{ "_id": "6ae24a7a3d0f299392dcfd40", "Package": "BayesAiken", "Type": "Package", "Title": "Bayesian Content Validity Coefficients with Credibility\nIntervals", "Version": "2.0.0", "Date": "2026-09-24", "Authors@R": "person(\"Jose\", \"Ventura-Leon\",\nemail = \"jventuraleon@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2996-4244\"))", "Description": "Bayesian inference for the six rating coefficients\nproposed by Aiken (1985) and\nAiken (1989) : V (content\nvalidity), H (homogeneity), C (congruence), R (repeatability),\nA (rater agreement) and I (item coherence). All six are\nexpressed as functionals of the population distribution of\nrating categories, so a single Dirichlet-Multinomial model\nyields their full posterior by push-forward. This treats each\njudge as one categorical observation and avoids the\npseudo-replication of Beta-Binomial formulations. R is modelled\non the c-by-c joint table because it is not a functional of the\nmarginal distribution. Provides classical estimates with the\ncorrect parity correction, highest density and equal-tailed\ncredibility intervals, posterior threshold probabilities, prior\nsensitivity analysis and a friendly step-by-step verbose\nreport.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "URL": "https://github.com/jventural/BayesAiken", "BugReports": "https://github.com/jventural/BayesAiken/issues", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-28 19:14:42 UTC", "User": "root" }, "Author": "Jose Ventura-Leon [aut, cre] (ORCID:\n)", "Maintainer": "Jose Ventura-Leon ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-10-28 11:00:18 UTC", "RemoteUrl": "https://github.com/cran/BayesAiken", "RemoteRef": "HEAD", "RemoteSha": "0fc3c039f87d9c005a85499c0e615e8755562afa", "_user": "cran", "_type": "src", "_file": "BayesAiken_2.0.0.tar.gz", "_fileid": "https://r2.ropensci.org/153a3ed30d439ff62aefa6c38e3e402f4abbbd22fc99836ffa2a608e49a60212", "_filesize": 357741, "_sha256": "153a3ed30d439ff62aefa6c38e3e402f4abbbd22fc99836ffa2a608e49a60212", "_expires": "2027-02-05T19:18:15.000Z", "_created": "2026-10-28T19:14:42.000Z", "_published": "2026-10-28T19:18:18.020Z", "_upstream": "https://github.com/cran/BayesAiken", "_status": "success", "_maintainer": { "name": "Jose Ventura-Leon", "email": "jventuraleon@gmail.com", "login": "jventural", "description": "", "uuid": 60574394, "orcid": "0000-0003-2996-4244" }, "_jobs": [ { "job": 123043003828, "time": 142, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12845490191" }, { "job": 123043003757, "time": 169, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12843429614" }, { "job": 123041460179, "time": 171, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12844926043" }, { "job": 123043003891, "time": 92, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12844207108" }, { "job": 123043003767, "time": 83, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12843318839" }, { "job": 123043003728, "time": 94, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12844217602" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/40752109617", "_commit": { "id": "0fc3c039f87d9c005a85499c0e615e8755562afa", "author": "Jose Ventura-Leon ", "committer": "cran-robot ", "message": "version 2.0.0\n", "time": 1793185218 }, "_distro": "resolute", "_host": "GitHub-Actions", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.0.0", "role": "Depends" }, { "package": "stats", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "shiny", "role": "Suggests" }, { "package": "readxl", "role": "Suggests" }, { "package": "writexl", "role": "Suggests" }, { "package": "openxlsx", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2026-44", "n": 1 } ], "_tags": [ { "name": "2.0.0", "date": "2026-10-28" } ], "_stars": 0, "_contributors": [ { "user": "jventural", "count": 1, "uuid": 60574394 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "followers": 634, "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BayesAiken" }, "_devurl": "https://github.com/jventural/bayesaiken", "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/BayesAiken.