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airpino
HistDAWass:Histogram-Valued Data Analysis
In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histogram-valued data and for histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.
Maintained by Antonio Irpino. Last updated 1 years ago.
5 stars 4.75 score 75 scripts
danielebizzarri
MiMIR:Metabolomics-Based Models for Imputing Risk
Provides an intuitive framework for ad-hoc statistical analysis of 1H-NMR metabolomics by Nightingale Health. It allows to easily explore new metabolomics measurements assayed by Nightingale Health, comparing the distributions with a large Consortium (BBMRI-nl); project previously published metabolic scores [<doi:10.1016/j.ebiom.2021.103764>, <doi:10.1161/CIRCGEN.119.002610>, <doi:10.1038/s41467-019-11311-9>, <doi:10.7554/eLife.63033>, <doi:10.1161/CIRCULATIONAHA.114.013116>, <doi:10.1007/s00125-019-05001-w>]; and calibrate the metabolic surrogate values to a desired dataset.
Maintained by Daniele Bizzarri. Last updated 2 years ago.
binary-risk-factorsbiomarkerslinear-regressionmetabolitesmetabolomicsnightingale-metabolomicsrisk-factor-modelsrisk-factorssurrogate-models
8 stars 4.11 score 32 scripts