Recent studies show that copy number variants (CNVs), due to their ubiquitous presence in eukaryotes, contribute to phenotypic variation, environmental adaptation, and fuel species divergence at a previously unknown rate. However, the detection of CNVs in genomes, especially in non-model organisms is challenging due to the need for costly genomic resources and complex computational infrastructure. Therefore, to provide researchers with a low-cost and easily accessible resource, we developed a robust statistical framework and an R software package to detect CNVs from SNPs. The methods implemented in the framework use read-depth variation from high throughput sequence data, and relies on the proportion of heterozygotes in populations and their allelic ratios. The framework consists of two main steps: 1) flagging SNP deviants using allelic ratio deviations from the expectation and excess of heterozygotes under Hardy-Weinberg Equilibrium; followed by 2) filtering putative CNVs from deviant SNPs using data-specific statistical approaches. Our framework is well-catered for most GBS technologies (e.g., RADseq, Exome-capture, WGS). As such, it allows calling CNVs from genomes of varying complexity. The framework is implemented in the R package "rCNV" which effortlessly automates the analysis. We tested our models on four datasets obtained from different sequencing technologies (i.e., RADseq: Chinook salmon - Oncorhynchus tshawytscha, American lobster - Homarus americanus, Exome-capture: Norway Spruce - Picea abies, and WGS: Malaria mosquito - Anopheles gambiae). Our models detected CNVs with substantial accuracy and were significantly efficient with statistically low confident data, where average read-depth and number of samples are limited.