Information Theoretic Analysis of Gene Expression Data R package SurprisalAnalysis version 3.0.1 Implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) <doi:10.1371/journal.pone.0108549>. Zadran S. et al. (2014) <doi:10.1073/pnas.1414714111>. Su Y. et al. (2019) <doi:10.1371/journal.pcbi.1007034>. Bogaert K. A. et al. (2018) <doi:10.1371/journal.pone.0195142>. Software Imports: matlib, shiny, ggplot2, shinythemes, shinyjs, shinycssloaders, patchwork, DT Suggests: knitr, rmarkdown, pheatmap, peakRAM, data.table, BiocManager, clusterProfiler, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db, httpuv Annice Najafi <annicenajafi27@gmail.com> Comprehensive R Archive Network (CRAN) Annice Najafi [aut, cre] (ORCID: <https://orcid.org/0000-0003-0679-9397>) MIT + file LICENSE (https://CRAN.R-project.org/package=SurprisalAnalysis/LICENSE) 2026-04-23 application/tgz https://CRAN.R-project.org/package=SurprisalAnalysis doi:10.32614/CRAN.package.SurprisalAnalysis