Dynamic CUR is an r package that boosts the CUR decomposition varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension. Mahoney & Drineas (2009) identified the singular vectors of the SVD as the PCs' interpretation problem and proposed another type of matrix factorization known as CUR Decomposition (Mahoney & Drineas, 2009; Mahoney, Maggioni, & Drineas, 2008; Bodor, Csabai, Mahoney, & Solymosi, 2012). The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics. One example is the one showed in Mahoney & Drineas (2009), in which cancer microarrays highlighted to recognize, based on 5000 variables, genetic patterns in patients with soft tissue tumors analyzed with cDNA microarrays. The objective of this package is to show an alternative to variable selection (columns) or individuals (rows) to the ones developed by Mahoney & Drineas (2009). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation \|A-CUR\|. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest.