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Robust statistical data modelling under potential model mis-specificationoften requires leaving the parametric world for the nonparametric. In thelatter, parameters are infinite dimensional objects such as functions,probability distributions or infinite vectors. In the Bayesian nonparametricapproach, prior distributions are designed for these parameters, which providea handle to manage the complexity of nonparametric models in practice. However,most modern Bayesian nonparametric models seem often out of reach topractitioners, as inference algorithms need careful design to deal with theinfinite number of parameters. The aim of this work is to facilitate thejourney by providing computational tools for Bayesian nonparametric inference.The article describes a set of functions available in the \R package BNPdensityin order to carry out density estimation with an infinite mixture model,including all types of censored data. The package provides access to a largeclass of such models based on normalized random measures, which represent ageneralization of the popular Dirichlet process mixture. One striking advantageof this generalization is that it offers much more robust priors on the numberof clusters than the Dirichlet. Another crucial advantage is the completeflexibility in specifying the prior for the scale and location parameters ofthe clusters, because conjugacy is not required. Inference is performed using atheoretically grounded approximate sampling methodology known as the Ferguson &Klass algorithm. The package also offers several goodness of fit diagnosticssuch as QQ-plots, including a cross-validation criterion, the conditionalpredictive ordinate. The proposed methodology is illustrated on a classicalecological risk assessment method called the Species Sensitivity Distribution(SSD) problem, showcasing the benefits of the Bayesian nonparametric framework.

BNPdensity: Bayesian nonparametric mixture modeling in R