This natural language processing toolkit provides language-agnostic
    'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency
    parsing' of raw text. Next to text parsing, the package also allows you to train
    annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided
    at <https://universaldependencies.org/format.html>. The techniques are explained
    in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0
    with UDPipe', available at <doi:10.18653/v1/K17-3009>. 
    The toolkit also contains functionalities for commonly used data manipulations on texts 
    which are enriched with the output of the parser. Namely functionalities and algorithms 
    for collocations, token co-occurrence, document term matrix handling, 
    term frequency inverse document frequency calculations,
    information retrieval metrics (Okapi BM25), handling of multi-word expressions,
    keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) 
    sentiment scoring and semantic similarity analysis.

Provides simple bindings to Unidata's udunits library.

Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.

Enables the user to calculate Value at Risk (VaR) and Expected 
    Shortfall (ES) by means of various parametric and semiparametric 
    GARCH-type models. For the latter the estimation of the nonparametric scale
    function is carried out by means of a data-driven smoothing approach. Model
    quality, in terms of forecasting VaR and ES, can be assessed by means of 
    various backtesting methods such as the traffic light test for VaR and a 
    newly developed traffic light test for ES. The approaches implemented in 
    this package are described in e.g. Feng Y., Beran J., Letmathe S. and 
    Ghosh S. (2020) <https://ideas.repec.org/p/pdn/ciepap/137.html> as well as 
    Letmathe S., Feng Y. and Uhde A. (2021) 
    <https://ideas.repec.org/p/pdn/ciepap/141.html>.