Trends

Trending R packages reveal a strong focus on advanced statistical modeling and analysis. Several packages address specialized experimental design needs, particularly for cDNA microarray experiments (soptdmaeA, optrcdmaeAT, optbdmaeAT), highlighting ongoing research in genomics and bioinformatics. Another significant trend is the rise of robust statistical methods, exemplified by the multiverse package for exploring the sensitivity of findings to analytical choices, and rnmamod for Bayesian network meta-analysis handling missing data.

Alongside statistical advancements, there's a notable emphasis on data-driven applications in specific domains. Packages like WaverideR for wavelet analysis in geoscience, rNeighborGWAS for incorporating neighbor effects in GWAS, and accept for COPD exacerbation prediction demonstrate the increasing use of R in specialized scientific fields. The omock package underscores the growing need for efficient data simulation and testing within the context of observational medical data.

Finally, the popularity of packages like FAfA (factor analysis), caviarpd (cluster analysis), and semsfa (stochastic frontier models) reflects a broader interest in user-friendly tools for complex statistical tasks. The inclusion of packages focused on tutorials (positron.tutorials) and utility functions (wrappedtools) suggests a community effort to improve accessibility and efficiency in R programming. The RAINBOWR package for genome-wide association studies further emphasizes the importance of advanced statistical methods in genetics research.