Methods to calculate sensitivities of financial option prices for
European, Asian, American and Digital Options options in the Black Scholes
model, and in more general jump diffusion models. Furthermore, methods to
compute implied volatilities are provided for a wide range of option types and
custom payoff functions. Classical formulas are implemented for European
options in the Black Scholes Model, as is presented in Hull, J. C. (2017).
Options, Futures, and Other Derivatives, Global Edition (9th Edition). Pearson.
In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see
Hudde, A. & Rüschendorf, L. (2016). European and Asian Greeks for exponential
Lévy processes. <arXiv:1603.00920>. For American options, the Binomial Tree
Method is implemented, as is presented in Hull, J. C. (2017).

Combine Categories Using Greenacre's Method
person("Jeff", "Jetton", , "jeff.jetton@gmail.com", c("aut", "cre"))
Implements a method of iteratively collapsing the rows of a
contingency table, two at a time, by selecting the pair of categories whose
combination yields a new table with the smallest loss of chi-squared, as
described by Greenacre, M.J. (1988) <doi:10.1007/BF01901670>. The result is
compatible with the class of object returned by the 'stats' package's
hclust() function and can be used similarly (plotted as a dendrogram,
cut, etc.). Additional functions are provided for automatic cutting and
diagnostic plotting.

c(
person("Olek", "Wojcik", email = "olkowaty@gmail.com", role = c("cre", "aut")),
person("Sam", "Olson", email = "samuel.david.olson@gmail.com", role = "aut"))
Functions which make using the Generalized Regression Estimator(GREG)
J.N.K. Rao, Isabel Molina, (2015) <doi:10.3390/f11020244>
and the Generalized Regression Estimator Operating on Resolutions of Y (GREGORY) easier.
The functions are designed to work well within a forestry context, and estimate multiple
estimation units at once. Compared to other survey estimation packages, this function has greater flexibility when
describing the linear model.