{ "_id": "6a141dfbacfb0bcc41d3ddbe", "Package": "SimMultiCorrData", "Type": "Package", "Title": "Simulation of Correlated Data with Multiple Variable Types", "Version": "0.2.2", "Author": "Allison Cynthia Fialkowski", "Maintainer": "Allison Cynthia Fialkowski ", "Description": "Generate continuous (normal or non-normal), binary,\nordinal, and count (Poisson or Negative Binomial) variables\nwith a specified correlation matrix. It can also produce a\nsingle continuous variable. This package can be used to\nsimulate data sets that mimic real-world situations (i.e.\nclinical or genetic data sets, plasmodes). All variables are\ngenerated from standard normal variables with an imposed\nintermediate correlation matrix. Continuous variables are\nsimulated by specifying mean, variance, skewness, standardized\nkurtosis, and fifth and sixth standardized cumulants using\neither Fleishman's third-order () or\nHeadrick's fifth-order ()\npolynomial transformation. Binary and ordinal variables are\nsimulated using a modification of the ordsample() function from\n'GenOrd'. Count variables are simulated using the inverse cdf\nmethod. There are two simulation pathways which differ\nprimarily according to the calculation of the intermediate\ncorrelation matrix. In Correlation Method 1, the\nintercorrelations involving count variables are determined\nusing a simulation based, logarithmic correlation correction\n(adapting Yahav and Shmueli's 2012 method,\n). In Correlation Method 2, the count\nvariables are treated as ordinal (adapting Barbiero and\nFerrari's 2015 modification of GenOrd,\n). There is an optional error loop that\ncorrects the final correlation matrix to be within a\nuser-specified precision value of the target matrix. The\npackage also includes functions to calculate standardized\ncumulants for theoretical distributions or from real data sets,\ncheck if a target correlation matrix is within the possible\ncorrelation bounds (given the distributions of the simulated\nvariables), summarize results (numerically or graphically), to\nverify valid power method pdfs, and to calculate lower\nstandardized kurtosis bounds.", "License": "GPL-2", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(wrap = FALSE)", "RoxygenNote": "6.0.1", "VignetteBuilder": "knitr", "URL": "https://github.com/AFialkowski/SimMultiCorrData", "Config/pak/sysreqs": "make", "Repository": "https://afialkowski.r-universe.dev", "Date/Publication": "2018-06-28 16:51:16 UTC", "RemoteUrl": "https://github.com/afialkowski/simmulticorrdata", "RemoteRef": "HEAD", "RemoteSha": "89bd4e5a3fc66c2fc9c637823d68d71bfac99368", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-25 09:57:04 UTC", "User": "root" }, "MD5sum": "68a83a08786c2670087cc4230e19fbae", "_user": "afialkowski", "_type": "src", "_file": "SimMultiCorrData_0.2.2.tar.gz", "_fileid": "b810a7e9a7c435ec17bdb8993cd29eb543c4e0591c82d674c49d9e6155278386", "_filesize": 2106702, "_sha256": "b810a7e9a7c435ec17bdb8993cd29eb543c4e0591c82d674c49d9e6155278386", "_created": "2026-05-25T09:57:04.000Z", "_published": "2026-05-25T10:01:31.019Z", "_distro": "noble", "_jobs": [ { "job": 77692361840, "time": 211, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7195995407" }, { "job": 77692361879, "time": 186, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7195989429" }, { "job": 77692361822, "time": 102, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7195968282" }, { "job": 77692361841, "time": 127, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7195974401" }, { "job": 77691867521, "time": 227, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7195941852" }, { "job": 77692361844, "time": 111, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7195970633" }, { "job": 77692361864, "time": 183, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7195992779" }, { "job": 77692361870, "time": 156, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7195981696" }, { "job": 77692361899, "time": 192, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7195990598" } ], "_buildurl": "https://github.com/r-universe/afialkowski/actions/runs/26394498660", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/afialkowski/simmulticorrdata", "_commit": { "id": "89bd4e5a3fc66c2fc9c637823d68d71bfac99368", "author": "AFialkowski ", "committer": "AFialkowski ", "message": "Updates to version 0.2.2 before CRAN submission.