Provides researchers and educators with easy-to-learn user friendly tools for calculating 
    key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. 
    These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, 
    Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation 
    (Global and Local Moran's I), several Geographically Weighted Regression techniques and other 
    Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for 
    measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.

Analysis of stock data ups and downs trend, the stock technical analysis indicators function have trend line, reversal pattern and market trend.

Implements latent Dirichlet allocation (LDA)
	     and related models.  This includes (but is not limited
	     to) sLDA, corrLDA, and the mixed-membership stochastic
	     blockmodel.  Inference for all of these models is
	     implemented via a fast collapsed Gibbs sampler written
	     in C.  Utility functions for reading/writing data
	     typically used in topic models, as well as tools for
	     examining posterior distributions are also included.

Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.

Contains the development of a tool that provides a web-based graphical
             user interface (GUI) to perform Biplots representations from a scraping of
             news from digital newspapers under the Bayesian approach of Latent Dirichlet 
             Assignment (LDA) and machine learning algorithms. Contains LDA methods 
             described by Blei , David M., Andrew Y. Ng and Michael I. Jordan (2003) 
             <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
             and Biplot methods described by Gabriel K.R(1971) <doi:10.1093/biomet/58.3.453>
             and Galindo-Villardon P(1986) <https://diarium.usal.es/pgalindo/files/2012/07/Questiio.pdf>.