`RinvD`

is no longer selected to be monitored in random intercept model (`RinvD`

is not used in such a model)- fixed various bugs for models in which only the intercept is used (no covariates)

`summary()`

: reduced default number of digits- continuous variables with two distinct values are converted to factor
- argument
`meth`

now uses default values if only specified for subst of incomplete variables `get_MIdat()`

: argument`minspace`

added to ensure spacing of iterations selected as imputations`densplot()`

: accepts additional options, e.g.,`lwd`

,`col`

, …

`coef()`

method added for`JointAI`

object and`summary.JointAI`

object`confint()`

method added for`JointAI`

object`print()`

method added for`JointAI`

object`survreg_imp()`

added to perform analysis of parametric (Weibull) survival models`glme_imp()`

added to perform generalized linear mixed modeling- extended documentation; two new vignettes on MCMC parameters and functions for after the model is estimated; added messages about coding of ordinal variables

# JointAI 0.3.4 |

## Bug fixes * `traceplot()` , `densplot()` : specification of `nrow` AND `ncol` possible; fixed bug when only `nrow` specified |

# JointAI 0.3.3 |

## Bug fixes * remove deprecated code specifying `contrast.arg` that now in some cases cause error * fixed problem identifying non-linear functions in formula when the name of another variable contains the function name |

`lme_imp()`

: fixed error in JAGS model when interaction between random slope variable and longiudinal variable

- unused levels of factores are dropped

# 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()` displayes output with rowname 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 linebreak 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 |

`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
- bugfix in re-ordering columns when a function is part of the linear predictor
- bugfix in intial values for categorical covariates
- bugfix in finding imputation method when function of variable is specified as auxiliary variable

`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`

`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 hyperparameters`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*

`md_pattern()`

: does not generate duplicate plot any more- corrected names of imputation methods in help file
- scaling when no continuous covariates are in the model or scaling is deselected fixed
- initial value specification for coefficient for auxiliary variables fixed
`get_MIdat()`

: imputed values are now filled in in the correct order`get_MIdat()`

: variables imputed with`lognorm`

are now included when extracting an imputed dataset`get_MIdat()`

: imputed values of transformed variables are now included in imputed datasets- problem with non valid names of factor labels fixed
- data matrix is now ordered according to order in user-specified
`meth`

argument

`md.pattern()`

: adaptation to new version of`md.pattern()`

from the**mice**package- internally change all
`NaN`

to`NA`

- allow for scaling of incomplete covariates with quadratic effects
- changed hyperparameter for precision in models with logit link from 4/9 to 0.001

`gamma`

and`beta`

imputation methods implemented