Packaged: 2019-05-13 07:48:54 UTC; sebastian.fox

Packaged: 2019-07-18 14:14:23 UTC; sebastian.fox

Imports: curl, dplyr, fingertipsR, geojsonio, ggplot2 (>= 3.0), httr,
Packaged: 2019-09-09 18:38:43 UTC; sebastian.fox

Title: Computation of the probability of ruin within a finite time
Description: In the Cramér–Lundberg risk process perturbed by a Wiener
process, this packages provides approximations to the probability of
ruin within a finite time horizon.  Currently, there are three methods
implemented: The first one uses saddlepoint approximation (two
variants are provided), the second one uses importance sampling and
the third one is based on the simulation of a dual process.  This last
method is not very accurate and only given here for completeness.
Author: Benjamin Baumgartner [aut, cre],

Title: Computation of the Probability of Ruin Within a Finite Time
Imports: sdprisk, numDeriv, utils, methods

Title: Test for Finiteness of Moments in a Distribution
Authors@R: c(person("Torsten", "Heinrich", role = c("aut", "cre"),
person("Julian", "Winkler", role = c("aut"),
Description: The purpose of this package is to tests whether a given
moment of the distribution of a given sample is finite or not. For
heavy-tailed distributions with tail exponent b, only moments of
order smaller than b are finite. Tail exponent and heavy-
tailedness are notoriously difficult to ascertain. But the
finiteness of moments (including fractional moments) can be tested
directly. This package does that following the test suggested by
Imports: Rcpp (>= 1.0.3), stabledist (>= 0.7)

person("Markus", "Virtanen", email = "markus.m.virtanen@gmail.com",
person("Kai", "Hippi", email = "kai.hippi@aalto.fi",
Description: R API client package for 'Fingrid Open Data' on the
electricity market and the power system. get_data() function
holds the main application logic to retrieve time-series data.
API calls require free user account registration.

Title: Microsoft Finance Time Series Forecasting Framework
Description: 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 want 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!
Imports: cli, Cubist, dials, doParallel, dplyr, earth, foreach,
generics, glmnet, gtools, hts, kernlab, lubridate, magrittr,
parsnip, plyr, purrr, recipes, rsample, rules, stringr, tibble,
tidyr, tidyselect, timetk, tune, workflows
Suggests: knitr, reactable, rmarkdown, testthat (>= 3.0.0)
Author: Mike Tokic [aut, cre] (<https://orcid.org/0000-0002-7630-7055>),