Enables filtering datasets by a prior specified identifiers
    which correspond to saved filter expressions.

Provides a collection of utility functions 
    to download and manage data sets from the Internet or from other 
    sources.

Generate regression results tables and plots in final 
    format for publication. Explore models and export directly to PDF 
    and 'Word' using 'RMarkdown'.

Calculate the final size of a
    susceptible-infectious-recovered epidemic in a population with
    demographic variation in contact patterns and susceptibility to
    disease, as discussed in Miller (2012)
    <doi:10.1007/s11538-012-9749-6>.

Functions for financial analysis and financial modeling, 
             including batch graphs generation, beta calculation, 
             descriptive statistics, annuity calculation, bond pricing 
             and financial data download.

Infrastructure for defining meta-data and
    relationships for financial instruments.

Contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam, and some topics in the 'Models for Financial Economics' exam.

A programmatic interface to the 'Finnish Biodiversity Information
    Facility' ('FinBIF') API (<https://api.laji.fi>). 'FinBIF' aggregates
    Finnish biodiversity data from multiple sources in a single open access
    portal for researchers, citizen scientists, industry and government.
    'FinBIF' allows users of biodiversity information to find, access, combine
    and visualise data on Finnish plants, animals and microorganisms. The
    'finbif' package makes the publicly available data in 'FinBIF' easily
    accessible to programmers. Biodiversity information is available on taxonomy
    and taxon occurrence. Occurrence data can be filtered by taxon, time,
    location and other variables. The data accessed are conveniently
    preformatted for subsequent analyses.

Learning bipartite and k-component bipartite graphs from financial
     datasets. This package contains implementations of the algorithms described
     in the paper: Cardoso JVM, Ying J, and Palomar DP (2022).
     <https://openreview.net/pdf?id=WNSyF9qZaMd>
     "Learning bipartite graphs: heavy tails and multiple components,
     Advances in Neural Informations Processing Systems" (NeurIPS).