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geomorphr

geomorph:Geometric Morphometric Analyses of 2D and 3D Landmark Data

Read, manipulate, and digitize landmark data, generate shape variables via Procrustes analysis for points, curves and surfaces, perform shape analyses, and provide graphical depictions of shapes and patterns of shape variation.

Maintained by Dean Adams. Last updated 1 months ago.

18.9 match 76 stars 12.05 score 700 scripts 6 dependents

husson

FactoMineR:Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Maintained by Francois Husson. Last updated 3 months ago.

7.2 match 47 stars 14.71 score 5.6k scripts 112 dependents

vegandevs

vegan:Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

Maintained by Jari Oksanen. Last updated 16 days ago.

ecological-modellingecologyordinationfortranopenblas

4.8 match 472 stars 19.41 score 15k scripts 440 dependents

zarquon42b

Morpho:Calculations and Visualisations Related to Geometric Morphometrics

A toolset for Geometric Morphometrics and mesh processing. This includes (among other stuff) mesh deformations based on reference points, permutation tests, detection of outliers, processing of sliding semi-landmarks and semi-automated surface landmark placement.

Maintained by Stefan Schlager. Last updated 5 months ago.

openblascppopenmp

9.2 match 51 stars 10.00 score 218 scripts 13 dependents

iandryden

shapes:Statistical Shape Analysis

Routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

Maintained by Ian Dryden. Last updated 4 months ago.

10.8 match 7 stars 8.50 score 225 scripts 24 dependents

hwborchers

pracma:Practical Numerical Math Functions

Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.

Maintained by Hans W. Borchers. Last updated 1 years ago.

5.0 match 29 stars 12.34 score 6.6k scripts 931 dependents

timcdlucas

paleomorph:Geometric Morphometric Tools for Paleobiology

Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses.

Maintained by Tim Lucas. Last updated 8 years ago.

morphometricspaleobiologyprocrustesstatistical-analysis

15.2 match 4 stars 3.60 score 20 scripts

momx

Momocs:Morphometrics using R

The goal of 'Momocs' is to provide a complete, convenient, reproducible and open-source toolkit for 2D morphometrics. It includes most common 2D morphometrics approaches on outlines, open outlines, configurations of landmarks, traditional morphometrics, and facilities for data preparation, manipulation and visualization with a consistent grammar throughout. It allows reproducible, complex morphometrics analyses and other morphometrics approaches should be easy to plug in, or develop from, on top of this canvas.

Maintained by Vincent Bonhomme. Last updated 1 years ago.

morphometrics

7.1 match 51 stars 7.42 score 346 scripts

pvermees

provenance:Statistical Toolbox for Sedimentary Provenance Analysis

Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.

Maintained by Pieter Vermeesch. Last updated 2 months ago.

9.2 match 14 stars 5.52 score 79 scripts 1 dependents

jongheepark

MCMCpack:Markov Chain Monte Carlo (MCMC) Package

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

Maintained by Jong Hee Park. Last updated 7 months ago.

cpp

5.3 match 13 stars 9.40 score 2.6k scripts 150 dependents

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

Maintained by William Revelle. Last updated 3 months ago.

3.0 match 52 stars 13.94 score 29k scripts 317 dependents

vanderleidebastiani

SYNCSA:Analysis of Functional and Phylogenetic Patterns in Metacommunities

Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).

Maintained by Vanderlei Julio Debastiani. Last updated 5 years ago.

7.6 match 3 stars 5.36 score 28 scripts 1 dependents

kisungyou

Rdimtools:Dimension Reduction and Estimation Methods

We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.

Maintained by Kisung You. Last updated 2 years ago.

dimension-estimationdimension-reductionmanifold-learningsubspace-learningopenblascppopenmp

4.8 match 52 stars 8.37 score 186 scripts 8 dependents

svkucheryavski

pcv:Procrustes Cross-Validation

Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.

Maintained by Sergey Kucheryavskiy. Last updated 1 years ago.

