Showing 9 of total 9 results (show query)
wviechtb
metafor:Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Maintained by Wolfgang Viechtbauer. Last updated 5 days ago.
meta-analysismixed-effectsmultilevel-modelsmultivariate
250 stars 16.32 score 4.9k scripts 92 dependents
brandmaier
semtree:Recursive Partitioning for Structural Equation Models
SEM Trees and SEM Forests -- an extension of model-based decision trees and forests to Structural Equation Models (SEM). SEM trees hierarchically split empirical data into homogeneous groups each sharing similar data patterns with respect to a SEM by recursively selecting optimal predictors of these differences. SEM forests are an extension of SEM trees. They are ensembles of SEM trees each built on a random sample of the original data. By aggregating over a forest, we obtain measures of variable importance that are more robust than measures from single trees. A description of the method was published by Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) <doi:10.1037/a0030001> and Arnold, Voelkle, & Brandmaier (2020) <doi:10.3389/fpsyg.2020.564403>.
Maintained by Andreas M. Brandmaier. Last updated 3 months ago.
bigdatadecision-treeforestmultivariaterandomforestrecursive-partitioningsemstatistical-modelingstructural-equation-modelingstructural-equation-models
15 stars 8.56 score 68 scripts
hsbadr
HiClimR:Hierarchical Climate Regionalization
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.
Maintained by Hamada S. Badr. Last updated 3 months ago.
clusteringcontiguityhomogeneitymultivariateregionalizationspatiotemporalfortran
16 stars 8.06 score 53 scripts 3 dependents
gzt
CholWishart:Cholesky Decomposition of the Wishart Distribution
Sampling from the Cholesky factorization of a Wishart random variable, sampling from the inverse Wishart distribution, sampling from the Cholesky factorization of an inverse Wishart random variable, sampling from the pseudo Wishart distribution, sampling from the generalized inverse Wishart distribution, computing densities for the Wishart and inverse Wishart distributions, and computing the multivariate gamma and digamma functions. Provides a header file so the C functions can be called directly from other programs.
Maintained by Geoffrey Thompson. Last updated 6 months ago.
cholesky-decompositioncholesky-factorizationdigamma-functionsgammamultivariatepseudo-wishartwishartwishart-distributionsopenblas
7 stars 7.05 score 41 scripts 13 dependents
furrer-lab
abn:Modelling Multivariate Data with Additive Bayesian Networks
The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website.
Maintained by Matteo Delucchi. Last updated 22 days ago.
bayesian-networkbinomialcategorical-datagaussiangrouped-datasetsmixed-effectsmultinomialmultivariatepoissonstructure-learninggslopenblascppopenmpjags
6 stars 6.88 score 90 scripts
mkln
meshed:Bayesian Regression with Meshed Gaussian Processes
Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2024) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.
Maintained by Michele Peruzzi. Last updated 8 months ago.
bayesianmcmcmultivariateregressionspatialspatiotemporalopenblascppopenmp
13 stars 6.11 score 49 scripts
numbats
cassowaryr:Compute Scagnostics on Pairs of Numeric Variables in a Data Set
Computes a range of scatterplot diagnostics (scagnostics) on pairs of numerical variables in a data set. A range of scagnostics, including graph and association-based scagnostics described by Leland Wilkinson and Graham Wills (2008) <doi:10.1198/106186008X320465> and association-based scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be computed. Summary and plotting functions are provided.
Maintained by Harriet Mason. Last updated 29 days ago.
data-sciencedata-visualizationedahigh-dimensional-datamultivariate
3 stars 6.02 score 26 scripts 1 dependents
nunofachada
micompr:Multivariate Independent Comparison of Observations
A procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) <doi:10.32614/RJ-2016-055>.
Maintained by Nuno Fachada. Last updated 8 months ago.
micomprmultivariatemultivariate-datamultivariate-distributionsmultivariate-observationsnon-parametricparametric-testsstatistical-analysisstatistical-datastatistical-methodsstatistical-tests
3 stars 5.19 score 52 scripts
coatless-rpkg
msos:Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden
Multivariate Analysis methods and data sets used in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>. This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC').
Maintained by James Balamuta. Last updated 1 years ago.
3 stars 4.16 score 32 scripts 1 dependents