Package: psfmi
Type: Package
Depends: R (>= 3.5.0),
Imports: survival (> 2.41-3), car (> 3.0-0), norm (>= 1.0-9.5),
        miceadds (> 2.10-14), mitools (>= 2.4), foreign (>= 0.8-72),
        pROC (> 1.11.0), rms (> 5.1-2), ResourceSelection (> 0.3-2),
        ggplot2 (> 2.2.1), dplyr (>= 0.8.3), magrittr (>= 1.5), rsample
        (>= 0.0.5), purrr (>= 0.3.3), tidyr (>= 1.0.0), tibble (>=
        2.1.3), lme4 (>= 1.1-21), mice (>= 3.6.0), mitml (>= 0.3-7)
Title: Prediction Model Selection and Performance Evaluation in
        Multiple Imputed Datasets
Version: 0.2.0
Authors@R: c(
  person("Martijn", "Heymans", email = "mw.heymans@amsterdamumc.nl", role=c("cre", "aut")),
  person("Iris", "Eekhout", email = "i.eekhout@tno.nl", role=c("ctb")))
Description: 
	Provides functions to apply pooling or backward selection 
	of logistic, Cox regression and Multilevel (mixed models) prediction 
	models in multiply imputed datasets. Backward selection can be done 
	from the pooled model using Rubin's Rules (RR), the D1, D2, D3 and 
	promising median p-values method. The model can contain 
	continuous, dichotomous, categorical predictors and interaction terms 
	between all	these type of 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 includes a function to evaluate the stability 
	of the models	using bootstrapping and cluster bootstrapping. The package further 
	contains functions to generate pooled model performance measures in multiply 
	imputed datasets as ROC/AUC, R-squares, Brier score, fit test values and 
	calibration	plots for logistic regression models. A function to apply 
	Bootstrap internal validation	is also available where two methods can be 
	used to combine bootstrapping and	multiple imputation. One method, boot_MI, 
	first draws	bootstrap samples and	subsequently performs multiple imputation and with 
	the other method, MI_boot, first bootstrap samples are drawn from each imputed 
	dataset before results are combined. The adjusted intercept after shrinkage of
	the pooled regression coefficients can be subsequently obtained. Backward selection	
	as part of internal validation is also an option. 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>.
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
License: GPL (>= 2)
URL: https://github.com/mwheymans/psfmi
BugReports: https://github.com/mwheymans/psfmi/issues
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-03 07:09:14 UTC; mwhey
Author: Martijn Heymans [cre, aut],
  Iris Eekhout [ctb]
Maintainer: Martijn Heymans <mw.heymans@amsterdamumc.nl>
Repository: CRAN
Date/Publication: 2020-02-03 07:30:02 UTC