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "LICENSE" ], "_cranurl": false, "_releases": [ { "version": "2.0.0", "date": "2026-10-28" } ], "_exports": [ "aiken_A_classic", "aiken_apa", "aiken_bayes", "aiken_bayes_table", "aiken_C_classic", "aiken_H_classic", "aiken_I_classic", "aiken_R_classic", "aiken_test", "aiken_V_classic", "aiken_V_ic_pg", "bayes_aiken", "bayes_aiken_capture", "bayes_aiken_export", "check_judge_heterogeneity", "coef_A", "coef_A_multi", "coef_C", "coef_C_multi", "coef_H", "coef_H_multi", "coef_I", "coef_I_multi", "coef_R", "coef_R_multi", "coef_V", "coef_V_dirichlet", "coef_V_dirichlet_multi", "coef_V_multi", "compare_items", "compare_judge_groups", "compare_priors", "compare_V_models", "compute_hdi", "decision_rule", "decision_rule_multi", "detect_judge_cols", "export_to_csv", "export_to_txt", "extract_results", "fmt_apa", "plot_all_coefficients", "plot_comparison", "plot_multiple_posteriors", "plot_posterior", "plot_V_beta_binomial", "plot_V_dirichlet", "print_apa", "recommend_model", "rope_analysis", "run_aiken_app", "sample_size_planning", "sensitivity_analysis", "simulate_judges_planning", "summary_table" ], "_help": [ { "page": "aiken_apa", "title": "Tabla de resultados en formato APA compacto", "topics": [ "aiken_apa" ] }, { "page": "aiken_bayes", "title": "Coeficientes de Aiken bayesianos por el modelo Dirichlet-Multinomial", "topics": [ "aiken_bayes" ] }, { "page": "aiken_bayes_table", "title": "Analisis bayesiano completo de una tabla de calificaciones de jueces", "topics": [ "aiken_bayes_table" ] }, { "page": "aiken_classic", "title": "Coeficientes clasicos de Aiken", "topics": [ "aiken_A_classic", "aiken_classic", "aiken_C_classic", "aiken_H_classic", "aiken_I_classic", "aiken_R_classic", "aiken_V_classic" ] }, { "page": "aiken_test", "title": "Prueba de significacion de Aiken (1985) para V, R y H", "topics": [ "aiken_test" ] }, { "page": "aiken_V_ic_pg", "title": "Intervalo de puntuacion de Penfield-Giacobbi para la V (frecuentista)", "topics": [ "aiken_V_ic_pg" ] }, { "page": "as.data.frame.aiken_table", "title": "Convertir un aiken_table a data.frame", "topics": [ "as.data.frame.aiken_table" ] }, { "page": "as.data.frame.bayes_coef", "title": "Convertir bayes_coef a data.frame", "topics": [ "as.data.frame.bayes_coef" ] }, { "page": "as.data.frame.bayes_simulation", "title": "Convertir resultados de simulacion a data.frame (formato Tabla 3)", "topics": [ "as.data.frame.bayes_simulation" ] }, { "page": "bayes_aiken", "title": "============================================================================= BAYES AIKEN - FUNCION PRINCIPAL Calcula todos los coeficientes de validez de contenido con enfoque bayesiano", "topics": [ "bayes_aiken" ] }, { "page": "bayes_aiken_capture", "title": "============================================================================= FUNCIONES DE EXPORTACION Y CAPTURA - BayesAiken ============================================================================= Capturar salida verbose de bayes_aiken", "topics": [ "bayes_aiken_capture" ] }, { "page": "bayes_aiken_export", "title": "Exportar resultados de bayes_aiken", "topics": [ "bayes_aiken_export" ] }, { "page": "check_judge_heterogeneity", "title": "============================================================================= UTILIDADES Y HELPERS - BayesAiken ============================================================================= Diagnostico de heterogeneidad entre jueces", "topics": [ "check_judge_heterogeneity" ] }, { "page": "coef_A", "title": "Calcular Coeficiente A (Acuerdo del Juez) con intervalos bayesianos", "topics": [ "coef_A" ] }, { "page": "coef_A_multi", "title": "Calcular A para multiples jueces", "topics": [ "coef_A_multi" ] }, { "page": "coef_C", "title": "Calcular Coeficiente C (Consenso) con intervalos bayesianos", "topics": [ "coef_C" ] }, { "page": "coef_C_multi", "title": "Calcular C para multiples items", "topics": [ "coef_C_multi" ] }, { "page": "coef_H", "title": "Calcular Coeficiente H (Homogeneidad) con intervalos bayesianos", "topics": [ "coef_H" ] }, { "page": "coef_H_multi", "title": "Calcular H para multiples items", "topics": [ "coef_H_multi" ] }, { "page": "coef_I", "title": "Calcular Coeficiente I (Consistencia Inter-Item) con intervalos bayesianos", "topics": [ "coef_I" ] }, { "page": "coef_I_multi", "title": "Calcular I para todos los pares de items", "topics": [ "coef_I_multi" ] }, { "page": "coef_R", "title": "Calcular Coeficiente R (Reproducibilidad) con intervalos bayesianos", "topics": [ "coef_R" ] }, { "page": "coef_R_multi", "title": "Calcular R para multiples items (requiere datos en formato especial)", "topics": [ "coef_R_multi" ] }, { "page": "coef_V", "title": "============================================================================= COEFICIENTES DE VALIDEZ DE CONTENIDO - FUNCIONES MODULARES Cada coeficiente puede usarse de forma independiente ============================================================================= Calcular Coeficiente V de Aiken con intervalos bayesianos", "topics": [ "coef_V" ] }, { "page": "coef_V_dirichlet", "title": "Calcular Coeficiente V de Aiken con modelo Dirichlet-Multinomial", "topics": [ "coef_V_dirichlet" ] }, { "page": "coef_V_dirichlet_multi", "title": "Calcular V de Aiken Dirichlet para multiples items", "topics": [ "coef_V_dirichlet_multi" ] }, { "page": "coef_V_multi", "title": "Calcular V de Aiken para multiples items", "topics": [ "coef_V_multi" ] }, { "page": "compare_items", "title": "Comparar dos items usando diferencias posteriores", "topics": [ "compare_items" ] }, { "page": "compare_judge_groups", "title": "Comparar grupos de jueces", "topics": [ "compare_judge_groups" ] }, { "page": "compare_priors", "title": "Comparar diferentes priors", "topics": [ "compare_priors" ] }, { "page": "compare_V_models", "title": "Comparar modelos Beta-Binomial vs Dirichlet-Multinomial", "topics": [ "compare_V_models" ] }, { "page": "compute_hdi", "title": "Calcular HDI (Highest Density Interval)", "topics": [ "compute_hdi" ] }, { "page": "decision_rule", "title": "============================================================================= FUNCIONES AVANZADAS - BayesAiken Analisis bayesianos avanzados para validez de contenido", "topics": [ "decision_rule" ] }, { "page": "decision_rule_multi", "title": "Aplicar regla de decision a multiples items", "topics": [ "decision_rule_multi" ] }, { "page": "detect_judge_cols", "title": "Detectar las columnas de jueces en un data.frame", "topics": [ "detect_judge_cols" ] }, { "page": "export_to_csv", "title": "Exportar a archivo CSV", "topics": [ "export_to_csv" ] }, { "page": "export_to_txt", "title": "Exportar a archivo de texto", "topics": [ "export_to_txt" ] }, { "page": "extract_results", "title": "Extraer resultados en formato data.frame", "topics": [ "extract_results" ] }, { "page": "fmt_apa", "title": "Formatear un numero acotado en 0-1 al estilo APA", "topics": [ "fmt_apa" ] }, { "page": "plot_all_coefficients", "title": "Graficar todos los coeficientes", "topics": [ "plot_all_coefficients" ] }, { "page": "plot_comparison", "title": "Grafico de comparacion de items", "topics": [ "plot_comparison" ] }, { "page": "plot_multiple_posteriors", "title": "Graficar multiples items en un panel", "topics": [ "plot_multiple_posteriors" ] }, { "page": "plot_posterior", "title": "Graficar distribucion posterior", "topics": [ "plot_posterior" ] }, { "page": "plot_V_beta_binomial", "title": "Graficar densidad posterior del modelo Beta-Binomial", "topics": [ "plot_V_beta_binomial" ] }, { "page": "plot_V_dirichlet", "title": "Graficar densidad posterior del modelo Dirichlet-Multinomial", "topics": [ "plot_V_dirichlet" ] }, { "page": "plot.aiken_bayes", "title": "Grafico de la posterior de un coeficiente de Aiken", "topics": [ "plot.aiken_bayes" ] }, { "page": "plot.bayes_aiken", "title": "============================================================================= FUNCIONES DE VISUALIZACION - BayesAiken ============================================================================= Plot del objeto bayes_aiken", "topics": [ "plot.bayes_aiken" ] }, { "page": "plot.bayes_comparison", "title": "Plot de comparacion de items", "topics": [ "plot.bayes_comparison" ] }, { "page": "plot.bayes_rope", "title": "Plot del analisis ROPE", "topics": [ "plot.bayes_rope" ] }, { "page": "plot.bayes_sample_plan", "title": "Plot del planeamiento de muestra", "topics": [ "plot.bayes_sample_plan" ] }, { "page": "plot.bayes_simulation", "title": "Grafico de resultados de simulacion", "topics": [ "plot.bayes_simulation" ] }, { "page": "plot.sensitivity_analysis", "title": "Grafico del analisis de sensibilidad", "topics": [ "plot.sensitivity_analysis" ] }, { "page": "print_apa", "title": "Imprimir la tabla APA en consola", "topics": [ "print_apa" ] }, { "page": "print.aiken_bayes", "title": "Imprimir un objeto aiken_bayes", "topics": [ "print.aiken_bayes" ] }, { "page": "print.aiken_table", "title": "Imprimir un objeto aiken_table", "topics": [ "print.aiken_table" ] }, { "page": "print.bayes_aiken", "title": "Print method for bayes_aiken objects", "topics": [ "print.bayes_aiken" ] }, { "page": "print.bayes_coef", "title": "Print method for bayes_coef objects", "topics": [ "print.bayes_coef" ] }, { "page": "print.bayes_simulation", "title": "Imprimir resultados de simulacion", "topics": [ "print.bayes_simulation" ] }, { "page": "print.judge_heterogeneity", "title": "Imprimir diagnostico de heterogeneidad", "topics": [ "print.judge_heterogeneity" ] }, { "page": "print.sensitivity_analysis", "title": "Imprimir analisis de sensibilidad", "topics": [ "print.sensitivity_analysis" ] }, { "page": "recommend_model", "title": "Recomendar modelo para V de Aiken", "topics": [ "recommend_model" ] }, { "page": "rope_analysis", "title": "Analisis ROPE para equivalencia practica", "topics": [ "rope_analysis" ] }, { "page": "run_aiken_app", "title": "Abrir la aplicacion AikenBayes", "topics": [ "run_aiken_app" ] }, { "page": "sample_size_planning", "title": "Planear numero de jueces usando posterior predictivo", "topics": [ "sample_size_planning" ] }, { "page": "sensitivity_analysis", "title": "Analisis de sensibilidad completo", "topics": [ "sensitivity_analysis" ] }, { "page": "simulate_judges_planning", "title": "Simulacion para planificacion del numero de jueces", "topics": [ "simulate_judges_planning" ] }, { "page": "summary_table", "title": "Crear tabla resumen formateada", "topics": [ "summary_table" ] }, { "page": "summary.aiken_bayes", "title": "Resumen de un objeto aiken_bayes", "topics": [ "summary.aiken_bayes" ] }, { "page": "summary.bayes_aiken", "title": "Summary method for bayes_aiken objects", "topics": [ "summary.bayes_aiken" ] } ], "_rundeps": [], "_score": 1, "_indexed": true, "_nocasepkg": "bayesaiken", "_universes": [ "cran", "jventural" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.0.0", "date": "2026-10-28T19:17:09.000Z", "distro": "resolute", "commit": "0fc3c039f87d9c005a85499c0e615e8755562afa", "fileid": "https://r2.ropensci.org/a59a6c6175d14c1bd5ec6707368cebe56d6ce75d4e13015ed0c8e50579d96fab", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40752109617" }, { "r": "4.6.1", "os": "linux", "version": "2.0.0", "date": "2026-10-28T19:17:33.000Z", "distro": "resolute", "commit": "0fc3c039f87d9c005a85499c0e615e8755562afa", "fileid": "https://r2.ropensci.org/8d24dd4b3269b4849706597802860fb86fee5a85bb036b537a4965fcdaf9ca34", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40752109617" }, { "r": "4.6.0", "os": "wasm", "version": "2.0.0", "date": "2026-10-28T19:16:40.000Z", "commit": "0fc3c039f87d9c005a85499c0e615e8755562afa", "fileid": "https://r2.ropensci.org/5c72df259704f0ef82bfd80cef7149393084860b675ac92a890526c3f36d4924", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/40752109617" }, { "r": "4.7.0", "os": "win", "version": "2.0.0", "date": "2026-10-28T19:16:06.000Z", "commit": "0fc3c039f87d9c005a85499c0e615e8755562afa", "fileid": "https://r2.ropensci.org/e13c135e66d2ee19548324e9af79768aceea2a5d1de53be73abdb8b7a6928906", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40752109617" }, { "r": "4.6.1", "os": "win", "version": "2.0.0", "date": "2026-10-28T19:16:12.000Z", "commit": "0fc3c039f87d9c005a85499c0e615e8755562afa", "fileid": "https://r2.ropensci.org/3c389280acc0bbbe4b1d2512b484866cdcd99d255e19a568592da23e20a7f408", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40752109617" } ] }