Connect to the 'Adobe Analytics' API v2.0 <https://github.com/AdobeDocs/analytics-2.0-apis>
which powers 'Analysis Workspace'. The package was developed
with the analyst in mind, and it will continue to be
developed with the guiding principles of iterative,
repeatable, timely analysis.
c(
person("Ben", "Woodard", email = "benrwoodard@gmail.com", role = c("aut", "cre")),
person("Tim", "Wilson", email = "timwilson7@gmail.com", role = c("aut", "ctb")),
person("Charles", "Gallagher", email = "charlesjgallagher15@gmail.com", role = c("ctb")),
person("Mark", "Edmondson", role = "ctb")
)
Ben Woodard [aut, cre],
Tim Wilson [aut, ctb],
Charles Gallagher [ctb],
Mark Edmondson [ctb]

assertthat (>= 0.2.0), jsonlite (>= 1.5), dplyr (>= 0.8.1),
stringr (>= 1.4.0), purrr (>= 0.3.3), httr (>= 1.3.1), tidyr
(>= 1.0.0), rlang (>= 0.4.8), lubridate (>= 1.7.9), ggplot2 (>=
3.3.2), scales (>= 1.1.1), R6, jose, openssl, lifecycle, glue,
tibble, vctrs, progress, memoise, utils

R (>= 3.0), RandomFieldsUtils (>= 0.5.3)
stats, graphics, methods, grDevices, utils, quadprog, tcltk,
tkrplot
The classical Bass (1969) <doi:10.1287/mnsc.15.5.215> model and the agent based models, such as that by Goldenberg, Libai and Muller (2010) <doi:10.1016/j.ijresmar.2009.06.006> have been two different approaches to model adoption processes in marketing. These two approaches can be unified by explicitly modelling the utility functions. This package provides a GUI that allows, in a unified way, the modelling of these two processes and other processes.

R (>= 3.0), RandomFieldsUtils (>= 0.5.4)

c(
person("Kevin", "Kunzmann", role = c("aut", "cre"), email = "kevin.kunzmann@mrc-bsu.cam.ac.uk"),
person("Maximilian", "Pilz", role = c("aut"), email = "pilz@imbi.uni-heidelberg.de")
)
Optimize one or two-arm, two-stage designs for clinical trials with respect to several
pre-implemented objective criteria or implement custom objectives.
Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported.
knitr, rmarkdown, testthat, covr, rpact, vdiffr
adoptr.R util.R DataDistribution.R Prior.R PointMassPrior.R
ContinuousPrior.R TwoStageDesign.R GroupSequentialDesign.R
OneStageDesign.R Scores.R AffineScoreFunction.R constraints.R
minimize.R ConditionalPower.R ConditionalSampleSize.R
regularization.R boundary_designs.R
Kevin Kunzmann [aut, cre],
Maximilian Pilz [aut]