A meta-analysis package for R
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Updated
Jun 12, 2024 - R
A meta-analysis package for R
Hierarchical Climate Regionalization
Recursive Partitioning for Structural Equation Models
Source code for R package chebpol on CRAN
Functions for Wishart distributions, including sampling from the inverse Wishart and sampling from the Cholesky factorization of a Wishart.
msos: Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden
Compute scagnostics on your scatterplots
Repository that contains a set of functions for bnlearn package discrete models: multi-variable prediction and evaluation metrics
Multivariate independent comparison of observations.
This link shows the codes in the paper: Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data. Please read readme.file first.
This repository contains R code that explains graphically how a few different multivariate statistical techniques work. Topics covered are distance measures, principal component analysis, permutational analysis of variance, and partial least squares regression.
Multivariate analysis of data and geographic display of results
R package implementing Multivariate Error Measures for time series forecasting
Bayesian network analysis in R
R code to reproduce analyses in "Rapid winter warming could disrupt coastal marine fish community structure" (Clark et al, Nature Climate Change, 2020)
R package for displaying multivariate data through a quasi-Chernoff visualization
R package implementing the multivariate (multi-univariate) extension of the benchmarks used for the M
R package implementing the Extended Dynamic Factor Machine Learner multivariate forecasting method
Multivariate quantile function from discrete approximation of continuous probability distribution function
Supervised Component Generalised Linear Regression for mixed models
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