dplyr, httr, lubridate, magrittr, RCurl, tidyr, stringr

curl, dplyr, httr, lubridate, magrittr, RCurl, tidyr, stringr

c(
person("Pawel", "Cwiek", , "prc.altodato@gmail.com", role = c("aut", "cre")),
person("David", "Megginson", role = "ctb",
comment = "Author of data set with airports list https://ourairports.com/data/"),
person("Greg", "Thompson", role = "ctb",
comment = "Author of data set with airports list https://www.aviationweather.gov/docs/metar/stations.txt")
)

Book Companion for Processing and Modelling Financial Data with
R
Provides access to material from the book "Processing and Modelling Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pmfdr/home>.

by John Verzani with contributions by Yvonnick Noel
lattice, MASS, gWidgets(>= 0.0-15), gWidgetsRGtk2(>= 0.0-13)

lattice, MASS, gWidgets(>= 0.0-21), gWidgetsRGtk2(>= 0.0-22)

lattice, MASS, proto, foreign, gWidgets(>= 0.0-22), gWidgetsRGtk2(>= 0.0-25)

John Verzani with contributions by Yvonnick Noel

lattice, MASS, proto, foreign, gWidgets(>= 0.0-36),
gWidgetsRGtk2(>= 0.0-53)

Minimal Working Examples for Particle Metropolis-Hastings
Routines for state estimate in a linear
Gaussian state space model and a simple stochastic volatility model using
particle filtering. Parameter inference is also carried out in these models
using the particle Metropolis-Hastings algorithm that includes the particle
filter to provided an unbiased estimator of the likelihood. This package is
a collection of minimal working examples of these algorithms and is only
meant for educational use and as a start for learning to them on your own.
mvtnorm, Quandl, grDevices, graphics, stats

Interface to the Penn Machine Learning Benchmarks Data
Repository
Check available classification and regression data sets from the PMLB repository and download them.
The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms.
These data sets cover a range of applications, and include binary/multi-class classification problems and
regression problems, as well as combinations of categorical, ordinal, and continuous features.
There are currently over 150 datasets included in the PMLB repository.
c(
person("Trang", "Le", email = "grixor@gmail.com", role = c("aut", "cre"), comment = "https://trang.page/"),
person("makeyourownmaker", email = "makeyourownmaker@gmx.com", role = "aut", comment = "https://github.com/makeyourownmaker"),
person("Jason", "Moore", email = "jhmoore@upenn.edu", role = "aut", comment = "http://www.epistasisblog.org/"),
person("University of Pennsylvania", role = "cph"))
Trang Le [aut, cre] (https://trang.page/),
makeyourownmaker [aut] (https://github.com/makeyourownmaker),
Jason Moore [aut] (http://www.epistasisblog.org/),
University of Pennsylvania [cph]