{ "_id": "6ae34b1295ca2370cc8ce54c", "Package": "networkscaleup", "Title": "Network Scale-Up Models for Aggregated Relational Data", "Version": "0.2-2", "Authors@R": "c(person(\"Ian\", \"Laga\", , \"ilaga25@gmail.com\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-5164-4856\")),\nperson(\"Owen G.\", \"Ward\", , \"oward@sfu.ca\", role = c(\"aut\", \"cre\")),\nperson(\"Anna L.\", \"Smith\", role = c(\"aut\")),\nperson(\"Benjamin\", \"Vogel\", role = c(\"aut\")),\nperson(\"Jieyun\", \"Wang\", role = c(\"aut\")),\nperson(\"Le\", \"Bao\", , \"lebao@psu.edu\", role = c(\"aut\")),\nperson(\"Xiaoyue\", \"Niu\", , \"Xiaoyue@psu.edu\", role = c(\"aut\")))", "Description": "Provides a variety of Network Scale-up Models for\nresearchers to analyze Aggregated Relational Data, through the\nuse of Stan and 'glmmTMB'. Also provides tools for model\nchecking In this version, the package implements models from\nLaga, I., Bao, L., and Niu, X (2023)\n, Zheng, T., Salganik, M.\nJ., and Gelman, A. (2006) ,\nKillworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A.,\nand Bernard, H. R. (1998) ,\nand Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G.\nA., and Johnsen, E. C. (1998) .", "License": "GPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "Biarch": "true", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "LazyData": "true", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-29 12:43:28 UTC", "User": "root" }, "Author": "Ian Laga [aut] (ORCID:\n), Owen G. Ward [aut,\ncre], Anna L. Smith [aut], Benjamin Vogel [aut], Jieyun Wang\n[aut], Le Bao [aut], Xiaoyue Niu [aut]", "Maintainer": "Owen G. Ward ", "URL": "https://github.com/ilaga/networkscaleup", "Config/pak/sysreqs": "make", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-06-26 08:00:02 UTC", "RemoteUrl": "https://github.com/cran/networkscaleup", "RemoteRef": "HEAD", "RemoteSha": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "_user": "cran", "_type": "src", "_file": "networkscaleup_0.2-2.tar.gz", "_fileid": "https://r2.ropensci.org/c912c052718ce134e37ee20f71f84b1c58f7ac41bd7ba4fcf9cb468bea81b4a3", "_filesize": 1151682, "_sha256": "c912c052718ce134e37ee20f71f84b1c58f7ac41bd7ba4fcf9cb468bea81b4a3", "_expires": "2027-02-06T13:33:03.000Z", "_created": "2026-10-29T12:43:28.000Z", "_published": "2026-10-29T13:33:06.085Z", "_upstream": "https://github.com/cran/networkscaleup", "_status": "success", "_maintainer": { "name": "Owen G. Ward", "email": "oward@sfu.ca" }, "_jobs": [ { "job": 123461994592, "time": 1036, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12900216486" }, { "job": 123461994601, "time": 1245, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12901165126" }, { "job": 123461994476, "time": 1056, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12900196989" }, { "job": 123461994760, "time": 818, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12900206192" }, { "job": 123444404879, "time": 2195, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12899770246" }, { "job": 123461994406, "time": 680, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12899049100" }, { "job": 123461994618, "time": 1111, "config": "windows-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12900267140" }, { "job": 123461994579, "time": 1915, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12901820672" }, { "job": 123461994431, "time": 1042, "config": "windows-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12900490475" }, { "job": 123461994675, "time": 1679, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12901495733" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647", "_commit": { "id": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "author": "Owen G. Ward ", "committer": "cran-robot ", "message": "version 0.2-2\n", "time": 1782460802 }, "_distro": "resolute", "_host": "GitHub-Actions", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "BH", "version": ">= 1.66.0", "role": "LinkingTo" }, { "package": "Rcpp", "version": ">= 0.12.0", "role": "LinkingTo" }, { "package": "RcppEigen", "version": ">= 0.3.3.3.0", "role": "LinkingTo" }, { "package": "rstan", "version": ">= 2.26.0", "role": "LinkingTo" }, { "package": "rstantools", "role": "LinkingTo" }, { "package": "StanHeaders", "version": ">= 2.26.0", "role": "LinkingTo" }, { "package": "methods", "role": "Imports" }, { "package": "Rcpp", "version": ">= 0.12.0", "role": "Imports" }, { "package": "rstan", "version": ">= 2.26.0", "role": "Imports" }, { "package": "LaplacesDemon", "version": ">= 16.1.6", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "readr", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "tidyr", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "glmmTMB", "role": "Imports" }, { "package": "gridExtra", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "stringr", "role": "Imports" }, { "package": "trialr", "role": "Imports" }, { "package": "tidyselect", "role": "Imports" }, { "package": "RMTstat", "role": "Imports" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2025-50", "n": 