11.0 match 8 stars 3.60 score 6 scripts

mpff

elastes:Elastic Full Procrustes Means for Sparse and Irregular Planar Curves

Provides functions for the computation of functional elastic shape means over sets of open planar curves. The package is particularly suitable for settings where these curves are only sparsely and irregularly observed. It uses a novel approach for elastic shape mean estimation, where planar curves are treated as complex functions and a full Procrustes mean is estimated from the corresponding smoothed Hermitian covariance surface. This is combined with the methods for elastic mean estimation proposed in Steyer, Stรถcker, Greven (2022) <doi:10.1111/biom.13706>. See Stรถcker et. al. (2022) <arXiv:2203.10522> for details.

Maintained by Manuel Pfeuffer. Last updated 2 years ago.

10.1 match 1 stars 3.70 score 7 scripts

acorg

Racmacs:Antigenic Cartography Macros

A toolkit for making antigenic maps from immunological assay data, in order to quantify and visualize antigenic differences between different pathogen strains as described in Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World Health Organization influenza vaccine strain selection process. Additional functions allow for the diagnostic evaluation of antigenic maps and an interactive viewer is provided to explore antigenic relationships amongst several strains and incorporate the visualization of associated genetic information.

Maintained by Sam Wilks. Last updated 9 months ago.

openblascppopenmp

3.8 match 21 stars 8.06 score 362 scripts

privefl

bigutilsr:Utility Functions for Large-scale Data

Utility functions for large-scale data. For now, package 'bigutilsr' mainly includes functions for outlier detection and unbiased PCA projection.

Maintained by Florian Privรฉ. Last updated 5 months ago.

cpp

5.3 match 10 stars 5.80 score 39 scripts 5 dependents

pmair78

smacof:Multidimensional Scaling

Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.

Maintained by Patrick Mair. Last updated 5 months ago.

3.5 match 5 stars 7.86 score 152 scripts 24 dependents

keefe-murphy

IMIFA:Infinite Mixtures of Infinite Factor Analysers and Related Models

Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.

Maintained by Keefe Murphy. Last updated 1 years ago.

bayesian-nonparametricsdimension-reductionfactor-analysisgaussian-mixture-modelmodel-based-clustering

5.3 match 7 stars 5.25 score 51 scripts

scollinselliott

lakhesis:Consensus Seriation for Binary Data

Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) <https://volweb.utk.edu/~scolli46/sceLakhesis.pdf>.

Maintained by Stephen A. Collins-Elliott. Last updated 4 months ago.

archaeologybinary-datacorrespondence-analysisecologyseriation

5.3 match 4 stars 5.08 score 2 scripts

adeverse

ade4:Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.

Maintained by Aurรฉlie Siberchicot. Last updated 12 days ago.

openblascpp

1.8 match 39 stars 14.96 score 2.2k scripts 256 dependents

elies-ramon

kerntools:Kernel Functions and Tools for Machine Learning Applications

Kernel functions for diverse types of data (including, but not restricted to: nonnegative and real vectors, real matrices, categorical and ordinal variables, sets, strings), plus other utilities like kernel similarity, kernel Principal Components Analysis (PCA) and features' importance for Support Vector Machines (SVMs), which expand other 'R' packages like 'kernlab'.

Maintained by Elies Ramon. Last updated 25 days ago.

kernel-methodspca

5.3 match 1 stars 4.73 score 12 scripts

umr-amap

BIOMASS:Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests

Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, (3) construct height-diameter models, (4) manage tree and plot coordinates, (5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty. To cite 'BIOMASS', please use citation("BIOMASS"). See more in the article of Rรฉjou-Mรฉchain et al. (2017) <doi:10.1111/2041-210X.12753>.

Maintained by Dominique Lamonica. Last updated 2 days ago.

2.3 match 26 stars 9.90 score 68 scripts 1 dependents

matthutchinson1

paco:Procrustes Application to Cophylogenetic Analysis

Procrustes analyses to infer co-phylogenetic matching between pairs of phylogenetic trees.

Maintained by Matthew Hutchinson. Last updated 4 years ago.

