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
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