\n", "time": 1530204676 }, "_maintainer": { "name": "Allison Cynthia Fialkowski", "email": "allijazz@uab.edu", "login": "afialkowski", "description": "I'm a 3rd year veterinary student at Auburn University CVM. I have a Ph.D. in Biostatistics, an M.S. and B.S. in Math, and a B.S. in Biology.", "uuid": 17101407 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.3.0", "role": "Depends" }, { "package": "BB", "role": "Imports" }, { "package": "nleqslv", "role": "Imports" }, { "package": "GenOrd", "role": "Imports" }, { "package": "psych", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "VGAM", "role": "Imports" }, { "package": "triangle", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "grid", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "printr", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" } ], "_owner": "afialkowski", "_selfowned": true, "_usedby": 10, "_updates": [], "_tags": [], "_stars": 12, "_contributors": [ { "user": "afialkowski", "count": 23, "uuid": 17101407 } ], "_userbio": { "uuid": 17101407, "type": "user", "name": "Allison Cynthia Fialkowski", "description": "I'm a 3rd year veterinary student at Auburn University CVM. 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"chat_pois", "denom_corr_cat", "error_loop", "error_vars", "find_constants", "findintercorr", "findintercorr_cat_nb", "findintercorr_cat_pois", "findintercorr_cont", "findintercorr_cont_cat", "findintercorr_cont_nb", "findintercorr_cont_nb2", "findintercorr_cont_pois", "findintercorr_cont_pois2", "findintercorr_nb", "findintercorr_pois", "findintercorr_pois_nb", "findintercorr2", "fleish", "fleish_Hessian", "fleish_skurt_check", "intercorr_fleish", "intercorr_poly", "max_count_support", "nonnormvar1", "ordnorm", "pdf_check", "plot_cdf", "plot_pdf_ext", "plot_pdf_theory", "plot_sim_cdf", "plot_sim_ext", "plot_sim_pdf_ext", "plot_sim_pdf_theory", "plot_sim_theory", "poly", "poly_skurt_check", "power_norm_corr", "rcorrvar", "rcorrvar2", "separate_rho", "sim_cdf_prob", "stats_pdf", "valid_corr", "valid_corr2", "var_cat" ], "_datasets": [ { "name": "H_params", "title": "Parameters for Examples of Constants Calculated by Headrick's Fifth-Order Polynomial Transformation", "object": 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"title": "Find Standardized Cumulants of Data based on Fisher's k-statistics", "topics": [ "calc_fisherk" ] }, { "page": "calc_lower_skurt", "title": "Find Lower Boundary of Standardized Kurtosis for Polynomial Transformation", "topics": [ "calc_lower_skurt" ] }, { "page": "calc_moments", "title": "Find Standardized Cumulants of Data by Method of Moments", "topics": [ "calc_moments" ] }, { "page": "calc_theory", "title": "Find Theoretical Standardized Cumulants for Continuous Distributions", "topics": [ "calc_theory" ] }, { "page": "cdf_prob", "title": "Calculate Theoretical Cumulative Probability for Continuous Variables", "topics": [ "cdf_prob" ] }, { "page": "chat_nb", "title": "Calculate Upper Frechet-Hoeffding Correlation Bound: Negative Binomial - Normal Variables", "topics": [ "chat_nb" ] }, { "page": "chat_pois", "title": "Calculate Upper Frechet-Hoeffding Correlation Bound: Poisson - Normal Variables", "topics": [ "chat_pois" ] }, { "page": "denom_corr_cat", "title": "Calculate Denominator Used in Intercorrelations Involving Ordinal Variables", "topics": [ "denom_corr_cat" ] }, { "page": "error_loop", "title": "Error Loop to Correct Final Correlation of Simulated Variables", "topics": [ "error_loop" ] }, { "page": "error_vars", "title": "Generate Variables for Error Loop", "topics": [ "error_vars" ] }, { "page": "find_constants", "title": "Find Power Method Transformation Constants", "topics": [ "find_constants" ] }, { "page": "findintercorr", "title": "Calculate