This insert introduces some kernel estimators that provide nonparametric density estimates along with ado-files to calculate them. Kernel density estimators are an essential component of many more complicated estimators as tools for the exploratory stage of data analysis. In this context, kernel estimators can be regarded as nonparametric histogram smoothers. From an exploratory point of view, density estimates are valuable because they can reveal skewness, heavy or light tails, and multimodality in the data, characteristics that can be investigated further at the confirmatory stabe (Silverman 1986).