1 }, { "week": "2026-26", "n": 1 } ], "_tags": [ { "name": "0.2-1", "date": "2025-12-10" }, { "name": "0.2-2", "date": "2026-06-26" } ], "_stars": 1, "_contributors": [ { "user": "ilaga", "count": 3, "uuid": 11948264 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "followers": 634, "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 426, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/networkscaleup" }, "_devurl": "https://github.com/ilaga/networkscaleup", "_searchresults": 0, "_topics": [ "onetbb", "cpp" ], "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/networkscaleup.html", "extra/readme.html", "extra/readme.md" ], "_homeurl": "https://github.com/ilaga/networkscaleup", "_realowner": "ilaga", "_cranurl": false, "_releases": [ { "version": "0.1-1", "date": "2022-04-06" }, { "version": "0.1-2", "date": "2024-02-25" }, { "version": "0.2-1", "date": "2025-12-11" }, { "version": "0.2-2", "date": "2026-06-26" } ], "_exports": [ "construct_pearson", "construct_rqr", "correlatedStan", "cov_plots", "dispersion_metric", "fit_mle", "get_surrogate", "hang_rootogram_ard", "killworth", "make_ard", "overdispersed", "overdispersedStan", "plot_fitted", "residual_correlation", "residual_heatmap", "scaling", "tw_group_corr_test" ], "_datasets": [ { "name": "example_data", "title": "Simulated ARD data set with z and x.", "object": "example_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "construct_pearson", "title": "Compute Pearson Residuals for ARD matrix and fitted model", "topics": [ "construct_pearson" ] }, { "page": "construct_rqr", "title": "Compute Randomized Quantile Residuals for ARD Models", "topics": [ "construct_rqr" ] }, { "page": "correlatedStan", "title": "Fit ARD using the uncorrelated or correlated model in Stan This function fits the ARD using either the uncorrelated or correlated model in Laga et al. (2021) in Stan. The population size estimates and degrees are scaled using a post-hoc procedure.", "topics": [ "correlatedStan" ] }, { "page": "cov_plots", "title": "Covariance plots", "topics": [ "cov_plots" ] }, { "page": "dispersion_metric", "title": "Dispersion Metric for Fitted ARD Model", "topics": [ "dispersion_metric" ] }, { "page": "example_data", "title": "Simulated ARD data set with z and x.", "topics": [ "example_data" ] }, { "page": "fit_mle", "title": "Fit basic Poisson and Negative Binomial models using glmmTMB", "topics": [ "fit_mle" ] }, { "page": "get_surrogate", "title": "Compute Surrogate Residuals for ARD Models", "topics": [ "get_surrogate" ] }, { "page": "hang_rootogram_ard", "title": "Hanging Rootogram for Fitted ARD Model", "topics": [ "hang_rootogram_ard" ] }, { "page": "killworth", "title": "Fit Killworth models to ARD. This function estimates the degrees and population sizes using the plug-in MLE and MLE estimator.", "topics": [ "killworth" ] }, { "page": "log_mix_uniform", "title": "log computed uniform quantile", "topics": [ "log_mix_uniform" ] }, { "page": "make_ard", "title": "Generate simulated ARD", "topics": [ "make_ard" ] }, { "page": "make_ard_tidy", "title": "Construct tibble from ARD matrix", "topics": [ "make_ard_tidy" ] }, { "page": "networkscaleup", "title": "The 'networkscaleup' package.", "topics": [ "networkscaleup-package", "networkscaleup" ] }, { "page": "overdispersed", "title": "Fit Overdispersed model to ARD (Gibbs-Metropolis)", "topics": [ "overdispersed" ] }, { "page": "overdispersedStan", "title": "Fit ARD using the Overdispersed model in Stan", "topics": [ "overdispersedStan" ] }, { "page": "plot_fitted", "title": "Plot residuals against fitted values", "topics": [ "plot_fitted" ] }, { "page": "plot_tw_statistic", "title": "Plot the TW Test Statistic", "topics": [ "plot_tw_statistic" ] }, { "page": "residual_correlation", "title": "Construction Residual (row/column) correlation matrix", "topics": [ "residual_correlation" ] }, { "page": "residual_heatmap", "title": "Construct heatmap of residuals", "topics": [ "residual_heatmap" ] }, { "page": "rqr_nbinom_logs", "title": "compute numerically stable negative binomial rqr", "topics": [ "rqr_nbinom_logs" ] }, { "page": "rqr_pois_logs", "title": "compute numerically stable Poisson rqr", "topics": [ "rqr_pois_logs" ] }, { "page": "scaling", "title": "Scale raw log degree and log prevalence estimates", "topics": [ "scaling" ] }, { "page": "tw_group_corr_test", "title": "Tracy-Widom test for residual group correlation", "topics": [ "tw_group_corr_test" ] } ], "_readme": "https://github.com/cran/networkscaleup/raw/HEAD/README.md", "_rundeps": [ "abind", "arrayhelpers", "backports", "BH", "binom", "bit", "bit64", "boot", "broom", "callr", "checkmate", "cli", "clipr", "coda", "colorspace", "cowplot", "cpp11", "crayon", "Deriv", "desc", "distributional", "doBy", "dplyr", "farver", "forecast", "fracdiff", "generics", "ggdist", "ggplot2", "glmmTMB", "glue", "gridExtra", "gtable", "gtools", "hms", "inline", "isoband", "labeling", "LaplacesDemon", "lattice", "lifecycle", "lme4", "lmtest", "loo", "magrittr", "MASS", "Matrix", "matrixStats", "mgcv", "minqa", "modelr", "nlme", "nloptr", "nnet", "numDeriv", "otel", "pbkrtest", "pillar", "pkgbuild", "pkgconfig", "posterior", "prettyunits", "processx", "progress", "ps", "purrr", "quadprog", "QuickJSR", "R6", "rbibutils", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "RcppParallel", "Rdpack", "readr", "reformulas", "rlang", "RMTstat", "rstan", "rstantools", "RTMB", "S7", "sandwich", "scales", "StanHeaders", "stringi", "stringr", "svUnit", "tensorA", "tibble", "tidybayes", "tidyr", "tidyselect", "timeDate", "TMB", "trialr", "tzdb", "urca", "utf8", "vctrs", "viridisLite", "vroom", "withr", "zoo" ], "_sysdeps": [ { "shlib": "libtbb", "package": "libtbb12", "headers": "libtbb-dev", "source": "onetbb", "version": "2022.3.0-2", "name": "onetbb", "homepage": "https://uxlfoundation.github.io/oneTBB/", "description": "parallelism library for C++ - runtime files" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "ARD-Diagnostics.Rmd", "filename": "ARD-Diagnostics.html", "title": "Diagnostic tools for ARD Models", "engine": "knitr::rmarkdown", "headings": [ "Simulating Data", "Hanging Rootograms", "Testing for Covariates" ], "created": "2025-12-10 18:40:02", "modified": "2025-12-10 18:40:02", "commits": 1 }, { "source": "FittingNetworkScaleup.Rmd", "filename": "FittingNetworkScaleup.html", "title": "Fitting Network Scale-up Models", "engine": "knitr::rmarkdown", "headings": [ "Overview", "Instructions", "PIMLE", "MLE", "Bayesian Models", "Overdispersed Model", "Correlated Models" ], "created": "2022-04-06 06:10:02", "modified": "2025-12-10 18:40:02", "commits": 2 } ], "_score": 3.3010299956639813, "_indexed": false, "_nocasepkg": "networkscaleup", "_universes": [ "cran" ], "_indexurl": "https://ilaga.r-universe.dev/networkscaleup", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.2-2", "date": "2026-10-29T13:03:10.000Z", "distro": "resolute", "arch": "aarch64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/43b95bd12881f1699b85d4714a38e7d7b971f566004fd0ae8d75f52e5aaae37f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.7.0", "os": "linux", "version": "0.2-2", "date": "2026-10-29T13:03:18.000Z", "distro": "resolute", "arch": "x86_64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/c273bbbb186cb539a05b69f0dec8bd9b78761c3e779a6292d5c415463843d886", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.6.1", "os": "linux", "version": "0.2-2", "date": "2026-10-29T13:03:14.000Z", "distro": "resolute", "arch": "aarch64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/50f89cc79bd8f1937d31bfb740577a6036bd8427c2d74e585b6970281787b36b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.6.1", "os": "linux", "version": "0.2-2", "date": "2026-10-29T13:03:26.000Z", "distro": "resolute", "arch": "x86_64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/2b5b9cfce1bd349eea45bbe26ac4b545da23979a902685248a73829b2e63cdf3", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.6.0", "os": "wasm", "version": "0.2-2", "date": "2026-10-29T13:03:26.000Z", "arch": "emscripten", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/806706a02b3a1f0ffd9666178abd21e8ffd77d65fee719842fce29035ee427a7", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.7.0", "os": "win", "version": "0.2-2", "date": "2026-10-29T13:04:32.000Z", "arch": "aarch64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/a07e57d30cd0f27f1694d6ee06cca62e4b60857ace26001fe8f937959b9059f5", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.7.0", "os": "win", "version": "0.2-2", "date": "2026-10-29T13:02:31.000Z", "arch": "x86_64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/7da24d79a7f9c9b17b9bd103aacf764ed5f98a2a6f68e36d2536fb0b44e4f4d4", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.6.1", "os": "win", "version": "0.2-2", "date": "2026-10-29T13:04:31.000Z", "arch": "aarch64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/1d087cfe991d432755bcf6adc4fcca6b2550ff7d1efcbe16c4f53fa58557e4b1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" }, { "r": "4.6.1", "os": "win", "version": "0.2-2", "date": "2026-10-29T13:02:32.000Z", "arch": "x86_64", "commit": "873c2ca7ae28b729c02ca2ce4a3e0bcac1edfd31", "fileid": "https://r2.ropensci.org/cc9062f9339ea942058ab6874fd4396a8bb777417ba5a85795754cacc0c7588b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40872895647" } ] }