multinomial conjoint analysis using R, Bayesian modeling, and statistical packages.
A detailed analysis of an optimal stopping problem involving drawing cards for reward, exploring mathematical strategies and first-principles reasoning.
