Package: BayesAiken 2.0.0

Jose Ventura-Leon

BayesAiken: Bayesian Content Validity Coefficients with Credibility Intervals

Bayesian inference for the six rating coefficients proposed by Aiken (1985) <doi:10.1177/0013164485451012> and Aiken (1989) <doi:10.1177/0013164489492003>: V (content validity), H (homogeneity), C (congruence), R (repeatability), A (rater agreement) and I (item coherence). All six are expressed as functionals of the population distribution of rating categories, so a single Dirichlet-Multinomial model yields their full posterior by push-forward. This treats each judge as one categorical observation and avoids the pseudo-replication of Beta-Binomial formulations. R is modelled on the c-by-c joint table because it is not a functional of the marginal distribution. Provides classical estimates with the correct parity correction, highest density and equal-tailed credibility intervals, posterior threshold probabilities, prior sensitivity analysis and a friendly step-by-step verbose report.

Authors:Jose Ventura-Leon [aut, cre]

BayesAiken_2.0.0.tar.gz
BayesAiken_2.0.0.zip(r-4.7-any)BayesAiken_2.0.0.zip(r-4.6-any)
BayesAiken_2.0.0.tar.gz(r-4.7-any)BayesAiken_2.0.0.tar.gz(r-4.6-any)
BayesAiken_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BayesAiken/json (API)

# Install 'BayesAiken' in R:
install.packages('BayesAiken', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jventural/bayesaiken/issues

On CRAN:

Conda:

1.00 score 55 exports 0 dependencies

Last updated from:0fc3c039f8. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK142
source / vignettesOK171
linux-releaseOK169
windows-develOK83
windows-releaseOK94
wasm-releaseOK92

Exports:aiken_A_classicaiken_apaaiken_bayesaiken_bayes_tableaiken_C_classicaiken_H_classicaiken_I_classicaiken_R_classicaiken_testaiken_V_classicaiken_V_ic_pgbayes_aikenbayes_aiken_capturebayes_aiken_exportcheck_judge_heterogeneitycoef_Acoef_A_multicoef_Ccoef_C_multicoef_Hcoef_H_multicoef_Icoef_I_multicoef_Rcoef_R_multicoef_Vcoef_V_dirichletcoef_V_dirichlet_multicoef_V_multicompare_itemscompare_judge_groupscompare_priorscompare_V_modelscompute_hdidecision_ruledecision_rule_multidetect_judge_colsexport_to_csvexport_to_txtextract_resultsfmt_apaplot_all_coefficientsplot_comparisonplot_multiple_posteriorsplot_posteriorplot_V_beta_binomialplot_V_dirichletprint_aparecommend_modelrope_analysisrun_aiken_appsample_size_planningsensitivity_analysissimulate_judges_planningsummary_table

