Performs regression analysis for longitudinal count data,  
   allowing for serial dependence among observations from a given 
   individual and two dimensional random effects on the linear predictor. 
   Estimation is via maximization of the exact likelihood of a suitably 
   defined model. Missing values and unbalanced data are allowed. 
   Details can be found in the accompanying scientific papers: 
   Goncalves & Cabral (2021, Journal of Statistical Software, 
   <doi:10.18637/jss.v099.i03>) and Goncalves et al. 
   (2007, Computational Statistics & Data Analysis, 
   <doi:10.1016/j.csda.2007.03.002>).

Performs least squares constrained optimization on a linear objective function. It contains
    a number of algorithms to choose from and offers a formula syntax similar to lm().

A large C/C++-based package for advanced data transformation and 
    statistical computing in R that is extremely fast, class-agnostic, robust, and 
    programmer friendly. Core functionality includes a rich set of S3 generic grouped 
    and weighted statistical functions for vectors, matrices and data frames, which 
    provide efficient low-level vectorizations, OpenMP multithreading, and skip missing 
    values by default. These are integrated with fast grouping and ordering algorithms 
    (also callable from C), and efficient data manipulation functions. The package also 
    provides a flexible and rigorous approach to time series and panel data in R, fast 
    functions for data transformation and common statistical procedures, detailed 
    (grouped, weighted) summary statistics, powerful tools to work with nested data, 
    fast data object conversions, functions for memory efficient R programming, and 
    helpers to effectively deal with variable labels, attributes, and missing data. It 
    seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/
    'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'.

Contains functions to help in selecting and exploring features ( or variables ) in binary classification problems.
             Provides functions to compute and display information value and weight of evidence (WoE) of the variables , and to convert numeric variables to categorical variables by binning.
             Functions are also provided  to determine which levels ( or categories ) of a categorical variable can be collapsed (or combined ) based on their response rates.
             The functions provided only work for binary classification problems.