Discriminant analysis plays an important role in multivariate statistics as a prediction and classification method. It has been successfully applied in many fields of work and research. Analysis for multiple groups can be performed using linear discriminant analysis, which can be applied to classify and predict observations into various populations. The poor performance of linear discriminant analysis is due to lack of robustness of the classical estimators to train the model. As it happens with other multivariate methods, discriminant analysis is highly vulnerable to the presence of outliers that commonly occur in many real world data sets. The lack of robustness on the classical estimators on which the linear discriminant function is based is a severe disadvantage and several authors have worked to find efficient ways to prevent the damage outliers can cause. There exists a statistical computing system, R. (rrlda) is an R package that uses a sparse estimation of the inverse covariance matrix to perform the Robust Regularized Linear Discriminant Analysis. The main contribution of this paper is to show that with general convex uncertainty models on the problem data, robust regularized linear discriminant analysis can be carried out using convex optimization. This paper focuses on the package, (rrlda), in R and its efficiency in performing the Robust Linear Discriminant Analysis for a given data.