Computes small-sample degrees of freedom adjustment for
heteroskedasticity robust standard errors, and for clustered standard errors
in linear regression. See Imbens and Kolesár (2016)
<doi:10.1162/REST_a_00552> for a discussion of these adjustments.

Phase I/II Adaptive Dose-Finding Design for Combination Studies
Marie-Karelle Riviere and Jacques-Henri Jourdan
src/arms.c and src/arms.h are copyright Wally Gilks. All
other files are copyright Institut de Recherches
Internationales Servier and Institut national de la sante et de
la recherche medicale.
Phase I/II adaptive dose-finding design for combination
studies. Several methods are proposed depending on the type of
combinations: (1) the combination of two cytotoxic agents, and (2)
combination of a molecularly targeted agent with a cytotoxic agent.
BH (>= 1.54), RcppProgress (>= 0.1), Rcpp

RcppArmadillo (>= 0.4.500), BH (>= 1.54), RcppProgress (>=
0.2), Rcpp

RcppArmadillo (>= 0.4.650), BH (>= 1.55), RcppProgress (>=
0.2.1), Rcpp

RcppArmadillo (>= 0.7.100), BH (>= 1.55), RcppProgress (>=
0.2.1), Rcpp

src/arms.c and src/arms.h are copyright Wally Gilks. All
other files are copyright Sanofi-Aventis R&D, Institut de
Recherches Internationales Servier and Institut national de la
sante et de la recherche medicale.
Phase I/II adaptive dose-finding design for combination
studies where toxicity rates are supposed to increase with both agents.

Dose-finding by the continual reassessment method
This package provides function to run the CRM and TITE-CRM
in phase I trials and calibration tools for trial planning purposes.

This package provides functions to run the CRM and
TITE-CRM in phase I trials and calibration tools for trial
planning purposes.

Dose-Finding by the Continual Reassessment Method
Provides functions to run the CRM and
TITE-CRM in phase I trials and calibration tools for trial
planning purposes.

Explore data.frames by plotting NA and classes of each variable
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.