Intermediate MVN Correlation for Ordinal, Continuous, Poisson, or Negative Binomial Variables: Correlation Method 1", "topics": [ "findintercorr" ] }, { "page": "findintercorr_cat_nb", "title": "Calculate Intermediate MVN Correlation for Ordinal - Negative Binomial Variables: Correlation Method 1", "topics": [ "findintercorr_cat_nb" ] }, { "page": "findintercorr_cat_pois", "title": "Calculate Intermediate MVN Correlation for Ordinal - Poisson Variables: Correlation Method 1", "topics": [ "findintercorr_cat_pois" ] }, { "page": "findintercorr_cont", "title": "Calculate Intermediate MVN Correlation for Continuous Variables Generated by Polynomial Transformation", "topics": [ "findintercorr_cont" ] }, { "page": "findintercorr_cont_cat", "title": "Calculate Intermediate MVN Correlation for Continuous - Ordinal Variables", "topics": [ "findintercorr_cont_cat" ] }, { "page": "findintercorr_cont_nb", "title": "Calculate Intermediate MVN Correlation for Continuous - Negative Binomial Variables: Correlation Method 1", "topics": [ "findintercorr_cont_nb" ] }, { "page": "findintercorr_cont_nb2", "title": "Calculate Intermediate MVN Correlation for Continuous - Negative Binomial Variables: Correlation Method 2", "topics": [ "findintercorr_cont_nb2" ] }, { "page": "findintercorr_cont_pois", "title": "Calculate Intermediate MVN Correlation for Continuous - Poisson Variables: Correlation Method 1", "topics": [ "findintercorr_cont_pois" ] }, { "page": "findintercorr_cont_pois2", "title": "Calculate Intermediate MVN Correlation for Continuous - Poisson Variables: Correlation Method 2", "topics": [ "findintercorr_cont_pois2" ] }, { "page": "findintercorr_nb", "title": "Calculate Intermediate MVN Correlation for Negative Binomial Variables: Correlation Method 1", "topics": [ "findintercorr_nb" ] }, { "page": "findintercorr_pois", "title": "Calculate Intermediate MVN Correlation for Poisson Variables: Correlation Method 1", "topics": [ "findintercorr_pois" ] }, { "page": "findintercorr_pois_nb", "title": "Calculate Intermediate MVN Correlation for Poisson - Negative Binomial Variables: Correlation Method 1", "topics": [ "findintercorr_pois_nb" ] }, { "page": "findintercorr2", "title": "Calculate Intermediate MVN Correlation for Ordinal, Continuous, Poisson, or Negative Binomial Variables: Correlation Method 2", "topics": [ "findintercorr2" ] }, { "page": "fleish", "title": "Fleishman's Third-Order Polynomial Transformation Equations", "topics": [ "fleish" ] }, { "page": "fleish_Hessian", "title": "Fleishman's Third-Order Transformation Hessian Calculation for Lower Boundary of Standardized Kurtosis in Asymmetric Distributions", "topics": [ "fleish_Hessian" ] }, { "page": "fleish_skurt_check", "title": "Fleishman's Third-Order Transformation Lagrangean Constraints for Lower Boundary of Standardized Kurtosis in Asymmetric Distributions", "topics": [ "fleish_skurt_check" ] }, { "page": "H_params", "title": "Parameters for Examples of Constants Calculated by Headrick's Fifth-Order Polynomial Transformation", "topics": [ "H_params" ] }, { "page": "Headrick.dist", "title": "Examples of Constants Calculated by Headrick's Fifth-Order Polynomial Transformation", "topics": [ "Headrick.dist" ] }, { "page": "intercorr_fleish", "title": "Fleishman's Third-Order Polynomial Transformation Intermediate Correlation Equations", "topics": [ "intercorr_fleish" ] }, { "page": "intercorr_poly", "title": "Headrick's Fifth-Order Polynomial Transformation Intermediate Correlation Equations", "topics": [ "intercorr_poly" ] }, { "page": "max_count_support", "title": "Calculate Maximum Support Value for Count Variables: Correlation Method 2", "topics": [ "max_count_support" ] }, { "page": "nonnormvar1", "title": "Generation of One Non-Normal Continuous Variable Using the Power Method", "topics": [ "nonnormvar1" ] }, { "page": "ordnorm", "title": "Calculate Intermediate MVN Correlation to Generate Variables Treated as Ordinal", "topics": [ "ordnorm" ] }, { "page": "pdf_check", "title": "Check Polynomial Transformation