- LPRelevance: A new framework to perform customized inference at individual level by taking contextual covariates into account. Three main functions are provided in this package: (i) LASER(): it generates specially-designed artificial relevant samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) rEB.proc(): performs empirical Bayes inference based on LASERs.
- LPCopula: It contains nonparametric universal LP-copula modeling code.
- LPKsample: A graph-based nonparametric algorithm for High-dimensional k-sample problem that includes (i) confirmatory test; (ii) exploratory results and (iii) options to output a data-driven LP-transformed matrix for classification.
- BayesGOF: It performs Bayesian exploratory data analysis, prior uncertainty modeling, Macro-and MicroInference.
- LPGraph: Fast and compressive nonparametric spectral algorithm for ordered graphs with application to the high-dimensional change point analysis.
- LPTime: Implements non-Gaussian nonlinear time series (Nonparametric) modeling algorithm with application to financial econometrics and eye-tracking data analysis.
- LPMode: Nonparametric modeling algorithm for large-scale bump hunting problems with applications in environmental science, ecology, econometrics, and analytical chemistry to astronomy and cancer genomics.
- CDfdr: Skew-beta comparison density based nonparametric fdr estimation algorithm.