Lazy read for drawings. A 'dplyr' back end for data sources supported by 
    'GDAL' vector drivers, that allows working with local or remote sources as if they 
    are in-memory data frames. Basic features works with any drawing format ('GDAL vector 
    data source') supported by the 'sf' package.

Helper functions to build SQL statements
    for dbGetQuery or dbSendQuery under program control.
    They are intended to increase speed of coding and
    to reduce coding errors. Arguments are carefully checked,
    in particular SQL identifiers such as names of tables or columns.
    More patterns will be added as required.

Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading.
    Main goal is to process information within "Decision Support System" to come up with analysis or predictions.
    There are several utilities such as dynamic and adaptive risk management using reinforcement learning
    and even functions to generate predictions of price changes using pattern recognition deep regression learning.
    Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, 
    Market Type research of Van Tharp Institute: <https://vantharp.com/>,
    Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.

Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.

Provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

A suite of tools for literature-based discovery in biomedical research. 
    Provides functions for retrieving scientific articles from 'PubMed' and 
    other NCBI databases, extracting biomedical entities (diseases, drugs, genes, etc.), 
    building co-occurrence networks, and applying various discovery models 
    including 'ABC', 'AnC', 'LSI', and 'BITOLA'. The package also includes 
    visualization tools for exploring discovered connections.