Package: covsim 1.1.0

Njaal Foldnes

covsim: VITA, IG and PLSIM Simulation for Given Covariance and Marginals

Random sampling from distributions with user-specified population covariance matrix. Marginal information may be fully specified, for which the package implements the VITA (VIne-To-Anything) algorithm Grønneberg and Foldnes (2017) <doi:10.1007/s11336-017-9569-6>. See also Grønneberg, Foldnes and Marcoulides (2022) <doi:10.18637/jss.v102.i03>. Alternatively, marginal skewness and kurtosis may be specified, for which the package implements the IG (independent generator) and PLSIM (piecewise linear) algorithms, see Foldnes and Olsson (2016) <doi:10.1080/00273171.2015.1133274> and Foldnes and Grønneberg (2021) <doi:10.1080/10705511.2021.1949323>, respectively.

Authors:Njaal Foldnes [aut, cre], Steffen Grønneberg [aut]

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NEWS

# Install 'covsim' in R:
install.packages('covsim', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 285 downloads 3 exports 25 dependencies

Last updated 10 months agofrom:ba41f68698. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 28 2025
R-4.5-linuxOKMar 28 2025
R-4.4-linuxOKMar 28 2025

Exports:rIGrPLSIMvita

Dependencies:assertthatBHgmmkde1dlatticelavaanMASSMatrixmnormtmvtnormnleqslvnumDerivpbivnormPearsonDSquadprograndtoolboxRcppRcppEigenRcppThreadrngWELLrvinecopulibsandwichtmvtnormwdmzoo

Covsim: Simulating non-normal data with given covariance matrix

Njål Foldnes and Steffen Grønneberg

Rendered fromvignette.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2024-06-01
Started: 2022-04-29

Citation

To cite covsim in publications use:

Grønneberg S, Foldnes N, Marcoulides KM (2022). “covsim: An R Package for Simulating Non-Normal Data for Structural Equation Models Using Copulas.” Journal of Statistical Software, 102(3), 1–45. doi:10.18637/jss.v102.i03.

Corresponding BibTeX entry: 

  @Article{,
    title = {{covsim}: An {R} Package for Simulating Non-Normal Data
      for Structural Equation Models Using Copulas},
    author = {Steffen Gr{\o}nneberg and Nj{\aa}l Foldnes and Katerina
      M. Marcoulides},
    journal = {Journal of Statistical Software},
    year = {2022},
    volume = {102},
    number = {3},
    pages = {1--45},
    doi = {10.18637/jss.v102.i03},
  }

Readme and manuals

covsim

This package contains implementations of three simulation procedures that simulate from a multivariate distributions with a pre-specified covariance matrix. In addition, the user may pre-specify marginal information. The main function is vita, which implements the very general VIne-To-Anything (VITA) algorithm of Grønneberg and Foldnes (Psychometrika, 2017).

How to install

You can install:

  • the stable release on CRAN:

    install.packages("covsim")

  • the latest development version:

    devtools::install_github("njaalf/covsim")

Package overview

VITA

The user must completely specify the marginals from the distributions available in the stats package. Also the user must specify the population covariance matrix. Note that the variances in this matrix must equal the population variances of the marginal distributions. A regular vine may also be provided, that is, a list of bicopulas and a sequence for their calibrations, i.e. a sequence of trees. If no vine is provided, a default D-vine is assumed. Note that in many cases the vine, the marginals and the covariance matrix are not compatible. That is, no vine distribution exists whose covariance equals the target covariance. Also note that calibration of the vine is computationally burdensome. To speed up calibration, one could reduce the numpoints and/or the Nmax parameters. This would reduce the precision of the adherence of the vita vine to the target population covariance matrix.

IG

In addition to vita, a simpler and much more restricted simulation algorithm is provided in the rIG function, which implements the independent generator approach of Foldnes and Olsson (MBR, 2016) The IG approach is comparable to the Vale-Maurelli approach, in that it is fast and precise. Only skewness and kurtosis of the marginals is provided. The marginal distributions are therefore not fully controlled using IG.

PLSIM

has a similar interface as that of IG. Skewness and kurtosis is pre-specified. PLSIM is flexible beyond the default values. Numsegments can be increased, or the breakpoints manually set by providing gammalist.

References

Grønneberg, S., Foldnes, N. Covariance Model Simulation Using Regular Vines. Psychometrika 82, 1035–1051 (2017). https://doi.org/10.1007/s11336-017-9569-6

Grønneberg, S., Foldnes, N., & Marcoulides, K. M. (2022). covsim: An r package for simulating non-normal data for structural equation models using copulas. Journal of Statistical Software. DOI: 10.18637/jss.v102.i03

Njål Foldnes & Ulf Henning Olsson (2016) A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness, Kurtosis, and Covariance Matrix, Multivariate Behavioral Research, 51:2-3, 207-219, DOI: 10.1080/00273171.2015.1133274

Foldnes, N., & Grønneberg, S. (2022). Non-normal data simulation using piecewise linear transforms. Structural Equation Modeling: A Multidisciplinary Journal, 29(1), 36-46.