Title: Asymptotic simultaneous confidence intervals for many-to-one
Description: Asymptotic simultaneous confidence intervals for
comparison of many treatments with one control, for the
difference of binomial proportions, allows for
Dunnett-like-adjustment, Bonferroni or unadjusted intervals.
Simulation of power of the above interval methods, approximate
calculation of any-pair-power, and sample size iteration based
on approximate any-pair power. Exact conditional maximum test
for many-to-one comparisons to a control.

on approximate any-pair power.  Exact conditional maximum test
Packaged: 2012-12-18 11:47:03 UTC; Schaarschmidt

Packaged: 2013-10-02 08:19:53 UTC; Schaarschmidt

Title: Many-to-One Comparisons of Proportions
iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control.
Packaged: 2018-04-19 11:09:37 UTC; Schaarschmidt

person('Gergely', 'Daróczi', email = 'daroczig@rapporter.net', role = c('aut', 'cre')),
person('David', 'Andel', email = 'andel@bli.uzh.ch', role = 'aut'))
Description: R client to the 'Binance' Public Rest API for data collection on cryptocurrencies, portfolio management and trading: <https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md>.
Imports: data.table, httr, digest, snakecase, logger, jsonlite

Title: Fit and Predict a Gaussian Process Model with (Time-Series)
Description: Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.
(>= 1.0.4), GPfit (>= 1.0-0), stats, graphics, utils, methods

Description: Convert to binary numbers (Base2). Shift, rotate, summary. Based on logical vector.