Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.

Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem <doi:10.18637/jss.v095.c01>.

Methods for Evaluating Principal Surrogates of Treatment
Response
Contains the core methods for the evaluation of principal
surrogates in a single clinical trial. Provides a flexible interface for
defining models for the risk given treatment and the surrogate, the models
for integration over the missing counterfactual surrogate responses, and the
estimation methods. Estimated maximum likelihood and pseudo-score can be used
for estimation, and the bootstrap for inference. A variety of post-estimation
summary methods are provided, including print, summary, plot, and testing.

c(
person("Michael C Sachs", email = "sachsmc@gmail.com", role = c("aut", "cre")),
person("Erin E Gabriel", email = "erin.gabriel14@gmail.com", role = "aut"))
ggplot2, testthat, knitr, printr, rmarkdown
Michael C Sachs [aut, cre],
Erin E Gabriel [aut]

Prediction Model Selection and Performance Evaluation in
Multiple Imputed Datasets
c(
person("Martijn", "Heymans", email = "mw.heymans@amsterdamumc.nl", role=c("cre", "aut")),
person("Iris", "Eekhout", email = "i.eekhout@tno.nl", role=c("ctb")))
Provides functions to apply pooling or backward selection
for logistic or Cox regression prediction models in multiple imputed
datasets. Backward selection can be done from the pooled model using
Rubin's Rules (RR), the total covariance matrix (D1 method), pooling
chi-square values (D2 method), pooling likelihood ratio statistics
(D3) or pooling the median p-values. The model can contain
continuous, dichotomous, categorical predictors and interaction terms
between all	type of these predictors. Continuous predictors	can also
be introduced as restricted cubic spline coefficients. It is also possible
to force (spline) predictors or interaction terms in the model during predictor
selection. The package also contains functions to generate apparent model performance
measures over imputed datasets as ROC/AUC, R-squares, fit test values and
calibration	plots. A wrapper function	over Frank Harrell's validate function
is used for that. Bootstrap internal validation is performed in each imputed dataset
and results are pooled. Backward selection as part of internal validation
is optional and recommended. Also a function to externally validate logistic
prediction models in multiple imputed datasets is available.
Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
Marshall (2009) <doi:10.1186/1471-2288-9-57>.
Martijn Heymans [cre, aut],
Iris Eekhout [ctb]