Packaged: Sat Nov  8 09:30:26 2008; cheung

Description: This package provides functions to run the CRM and
TITE-CRM in phase I trials and calibration tools for trial

Title: Dose-Finding by the Continual Reassessment Method
Description: Provides functions to run the CRM and

Packaged: 2013-08-02 03:29:47 UTC; workspace

Title: Explore data.frames by plotting NA and classes of each variable
Description: Quickly and graphically show missing values and classes of each variable and each observation of a data.frame. The aim is to show pattern of missing values and if there is wrong class attribution.

Description: Quickly and graphicaly show NA and classes of each variable and each observation of a data.frame. The aim is to show pattern of NA

Suggests: knitr, rmarkdown, AER, mlogit, plm
Description: Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.

Title: Meta-Analysis of Phase I Dose-Finding Early Clinical Trials
Author: Artemis Toumazi <artemis.toumazi@gmail.com>, Sarah Zohar <sarah.zohar@inserm.fr>, Anand N. Vidyashankar <avidyash@gmu.edu>, Jie Xu <jxu13@gmu.edu> and Moreno Ursino <moreno.ursino@inserm.fr>
Description: Meta-analysis approaches for Phase I dose finding early phases clinical trials in order to better suit requirements in terms of maximum tolerated dose (MTD) and maximal dose regimen (MDR). This package has currently three different approaches: (a) an approach proposed by Zohar et al, 2011, <doi:10.1002/sim.4121> (denoted as ZKO), (b) the Variance Weighted pooling analysis (called VarWT) and (c) the Random Effects Model Based (REMB) algorithm, where user can input his/her own model based approach or use the existing random effect logistic regression model (named as glimem) through the 'dfmeta' package.
Depends: R (>= 3.0.2), lme4, stats4, plyr, data.table
Imports: ggplot2 (>= 2.0.0), methods, graphics, grDevices, stats, utils
Packaged: 2018-03-25 14:13:52 UTC; artemistoumazi

Authors@R: c(person("Sebastian", "Krantz", role = c("aut", "cre"), email = "sebastian.krantz@graduateinstitute.ch"),
person("Rytis", "Bagdziunas", role = "aut"))
Description: Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm
or Two-Step (2S) estimation, supporting datasets with missing data. The implementation follows advances in the
econometric literature: estimation can be done either by running the Kalman Filter and Smoother once with initial
values from PCA - 2S estimation following Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> -
or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012)
<doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>,
allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and
the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports
interpretation and visualization of the model as well as forecasting. Information criteria to choose the number
of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
Suggests: xts, vars, magrittr, testthat (>= 3.0.0), knitr, rmarkdown,
Packaged: 2022-10-10 21:53:04 UTC; sebastiankrantz