Imports: actuar, aplpack, binom, boot, coin, distrEx, e1071, HH (>=
2.1-32), Hmisc, lmtest, mvtnorm, prob, qcc, RcmdrPlugin.IPSUR
(>= 0.1-6), reshape, scatterplot3d, TeachingDemos (>= 2.5), vcd
Suggests: diagram, emdbook, ggplot2, knitr, lattice, RcmdrMisc,
Description: An introductory probability and statistics textbook, alongside other supplementary materials.  The book is released under the GNU Free Documentation License.

person("Nils", "Petras", email = "nils.petras@mailbox.org",
person("Michael", "Dantlgraber", role = "aut"),
person("Ulf-Dietrich", "Reips", role = "ctb"),
person("Matthias", "Bannert", role = "ctb"))
Description: Generate plots based on the Item Pool Visualization concept for
latent constructs. Item Pool Visualizations are used to display the
conceptual structure of a set of items (self-report or psychometric).
Suggests: knitr, rmarkdown, extrafont, readxl

Imports: ggforce, ggplot2, stats, stringr, lavaan, Matrix, reshape2
Suggests: knitr, rmarkdown, extrafont, readxl, cowplot, gplots

Title: Adapted Boxplot to Missing Observations
Author: Ana Maria Bianco [aut], Graciela Boente [aut], Ana Perez-Gonzalez [aut]
Description: Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or  modelled  using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.

Title: Inverse-Propensity-Weighting for Partially Nested Designs
Description: Use inverse-propensity-weighted estimation approaches to estimating the treatment effect from a partially nested design where one study arm (the treatment arm) is nested and the other study arm (the control arm) is not. Two estimators are provided: IPW mean difference and IPW multilevel modeling. <https://github.com/xliu12/IPWpn>.
Imports: MplusAutomation, tidyverse, mvtnorm, stats, utils, dplyr,

Title: Propensity Score Based Adjusted Survival Curves and
Author: F. Le Borgne <fleborgne@idbc.fr> and Y. Foucher <Yohann.Foucher@univ-nantes.fr>
Description: In observational studies, the presence of confounding factors is common and the comparison of different groups of subjects requires adjustment. In this package, we propose simple functions to estimate adjusted survival curves and log-rank test based on inverse probability weighting (IPW).