tipsae-package {tipsae} | R Documentation |
It provides tools for mapping proportions and indicators defined on the unit interval, widely used to measure, for instance, unemployment, educational attainment and also disease prevalence. It implements Beta-based small area methods, particularly indicated for unit interval responses, comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions. Such methods, developed within a Bayesian framework, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A customized parallel computing is built-in to reduce the computational time. The features of the tipsae package assist the user in carrying out a complete SAE analysis through the entire process of estimation, validation and results presentation, making the application of Bayesian algorithms and complex SAE methods straightforward. A Shiny application with a user-friendly interface can be launched to further simplify the process.
Silvia De Nicolò, silvia.denicolo@unibo.it
Aldo Gardini, aldo.gardini@unibo.it
De Nicolò S, Gardini A (2024). “The R Package tipsae: Tools for Mapping Proportions and Indicators on the Unit Interval.” Journal of Statistical Software, 108(1), 1–36. doi:10.18637/jss.v108.i01.
Stan Development Team (2020). “RStan: the R interface to Stan.” R package version 2.21.2, https://mc-stan.org/.
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, Brubaker M, Guo J, Li P, Riddell A (2017). “Stan: A probabilistic programming language.” Journal of Statistical Software, 76(1), 1–32.
Janicki R (2020). “Properties of the beta regression model for small area estimation of proportions and application to estimation of poverty rates.” Communications in Statistics-Theory and Methods, 49(9), 2264–2284.
Vehtari A, Gelman A, Gabry J (2017). “Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.” Statistics and Computing, 27(5), 1413–1432.
Datta GS, Ghosh M, Steorts R, Maples J (2011). “Bayesian benchmarking with applications to small area estimation.” Test, 20(3), 574–588.
Kish L (1992). “Weighting for Unequal Pi.” Journal of Official Statistics, 8(2), 183.
Fabrizi E, Ferrante MR, Pacei S, Trivisano C (2011). “Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains.” Computational Statistics & Data Analysis, 55(4), 1736–1747.
Morris M, Wheeler-Martin K, Simpson D, Mooney SJ, Gelman A, DiMaggio C (2019). “Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan.” Spatial and Spatio-Temporal Epidemiology, 31, 100301.
De Nicolò S, Ferrante MR, Pacei S (2023). “Small area estimation of inequality measures using mixtures of Beta.” https://doi.org/10.1093/jrsssa/qnad083.
Chang W, Cheng J, Allaire JJ, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2021). “shiny: Web Application Framework for R.” R package version 1.6.0, https://CRAN.R-project.org/package=shiny.