Description: Methods for working with dose-finding clinical trials. We provide
implementations of many dose-finding clinical trial designs, including the
<doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji
interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox
described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs
are implemented with a common interface. We also offer optional additional
classes to tailor the behaviour of all designs, including avoiding skipping
doses, stopping after n patients have been treated at the recommended dose,
stopping when a toxicity condition is met, or demanding that n patients are
treated before stopping is allowed. By daisy-chaining together these classes
using the pipe operator from 'magrittr', it is simple to tailor the
behaviour of a dose-finding design so it behaves how the trialist wants.
Having provided a flexible interface for specifying designs, we then provide
functions to run simulations and calculate dose-paths for future cohorts of
gtools, dfcrm, BOIN, trialr (>= 0.1.5), DiagrammeR,

Depends: clim.pact, cyclones, akima, ncdf, sgeostat, fields, spam, R
Description: functions for post-processing and gridding

person("Michael", "Dietze", role = c("cre", "aut", "trl"), email = "mdietze@gfz-potsdam.de"),
person("Christoph", "Burow", role = c("ctb")),
person("Sophie", "Lagarde", role = c("ctb", "trl")))
Description: Environmental seismology is a scientific field that studies the
seismic signals, emitted by Earth surface processes. This package
provides all relevant functions to read/write seismic data files, prepare,
analyse and visualise seismic data, and generate reports of the processing
Imports: sp, multitaper, raster, rgdal, caTools, signal, fftw,
matrixStats, methods, IRISSeismic, XML, rmarkdown, Rcpp (>=

matrixStats, methods, IRISSeismic, XML, shiny, rmarkdown,

matrixStats, methods, IRISSeismic, XML, shiny, shinyFiles,
rmarkdown, reticulate, EMMAgeo, limSolve, extraDistr,

Package: esemifar
Type: Package
Title: Smoothing Long-Memory Time Series
Version: 1.0.0
Description: The nonparametric trend and its derivatives in equidistant time
series (TS) with long-memory errors can be estimated. The
estimation is conducted via local polynomial regression using an
automatically selected bandwidth obtained by a built-in iterative plug-in
algorithm or a bandwidth fixed by the user.
The smoothing methods of the package are described in Letmathe, S., Beran,
J. and Feng, Y., (2021) <https://ideas.repec.org/p/pdn/ciepap/145.html>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: fracdiff, stats, smoots, graphics, grDevices
Depends: R (>= 2.10)
Authors@R:
c(person("Yuanhua", "Feng", role = "aut",
comment = "Paderborn University, Germany"),
person("Sebastian", "Letmathe", role = c("aut", "cre"),
email = "sebastian.letmathe@uni-paderborn.de",
comment = "Paderborn University, Germany"),
person("Dominik", "Schulz", role = c("aut"),
comment = "Paderborn University, Germany"))
URL: https://wiwi.uni-paderborn.de/en/dep4/feng/
Acknowledgments: This work was supported by the German DFG project
GZ-FE-1500-2-1.
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-10-27 14:48:40 UTC; Letmode
Author: Yuanhua Feng [aut] (Paderborn University, Germany),
Sebastian Letmathe [aut, cre] (Paderborn University, Germany),
Dominik Schulz [aut] (Paderborn University, Germany)
Maintainer: Sebastian Letmathe <sebastian.letmathe@uni-paderborn.de>
Repository: CRAN
Date/Publication: 2021-10-28 19:30:05 UTC