Multivariate Double Hierarchical Generalized Linear Models
Allows different models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
R (>= 3.2.0), methods, Matrix, numDeriv, boot, mvtnorm

Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.

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

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
permutation approach.
Ditzhaus, M. and Friedrich, S. (2018) <arXiv:1807.05504>.

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.
Ditzhaus, M. and Friedrich, S. (2018) <arXiv:1807.05504>.
Ditzhaus, M. and Pauly, M. (2018) <arXiv:1808.05627>.

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.
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
(Ibrahim et al., 2005; <doi:10.1198/016214504000001844>).
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.
CDM, coda, graphics, MASS, miceadds (>= 2.13-60), Rcpp, sirt,
stats, TAM, utils