Other packages > Find by keyword >

SimJoint  

Simulate Joint Distribution
View on CRAN: Click here


Download and install SimJoint package within the R console
Install from CRAN:
install.packages("SimJoint")

Install from Github:
library("remotes")
install_github("cran/SimJoint")

Install by package version:
library("remotes")
install_version("SimJoint", "0.3.12")



Attach the package and use:
library("SimJoint")
Maintained by
Charlie Wusuo Liu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-13
Latest Update: 2024-01-14
Description:
Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator.
How to cite:
Charlie Wusuo Liu (2019). SimJoint: Simulate Joint Distribution. R package version 0.3.12, https://cran.r-project.org/web/packages/SimJoint. Accessed 29 Oct. 2026.
Previous versions and publish date:
(2026-07-09 08:26), 0.1.1 (2019-06-13 15:10), 0.2.1 (2019-06-18 23:40), 0.2.2 (2019-08-08 00:40), 0.3.4 (2020-01-10 17:10), 0.3.5 (2020-01-13 20:40), 0.3.7 (2020-04-14 19:20), 0.3.9 (2021-12-09 21:20), 0.3.11 (2024-01-09 02:10)
Other packages that cited SimJoint R package
View SimJoint citation profile
Other R packages that SimJoint depends, imports, suggests or enhances
Complete documentation for SimJoint
Functions, R codes and Examples using the SimJoint R package
Some associated functions: LHSpmf . SJpearson . SJpearsonPMF . SJspearman . SJspearmanPMF . decor . exportRandomState . postSimOpt . xSJpearson . xSJpearsonPMF . 
Some associated R codes: RcppExports.R .  Full SimJoint package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

GEVcdn  
GEV Conditional Density Estimation Network
Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value anal ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
binovisualfields  
Depth-Dependent Binocular Visual Fields Simulation
Simulation and visualization depth-dependent integrated visual fields. Visual fields are measured mo ...
Download / Learn more Package Citations See dependency  
personalized2part  
Two-Part Estimation of Treatment Rules for Semi-Continuous Data
Implements the methodology of Huling, Smith, and Chen (2020) , w ...
Download / Learn more Package Citations See dependency  
fastRG  
Sample Generalized Random Dot Product Graphs in Linear Time
Samples generalized random product graphs, a generalization of a broad class of network models. Giv ...
Download / Learn more Package Citations See dependency  

29,218

R Packages

247,686

Dependencies

77,281

Author Associations

29,219

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA