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/>.

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time 
    Series Forecasting Framework, aka Finn, can be used to forecast any component of the income 
    statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, 
    Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built 
    to meet the needs of financial analysts to better forecast their businesses within a company, and has 
    a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

Download and display company financial data from the U.S. Securities
    and Exchange Commission's EDGAR database. It contains a suite of functions with
    web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR 
    in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
    for more information.

R companion to Tsay (2005) Analysis of Financial Time
   Series, second edition (Wiley).  Includes data sets, functions and
   script files required to work some of the examples.  Version 0.3-x
   includes R objects for all data files used in the text and script
   files to recreate most of the analyses in chapters 1-3 and 9 plus
   parts of chapters 4 and 11.

Provides a lightweight suite
             of functions for retrieving information
             about 5-digit or 2-digit US FIPS codes.

Computes implicitly induced quantities from prior/hyperparameter    
    specifications of three Mixtures of Finite Mixtures models: Dirichlet 
    Process Mixtures (DPMs; Escobar and West (1995) 
    <doi:10.1080/01621459.1995.10476550>), Static Mixtures of Finite Mixtures 
    (Static MFMs; Miller and Harrison (2018) 
    <doi:10.1080/01621459.2016.1255636>), and Dynamic Mixtures of Finite 
    Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020)
    <arXiv:2005.09918>). For methodological details, please refer to 
    Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020)
    <arXiv:2012.12337>) as well as the package vignette.