Flat text files provide a robust, compressible, and portable
  way to store tables from databases.  This package provides convenient
  functions for exporting tables from relational database connections
  into compressed text files and streaming those text files back into
  a database without requiring the whole table to fit in working memory.

A dependency-free collection of simple functions for cleaning
    rectangular data. This package allows to detect, count and replace
    values or discard rows/columns using a predicate function. In
    addition, it provides tools to check conditions and return informative
    error messages.

Implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.

Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.

Providing ways to estimate the value of European stock options 
    given historical stock price data. It includes functions for calculating option values 
    based on autoregressive–moving-average (ARMA) models and generates information about these models. 
    This package is made to be easy to understand and for financial analysis capabilities.