Formula

R package Formula: Extended Model Formulas. Infrastructure for extended formulas with multiple parts on the right-hand side and/or multiple responses on the left-hand side (see <doi:10.18637/jss.v034.i01>). Extended Model Formulas in R: Multiple Parts and Multiple Responses. Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they have certain limitations including being confined to single responses and not providing convenient support for processing formulas with multiple parts. The latter is relevant for models with two or more sets of variables, e.g., different equations for different model parameters (such as mean and dispersion), regressors and instruments in instrumental variable regressions, two-part models such as hurdle models, or alternative-specific and individual-specific variables in choice models among many others. The R package Formula addresses these two problems by providing a new class “Formula” (inheriting from “formula”) that accepts an additional formula operator | separating multiple parts and by allowing all formula operators (including the new |) on the left-hand side to support multiple responses.


References in zbMATH (referenced in 18 articles , 1 standard article )

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  1. Yves Croissant: Estimation of Random Utility Models in R: The mlogit Package (2020) not zbMATH
  2. David Smith; Malcolm Faddy: Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data (2019) not zbMATH
  3. Zhang, Qihuang; Yi, Grace Y.: R package for analysis of data with mixed measurement error and misclassification in covariates: augSIMEX (2019)
  4. Georgios Papageorgiou: BNSP: an R Package for Fitting Bayesian Regression Models With Semiparametric Mean and Variance Functions (2018) arXiv
  5. Guido Masarotto and Cristiano Varin: Gaussian Copula Regression in R (2017) not zbMATH
  6. Woodrow Burchett and Amanda Ellis and Solomon Harrar and Arne Bathke: Nonparametric Inference for Multivariate Data: The R Package npmv (2017) not zbMATH
  7. Peng Zhang and Zhenguo Qiu and Chengchun Shi: simplexreg: An R Package for Regression Analysis of Proportional Data Using the Simplex Distribution (2016) not zbMATH
  8. Vanegas, Luis Hernando; Paula, Gilberto A.: An extension of log-symmetric regression models: R codes and applications (2016)
  9. Weihua An; Xuefu Wang: LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions (2016) not zbMATH
  10. Andrew Finley; Sudipto Banerjee; Alan Gelfand: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models (2015) not zbMATH
  11. Brenton Kenkel; Curtis Signorino: Estimating Extensive Form Games in R (2014) not zbMATH
  12. Terrance Savitsky; Susan Paddock: Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R (2014) not zbMATH
  13. Birgit Erni; Bo Bonnevie; Hans-Dieter Oschadleus; Res Altwegg; Les Underhill: moult: An R Package to Analyze Moult in Birds (2013) not zbMATH
  14. Donald Hedeker; Rachel Nordgren: MIXREGLS: A Program for Mixed-Effects Location Scale Analysis (2013) not zbMATH
  15. Bettina Grün; Ioannis Kosmidis; Achim Zeileis: Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned (2012) not zbMATH
  16. Achim Zeileis; Yves Croissant: Extended Model Formulas in R: Multiple Parts and Multiple Responses (2010) not zbMATH
  17. Francisco Cribari-Neto; Achim Zeileis: Beta Regression in R (2010) not zbMATH
  18. Wolfgang Viechtbauer: Conducting Meta-Analyses in R with the metafor Package (2010) not zbMATH