BayesX

R package BayesX: R Utilities Accompanying the Software Package BayesX. Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (<http://www.BayesX.org>). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 73 articles , 3 standard articles )

Showing results 21 to 40 of 73.
Sorted by year (citations)
  1. Choi, Taeryon; Woo, Yoonsung: A partially linear model using a Gaussian process prior (2015)
  2. Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015) not zbMATH
  3. Eilers, Paul H. C.; Marx, Brian D.; Durbán, Maria: Twenty years of P-splines (invited article) (2015)
  4. Klein, Nadja; Kneib, Thomas; Lang, Stefan; Sohn, Alexander: Bayesian structured additive distributional regression with an application to regional income inequality in Germany (2015)
  5. Lang, Stefan; Steiner, Winfried J.; Weber, Anett; Wechselberger, Peter: Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits (2015)
  6. Page, Garritt L.; Quintana, Fernando A.: Predictions based on the clustering of heterogeneous functions via shape and subject-specific covariates (2015)
  7. Perry de Valpine; Daniel Turek; Christopher J. Paciorek; Clifford Anderson-Bergman; Duncan Temple Lang; Rastislav Bodik: Programming with models: writing statistical algorithms for general model structures with NIMBLE (2015) arXiv
  8. Sperlich, Stefan; Theler, Raoul: Modeling heterogeneity: a praise for varying-coefficient models in causal analysis (2015)
  9. Zhou, Haiming; Hanson, Timothy; Knapp, Roland: Marginal Bayesian nonparametric model for time to disease arrival of threatened amphibian populations (2015)
  10. Benjamin Hofner, Andreas Mayr, Matthias Schmid: gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework (2014) arXiv
  11. Bosq, Denis; Ruiz-Medina, María D.: Bayesian estimation in a high dimensional parameter framework (2014)
  12. Duarte, Elisa; De Sousa, Bruno; Cadarso-Suarez, Carmen; Rodrigues, Vitor; Kneib, Thomas: Structured additive regression modeling of age of menarche and menopause in a breast cancer screening program (2014)
  13. Kim, Hea-Jung; Choi, Taeryon: On Bayesian estimation of regression models subject to uncertainty about functional constraints (2014)
  14. Takele, Kasahun; Taye, Ayele: Bayesian modelling of growth retardation among children under-five years old (2014)
  15. Yee, Thomas W.: Reduced-rank vector generalized linear models with two linear predictors (2014)
  16. Bivand, Roger S.; Pebesma, Edzer J.; Gómez-Rubio, Virgilio: Applied spatial data analysis with R (2013)
  17. Duncan Lee: CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors (2013) not zbMATH
  18. Kris De Brabanter; Johan Suykens; Bart De Moor: Nonparametric Regression via StatLSSVM (2013) not zbMATH
  19. Scheipl, Fabian; Kneib, Thomas; Fahrmeir, Ludwig: Penalized likelihood and Bayesian function selection in regression models (2013)
  20. Brombin, Chiara; Crippa, Massimo; Di Serio, Clelia: Modeling cancer cells growth (2012)