Defines S3 vector data types for vectors of functional data 
   (grid-based, spline-based or functional principal components-based) with all 
   arithmetic and summary methods, derivation, integration and smoothing, 
   plotting, data import and export, and data wrangling, such as re-evaluating, 
   subsetting, sub-assigning, zooming into sub-domains, or extracting functional 
   features like minima/maxima and their locations. 
   The implementation allows including such vectors in data frames for joint 
   analysis of functional and scalar variables.

R implementation of 'TFactS' to predict which are the 
    transcription factors (TFs), regulated in a biological condition based on
    lists of differentially expressed genes (DEGs) obtained from transcriptome
    experiments. This package is based on the 'TFactS' concept by 
    Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands
    it. It allows users to perform 'TFactS'-like enrichment approach. The
    package can import and use the original catalogue file from the 'TFactS'
    as well as users' defined catalogues of interest that are not supported 
    by 'TFactS' (e.g., Arabidopsis).

'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository 
             of community contributions that conform to well-established API patterns, 
             but implement new functionality not available in core 'TensorFlow'. 
             'TensorFlow' natively supports a large number of operators, layers, metrics, 
             losses, optimizers, and more. However, in a fast moving field like Machine Learning, 
             there are many interesting new developments that cannot be integrated into 
             core 'TensorFlow' (because their broad applicability is not yet clear, or 
             it is mostly used by a smaller subset of the community).

Build customized transfer function and ARIMA models with multiple operators 
    and parameter restrictions. Provides tools for model identification, estimation 
    using exact or conditional maximum likelihood, diagnostic checking, automatic outlier 
    detection, calendar effects, forecasting, and seasonal adjustment. The new version 
    also supports unobserved component ARIMA model specification and estimation 
    for structural time series analysis.