JointAI 0.4.0

Bug fixes

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)

Minor changes

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, …

Extensions

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

JointAI 0.3.2

Bug fixes

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

Minor changes

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

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

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 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

JointAI 0.2.0

Bug fixes

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

Minor changes

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

Extensions

gamma and beta imputation methods implemented