Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. 
    Including equal frequency binning, equidistant binning, 
    K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, 
    fully automatic generation of scorecards, etc. 
    This package is designed to make scorecard development easier and faster.
    References include:
    1. <http://shichen.name/posts/>. 
    2. Dong-feng Li(Peking University),Class PPT.
    3. <https://zhuanlan.zhihu.com/p/389710022>. 
    4. <https://www.zhangshengrong.com/p/281oqR9JNw/>.

For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease,
  we developed this program to acquire disease-related genes.
  The data is integrated from three public databases. The three databases are 'eDGAR', 'DrugBank'
  and 'MalaCards'. The 'eDGAR' is a comprehensive database, containing data on the relationship between disease and genes.
  'DrugBank' contains information on 13443 drugs and 5157 targets. 'MalaCards' integrates human disease information, including disease-related genes.

Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at <https://github.com/alekrutkowski/autoshiny>.

The normal process of creating clinical study slides is that
    a statistician manually type in the numbers from outputs and a
    separate statistician to double check the typed in numbers. This
    process is time consuming, resource intensive, and error prone.
    Automatic slide generation is a solution to address these issues. It
    reduces the amount of work and the required time when creating slides,
    and reduces the risk of errors from manually typing or copying numbers
    from the output to slides. It also helps users to avoid unnecessary
    stress when creating large amounts of slide decks in a short time
    window.

Automatically do statistical exploration. Create formulas using 'tidyselect' syntax, and then determine cross-validated model accuracy and variable contributions using 'glm' and 'xgboost'. Contains additional helper functions to create and modify formulas. Has a flagship function to quickly determine relationships between categorical and continuous variables in the data set.