5.5 match 4.00 score 33 scripts 1 dependents

r-forge

smacofx:Flexible Multidimensional Scaling and 'smacof' Extensions

Flexible multidimensional scaling (MDS) methods and extensions to the package 'smacof'. This package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different flexible MDS models. These are: Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459) with powers, Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>) with ratio and interval optimal scaling, Multiscale MDS (Ramsay, 1977, <doi:10.1007/BF02294052>) with ratio and interval optimal scaling, s-stress MDS (ALSCAL; Takane, Young & De Leeuw, 1977, <doi:10.1007/BF02293745>) with ratio and interval optimal scaling, elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>) with ratio and interval optimal scaling, r-stress MDS (De Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>) with ratio, interval, splines and nonmetric optimal scaling, power-stress MDS (POST-MDS; Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>) with ratio and interval optimal scaling, restricted power-stress (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>) with ratio and interval optimal scaling, approximate power-stress with ratio optimal scaling (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>), Box-Cox MDS (Chen & Buja, 2013, <https://jmlr.org/papers/v14/chen13a.html>), local MDS (Chen & Buja, 2009, <doi:10.1198/jasa.2009.0111>), curvilinear component analysis (Demartines & Herault, 1997, <doi:10.1109/72.554199>), curvilinear distance analysis (Lee, Lendasse & Verleysen, 2004, <doi:10.1016/j.neucom.2004.01.007>), nonlinear MDS with optimal dissimilarity powers functions (De Leeuw, 2024, <https://github.com/deleeuw/smacofManual/blob/main/smacofPO/smacofPO.pdf>), sparsified (power) MDS and sparsified multidimensional (power) distance analysis (Rusch, 2024, <doi:10.57938/355bf835-ddb7-42f4-8b85-129799fc240e>). Some functions are suitably flexible to allow any other sensible combination of explicit power transformations for weights, distances and input proximities with implicit ratio, interval, splines or nonmetric optimal scaling of the input proximities. Most functions use a Majorization-Minimization algorithm. Currently the methods are only available for one-mode data (symmetric dissimilarity matrices).

Maintained by Thomas Rusch. Last updated 2 months ago.

5.5 match 1 stars 3.89 score 2 dependents

peterwmacd

fase:Functional Adjacency Spectral Embedding

Latent process embedding for functional network data with the Functional Adjacency Spectral Embedding. Fits smooth latent processes based on cubic spline bases. Also generates functional network data from three models, and evaluates a network generalized cross-validation criterion for dimension selection. For more information, see MacDonald, Zhu and Levina (2022+) <arXiv:2210.07491>.

Maintained by Peter W. MacDonald. Last updated 9 months ago.

5.8 match 3.40 score 3 scripts

leondap

recluster:Ordination Methods for the Analysis of Beta-Diversity Indices

The analysis of different aspects of biodiversity requires specific algorithms. For example, in regionalisation analyses, the high frequency of ties and zero values in dissimilarity matrices produced by Beta-diversity turnover produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, visualisation of biogeographical regionalisation can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to visualise and analyse pattern of biodiversity and to improve occurrence data for cryptic taxa.

Maintained by Leonardo Dapporto. Last updated 4 months ago.

3.9 match 4 stars 4.69 score 41 scripts

r-forge

cops:Cluster Optimized Proximity Scaling

Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) for any Minkowski distance with given explicit power transformations and implicit ratio, interval and nonmetric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.

Maintained by Thomas Rusch. Last updated 2 months ago.

3.7 match 1 stars 4.48 score 23 scripts

timbeechey

clubpro:Classification Using Binary Procrustes Rotation

Implements a classification method described by Grice (2011, ISBN:978-0-12-385194-9) using binary procrustes rotation; a simplified version of procrustes rotation.

Maintained by Timothy Beechey. Last updated 9 months ago.

classificationdata-analysispsychology-experimentsrcppstatistical-analysisstatisticsopenblascppopenmp

3.8 match 4.30 score 2 scripts

melff

munfold:Metric Unfolding

Multidimensional unfolding using Schoenemann's algorithm for metric and Procrustes rotation of unfolding results.

Maintained by Martin Elff. Last updated 1 years ago.

5.8 match 1 stars 2.70 score 2 scripts

jivfur

Irescale:Calculate and Rectify Moran's I

Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.

Maintained by Ivan Fuentes. Last updated 5 years ago.

4.9 match 2.70 score 3 scripts

juanmac17

Evomorph:Evolutionary Morphometric Simulation

Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.

Maintained by Juan Manuel Cabrera. Last updated 9 years ago.

9.5 match 1 stars 1.38 score 12 scripts

bioc

flowStats:Statistical methods for the analysis of flow cytometry data

Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package.

