Showing 10 of total 10 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 dependents
spatstat
spatstat.geom:Geometrical Functionality of the 'spatstat' Family
Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)
Maintained by Adrian Baddeley. Last updated 4 days ago.
classes-and-objectsdistance-calculationgeometrygeometry-processingimagesmensurationplottingpoint-patternsspatial-dataspatial-data-analysis
7 stars 12.14 score 241 scripts 229 dependents
muvisu
biplotEZ:EZ-to-Use Biplots
Provides users with an EZ-to-use platform for representing data with biplots. Currently principal component analysis (PCA), canonical variate analysis (CVA) and simple correspondence analysis (CA) biplots are included. This is accompanied by various formatting options for the samples and axes. Alpha-bags and concentration ellipses are included for visual enhancements and interpretation. For an extensive discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.
Maintained by Sugnet Lubbe. Last updated 19 days ago.
7 stars 8.39 score 30 scripts 1 dependents
friendly
ggbiplot:A Grammar of Graphics Implementation of Biplots
A 'ggplot2' based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using 'ggplot2' techniques.
Maintained by Michael Friendly. Last updated 6 months ago.
biplotdata-visualizationdimension-reductionprincipal-component-analysis
12 stars 8.15 score 2.4k scripts 1 dependents
thomasp85
ambient:A Generator of Multidimensional Noise
Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The 'ambient' package provides an interface to the 'FastNoise' C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions.
Maintained by Thomas Lin Pedersen. Last updated 3 years ago.
97 stars 8.09 score 956 scripts 2 dependents
sportsdataverse
sportyR:Plot Scaled 'ggplot' Representations of Sports Playing Surfaces
Create scaled 'ggplot' representations of playing surfaces. Playing surfaces are drawn pursuant to rule-book specifications. This package should be used as a baseline plot for displaying any type of tracking data.
Maintained by Ross Drucker. Last updated 2 months ago.
footballggplot2playing-surfacessportsports-analyticssports-datasports-tracking-datasportsdataversevisualization
104 stars 8.08 score 97 scripts
dgerbing
lessR:Less Code, More Results
Each function replaces multiple standard R functions. For example, two function calls, Read() and CountAll(), generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate. Other functions provide for summary statistics via pivot tables, a comprehensive regression analysis, ANOVA and t-test, visualizations including the Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, time series with aggregation and forecasting, color themes, and Trellis (facet) graphics. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, generation and rendering of regression instructions for interpretative output, and interactive visualizations.
Maintained by David W. Gerbing. Last updated 12 days ago.
6 stars 7.42 score 394 scripts 3 dependents
robinhankin
lorentz:The Lorentz Transform in Relativistic Physics
The Lorentz transform in special relativity; also the gyrogroup structure of three-velocities. Performs active and passive transforms and has the ability to use units in which the speed of light is not unity. Includes some experimental functionality for celerity and rapidity. For general relativity, see the 'schwarzschild' package.
Maintained by Robin K. S. Hankin. Last updated 2 months ago.
4 stars 5.26 score 15 scripts
valentint
rrcov3way:Robust Methods for Multiway Data Analysis, Applicable also for Compositional Data
Provides methods for multiway data analysis by means of Parafac and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>). Several optimization methods alternative to ALS are available (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).
Maintained by Valentin Todorov. Last updated 1 years ago.
4.28 score 38 scripts