Joint Analysis and Imputation of Incomplete Data
Add line breaks to a linear predictor string
Continue sampling from an object of class JointAI
Extract names of variables from a (list of) formula(s)
B-Spline Basis for Polynomial Splines
Check/convert formula to list
Replace a full with a block-diagonal variance covariance matrix Check ...
Check / create the random effects variance-covariance matrix specifica...
First validation for rd_vcov
Convert a survival outcome to a model name
Combine fixed and random effects formulas
Combine a fixed and random effects formula
Get the default values for hyper-parameters
Plot the posterior density from object of class JointAI
Converts a difftime object to a data.frame
Create a duration object
Expand rd_vcov using variable names in case "full" is used
Extract all id variables from a list of random effects formulas
Extract the left hand side of a formula
Return the current state of a 'JointAI' model
Identify the family from the covariate model type
Extract multiple imputed datasets from an object of class JointAI
Obtain a summary of the missing values involved in an object of class ...
Re-create the full Mlist from a "JointAI" object
Identify the general model type from the covariate model type
Extract the number of random effects
Identify the data matrix containing a given response variable
Gelman-Rubin criterion for convergence
Get info on the main effects in a random slope structure for a given l...
Get info on the interactions with random slopes for a given level and ...
JointAI: Joint Analysis and Imputation of Incomplete Data
Fitted object of class 'JointAI'
List model details
Calculate and plot the Monte Carlo error
Missing data pattern
Joint Analysis and Imputation of incomplete data
Generate a Basis Matrix for Natural Cubic Splines
Parameter names of an JointAI object
Write the coefficient part of a linear predictor
Write the data element of a linear predictor
Write a linear predictor
Create the scaling in a data element of a linear predictor
Wrap a data element of a linear predictor in scaling syntax
PBC data
Plot an object object inheriting from class 'JointAI'
Visualize the distribution of all variables in the dataset
Plot the distribution of observed and imputed values
Create a new data frame for prediction
Predict values from an object of class JointAI
Extract the random effects variance covariance matrix Returns the post...
Set all elements of a difftime object to the same, largest meaningfu...
Remove the left hand side of a (list of) formula(s)
Extract residuals from an object of class JointAI
Specify reference categories for all categorical covariates in the mod...
Parameters used by several functions in JointAI
Split a formula into fixed and random effects parts
Split a list of formulas into fixed and random effects parts.
Calculate the sum of the computational duration of a JointAI object
Summarize the results from an object of class JointAI
Create a Survival Object
Create traceplots for a MCMC sample
Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.