Oleksandr Castello [aut, cre]
Marina Resta [ctb, cre]
Modeling the zero coupon yield curve using the dynamic De Rezende and
Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable
or fixed decaying parameters. For explanatory purposes, the package also
includes various short datasets of interest rates for the BRICS countries.

Dealing GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) RAW.tar file using the robust multi-array average expression measure and returning expression profile.

Implementation of Multi Objective Decision Analysis
c(
person("Josh", "Deehr",
email = "josh.deehr@gmail.com",
role = c("aut","cre")),
person("Christopher", "Smith",
email = "Cms3am@virginia.edu",
role = c("aut")),
person("Jason", "Freels",
email = "auburngrads@live.com",
role = c("aut")),
person("Emily", "Meyer",
email = "emily.meyer@theperducogroup.com",
role = c("aut"))
)
Aides in the multi objective decision analysis process by simplifying
the creation of value hierarchy tree plots, calculating and plotting single and
multi attribute value function scores, and conducting sensitivity analysis. Linear,
exponential, and categorical single attribute value functions are supported. For
details see Parnell (2013, ISBN:978-1-118-17313-8) Kirkwood (1997, ISBN:0-534-51692-0).
stats, ggplot2, tidyr, dplyr, graphics, data.tree, gridExtra,
viridisLite, Cairo
Josh Deehr [aut, cre],
Christopher Smith [aut],
Jason Freels [aut],
Emily Meyer [aut]

stats, ggplot2, tidyr, dplyr, graphics, data.tree, gridExtra,
viridisLite, Cairo, DiagrammeR

stats, ggplot2, tidyr, dplyr, graphics, data.tree, gridExtra,
viridisLite, Cairo, methods, qpdf, DiagrammeR

c(person("Graeme", "Blair", email = "graeme.blair@ucla.edu", role = c("aut", "cre")),
person("Jasper", "Cooper", email = "jaspercooper@gmail.com", role = c("aut")),
person("Alexander", "Coppock", email = "acoppock@gmail.com", role = c("aut")),
person("Macartan", "Humphreys", email = "macartan@gmail.com", role = c("aut")),
person("Neal", "Fultz", email = "nfultz@gmail.com", role = c("aut")))
Researchers can characterize and learn about the properties of
research designs before implementation using `DeclareDesign`. Ex ante
declaration and diagnosis of designs can help researchers clarify the
strengths and limitations of their designs and to improve their
properties, and can help readers evaluate a research strategy prior
to implementation and without access to results. It can also make it
easier for designs to be shared, replicated, and critiqued.
testthat, knitr, rmarkdown, AER, dplyr, data.table, ggplot2,
future.apply, broom, MASS, Matching, betareg, biglm, gam, lfe,
sf, reshape2
Graeme Blair [aut, cre],
Jasper Cooper [aut],
Alexander Coppock [aut],
Macartan Humphreys [aut],
Neal Fultz [aut]