Maintainer: Alberto Vico Moreno <avm00016@red.ujaen.es>
Description: Makes the time series prediction easier by automatizing this process
using four main functions: prep(), modl(), pred() and postp(). Features different
preprocessing methods to homogenize variance and to remove trend and seasonality.
Also has the potential to bring together different predictive models to make comparatives.
Features ARIMA and Data Mining Regression models (using caret).
Imports: caret, forecast, graphics, methods, Metrics, stats, TSPred,
Packaged: 2017-09-19 16:00:57 UTC; Alberto Vico Moreno

Authors@R: c(person("Alberto", "Vico Moreno", role=c("aut","cre"),email="avm00016@red.ujaen.es"),
Packaged: 2018-04-29 13:45:20 UTC; Alberto Vico Moreno

Title: R Package for Pairwise Preference Elicitation
Description: Allows users to derive multi-objective weights from pairwise comparisons, which
research shows is more repeatable, transparent, and intuitive other techniques. These weights
can be rank existing alternatives or to define a multi-objective utility function for optimization.

Maintainer: Amir Masoud Abdol <i@amirmasoudabdol.name>
Description: This is an accessible template for 'pkgdown'. It uses two
bootstrap themes, Flatly and Darkly and utilizes the
'prefers-color-scheme' CSS variable to automatically serve either of
the two based on user’s operating system setting, or allowing them to

Title: 2-Stage Clinical Trial Design and Analysis
Authors@R: c(person(given="Briana", family="Cameron", email="briana.cameron@yale.edu", role=c("aut", "cph", "cre")), person(given="Denise", family="Esserman", email="denise.esserman@yale.edu", role="ctb"), person(given="Michael",family="Kane",email="michael.kane@yale.edu",role="ctb"))
Description: Design and analyze two-stage randomized trials with a continuous
outcome measure. The package contains functions to compute the required sample
size needed to detect a given preference, treatment, and selection effect;
alternatively, the package contains functions that can report the study power
given a fixed sample size. Finally, analysis functions are provided to test each
effect using either summary data (i.e. means, variances) or raw study data.
Suggests: knitr, covr, testthat, ggplot2, devtools
Packaged: 2017-06-16 13:08:55 UTC; bricameron