Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see <http://crexplorer.net>). Further extension to more plot variants is planned.

Developed for the following tasks. 1- Simulating and computing the maximum likelihood 
              estimator for the Birnbaum-Saunders (BS) distribution, 2- Computing the Bayesian estimator for
              the parameters of the BS distribution based on reference prior proposed by Xu and Tang (2010)
              <doi:10.1016/j.csda.2009.08.004> and conjugate prior. 3- Computing the Bayesian estimator for
              the BS distribution based on conjugate prior. 4- Computing the Bayesian estimator for the BS
              distribution based on Jeffrey prior given by Achcar (1993) <doi:10.1016/0167-9473(93)90170-X>
              5- Computing the Bayesian estimator for the BS distribution under progressive type-II censoring
              scheme.

A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j.  The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.

Implements methods for sample size 
  reduction within Linear and Quadratic Discriminant Analysis in Lapanowski and Gaynanova (2020) <arXiv:2005.03858>.
  Also includes methods for non-linear discriminant analysis with simultaneous sparse feature selection in Lapanowski and Gaynanova (2019) PMLR 89:1704-1713.