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STATassist - Standardised Statistical Comparison Workflows

Runs the applicable tests for a comparison in one call and returns standardised result tables. One-sample, two-group, multi-group, factorial and categorical workflows report parametric, rank-based and robust results side by side with effect sizes, confidence intervals and multiplicity-adjusted p-values. Supervised fits, embeddings, clustering and simulators follow the same result contracts. Methods include those of Welch (1947) <doi:10.1093/biomet/34.1-2.28>, Wilcoxon (1945) <doi:10.2307/3001968>, Mann and Whitney (1947) <doi:10.1214/aoms/1177730491>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Friedman (1937) <doi:10.1080/01621459.1937.10503522>, Tukey (1949) <doi:10.2307/3001913>, Dunn (1964) <doi:10.1080/00401706.1964.10490181>, Yuen (1974) <doi:10.1093/biomet/61.1.165>, Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>, Algina, Keselman and Penfield (2005) <doi:10.1037/1082-989X.10.3.317>, DeLong, DeLong and Clarke-Pearson (1988) <doi:10.2307/2531595>, Sun and Xu (2014) <doi:10.1109/LSP.2014.2337313>, Pencina, D'Agostino, D'Agostino and Vasan (2008) <doi:10.1002/sim.2929>, and Pencina, D'Agostino and Steyerberg (2011) <doi:10.1002/sim.4085>.

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3.88 score 3 stars 394 downloads

MALDIassist - Mathematical Utilities for MALDI-TOF Mass Spectrometry

Supports matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry workflows from raw Bruker spectra to cohort-level peak matrices. Provides spectrum loading, Savitzky-Golay smoothing, baseline correction (SNIP and TopHat), Gaussian kernel-regression-based peak detection including shoulder peaks, peak-quality assessment, filtering, and cohort feature analysis. Computationally intensive routines are implemented in C++ using 'Rcpp'. The implemented signal-processing methods include those described by Savitzky and Golay (1964) <doi:10.1021/ac60214a047>, Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, Stanford, Bagley and Solomon (2016) <doi:10.1186/s12953-016-0107-8>, and Nadaraya-Watson kernel regression (Nadaraya (1964) <doi:10.1137/1109020>; Watson (1964) <https://www.jstor.org/stable/25049340>).

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cpp

3.30 score 1 stars 223 downloads