R-universe search: topic:dimensionality-reduction

Showing 20 of total 20 results (show query)

jlmelville

uwot:The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction

An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) <doi:10.48550/arXiv.1802.03426>. It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) <doi:10.48550/arXiv.1602.00370> is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website (<https://github.com/jlmelville/uwot>) for more documentation and examples.

Maintained by James Melville. Last updated 7 days ago.

dimensionality-reduction umap cpp

329 stars 16.08 score 2.0k scripts 145 dependents

tkonopka

umap:Uniform Manifold Approximation and Projection

Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by McInnes and Healy (2018) in <arXiv:1802.03426>. This package provides an interface for two implementations. One is written from scratch, including components for nearest-neighbor search and for embedding. The second implementation is a wrapper for 'python' package 'umap-learn' (requires separate installation, see vignette for more details).

Maintained by Tomasz Konopka. Last updated 11 months ago.

dimensionality-reduction umap cpp

132 stars 12.82 score 3.6k scripts 45 dependents

bioc

destiny:Creates diffusion maps

Create and plot diffusion maps.

Maintained by Philipp Angerer. Last updated 4 months ago.

cellbiology cellbasedassays clustering software visualization diffusion-maps dimensionality-reduction cpp

82 stars 11.44 score 792 scripts 1 dependents

uscbiostats

partition:Agglomerative Partitioning Framework for Dimension Reduction

A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

Maintained by Malcolm Barrett. Last updated 5 months ago.

data-reduction dimensionality-reduction partitional-clustering openblas cpp

36 stars 7.72 score 27 scripts 1 dependents

hendersontrent

theft:Tools for Handling Extraction of Features from Time Series

Consolidates and calculates different sets of time-series features from multiple 'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

Maintained by Trent Henderson. Last updated 2 months ago.

data-visualisation data-visualization dimensionality-reduction machine-learning time-series

40 stars 7.48 score 50 scripts 1 dependents

erichson

sparsepca:Sparse Principal Component Analysis (SPCA)

Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data.

Maintained by N. Benjamin Erichson. Last updated 7 years ago.

dimension-reduction dimensionality-reduction pca spca

68 stars 7.28 score 86 scripts 3 dependents

terrytangyuan

lfda:Local Fisher Discriminant Analysis

Functions for performing and visualizing Local Fisher Discriminant Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised Local Fisher Discriminant Analysis(SELF).

Maintained by Yuan Tang. Last updated 2 years ago.

dimensionality-reduction distance-metric-learning machine-learning metric-learning statistics

76 stars 6.50 score 74 scripts 3 dependents

grvanderploeg

parafac4microbiome:Parallel Factor Analysis Modelling of Longitudinal Microbiome Data

Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.

Maintained by Geert Roelof van der Ploeg. Last updated 7 hours ago.

dimensionality-reduction microbiome microbiome-data multiway multiway-algorithms parallel-factor-analysis

7 stars 6.26 score 13 scripts

mmedl94

lionfish:Interactive 'tourr' Using 'python'

Extends the functionality of the 'tourr' package by an interactive graphical user interface. The interactivity allows users to effortlessly refine their 'tourr' results by manual intervention, which allows for integration of expert knowledge and aids the interpretation of results. For more information on 'tourr' see Wickham et. al (2011) <doi:10.18637/jss.v040.i02> or <https://github.com/ggobi/tourr>.

Maintained by Matthias Medl. Last updated 8 days ago.

data-sience data-visualization dimensionality-reduction exploratory-data-analysis interactive interactive-visualizations tourr

1 stars 5.98 score

bioc

scPCA:Sparse Contrastive Principal Component Analysis

A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA.

Maintained by Philippe Boileau. Last updated 2 months ago.

principalcomponent geneexpression differentialexpression sequencing microarray rnaseq bioconductor contrastive-learning dimensionality-reduction

12 stars 5.94 score 29 scripts

terrytangyuan

dml:Distance Metric Learning in R

State-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.

Maintained by Yuan Tang. Last updated 2 years ago.

dimensionality-reduction distance-metric-learning machine-learning metric-learning statistics

58 stars 5.94 score 8 scripts 1 dependents

nanxstats

enpls:Ensemble Partial Least Squares Regression

An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.

Maintained by Nan Xiao. Last updated 3 years ago.

chemometrics dimensionality-reduction ensemble-learning machine-learning outlier-detection partial-least-squares-regression

18 stars 5.56 score 40 scripts

columbia-prime

pcpr:Principal Component Pursuit for Environmental Epidemiology

Implementation of the pattern recognition technique Principal Component Pursuit tailored to environmental health data, as described in Gibson et al (2022) <doi:10.1289/EHP10479>.

Maintained by Lawrence G. Chillrud. Last updated 5 days ago.

dimensionality-reduction environmental-health environmental-mixtures epidemiology machine-learning pattern-recognition public-health statistical-modeling

4 stars 5.48 score

gmgeorg

ForeCA:Forecastable Component Analysis

Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.

Maintained by Georg M. Goerg. Last updated 5 years ago.

blind-source-separation dimensionality-reduction forecasting multivariate-timeseries signal-processing spectrum time-series time-series-analysis

15 stars 5.47 score 39 scripts

gdkrmr

coRanking:Co-Ranking Matrix

Calculates the co-ranking matrix to assess the quality of a dimensionality reduction.

Maintained by Guido Kraemer. Last updated 6 months ago.

dimensionality-reduction manifold-learning quality statistics unsupervised-learning cpp

9 stars 5.43 score 20 scripts 1 dependents

bioc

NewWave:Negative binomial model for scRNA-seq

A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.

Maintained by Federico Agostinis. Last updated 5 months ago.

software geneexpression transcriptomics singlecell batcheffect sequencing coverage regression batch-effects dimensionality-reduction negative-binomial scrna-seq

4 stars 5.33 score 27 scripts

gdkrmr

DRR:Dimensionality Reduction via Regression

An Implementation of Dimensionality Reduction via Regression using Kernel Ridge Regression.

Maintained by Guido Kraemer. Last updated 2 years ago.

dimensionality-reduction kernel-methods non-linear regression-models

9 stars 5.24 score 8 scripts 1 dependents

egarpor

ridgetorus:PCA on the Torus via Density Ridges

Implementation of a Principal Component Analysis (PCA) in the torus via density ridge estimation. The main function, ridge_pca(), obtains the relevant density ridge for bivariate sine von Mises and bivariate wrapped Cauchy distribution models and provides the associated scores and variance decomposition. Auxiliary functions for evaluating, fitting, and sampling these models are also provided. The package provides replicability to García-Portugués and Prieto-Tirado (2023) <doi:10.1007/s11222-023-10273-9>.

Maintained by Eduardo García-Portugués. Last updated 2 years ago.

circular-statistics dimensionality-reduction directional-statistics openblas cpp

4 stars 4.30 score 9 scripts

clement-w

SIRthresholded:Sliced Inverse Regression with Thresholding

Implements a thresholded version of the Sliced Inverse Regression method, which allows to do variable selection.

Maintained by Clement Weinreich. Last updated 6 months ago.

dimensionality-reduction inverse-regression statistical-learning variable-selection

4 stars 4.30 score 4 scripts

bioc

ReducedExperiment:Containers and tools for dimensionally-reduced -omics representations

Provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA).

Maintained by Jack Gisby. Last updated 3 months ago.

geneexpression infrastructure datarepresentation software dimensionreduction network bioconductor-package bioinformatics dimensionality-reduction

3 stars 4.13 score 8 scripts