https://annicenajafi.r-universe.devRecent package updates in annicenajafiR-universehttps://github.com/annicenajafi.pnghttps://annicenajafi.r-universe.devSat, 17 Oct 2026 04:40:59 GMTannicenajafi27@gmail.com (Annice Najafi)Provides different methods of multi-criteria decision analysis.https://github.com/r-universe/annicenajafi/actions/runs/39006609145Sat, 17 Oct 2026 04:40:59 GMTRMCDA0.3successhttps://annicenajafi.r-universe.devhttps://github.com/annicenajafi/rmcdaRMCDA_guidelines.RmdRMCDA_guidelines.htmlRMCDA Guidelines2025-01-21 03:52:012025-05-20 20:57:17annicenajafi27@gmail.com (Annice Najafi)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, which helps to capture coordinated gene expression patterns under different biological conditions. References where Surprisal analysis has been used for analyzing gene expression data (using the same methodology provided within this R package) are Kravchenko-Balasha et al (2014) <doi:10.1371/journal.pone.0108549>, Zadran et al. (2014) <doi:10.1073/pnas.1414714111>, Su et al. (2019) <doi:10.1371/journal.pcbi.1007034>, Bogaert et al. (2018) <doi:10.1371/journal.pone.0195142>.https://github.com/r-universe/annicenajafi/actions/runs/42015155430Wed, 08 Jul 2026 17:33:31 GMTSurprisalAnalysis0.2successhttps://annicenajafi.r-universe.devhttps://github.com/annicenajafi/surprisalanalysisNormalizationsZeroHandlingComparisons.RmdNormalizationsZeroHandlingComparisons.htmlNormalizations Comparisons2025-08-23 07:28:132025-08-23 07:28:13RunTimeRecording.RmdRunTimeRecording.htmlRun Time Recording2025-08-23 07:28:132025-08-25 04:18:32SurprisalAnalysisGuideline.RmdSurprisalAnalysisGuideline.htmlSurprisal Analysis Guidelines2025-08-23 07:28:132025-08-24 07:26:12