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Tabla de resultados en formato APA compactoaiken_apa
Coeficientes de Aiken bayesianos por el modelo Dirichlet-Multinomialaiken_bayes
Analisis bayesiano completo de una tabla de calificaciones de juecesaiken_bayes_table
Coeficientes clasicos de Aikenaiken_A_classic aiken_classic aiken_C_classic aiken_H_classic aiken_I_classic aiken_R_classic aiken_V_classic
Prueba de significacion de Aiken (1985) para V, R y Haiken_test
Intervalo de puntuacion de Penfield-Giacobbi para la V (frecuentista)aiken_V_ic_pg
Convertir un aiken_table a data.frameas.data.frame.aiken_table
Convertir bayes_coef a data.frameas.data.frame.bayes_coef
Convertir resultados de simulacion a data.frame (formato Tabla 3)as.data.frame.bayes_simulation
============================================================================= BAYES AIKEN - FUNCION PRINCIPAL Calcula todos los coeficientes de validez de contenido con enfoque bayesianobayes_aiken
============================================================================= FUNCIONES DE EXPORTACION Y CAPTURA - BayesAiken ============================================================================= Capturar salida verbose de bayes_aikenbayes_aiken_capture
Exportar resultados de bayes_aikenbayes_aiken_export
============================================================================= UTILIDADES Y HELPERS - BayesAiken ============================================================================= Diagnostico de heterogeneidad entre juecescheck_judge_heterogeneity
Calcular Coeficiente A (Acuerdo del Juez) con intervalos bayesianoscoef_A
Calcular A para multiples juecescoef_A_multi
Calcular Coeficiente C (Consenso) con intervalos bayesianoscoef_C
Calcular C para multiples itemscoef_C_multi
Calcular Coeficiente H (Homogeneidad) con intervalos bayesianoscoef_H
Calcular H para multiples itemscoef_H_multi
Calcular Coeficiente I (Consistencia Inter-Item) con intervalos bayesianoscoef_I
Calcular I para todos los pares de itemscoef_I_multi
Calcular Coeficiente R (Reproducibilidad) con intervalos bayesianoscoef_R
Calcular R para multiples items (requiere datos en formato especial)coef_R_multi
============================================================================= COEFICIENTES DE VALIDEZ DE CONTENIDO - FUNCIONES MODULARES Cada coeficiente puede usarse de forma independiente ============================================================================= Calcular Coeficiente V de Aiken con intervalos bayesianoscoef_V
Calcular Coeficiente V de Aiken con modelo Dirichlet-Multinomialcoef_V_dirichlet
Calcular V de Aiken Dirichlet para multiples itemscoef_V_dirichlet_multi
Calcular V de Aiken para multiples itemscoef_V_multi
Comparar dos items usando diferencias posteriorescompare_items
Comparar grupos de juecescompare_judge_groups
Comparar diferentes priorscompare_priors
Comparar modelos Beta-Binomial vs Dirichlet-Multinomialcompare_V_models
Calcular HDI (Highest Density Interval)compute_hdi
============================================================================= FUNCIONES AVANZADAS - BayesAiken Analisis bayesianos avanzados para validez de contenidodecision_rule
Aplicar regla de decision a multiples itemsdecision_rule_multi
Detectar las columnas de jueces en un data.framedetect_judge_cols
Exportar a archivo CSVexport_to_csv
Exportar a archivo de textoexport_to_txt
Extraer resultados en formato data.frameextract_results
Formatear un numero acotado en 0-1 al estilo APAfmt_apa
Graficar todos los coeficientesplot_all_coefficients
Grafico de comparacion de itemsplot_comparison
Graficar multiples items en un panelplot_multiple_posteriors
Graficar distribucion posteriorplot_posterior
Graficar densidad posterior del modelo Beta-Binomialplot_V_beta_binomial
Graficar densidad posterior del modelo Dirichlet-Multinomialplot_V_dirichlet
Grafico de la posterior de un coeficiente de Aikenplot.aiken_bayes
============================================================================= FUNCIONES DE VISUALIZACION - BayesAiken ============================================================================= Plot del objeto bayes_aikenplot.bayes_aiken
Plot de comparacion de itemsplot.bayes_comparison
Plot del analisis ROPEplot.bayes_rope
Plot del planeamiento de muestraplot.bayes_sample_plan
Grafico de resultados de simulacionplot.bayes_simulation
Grafico del analisis de sensibilidadplot.sensitivity_analysis
Imprimir la tabla APA en consolaprint_apa
Imprimir un objeto aiken_bayesprint.aiken_bayes
Imprimir un objeto aiken_tableprint.aiken_table
Print method for bayes_aiken objectsprint.bayes_aiken
Print method for bayes_coef objectsprint.bayes_coef
Imprimir resultados de simulacionprint.bayes_simulation
Imprimir diagnostico de heterogeneidadprint.judge_heterogeneity
Imprimir analisis de sensibilidadprint.sensitivity_analysis
Recomendar modelo para V de Aikenrecommend_model
Analisis ROPE para equivalencia practicarope_analysis
Abrir la aplicacion AikenBayesrun_aiken_app
Planear numero de jueces usando posterior predictivosample_size_planning
Analisis de sensibilidad completosensitivity_analysis
Simulacion para planificacion del numero de juecessimulate_judges_planning
Crear tabla resumen formateadasummary_table
Resumen de un objeto aiken_bayessummary.aiken_bayes
Summary method for bayes_aiken objectssummary.bayes_aiken