densTrue

densTrue is a software library designed for efficient and accurate density estimation. It provides a suite of algorithms that allow users to estimate probability density functions from data. The library is particularly useful in fields such as machine learning, statistical modeling, and data analysis where understanding the underlying distribution of data is crucial.

The core functionality of densTrue revolves around kernel density estimation (KDE), a non-parametric way to estimate the probability density function of a random variable. It supports various kernel functions, including Gaussian, Epanechnikov, and uniform, allowing users to choose the most appropriate one for their specific application. The library also offers methods for bandwidth selection, a critical parameter that controls the smoothness of the estimated density.

densTrue is implemented in Python and leverages the power of NumPy and SciPy for high-performance numerical computations. This integration makes it easy for users already working within the scientific Python ecosystem to incorporate densTrue into their existing workflows. The library aims to be both user-friendly and computationally efficient, providing accessible tools for complex statistical tasks.

Key features of densTrue include its support for multi-dimensional data, enabling density estimation in feature spaces with many dimensions. It also offers functionalities for visualizing the estimated densities, aiding in interpretation and analysis. The library is actively maintained, with a focus on improving performance, adding new algorithms, and enhancing usability based on community feedback. densTrue is distributed under an open-source license, making it freely available for both academic and commercial use.