types), nested list or simplified R objects ready to be used in the

Maintainer: Michele De Meo <micheledemeo@gmail.com>
Description: The sample size according to the Bethel's procedure.
Packaged: Fri Mar 27 10:54:06 2009; Administrator

Title: The BETS Model for Early Epidemic Data
Authors@R: c(person("Qingyuan", "Zhao", email = "qyzhao@statslab.cam.ac.uk", role = c("aut", "cre")),person("Nianqiao", "Ju", email = "nju@g.harvard.edu", role = "aut"))
Description: Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.

comment = "'etry()' and its print method borrow a lot from base R"))
Description: Drop-in replacement for 'parallel::mclapply()' adding e.g.
tracebacks, crash dumps, retries, condition handling, improved seeding,
progress bars and faster inter process communication. Some of the internal
functions are also exported for other use: 'etry()' (extended try),
'copy2shm()/allocate_from_shm()' (copy to and allocate from POSIX shared
memory), 'char_map/map2char()' (split a character vector into its unique
elements and a mapping on these) and various semaphore related functions.
Suggests: progress, spelling, testthat (>= 2.1.0)
Author: Andreas Kersting [aut, cre, cph],
R Core team [ctb] ('etry()' and its print method borrow a lot from base

Title: Bayesian Spectral Inference for Stationary Time Series
person("Alexander", "Meier", email="alexander.meier@ovgu.de", role=c("aut", "cre")),
person("Matthew C.", "Edwards", email="matt.edwards@auckland.ac.nz", role="aut"),
Description: Implementations of a Bayesian parametric (autoregressive), a Bayesian nonparametric (Whittle likelihood with Bernstein-Dirichlet prior) and a Bayesian semiparametric (autoregressive likelihood with Bernstein-Dirichlet correction) procedure are provided. The work is based on the corrected parametric likelihood by C. Kirch et al (2017) <arXiv:1701.04846>. It was supported by DFG grant KI 1443/3-1.
Packaged: 2017-01-23 01:48:29 UTC; alexander