Testing the homogeneity of variances among normal populations is of interest in several research areas. It is widely applied to assess uniformity in quality control, biology, agricultural production systems, human performance studies, and even in the development of educational methods. For this reason, in this paper, we introduce an R package named homnormal, which includes some of the most powerful tests for homogeneity of variances, such as Bartlett’s test, Levene’s test, Brown-Forsthye’s test, Bandairy Dai’s test, generalized p-approach, computational approach test, likelihood ratio test based on the computational approach test, and standardized likelihood ratio test. The homnormal package is designed to be user-friendly, making it accessible for researchers across multidisciplinary fields. Additionally, we compare these tests based on their empirical power and type I error rates to determine the best-performing test in specific situations. Simulated type I error rates and powers of these tests are provided and evaluated through an extensive Monte Carlo simulation study. The simulation results indicate that, regardless of the number of groups or whether sample sizes are equal or unequal, small or large, the standardized likelihood ratio test and the likelihood ratio test based on the computational approach test consistently outperform other methods in terms of performance.