Statistical data products for Multidisciplinary Sciences

**LPKsample**: LP nonparametric high-dimensional K-sample comparison method that includes (i) confirmatory test; (ii) exploratory results and (iii) options to output a data-driven LP-transformed matrix for classification.**Updated to Version 2.0****BayesGOF**: It performs Bayesian exploratory data analysis, prior uncertainty modeling, Macro-and MicroInference.**Updated to Version 5.2****QDComparison**: Modern Nonparametric Tools for Two-Sample Quantile and Distribution Comparisons. Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles).**LPGraph**: Nonparametric smoothing of Laplacian graph spectra via LP-Compressive basis. The approximation results can then be used for tasks like change point detection, k-sample testing, and so on.**LPTime**:**LPBigD**: A unified learning algorithm for Big probability Distributions that*outperforms*recent `breakthrough’ (sub-linear sample complexity) algorithms of theoretical computer science.**MetaLP**: Nonparametric distributed learning framework that addresses two main challenges of large datasets: (1) massive volume, and (2) variety or mixed data problem. It provides a*new statistically motivated*computing architecture for large-scale data modeling.**LPiTrack**: Eye-movement pattern recognition (Nonparametric) algorithm. This same technology is also applicable for analyzing other types of trajectory data like Insurance Telematics.**LPMode**: This algorithm provides a systematic (and automatic) nonparametric modeling strategy for*large-scale*bump-hunting problems. We have used this algorithm to answer important research questions arising in various fields starting from environmental science, ecology, econometrics, and analytical chemistry to astronomy and cancer genomics.