Title: Adaptive Subgroup Selection in Group Sequential Trials
Authors@R: c(person("Tze Leung", "Lai", role=c("ctb"),
Description: Clinical trial design for subgroup selection in three-stage group
sequential trial. Includes facilities for design, exploration and analysis of
such trials. An implementation of the initial DEFUSE-3 trial is also provided

Imports: R6, mvtnorm, knitr, magrittr, dplyr

Title: Interactive Document for Working with Association Rule Mining
Description: An interactive document on  the topic of association rule mining analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/ASSOCShiny/>.
Packaged: 2019-05-11 23:20:37 UTC; KARTIKEYA

Author: Parinaz Mehdipour <mehdipoor.p@gmail.com> [aut], Ali Ghanbari <a.ghanbari541@gmail.com> [cre,aut], Farshad Farzadfar <farzadfar3@yahoo.com> [cph]
Description: Fits a model to adjust and consider additional variations in three dimensions of age groups, time, and space on residuals excluded from a prediction model that have residual such as: linear regression, mixed model and so on. Details are given in Foreman et al. (2015) <doi:10.1186/1478-7954-10-1>.

Title: Stochastic Volatility Models with or without Leverage
Authors@R: c(person("Yasuhiro", "Omori", email = "omori.yasuhiro@gmail.com", role = c("aut", "cre")),person("Ryuji", "Hashimoto", role = "ctr"))
Description: The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters.
Imports: Rcpp (>= 1.0.7), freqdom, stats, graphics

Title: Automatic Time Series Analysis and Forecasting Using the Ata
person("Ali Sabri", "Taylan", email="alisabritaylan@gmail.com", role=c("aut", "cre", "cph"), comment = c(ORCID = "0000-0001-9514-934X")),
person("Hanife", "Taylan Selamlar", role=c("aut", "cph"), comment = c(ORCID = "0000-0002-4091-884X")),
Description: The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>,
Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the
initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy
implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend
and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
Maintainer: Ali Sabri Taylan <alisabritaylan@gmail.com>
Imports: graphics, forecast, Rcpp, Rdpack, seasonal, stats, stlplus,
stR, timeSeries, TSA, tseries, utils, xts
Author: Ali Sabri Taylan [aut, cre, cph]
Guckan Yapar [aut, ths, cph] (<https://orcid.org/0000-0002-0971-6676>)
Packaged: 2021-03-04 23:43:04 UTC; sabri.taylan