Title: Pricing of Different Types of Call
Author: Elia Degiorgi, Federico Milan, Davide Zaramella, Valerija Stoeva
Description: Compute the price of different types of call using different methods. The types available are Vanilla European Calls, Vanilla American Calls and American Digital Calls. Available methods are Montecarlo Simulation, Montecarlo Simulation with Antithetic Variates, Black-Scholes and the Binary Tree.

Title: Randomized Singular Value Decomposition Algorithms with
Description: Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2022) <doi:10.1101/2022.05.25.493261>, where we implement and propose two RSVD methods. One is based on Yu (2017) <arXiv:1704.07669> single pass RSVD but with power iteration scheme. The other is our new window based RSVD.

Description: Provides an R interface to the 'PCATS' API
allowing R users to submit tasks and retrieve results.
Imports: httr, jsonlite, methods, testthat

Title: Pediatric Complex Chronic Conditions
person("Peter", "DeWitt", , "dewittpe@gmail.com", role = c("aut")),
person("Tell",  "Bennett", , "tell.bennett@ucdenver.edu", role = c("aut")),
person("James", "Feinstein", , "james.feinstein@ucdenver.edu", role = c("aut")),
person("Seth", "Russell", , "seth.russell@ucdenver.edu", role = c("aut", "cre"))
Description: An implementation of the pediatric complex chronic conditions (CCC)
Suggests: covr, knitr, rmarkdown, testthat, readr

Title: Dynamic Principal Components for Periodically Correlated
Authors@R: c(person("Lukasz", "Kidzinski", email = "lukasz.kidzinski@stanford.edu", role = c("aut", "cre")), person("Neda", "Jouzdani", email = "neda.jouzdani@colostate.edu", role = c("aut")), person("Piotr", "Kokoszka", email = "piotr.kokoszka@colostate.edu", role = c("aut")))
Description: Method extends multivariate dynamic principal components to periodically correlated multivariate time series.

Description: Method extends multivariate and functional dynamic principal components
to periodically correlated multivariate time series. This package allows you to
compute true dynamic principal components in the presence of periodicity.
We follow implementation guidelines as described in Kidzinski, Kokoszka and
Jouzdani (2017), in Principal component analysis of periodically correlated