Description: The ragt2ridges-package provides ridge maximum likelihood estimation of vector auto-regressive processes: the VAR(1), VAR(2) and VARX(1) model (more to be added). Prior knowledge may be incorporated in the estimation through a) specification of the edges believed to be absent in the time series chain graph, and b) a shrinkage target towards which the parameter estimate is shrunken for large penalty parameter values. Estimation functionality is accompanied by methodology for penalty parameter selection. In addition, the package offers supporting functionality for the exploitation of estimated models. Among others, i) a procedure to infer the support of the non-sparse ridge estimate (and thereby of the time series chain graph) is implemented, ii) a table of node-wise network summary statistics, iii) mutual information analysis, and iv) impulse response analysis. Cf. Miok et al. (2017) <DOI:10.1002/bimj.201500269> and Miok et al. (2019) <DOI:10.1002/bimj.201700195> for details on the implemented methods.

methods, mvtnorm, stats, Biobase, CGHbase

Title: Pricing Equity Derivatives with Extensions of Black-Scholes
Maintainer: Brian K. Boonstra <ragtop@boonstra.org>
Description: Algorithms to price American and European
variety of other financial derivatives. It uses an
extension of the usual Black-Scholes model in which
jump to default may occur at a probability specified
by a power-law link between stock price and hazard
rate as found in the paper by Takahashi, Kobayashi,
use ideas and techniques from Andersen and
Suggests: testthat, roxygen2, knitr, rmarkdown, reshape2, stringr,
ggplot2, MASS, RColorBrewer, R.cache, Quandl

ggplot2, MASS, RColorBrewer, BondValuation, R.cache, Quandl

Title: Revisiting-Alpha-Investing for Polynomial Regression
person("Kory D.", "Johnson", email = "korydjohnson@gmail.com", role = c("aut", "cre")),
person("Robert A.", "Stine", email = "stine@wharton.upenn.edu", role = "aut")
Description: A modified implementation of stepwise regression that greedily searches
the space of interactions among features in order to build polynomial regression models.
Furthermore, the hypothesis tests conducted are valid-post model selection
due to the use of a revisiting procedure that implements an alpha-investing
rule. As a result, the set of rejected sequential hypotheses is proven to
control the marginal false discover rate. When not searching for polynomials,
the package provides a statistically valid algorithm
to run and terminate stepwise regression. For more information, see
Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.
Imports: stats, dplyr, ggplot2, readr, rlang
Maintainer: Kory D. Johnson <korydjohnson@gmail.com>