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.

Provides a Scheme-inspired Lisp dialect embedded in R, with
    macros, tail-call optimization, and seamless interoperability with R
    functions and data structures. (The name 'arl' is short for 'An R Lisp.')
    Implemented in pure R with no compiled code.

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.

Provides function declarations and inline function definitions
    that facilitate communication between R and the 'Armadillo' 'C++' library
    for linear algebra and scientific computing. This implementation is derived
    from Vargas Sepulveda and Schneider Malamud (2024)
    <doi:10.1016/j.softx.2025.102087>.

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>.