A package for array-based CNV (Copy Number Variation) analysis which is designed to control the FDR (False Discovery Rate) while ensuring high sensitivity. For controlling the FDR, we propose a probabilistic latent variable model, cn.FARMS, which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. In experiments, cn.FARMS outperformed its competitors both with respect to FDR and sensitivity, i.e. has fewer false positives while detecting more true CNVs. The reduced FDR increases the discovery power of studies and avoids that researchers are misguided by spurious correlations between CNVs and diseases.