Frbs: Fuzzy Rule-Based Systems for Classification and Regression in R Riza, Lala Septem Bergmeir, Christoph Norbert Herrera Triguero, Francisco Benítez Sánchez, José Manuel Fuzzy inference systems Soft computing Fuzzy sets Genetic fuzzy systems Fuzzy neural networks Fuzzy rule-based systems (FRBSs) are a well-known method family within soft computing. They are based on fuzzy concepts to address complex real-world problems. We present the R package frbs which implements the most widely used FRBS models, namely, Mamdani and Takagi Sugeno Kang (TSK) ones, as well as some common variants. In addition a host of learning methods for FRBSs, where the models are constructed from data, are implemented. In this way, accurate and interpretable systems can be built for data analysis and modeling tasks. In this paper, we also provide some examples on the usage of the package and a comparison with other common classification and regression methods available in R. 2016-01-22T08:32:53Z 2016-01-22T08:32:53Z 2015 info:eu-repo/semantics/article Riza, L.S.; et al. Frbs: Fuzzy Rule-Based Systems for Classification and Regression in R. Journal of Statistical Software, 65(6): online (2015). [http://hdl.handle.net/10481/39549] 1548-7660 http://hdl.handle.net/10481/39549 10.18637/jss.v065.i06 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License American Statistical Association