used by Amelia [http://gking.harvard.edu/amelia/] but is much
faster when filling in values for a single line of data.

can be transformed into approximating and stores this information
as an object of class FastImputationPatterns. The FastImputation
function uses this FastImputationPatterns object to impute (make
a good guess at) missing data in a single line or a whole dataframe
of data.  This approximates the process used by Amelia
[http://gking.harvard.edu/amelia/] but is much faster when
filling in values for a single line of data.

Title: Learn from Training Data then Quickly Fill in Missing Data
Description: TrainFastImputation() uses training data to describe a
as an object of class 'FastImputationPatterns'. FastImputation()
function uses this 'FastImputationPatterns' object to impute (make
a good guess at) missing data in a single line or a whole data frame

of data.  This approximates the process used by 'Amelia'
<http://gking.harvard.edu/amelia/> but is much faster when

<https://gking.harvard.edu/amelia> but is much faster when

Title: Semi-Parametric Joint Modeling of Survival and Longitudinal Data
person("Shanpeng", "Li", email = "lishanpeng0913@ucla.edu",
person("Ning", "Li", email = "liningpv@gmail.com",
person("Hong", "Wang", email = "wh@csu.edu.cn",
person("Jin", "Zhou", email = "jinjinzhou@g.ucla.edu",
person("Hua", "Zhou", email = "huazhou@ucla.edu",
person("Gang", "Li", email = "vli@ucla.edu",
Description: Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data
applying one way scan algorithm, proposed by Li and colleagues (2021) <arXiv:2110.14822>. The time-to-event data is
modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal
outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model
is estimated using am Expectation Maximization algorithm.
Imports: Rcpp (>= 1.0.7), MASS, statmod, survival, dplyr, nlme, mvtnorm
Packaged: 2022-01-05 14:13:11 UTC; shanpengli