Depends: R (>= 3.4.0), methods, Matrix, boot, mvtnorm

Author: Marc Ditzhaus and Sarah Friedrich
Description: Implemented is the multiple-direction logrank test
for two-sample right censored data. In addition to the
statistic two p-values are calculated: the first one is based on a
chi-squared approximation and the second one is based on a
Ditzhaus, M. and Friedrich, S. (2018) <arXiv:1807.05504>.

Description: Implemented are the one-sided and two-sided
multiple-direction logrank test for two-sample right
censored data. In addition to the statistics p-values are calculated:
1. For the one-sided testing problem one p-value based on a
wild bootstrap approach is determined. 2. In the two-sided case
one p-value based on a chi-squared approximation and
a second p-values based on a permutation approach are calculated.

Title: Model Based Treatment of Missing Data
Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Contains model-based treatment of missing data for regression models
with missing values in covariates or the dependent variable
using maximum likelihood or Bayesian estimation.
Multiple imputation can be also conducted.
Imports: CDM, coda, graphics, miceadds, sirt, stats, TAM, utils

Contains model-based treatment of missing data for regression
models with missing values in covariates or the dependent
variable using maximum likelihood or Bayesian estimation
The regression model can be nonlinear (e.g., interaction
effects, quadratic effects or B-spline functions).
Multilevel models with missing data in predictors are
available for Bayesian estimation. Substantive-model compatible
multiple imputation can be also conducted.
Imports: CDM, coda, graphics, MASS, miceadds (>= 2.13-60), Rcpp, sirt,

using maximum likelihood or Bayesian estimation. The regression
model can be nonlinear (e.g., including interaction or quadratic
effects). Multiple imputation can be also conducted.

Imports: CDM, coda, graphics, MASS, miceadds, sirt, stats, TAM, utils

using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005;
<doi:10.1198/016214504000001844>). The regression model can be nonlinear
(e.g., including interaction or quadratic effects).
Imports: CDM, coda, graphics, MASS, miceadds, Rcpp, sirt, stats, TAM,