Constants for Valid Power Method PDF", "topics": [ "pdf_check" ] }, { "page": "plot_cdf", "title": "Plot Theoretical Power Method Cumulative Distribution Function for Continuous Variables", "topics": [ "plot_cdf" ] }, { "page": "plot_pdf_ext", "title": "Plot Theoretical Power Method Probability Density Function and Target PDF of External Data for Continuous Variables", "topics": [ "plot_pdf_ext" ] }, { "page": "plot_pdf_theory", "title": "Plot Theoretical Power Method Probability Density Function and Target PDF by Distribution Name or Function for Continuous Variables", "topics": [ "plot_pdf_theory" ] }, { "page": "plot_sim_cdf", "title": "Plot Simulated (Empirical) Cumulative Distribution Function for Continuous, Ordinal, or Count Variables", "topics": [ "plot_sim_cdf" ] }, { "page": "plot_sim_ext", "title": "Plot Simulated Data and Target External Data for Continuous or Count Variables", "topics": [ "plot_sim_ext" ] }, { "page": "plot_sim_pdf_ext", "title": "Plot Simulated Probability Density Function and Target PDF of External Data for Continuous or Count Variables", "topics": [ "plot_sim_pdf_ext" ] }, { "page": "plot_sim_pdf_theory", "title": "Plot Simulated Probability Density Function and Target PDF by Distribution Name or Function for Continuous or Count Variables", "topics": [ "plot_sim_pdf_theory" ] }, { "page": "plot_sim_theory", "title": "Plot Simulated Data and Target Distribution Data by Name or Function for Continuous or Count Variables", "topics": [ "plot_sim_theory" ] }, { "page": "poly", "title": "Headrick's Fifth-Order Polynomial Transformation Equations", "topics": [ "poly" ] }, { "page": "poly_skurt_check", "title": "Headrick's Fifth-Order Transformation Lagrangean Constraints for Lower Boundary of Standardized Kurtosis", "topics": [ "poly_skurt_check" ] }, { "page": "power_norm_corr", "title": "Calculate Power Method Correlation", "topics": [ "power_norm_corr" ] }, { "page": "rcorrvar", "title": "Generation of Correlated Ordinal, Continuous, Poisson, and/or Negative Binomial Variables: Correlation Method 1", "topics": [ "rcorrvar" ] }, { "page": "rcorrvar2", "title": "Generation of Correlated Ordinal, Continuous, Poisson, and/or Negative Binomial Variables: Correlation Method 2", "topics": [ "rcorrvar2" ] }, { "page": "separate_rho", "title": "Separate Target Correlation Matrix by Variable Type", "topics": [ "separate_rho" ] }, { "page": "sim_cdf_prob", "title": "Calculate Simulated (Empirical) Cumulative Probability", "topics": [ "sim_cdf_prob" ] }, { "page": "SimMultiCorrData", "title": "Simulation of Correlated Data with Multiple Variable Types", "topics": [ "SimMultiCorrData-package", "SimMultiCorrData" ] }, { "page": "stats_pdf", "title": "Calculate Theoretical Statistics for a Valid Power Method PDF", "topics": [ "stats_pdf" ] }, { "page": "valid_corr", "title": "Determine Correlation Bounds for Ordinal, Continuous, Poisson, and/or Negative Binomial Variables: Correlation Method 1", "topics": [ "valid_corr" ] }, { "page": "valid_corr2", "title": "Determine Correlation Bounds for Ordinal, Continuous, Poisson, and/or Negative Binomial Variables: Correlation Method 2", "topics": [ "valid_corr2" ] }, { "page": "var_cat", "title": "Calculate Variance of Binary or Ordinal Variable", "topics": [ "var_cat" ] } ], "_readme": "https://github.com/afialkowski/simmulticorrdata/raw/HEAD/README.md", "_rundeps": [ "BB", "bbmle", "bdsmatrix", "boot", "cli", "cpp11", "cubature", "farver", "GenOrd", "ggplot2", "glue", "GPArotation", "gtable", "isoband", "labeling", "lattice", "lifecycle", "MASS", "Matrix", "mnormt", "mvtnorm", "nleqslv", "nlme", "numDeriv", "psych", "quadprog", "R6", "RColorBrewer", "Rcpp", "rlang", "S7", "scales", "triangle", "vctrs", "VGAM", "viridisLite", "withr" ], "_vignettes": [ { "source": "benefits.Rmd", "filename": "benefits.html", "title": "Benefits of SimMultiCorrData and Comparison to Other Packages", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "Benefits of this package:", "Comparison to other R packages:", "Barbiero & Ferrari's (2015) GenOrd", "Amatya & Demirtas' (2016) MultiOrd", "Leisch, Kaiser, & Hornik's (2010) orddata", "Demirtas, Nordgren, & Allozi's (2017) PoisBinOrdNonNor", "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-10-25 13:44:15", "commits": 7 }, { "source": "method_comp.Rmd", "filename": "method_comp.html", "title": "Comparison of Correlation Method 1 and Correlation Method 2", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "Methods Used in Both Pathways:", "Ordinal Variables:", "Continuous Variables:", "Continuous-Ordinal Pairs:", "Overview of Correlation Method 1:", "Simulation Process:", "Overview of Correlation Method 2:", "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-10-25 13:44:15", "commits": 6 }, { "source": "dist_comp.Rmd", "filename": "dist_comp.html", "title": "Comparison of Simulated Distribution to Theoretical Distribution or Empirical Data", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "Example", "Step 1: Obtain the standardized cumulants", "Step 2: Simulate the variable", "Step 3: Determine if the constants generate a valid power method pdf", "Step 4: Select a critical value", "Step 5: Solve for $\\Large z'$", "Step 6: Calculate $\\Large \\Phi(z')$", "Step 7: Plot graphs", "Calculate descriptive statistics.", "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-10-25 13:44:15", "commits": 6 }, { "source": "functions.Rmd", "filename": "functions.html", "title": "Functions by Topic", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "Simulation Functions:", "Power Method Constants Functions:", "Data Description (Summary) Functions:", "Lower Kurtosis Boundary Functions:", "Correlation Validation Functions:", "Intermediate Correlation Functions:", "Error Loop Functions:", "Graphing Functions:", "Additional Helper Functions:", "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-10-25 13:44:15", "commits": 6 }, { "source": "workflow.Rmd", "filename": "workflow.html", "title": "Overall Workflow for Data Simulation", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "Example", "Step 1: Set up the distributions and obtain the standardized cumulants", "Step 2: Calculate the lower kurtosis bounds for the continuous variables", "Correlation Method 1", "Step 3: Verify the target correlation matrix falls within the feasible correlation bounds", "Step 4: Generate the variables", "Step 5: Summarize the results numerically", "Step 6: Summarize the results graphically", "Correlation Method 2", "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-10-25 13:44:15", "commits": 6 }, { "source": "errorloop.Rmd", "filename": "errorloop.html", "title": "Overview of Error Loop", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-11-05 12:37:33", "commits": 5 }, { "source": "sixth_validpdf.Rmd", "filename": "sixth_validpdf.html", "title": "Using the Sixth Cumulant Correction to Find Valid Power Method Pdfs", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "Example", "Step 1) Find the theoretical standardized cumulants for each distribution.", "Step 2) Use the cumulants to find the constants for each distribution.", "Step 3) Look at results to see which distributions still have invalid power method pdfs", "Step 4) Look at constants and sixth cumulant corrections", "Step 5) Simulate distributions", "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-10-25 13:44:15", "commits": 7 }, { "source": "variable_types.Rmd", "filename": "variable_types.html", "title": "Variable Types", "author": "Allison C Fialkowski", "engine": "knitr::rmarkdown", "headings": [ "References" ], "created": "2017-06-22 17:30:57", "modified": "2017-10-25 13:44:15", "commits": 6 } ], "_score": 7.857332496431269, "_indexed": true, "_nocasepkg": "simmulticorrdata", "_universes": [ "afialkowski" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.2.2", "date": "2026-05-25T09:59:48.000Z", "distro": "noble", "commit": "89bd4e5a3fc66c2fc9c637823d68d71bfac99368", "fileid": "0c000b6e3e1090c6554f3b4f849fa30a575e946919bffdfe7c8e192a36f831ea", "status": "success", "check": "OK", 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