In the Cramér–Lundberg risk process perturbed by a Wiener
    process, this packages provides approximations to the probability of
    ruin within a finite time horizon.  Currently, there are three methods
    implemented: The first one uses saddlepoint approximation (two
    variants are provided), the second one uses importance sampling and
    the third one is based on the simulation of a dual process.  This last
    method is not very accurate and only given here for completeness.

The purpose of this package is to tests whether a given 
             moment of the distribution of a given sample is finite or not. For 
             heavy-tailed distributions with tail exponent b, only moments of 
             order smaller than b are finite. Tail exponent and heavy-
             tailedness are notoriously difficult to ascertain. But the 
             finiteness of moments (including fractional moments) can be tested 
             directly. This package does that following the test suggested by 
             Trapani (2016) <doi:10.1016/j.jeconom.2015.08.006>.

Providing classes, methods, and functions to deal with financial networks. 
    Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as 'rvest' and 'RSelenium'.
    Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, 'igraph', 'network').

R API client package for 'Fingrid Open Data' on the 
    electricity market and the power system. get_data() function
    holds the main application logic to retrieve time-series data.
    API calls require free user account registration.

Annotates Finnish textual survey responses into CoNLL-U format using Finnish treebanks from <https://universaldependencies.org/format.html> using UDPipe as described in Straka and Straková (2017) <doi:10.18653/v1/K17-3009>. Formatted data is then analysed using single or comparison n-gram plots, wordclouds, summary tables and Concept Network plots. The Concept Network plots use the TextRank algorithm as outlined in Mihalcea, Rada & Tarau, Paul (2004) <https://aclanthology.org/W04-3252/>.