JointAI 1.0.5

(update request by CRAN)


JointAI 1.0.4

New features

Bug fixes

Small improvements


JointAI 1.0.3

New features

Minor improvements and bug fixes


JointAI 1.0.2

New features

Minor improvements and bug fixes


JointAI 1.0.1

Minor improvements and bug fixes


JointAI 1.0.0

This version of JointAI contains some major changes. To extend the package it was necessary to change the internal structure and it was not possible to assure backward compatibility.

New features

New analysis model types

Hierarchical models with multiple levels of grouping

It is now possible to fit hierarchical models with more than one level of grouping, with nested as well as crossed random effects (check the help file) of the main model function for details on how to specify such random effects structures.

This does also apply to survival models, i.e., it is possible to specify a random effects structure to model survival outcomes in data with a hierarchical structure, e.g., in a multi-centre setting.

Proportional hazards model with time-dependent covariates

coxph_imp() can now handle time-dependent covariates using last-observation-carried-forward. This requires to add (1 | <id variable>) to the model formula to identify which rows belong to the same subject, and to specify the argument timevar to identify the variable that contains the observation time of the longitudinal measurements.

Multivariate models

By providing a list of model formulas it is possible to fit multiple analysis models (of different types) simultaneously. The models can share covariates and it is possible to have the response of one model as covariate in another model (in a sequential manner, however, not circular).

Partial proportional odds models for ordinal responses

As before, proportional odds are assumed by default for all covariates of a cumulative logit model. The argument nonprop accepts a one-sided formula or a named list of one-sided formulas in which the covariates are specified for which non-proportional odds should be assumed.

Additionally, the argument rev is available to specify a vector of names of ordinal responses for which the odds should be inverted. For details, see the the help file.

Other new features

Other changes

Bug fixes


JointAI 0.6.1

Bug fixes


JointAI 0.6.0

Bug fixes

Minor changes

New Features / Extensions


JointAI 0.5.2

Bug fixes


JointAI 0.5.1

Bug fixes

Minor changes


JointAI 0.5.0

Important

Bug fixes

Minor changes

New Features / Extensions


JointAI 0.4.0

Bug fixes

Minor changes

Extensions


JointAI 0.3.4

Bug fixes

JointAI 0.3.3

Bug fixes

# JointAI 0.3.2
# JointAI 0.3.1
## Bug fixes * plot_all() uses correct level-2 %NA in title * simWide: case with no observed bmi values removed * traceplot(), densplot(): ncol and nrow now work with use_ggplot = TRUE * traceplot(), densplot(): error in specification of nrow fixed * densplot(): use of color fixed * functions with argument subset now return random effects covariance matrix correctly * summary() displays output with row name when only one node is returned and fixed display of D matrix * GR_crit(): Literature reference corrected * predict(): prediction with varying factor fixed * no scaling for variables involved in a function to avoid problems with re-scaling
## Minor changes * plot_all() uses xpd = TRUE when printing text for character variables * list_impmodels() uses line break when output of predictor variables exceeds getOption("width") * summary() now displays tail-probabilities for off-diagonal elements of D * added option to show/hide constant effects of auxiliary variables in plots * predict(): now also returns newdata extended with prediction

JointAI 0.3.0

Bug fixes

Minor changes

Extensions


JointAI 0.2.0

Bug fixes

Minor changes

Extensions