Maintained by Greg Finak. Last updated 5 months ago.

immunooncologyflowcytometrycellbasedassays

1.6 match 13 stars 8.24 score 195 scripts 1 dependents

khliland

multiblock:Multiblock Data Fusion in Statistics and Machine Learning

Functions and datasets to support Smilde, Nรฆs and Liland (2021, ISBN: 978-1-119-60096-1) "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications.

Maintained by Kristian Hovde Liland. Last updated 2 months ago.

cpp

1.9 match 14 stars 6.68 score 19 scripts

aiorazabala

qmethod:Analysis of Subjective Perspectives Using Q Methodology

Analysis of Q methodology, used to identify distinct perspectives existing within a group. This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>). A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors. The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options. Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards. The package also offers functions to print Q cards and to generate Q distributions for study administration. See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a graphical user interface (GUI) for online and offline use.

Maintained by Aiora Zabala. Last updated 1 years ago.

1.8 match 38 stars 6.03 score 47 scripts

asa12138

pctax:Professional Comprehensive Omics Data Analysis

Provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.

Maintained by Chen Peng. Last updated 4 months ago.

microbiomesoftwarevisualizationomics

1.8 match 14 stars 5.89 score 14 scripts

cran

mvdalab:Multivariate Data Analysis Laboratory

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

Maintained by Nelson Lee Afanador. Last updated 2 years ago.

4.7 match 2.18 score 1 dependents

kisungyou

maotai:Tools for Matrix Algebra, Optimization and Inference

Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).

Maintained by Kisung You. Last updated 19 hours ago.

openblascppopenmp

1.8 match 8 stars 5.51 score 15 scripts 9 dependents

cran

prolsirm:Procrustes Matching for Latent Space Item Response Model

Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.

Maintained by Jinwen Luo. Last updated 1 years ago.

9.4 match 1.00 score

khliland

MatrixCorrelation:Matrix Correlation Coefficients

Computation and visualization of matrix correlation coefficients. The main method is the Similarity of Matrices Index, while various related measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's linear correlation and Coxhead's coefficient are included for comparison and flexibility.

Maintained by Kristian Hovde Liland. Last updated 2 years ago.

openblascpp

2.0 match 2 stars 4.66 score 38 scripts 2 dependents

bpoconnor

EFA.dimensions:Exploratory Factor Analysis Functions for Assessing Dimensionality

Functions for eleven procedures for determining the number of factors, including functions for parallel analysis and the minimum average partial test. There are also functions for conducting principal components analysis, principal axis factor analysis, maximum likelihood factor analysis, image factor analysis, and extension factor analysis, all of which can take raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations. Varimax rotation, promax rotation, and Procrustes rotations can be performed. Additional functions focus on the factorability of a correlation matrix, the congruences between factors from different datasets, the assessment of local independence, the assessment of factor solution complexity, and internal consistency. Auerswald & Moshagen (2019, ISSN:1939-1463); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, <doi:10.3758/bf03200807>); O'Connor (2001, ISSN:0146-6216).

Maintained by Brian P. OConnor. Last updated 9 months ago.

5.5 match 1 stars 1.57 score 33 scripts

kharchenkolab

vrnmf:Volume-Regularized Structured Matrix Factorization

Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.

Maintained by Evan Biederstedt. Last updated 3 years ago.

1.7 match 17 stars 4.52 score 13 scripts

psychbruce

PsychWordVec:Word Embedding Research Framework for Psychological Science

An integrative toolbox of word embedding research that provides: (1) a collection of 'pre-trained' static word vectors in the '.RData' compressed format <https://psychbruce.github.io/WordVector_RData.pdf>; (2) a series of functions to process, analyze, and visualize word vectors; (3) a range of tests to examine conceptual associations, including the Word Embedding Association Test <doi:10.1126/science.aal4230> and the Relative Norm Distance <doi:10.1073/pnas.1720347115>, with permutation test of significance; (4) a set of training methods to locally train (static) word vectors from text corpora, including 'Word2Vec' <arXiv:1301.3781>, 'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <arXiv:1607.04606>; (5) a group of functions to download 'pre-trained' language models (e.g., 'GPT', 'BERT') and extract contextualized (dynamic) word vectors (based on the R package 'text').

