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
## Bug fixes * lme_imp(): fixed error in JAGS model when interaction between random slope variable and longitudinal variable
## Minor changes * unused levels of factors are dropped

JointAI 0.3.1

Bug fixes

Minor changes

# JointAI 0.3.0
## Bug fixes * monitor_params is now checked to avoid problems when only part of the main parameters is selected * categorical imputation models now use min-max trick to prevent probabilities outside [0, 1] * initial value generation for logistic analysis model fixed * bug-fix in re-ordering columns when a function is part of the linear predictor * bug-fix in initial values for categorical covariates * bug-fix in finding imputation method when function of variable is specified as auxiliary variable
## Minor changes * md.pattern() now uses ggplot, which scales better than the previous version * lm_imp(), glm_imp() and lme_imp() now ask about overwriting a model file * analysis_main = T stays selected when other parameters are followed as well * get_MIdat(): argument include added to select if original data are included and id variable .id is added to the dataset * subset argument uses same logit as monitor_params argument * added switch to hide messages; distinction between messages and warnings * lm_imp(), glm_imp() and lme_imp() now take argument trunc in order to truncate the distribution of incomplete variables * summary() now omits auxiliary variables from the output * imp_par_list is now returned from JointAI models * cat_vars is no longer returned from lm_imp(), glm_imp() and lme_imp(), because it is contained in Mlist$refs
## Extensions * plot_all() function added * densplot() and traceplot() optional with ggplot * densplot() option to combine chains before plotting * example datasets NHANES, simLong and simWide added * list_impmodels to print information on the imputation models and hyper-parameters * parameters() added to display the parameters to be/that were monitored * set_refcat() added to guide specification of reference categories * extension of possible functions of variables in model formula to (almost all) functions that are available in JAGS * added vignettes Minimal Example, Visualizing Incomplete Data, Parameter Selection and Model Specification

JointAI 0.2.0

Bug fixes

Minor changes

Extensions