Description: Detect / classify events in time-series data. Combines multiple well-known R packages like 'forecast' to deliver an easily configurable tool for event detection.
person("Frederik", "Rehbach", email="frederik.re@gmail.com", role=c("aut", "cre")),
person("Steffen", "Moritz", email="steffen.moritz10@gmail.com", role=c("aut"), comment = c(ORCID = "0000-0002-0085-1804"))

person("Margarita", "Rebolledo", email="tarigarma2@gmail.com", role=c("aut", "cre")),
Imports: imputeTS, forecast, ggplot2, gridExtra

Description: Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.
person("Sowmya", "Chandrasekaran", email="sowzz.17@gmail.com", role=c("aut", "cre")),
Imports: imputeTS, forecast, ggplot2, gridExtra, neuralnet

Description: Perform Event Studies from through our EventStudyTools.com API interface, parse the results, visualize it, and / or use the results in further analysis.
Imports: shiny, miniUI, rstudioapi, httr, curl, jsonlite, magrittr (>=
1.5), data.table, dplyr, purrr, scales, tidyquant,

Description: Perform Event Studies from through our <http://EventStudyTools.com> Application Programming Interface, parse the results, visualize it, and / or use the results in further analysis.
1.5), data.table, testthat, dplyr, scales, tidyquant,

RColorBrewer, stringr, purrr, readr, openxlsx

1.5), data.table, testthat, dplyr, tidyr, rlang, scales,
tidyquant, RColorBrewer, stringr, purrr, readr, openxlsx

Description: Perform Event Studies from through our <https://EventStudyTools.com> Application Programming Interface, parse the results, visualize it, and / or use the results in further analysis.

Maintainer: Dr. Simon Mueller <sm@data-zoo.de>
Imports: httr, curl, jsonlite, magrittr (>= 1.5), data.table, testthat,
dplyr, tidyr, rlang, scales, RColorBrewer, stringr, purrr,
readr, shiny, miniUI, rstudioapi, tidyquant

Package: EventWinRatios
Type: Package
Title: Event-Specific Win Ratios for Terminal and Non-Terminal Events
Version: 0.1.0
Authors@R: c(
person(given = "Daewoo", family = "Pak", email = "dpak@yonsei.ac.kr", role = c("aut", "cre")),
person(given = "Song", family = "Yang", email = "yangso@nhlbi.nih.gov", role = c("aut"))
)
Maintainer: Daewoo Pak <dpak@yonsei.ac.kr>
Description: Provides several confidence interval and testing procedures using
event-specific win ratios for semi-competing risks data with non-terminal
and terminal events, as developed in Yang et al. (2021, preprint). Compared with
conventional methods for survival data, these procedures are designed to utilize
more data for improved inference procedures with semi-competing risks data.
The event-specific win ratios were introduced in Yang and Troendle
(2021<doi:10.1177/1740774520972408>). In this package, the event-specific win
ratios and confidence intervals are obtained for each event type, and several
testing procedures are developed for the global null of no treatment effect on
either terminal or non-terminal events. Furthermore, a test of proportional
hazard assumptions, under which the event-specific win ratios converge to the
hazard ratios, and a test of equal hazard ratios are provided. For summarizing
the treatment effect on all events, confidence intervals for linear combinations
of the event-specific win ratios are available using pre-determined or data-driven
weights. Asymptotic properties of these inference procedures are discussed in
Yang et al (2021, preprint). Also, transformations are used to yield better control
of the type one error rates for moderately sized data sets.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2021-10-01 08:43:02 UTC; heavy
Author: Daewoo Pak [aut, cre],
Song Yang [aut]
Repository: CRAN
Date/Publication: 2021-10-03 08:50:05 UTC