Showing 15 of total 15 results (show query)
bioc
IRanges:Foundation of integer range manipulation in Bioconductor
Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames.
Maintained by Hervé Pagès. Last updated 2 months ago.
infrastructuredatarepresentationbioconductor-packagecore-package
22 stars 16.09 score 2.1k scripts 1.8k dependentsbioc
S4Vectors:Foundation of vector-like and list-like containers in Bioconductor
The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).
Maintained by Hervé Pagès. Last updated 2 months ago.
infrastructuredatarepresentationbioconductor-packagecore-package
18 stars 16.05 score 1.0k scripts 1.9k dependentsxrobin
pROC:Display and Analyze ROC Curves
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Maintained by Xavier Robin. Last updated 5 months ago.
bootstrappingcovariancehypothesis-testingmachine-learningplotplottingrocroc-curvevariancecpp
125 stars 15.18 score 16k scripts 445 dependentsprojectmosaic
mosaic:Project MOSAIC Statistics and Mathematics Teaching Utilities
Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.
Maintained by Randall Pruim. Last updated 1 years ago.
93 stars 13.32 score 7.2k scripts 7 dependentsr-hyperspec
hyperSpec:Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)
Comfortable ways to work with hyperspectral data sets, i.e. spatially or time-resolved spectra, or spectra with any other kind of information associated with each of the spectra. The spectra can be data as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS, etc. More generally, any data that is recorded over a discretized variable, e.g. absorbance = f(wavelength), stored as a vector of absorbance values for discrete wavelengths is suitable.
Maintained by Claudia Beleites. Last updated 10 months ago.
data-wranglinghyperspectralimaginginfrarednmrramanspectroscopyuv-visxrf
16 stars 8.10 score 233 scripts 2 dependentsbioc
BumpyMatrix:Bumpy Matrix of Non-Scalar Objects
Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects.
Maintained by Aaron Lun. Last updated 3 months ago.
softwareinfrastructuredatarepresentation
1 stars 6.62 score 39 scripts 12 dependentscran
compositions:Compositional Data Analysis
Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.
1 stars 6.35 score 36 dependentsbioc
GPA:GPA (Genetic analysis incorporating Pleiotropy and Annotation)
This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture.
Maintained by Dongjun Chung. Last updated 5 months ago.
softwarestatisticalmethodclassificationgenomewideassociationsnpgeneticsclusteringmultiplecomparisonpreprocessinggeneexpressiondifferentialexpressioncpp
14 stars 6.15 score 7 scriptsbioc
zitools:Analysis of zero-inflated count data
zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.
Maintained by Carlotta Meyring. Last updated 5 months ago.
softwarestatisticalmethodmicrobiome
4.40 score 6 scriptskiangkiangkiang
ggESDA:Exploratory Symbolic Data Analysis with 'ggplot2'
Implements an extension of 'ggplot2' and visualizes the symbolic data with multiple plot which can be adjusted by more general and flexible input arguments. It also provides a function to transform the classical data to symbolic data by both clustering algorithm and customized method.
Maintained by Bo-Syue Jiang. Last updated 2 years ago.
21 stars 4.02 score 9 scriptswrathematics
kazaam:Tools for Tall Distributed Matrices
Many data science problems reduce to operations on very tall, skinny matrices. However, sometimes these matrices can be so tall that they are difficult to work with, or do not even fit into main memory. One strategy to deal with such objects is to distribute their rows across several processors. To this end, we offer an 'S4' class for tall, skinny, distributed matrices, called the 'shaq'. We also provide many useful numerical methods and statistics operations for operating on these distributed objects. The naming is a bit "tongue-in-cheek", with the class a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he starred in the film 'Kazaam'.
Maintained by Drew Schmidt. Last updated 8 years ago.
3.82 score 133 scriptscran
ibmdbR:IBM in-Database Analytics for R
Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the 'SparkR' package (<https://spark.apache.org/docs/latest/sparkr.html>). The optional 'ggplot2' package is needed for the plot.idaLm() function only.
Maintained by Shaikh Quader. Last updated 1 years ago.
2 stars 3.00 scorequantumofmoose
complexlm:Linear Fitting for Complex Valued Data
Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'.
Maintained by William Ryan. Last updated 1 years ago.
complex-numbersfittinglinear-modelslinear-regressionrobust-statisticsstatistics
1 stars 2.00 score 6 scripts