Data structures and algorithms for sparse arrays and matrices,
  based on index arrays and simple triplet representations, respectively.

Slanted matrices and ordered clustering for better visualization of similarity data.

Alternative to using withCallingHandlers() in the simple case of catch 
  and rethrow. The `%!%` operator evaluates the expression on its left hand side, 
  and if an error occurs, the right hand side is used to construct a new error
  that embeds the original error.

Allows users to list data structures using path-based navigation. 
    Provides intuitive methods for storing, accessing,
    and manipulating nested data through simple path strings.
    Key features include strict mode validation, path existence checking, recursive
    operations, and automatic parent-level creation. Designed for use cases requiring
    organized storage of complex nested data while maintaining simple access patterns.
    Particularly useful for configuration management, nested settings, and any
    application where data naturally forms a tree-like structure.

Implements the smooth LASSO estimator for the function-on-function linear regression model described in Centofanti et al. (2020) <arXiv:2007.00529>.

Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book 'Statistical Learning with Big Dependent Data' by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.

Provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including
    Latent Transition Analysis (LTA; Linda M. Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>,
    Latent Class Profile Analysis (LCPA; Hwan Chung et al., 2010) <doi:10.1111/j.1467-985x.2010.00674.x>, and
    Joint Latent Class Analysis (JLCA; Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and 
    any other extended models involving multiple latent class variables.