Software Development

Research in biostatistics produces frequently produces new methods for analyzing complex data.  In developing these methods, researchers in biostatistics often concurrently develop software to apply these new methods.  Members of the department of bioinformatics and biostatistics have been active in developing software that implements the methods they have developed.  Below is a brief list of some of the software programs developed by our faculty, staff, and students.

Statistics and Biostatistics

htestClust, Reweighted Marginal Hypothesis Tests for Clustered Data, R package
msSurv, Nonparametric estimation for multistate models, R package
RankAggreg, Rank aggregation of ordered lists, R package
clValid, Validation of clustering results, R package
Rank sum tests for clustered data, R function
Signed rank test for clustered data, R function
Marginal correlation for clustered data, R function
Synergy, R function for interaction testing of drugs
CI of Interaction Index, R function for estimating interaction indices



MmPalateMiRNA, Murine Palate miRNA Expression Analysis, Bioconductor package
dna, Differential network analysis, R package
svapls, Surrogate variable analysis via partial least squares, R package
Ensemble classification, R function
Empirical Bayes screening, R function



Standardization and denoising of mass spectra, FORTRAN function
pkDACLASS Peak Detection and Classification using MALDI-TOF Mass Spectra, Archived R package

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