Complete access from 'R' to the FIS 'MarketMap
C-Toolkit' ('FAME C-HLI'). 'FAME' is a fully integrated software and database
management system from FIS that provides the following capabilities:
Time series and cross-sectional data management;
Financial calculation, data analysis, econometrics, and forecasting;
Table generation and detailed multicolor, presentation-quality report writing;
Multicolor, presentation-quality graphics;
"What-if" analysis;
Application development and structured programming;
Data transfer to and from other applications;
Tools for building customized graphical user interfaces.

An R Implementation of the FIS MarketMap C-Toolkit

person("Victhor", "Sartório", email = "victhor@dme.ufrj.br", role = c("aut", "cre", "cph"))
Implements simple Hamiltonian Monte Carlo routines in R for
sampling from any desired target distribution which is continuous
and smooth. See Neal (2017) <arXiv:1701.02434> for further details
on Hamiltonian Monte Carlo. Automatic parameter selection is not
supported.

'HMMER' is a profile hidden Markov model tool used primarily for sequence
analysis in bioinformatics (<http://hmmer.org/>). 'rhmmer' provides
utilities for parsing the 'HMMER' output into tidy data frames.

Random Heteroscedastic Nested Error Regression
Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>.

c(person("David", "Williamson Shaffer", email = "dws@education.wisc.edu", role = c("aut")),person("Brad", "Rogers", email = "bjrogers2@wisc.edu", role = c("aut")),person("Brendan", "Eagan", email="beagan@wisc.edu", role = c("aut")),person("Cody L", "Marquart", email="cody.marquart@wisc.edu", role = c("cre", "aut")))
Rho is used to test the generalization of inter rater reliability
(IRR) statistics. Calculating rho starts by generating a large number of
simulated, fully-coded data sets: a sizable collection of hypothetical
populations, all of which have a kappa value below a given threshold -- which
indicates unacceptable agreement. Then kappa is calculated on a sample from
each of those sets in the collection to see if it is equal to or higher than
the kappa in then real sample. If less than five percent of the distribution
of samples from the simulated data sets is greater than actual observed kappa,
the null hypothesis is rejected and one can conclude that if the two raters had
coded the rest of the data, we would have acceptable agreement (kappa above the
threshold).
'rhoR.R' 'rho.R' 'rhoK.R' 'rhoSet.R' 'rhoCT.R' 'kappa.R'
'kappaSet.R' 'kappaCT.R' 'rhoMin.R' 'baserate.R'
'baserateSet.R' 'baserateCT.R' 'calcKappa.R' 'calcRho.R'
'checkBRPKcombo.R' 'contingencyTable.R' 'codeSet.R'
'contingencyToSet.R' 'createRandomSet.R'
'createSimulatedCodeSet.R' 'genPKcombo.R' 'genPcombo.R'
'generateKPs.R' 'getBootPvalue.R' 'getHandSet.R' 'getR.R'
'getTestSet.R' 'prset.R'
David Williamson Shaffer [aut],
Brad Rogers [aut],
Brendan Eagan [aut],
Cody L Marquart [cre, aut]