Tools to read, write, parse, and analyze forest fire history data (e.g. FHX). Described in Malevich et al. (2018) <doi:10.1016/j.dendro.2018.02.005>.

Call the data wrappers for Bursa Metropolitan Municipality's Open Data Portal <https://acikyesil.bursa.bel.tr/>. This will return all datasets stored in different formats.

A suite of functions for finance, including the estimation
	of variance matrices via a statistical factor model or
	Ledoit-Wolf shrinkage.

Script search, corner, genetic optimization, permutation tests, write expect test.

An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003) <doi:10.1023/A:1024940629314>).  Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.

Get a current financial year, start of current
    month, End of current month, start of financial year and end of it.
    Allow for offset from the date.

Data on the first 24 seasons of the UK TV show 'I'm a Celebrity, 
  Get Me Out of Here', broadcast from 2002-2024. Taken from the Wikipedia pages
  for each season and the main page available at
  <https://en.wikipedia.org/wiki/I%27m_a_Celebrity...Get_Me_Out_of_Here!_(British_TV_series)>.

Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours.

A collection of S4 classes, methods and functions to create
        and visualize business plans. Different types of cash flows can be
        defined, which can then be used and tabulated to create profit and
        loss statements, cash flow plans, investment and depreciation
        schedules, loan amortization schedules, etc. The methods are
        designed to produce handsome tables in both PDF and HTML using
        'RMarkdown' or 'Shiny'.

Provides a set of S3 generics to axe components of fitted
    model objects and help reduce the size of model objects saved to disk.

Verification of continually updating time series data where we
    expect new values, but want to ensure previous data remains unchanged. Data
    previously recorded could change for a number of reasons, such as discovery
    of an error in model code, a change in methodology or instrument recalibration.
    Monitoring data sources for these changes is not always possible. Other unnoticed
    changes could include a jump in time or measurement frequency, due to instrument
    failure or software updates. Functionality is provided that can be
    used to check and flag changes to previous data to prevent changes going
    unnoticed, as well as unexpected jumps in time.