The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013; AOAS). The proposed method is applicable, for example, when selecting a small number of most (or least) efficacious treatments from a large number of alternative treatments as well as when identifying subsets of the population who benefit (or are harmed by) a treatment of interest. The method adapts the Support Vector Machine classifier by placing separate LASSO constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. This allows for the qualitative distinction between causal and other parameters, thereby making the variable selection suitable for the exploration of causal heterogeneity. The package also contains the function, CausalANOVA, which estimates the average marginal interaction effects by a regularized ANOVA as proposed by Egami and Imai (2019; JASA).