Showing 11 of total 11 results (show query)
bwlewis
irlba:Fast Truncated Singular Value Decomposition and Principal Components Analysis for Large Dense and Sparse Matrices
Fast and memory efficient methods for truncated singular value decomposition and principal components analysis of large sparse and dense matrices.
Maintained by B. W. Lewis. Last updated 2 years ago.
pcaprincipal-component-analysissingular-value-decompositionsparse-principal-componentssvdopenblas
128 stars 13.85 score 1.5k scripts 293 dependents
erichson
rsvd:Randomized Singular Value Decomposition
Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.
Maintained by N. Benjamin Erichson. Last updated 4 years ago.
dimension-reductionmatrix-approximationpcaprincipal-component-analysisprobabilistic-algorithmsrandomized-algorithmsingular-value-decompositionsvd
99 stars 10.88 score 408 scripts 124 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
bioc
GenomicSuperSignature:Interpretation of RNA-seq experiments through robust, efficient comparison to public databases
This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package.
Maintained by Sehyun Oh. Last updated 5 months ago.
transcriptomicssystemsbiologyprincipalcomponentrnaseqsequencingpathwaysclusteringbioconductor-packageexploratory-data-analysisgseameshprincipal-component-analysisrna-sequencing-profilestransferlearning
16 stars 6.97 score 59 scripts
idblr
ndi:Neighborhood Deprivation Indices
Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available: including: (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x> and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002> who use variables chosen by Roux and Mair (2010) <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute indices of racial or ethnic residential segregation, including but limited to those discussed in Massey & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of socioeconomic disparity.
Maintained by Ian D. Buller. Last updated 7 months ago.
censuscensus-apicensus-datadeprivationdeprivation-statsdisparitygeospatialgeospatial-datametric-developmentprincipal-component-analysissegregation-measuressocio-economic-indicators
21 stars 6.67 score 7 scripts 1 dependents
christiangoueguel
HotellingEllipse:Hotelling’s T-Squared Statistic and Ellipse
Functions to calculate the Hotelling’s T-squared statistic and corresponding confidence ellipses. Provides the semi-axes of the Hotelling’s T-squared ellipses at 95% and 99% confidence levels. Enables users to obtain the coordinates in two or three dimensions at user-defined confidence levels, allowing for the construction of 2D or 3D ellipses with customized confidence levels. Bro and Smilde (2014) <DOI:10.1039/c3ay41907j>. Brereton (2016) <DOI:10.1002/cem.2763>.
Maintained by Christian L. Goueguel. Last updated 3 months ago.
confidence-ellipsehotelling-ellipsehotelling-s-t-squarehotelling-t2hotellings-t2-distributionmultivariate-distributionoutlierspartial-least-squares-regressionpcaplsprincipal-component-analysis
7 stars 5.29 score 14 scriptsfriendly
genridge:Generalized Ridge Trace Plots for Ridge Regression
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Maintained by Michael Friendly. Last updated 4 months ago.
bias-variancegraphicsprincipal-component-analysisregression-modelsridge-regressionsingular-value-decomposition
4 stars 4.84 score 69 scripts
fchen365
epca:Exploratory Principal Component Analysis
Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation.
Maintained by Fan Chen. Last updated 11 months ago.
community-detectionexploratory-data-analysismatrix-decompositionspcaprincipal-component-analysissparse-matrix
11 stars 4.74 score 8 scripts
lance-waller-lab
envi:Environmental Interpolation using Spatial Kernel Density Estimation
Estimates an ecological niche using occurrence data, covariates, and kernel density-based estimation methods. For a single species with presence and absence data, the 'envi' package uses the spatial relative risk function that is estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Maintained by Ian D. Buller. Last updated 5 months ago.
ecological-nicheecological-niche-modellinggeospatialgeospatial-analysiskernel-density-estimationniche-modelingniche-modellingnon-euclidean-spacespoint-patternpoint-pattern-analysisprincipal-component-analysisspatial-analysisspecies-distribution-modelingspecies-distribution-modelling
1 stars 4.22 score 33 scripts
reckziegel
epo:Enhanced Portfolio Optimization (EPO)
Implements the Enhanced Portfolio Optimization (EPO) method as described in Pedersen, Babu and Levine (2021) <doi:10.2139/ssrn.3530390>.
Maintained by Bernardo Reckziegel. Last updated 1 years ago.
bayesian-optimizationblack-littermanmean-variance-optimizationprincipal-component-analysisrobust-optimization
11 stars 3.74 score 4 scripts
annennenne
PCADSC:Tools for Principal Component Analysis-Based Data Structure Comparisons
A suite of non-parametric, visual tools for assessing differences in data structures for two datasets that contain different observations of the same variables. These tools are all based on Principal Component Analysis (PCA) and thus effectively address differences in the structures of the covariance matrices of the two datasets. The PCADSC tools consist of easy-to-use, intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The angle plot presents the information loss when moving from the PCA decomposition of one dataset to the PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby presenting the relative weighting and importance of the variables from the original dataset.
Maintained by Anne Helby Petersen. Last updated 3 years ago.
data-structuresexploratory-data-visualizationsprincipal-component-analysis
1 stars 3.11 score 13 scripts