Maintained by Han-Wu-Shuang Bao. Last updated 1 years ago.

bertcosine-similarityfasttextglovegptlanguage-modelnatural-language-processingnlppretrained-modelspsychologysemantic-analysistext-analysistext-miningtsneword-embeddingsword-vectorsword2vecopenjdk

1.8 match 22 stars 4.04 score 10 scripts

mllaberia

Rtapas:Random Tanglegram Partitions

Applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and nodes that maximize phylogenetic (in)congruence. It also includes functions to compute more easily the confidence intervals of classification metrics and plot results, reducing computational time. See "Llaberia-Robledillo et al. (2023, <doi:10.1093/sysbio/syad016>)".

Maintained by Mar Llaberia-Robledillo. Last updated 9 months ago.

1.7 match 5 stars 3.70 score 9 scripts

mqbssppe

factor.switching:Post-Processing MCMC Outputs of Bayesian Factor Analytic Models

A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) <DOI:10.1007/s11222-022-10084-4>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.

Maintained by Panagiotis Papastamoulis. Last updated 1 years ago.

3.9 match 1 stars 1.48 score 2 scripts 1 dependents

laura20vg

PERMANOVA:Multivariate Analysis of Variance Based on Distances and Permutations

Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.

Maintained by Laura Vicente-Gonzalez. Last updated 4 years ago.

2.0 match 2.71 score 17 scripts 1 dependents

schoonees

cds:Constrained Dual Scaling for Detecting Response Styles

This is an implementation of constrained dual scaling for detecting response styles in categorical data, including utility functions. The procedure involves adding additional columns to the data matrix representing the boundaries between the rating categories. The resulting matrix is then doubled and analyzed by dual scaling. One-dimensional solutions are sought which provide optimal scores for the rating categories. These optimal scores are constrained to follow monotone quadratic splines. Clusters are introduced within which the response styles can vary. The type of response style present in a cluster can be diagnosed from the optimal scores for said cluster, and this can be used to construct an imputed version of the data set which adjusts for response styles.

Maintained by Pieter Schoonees. Last updated 9 years ago.

2.0 match 2.65 score 37 scripts 1 dependents

itziari

ICGE:Estimation of Number of Clusters and Identification of Atypical Units

It is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.

Maintained by Itziar Irigoien. Last updated 2 years ago.

2.0 match 2.16 score 16 scripts 3 dependents

jessica-arbour

LOST:Missing Morphometric Data Simulation and Estimation

Functions for simulating missing morphometric data randomly, with taxonomic bias and with anatomical bias. LOST also includes functions for estimating linear and geometric morphometric data.

Maintained by J. Arbour. Last updated 2 months ago.

1.8 match 2.30 score 4 scripts

schoonees

lsbclust:Least-Squares Bilinear Clustering for Three-Way Data

Functions for performing least-squares bilinear clustering of three-way data. The method uses the bilinear decomposition (or bi-additive model) to model two-way matrix slices while clustering over the third way. Up to four different types of clusters are included, one for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously on (a subset of) their overall means, row margins, column margins and row-column interactions. The orthogonality of the bilinear model results in separability of the joint clustering problem into four separate ones. Three of these sub-problems are specific k-means problems, while a special algorithm is implemented for the interactions. Plotting methods are provided, including biplots for the low-rank approximations of the interactions.

Maintained by Pieter Schoonees. Last updated 6 years ago.

cpp

2.0 match 1.91 score 27 scripts 1 dependents

jo-karl

ccpsyc:Methods for Cross-Cultural Psychology

With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods.

Maintained by Johannes Karl. Last updated 2 years ago.

1.8 match 1 stars 2.00 score 1 scripts

mjderooij

lmap:Logistic Mapping

Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu).

Maintained by Mark de Rooij. Last updated 2 months ago.

1.9 match 1.48 score 3 scripts

dppalomar

sparseEigen:Computation of Sparse Eigenvectors of a Matrix

Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar, "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing, IEEE Trans. on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016. <doi:10.1109/TSP.2016.2605073>.

Maintained by Daniel P. Palomar. Last updated 6 years ago.

covariance-matrixeigenvectorspcasparse

0.5 match 12 stars 5.42 score 22 scripts

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