Authors@R: c(person("David", "Kahle", email = "david.kahle@gmail.com", role = c("aut", "cph", "cre")),
person("Christopher", "O'Neill", email = "musicman3320@gmail.com", role = c("aut", "cph")),
person("Jeff", "Sommars", email = "sommars1@uic.edu", role = c("aut", "cph")))
Description: Persistent interface to Macaulay2 (<http://www.math.uiuc.edu/Macaulay2/>)
and front-end tools facilitating its use in the R ecosystem.
Packaged: 2017-06-22 19:46:35 UTC; david_kahle

Description: Persistent interface to 'Macaulay2' <http://www.math.uiuc.edu/Macaulay2/>
and front-end tools facilitating its use in the 'R' ecosystem.
Imports: stringr, memoise, gmp, usethis, Rcpp
Suggests: knitr, rmarkdown, microbenchmark, testthat, R.rsp
Packaged: 2020-05-06 04:08:24 UTC; david_kahle

and front-end tools facilitating its use in the 'R' ecosystem. For details see
Packaged: 2020-05-27 22:32:36 UTC; david_kahle

Description: Provides binomial tree models for European, American and Asian
Options as well as Interest Rates. Monte Carlo Simulation and Methods for
Solving Differential Equations are also included.

Description: Contains functions, which facilitate downloading, loading and preparing data from 'M5 Forecasting' challenges (by 'University of Nicosia', hosted on 'Kaggle').
The data itself is set of time series of different product sales in 'Walmart'.
The package also includes a ready-to-use built-in M5 subset named 'tiny_m5'.
For detailed information about the challenges, see:
Makridakis, S. & Spiliotis, E. & Assimakopoulos, V. (2020). <doi:10.1016/j.ijforecast.2021.10.009>.
Suggests: ggplot2, knitr, testthat (>= 3.0.0), rmarkdown, zeallot
Packaged: 2022-09-09 18:28:59 UTC; krzysztof

Authors@R: c(person("Jean-Marie","Lepioufle", , "pv71u98h1@gmail.com", c("aut","cre")))
Title: Package About Data Manipulation in Pure Base R
Description: Data manipulation in one package and in base R.
Nothing else than base R to build the package.

Title: functions for multivariate AutoRegressive analysis
Description: Estimation of multivariate AR models through a computationally-efficient stepwise least-squares algorithm (Neumaier and Schneider, 2001); the procedure is of particular interest for high-dimensional data without missing values such as geophysical fields.