Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

Automatically Plot, Analyse and Revises Limits of Multiple Run
Charts
Plots multiple run charts, finds successive signals of
improvement, and revises medians when each signal occurs. Finds runs
above, below, or on both sides of the median, and returns a plot and
a data.table summarising original medians and any revisions, for all
groups within the supplied data.
knitr, rmarkdown, covr, pkgdown, testthat, NHSRdatasets

person(given = "Bryan",
family = "Jenks",
role = c("aut", "cre"),
email = "bryanjenks@protonmail.com",
comment = c(ORCID = "0000-0002-9604-3069"))
Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.

Softball Run Expectancy using Markov Chains and Simulation
c(
person("Annie", "Sauer", role = c("aut", "cre"), email = "anniees@vt.edu"),
person("Sierra", "Merkes", role = "aut", email = "smerkes@vt.edu"))
Implements two methods of estimating runs scored in a softball
scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical
distribution using multinomial random simulation.  Scores are based on player-specific input
probabilities (out, single, double, triple, walk, and homerun).  Optional inputs include probability
of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether
a player is "fast" (e.g. the player could stretch home).  These probabilities may be
calculated from common player statistics that are publicly available on team's webpages.
Scores are evaluated based on a nine-player lineup and may be used to compare lineups,
evaluate base scenarios, and compare the offensive potential of individual players.
Manuscript forthcoming.  See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for
implementation of discrete Markov chains.
Annie Sauer [aut, cre],
Sierra Merkes [aut]