{"id":28148546,"url":"https://github.com/amoneva/cacc","last_synced_at":"2025-05-15T01:13:46.396Z","repository":{"id":48273213,"uuid":"516825144","full_name":"amoneva/cacc","owner":"amoneva","description":"An R Package to compute Conjunctive Analysis of Case Configurations (CACC), Situational Clustering Tests, and Main Effects","archived":false,"fork":false,"pushed_at":"2024-10-04T09:58:12.000Z","size":152,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-11T15:17:50.801Z","etag":null,"topics":["criminology","data-analysis","r","social-science"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amoneva.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-07-22T16:54:30.000Z","updated_at":"2024-10-03T16:08:07.000Z","dependencies_parsed_at":"2023-01-20T11:17:59.818Z","dependency_job_id":null,"html_url":"https://github.com/amoneva/cacc","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amoneva%2Fcacc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amoneva%2Fcacc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amoneva%2Fcacc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amoneva%2Fcacc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amoneva","download_url":"https://codeload.github.com/amoneva/cacc/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254179905,"owners_count":22027884,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["criminology","data-analysis","r","social-science"],"created_at":"2025-05-15T01:13:45.365Z","updated_at":"2025-05-15T01:13:46.391Z","avatar_url":"https://github.com/amoneva.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n```{r}\n#| include: false\n\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# `cacc`: Conjunctive Analysis of Case Configurations\n\n\u003c!-- badges: start --\u003e\n[![CRAN status](https://www.r-pkg.org/badges/version/cacc)](https://CRAN.R-project.org/package=cacc)\n\n[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)\n\n[![R-CMD-check](https://github.com/amoneva/cacc/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/amoneva/cacc/actions/workflows/R-CMD-check.yaml)\n\u003c!-- badges: end --\u003e\n\nAn R Package to compute Conjunctive Analysis of Case Configurations (CACC), Situational Clustering Tests, and Main Effects\n\n## Overview\n\nA set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) (Miethe, Hart \u0026 Regoeczi, 2008), to identify and quantify situational clustering in dominant case configurations (Hart, 2019), and to determine the main effects of specific variable values on the probabilities of outcome (Hart, Rennison \u0026 Miethe, 2017). Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods.\n\n## Installation\n\nTo install cacc, you can run:\n\n```{r install}\n#| eval: false\n\n# Install {cacc} from CRAN\ninstall.packages(\"cacc\")\n```\n\nYou can also install the development version of cacc from [GitHub](https://github.com/) with:\n\n```{r install-devel}\n#| eval: false\n\n# Check if the `devtools` package needs to be installed\nif (!require(\"devtools\")) install.package(\"devtools\")\n\n# Install {cacc} from GitHub\ndevtools::install_github(\"amoneva/cacc\")\n```\n\n## Examples\n\n```{r load}\n#| warning: false\n\n# Load {cacc} and the {tidyverse}\nlibrary(cacc)\nlibrary(tidyverse)\n```\n\n```{r explore}\n# Explore the dataset\nonharassment |\u003e glimpse()\n```\n\n### CACC\n\n```{r cacc}\n# Calculate the CACC matrix\ncacc_matrix \u003c- onharassment |\u003e \n  cacc(\n    ivs = sex:privacy, \n    dv = rep_victim\n  )\n\n# Look at the first few rows\ncacc_matrix |\u003e head()\n```\n\n### Situational Clustering Tests\n\n```{r chi}\n# Compute a Chi-Square Goodness-of-Fit Test\ncacc_matrix |\u003e cluster_xsq()\n```\n\n```{r sci}\n# Compute a Situational Clustering Index (SCI)\ncacc_matrix |\u003e cluster_sci()\n\n# Plot a Lorenz Curve to visualize the SCI\ncacc_matrix |\u003e plot_sci()\n```\n\n### Main Effects\n\n```{r effect}\n# Compute the main effects for a specific variable value\ncacc_matrix |\u003e \n  main_effect(\n    iv = sex,\n    value = \"female\",\n    # Set to `FALSE` for a numeric vector of effects\n    summary = TRUE\n  )\n\n# Plot the distribution of the main effect\ncacc_matrix |\u003e \n  plot_effect(\n    iv = sex,\n    value = \"female\"\n  )\n```\n\n## References\n\n-   Hart, T. C. (2019). Identifying Situational Clustering and Quantifying Its Magnitude in Dominant Case Configurations: New Methods for Conjunctive Analysis. *Crime \u0026 Delinquency, 66*(1), 143-159. https://doi.org/10.1177/0011128719866123\n-   Hart, T. C., Rennison, C. M., \u0026 Miethe, T. D. (2017). Identifying Patterns of Situational Clustering and Contextual Variability in Criminological Data: An Overview of Conjunctive Analysis of  Case  Configurations. *Journal  of  Contemporary Criminal  Justice, 33*(2),  112–120. https://doi.org/10.1177/1043986216689746\n-   Miethe, T. D., Hart, T. C., \u0026 Regoeczi, W. C. (2008). The Conjunctive Analysis of Case Configurations: An Exploratory Method for Discrete Multivariate Analyses of Crime Data. *Journal of Quantitative Criminology, 24*, 227–241. https://doi.org/10.1007/s10940-008-9044-8\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famoneva%2Fcacc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famoneva%2Fcacc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famoneva%2Fcacc/lists"}