Streamline searching, downloading and formatting of nature media files (e.g. audios, photos) from online repositories. The package offers functions for obtaining media metadata from online repositories, downloading associated media files and updating data sets with new records.

Implements a method to combine multiple levels of multiple
  sequence alignment to uncover the structure of complex DNA rearrangements.

Implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.

Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. 
             Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural 
             impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>),  patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>),
             independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), 
             smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).

Implements Transmission Channel Analysis (TCA) for structural
    vector autoregressive (SVAR) models following the methodology of
    Wegner, Lieb, and Smeekes (2025) <doi:10.48550/arXiv.2405.18987>. TCA decomposes
    impulse response functions (IRFs) into contributions from distinct
    transmission channels using a systems form representation and directed
    acyclic graph (DAG) path analysis. Supports overlapping channels,
    exhaustive 3-way and 4-way decompositions via inclusion-exclusion
    principle. This is a parallel R implementation of the
    'tca-matlab-toolbox' (<https://github.com/enweg/tca-matlab-toolbox>).