The article explores new types of models for isotropic covariance functions, which are important in many practical applications. These new models are more flexible than traditional ones, allowing for both positive and negative covariance values in different parts of their domain. They can also exhibit different behaviors near the origin, like parabolic or linear. The researchers found that these new models are simple to use and can be adapted to various real-world scenarios, making them valuable tools for practitioners.