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bioc
pcaMethods:A collection of PCA methods
Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.
Maintained by Henning Redestig. Last updated 5 months ago.
49 stars 13.10 score 538 scripts 73 dependents
trackerproject
trackeR:Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices
Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.
Maintained by Ioannis Kosmidis. Last updated 1 years ago.
90 stars 6.37 score 58 scripts 1 dependentsbioc
SpatialOmicsOverlay:Spatial Overlay for Omic Data from Nanostring GeoMx Data
Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression.
Maintained by Maddy Griswold. Last updated 5 months ago.
geneexpressiontranscriptioncellbasedassaysdataimporttranscriptomicsproteomicsproprietaryplatformsrnaseqspatialdatarepresentationvisualizationopenjdk
4.30 score 8 scripts
laats
PrivateLR:Differentially Private Regularized Logistic Regression
Implements two differentially private algorithms for estimating L2-regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon for any pair D, D' of datasets that differ in exactly one record, any measurable set S, and the randomness is taken over the choices F makes.
Maintained by Staal A. Vinterbo. Last updated 7 years ago.
1.00 score 1 scripts