TrainFastImputation uses training data to describe a
multivariate normal distribution that the data approximates or
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 data frame
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.

TrainFastImputation() uses training data to describe a
multivariate normal distribution that the data approximates or
can be transformed into approximating and stores this information
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
filling in values for a single line of data.
'FastImputation.R' 'TrainFastImputation.R' 'UnfactorColumns.R'
'BoundNormalizedVariable.R' 'NormalizeBoundedVariable.R'
'CovarianceWithMissing.R' 'FI_train.R' 'FI_test.R' 'FI_true.R'

TrainFastImputation() uses training data to describe a
multivariate normal distribution that the data approximates or
can be transformed into approximating and stores this information
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'
<https://gking.harvard.edu/amelia> but is much faster when
filling in values for a single line of data.

Semi-Parametric Joint Modeling of Survival and Longitudinal Data
c(
person("Shanpeng", "Li", email = "lishanpeng0913@ucla.edu",
role = c("aut", "cre")),
person("Ning", "Li", email = "liningpv@gmail.com",
role = "ctb"),
person("Hong", "Wang", email = "wh@csu.edu.cn",
role = "ctb"),
person("Jin", "Zhou", email = "jinjinzhou@g.ucla.edu",
role = "ctb"),
person("Hua", "Zhou", email = "huazhou@ucla.edu",
role = "ctb"),
person("Gang", "Li", email = "vli@ucla.edu",
role = "ctb")
)
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.
Rcpp (>= 1.0.7), MASS, statmod, survival, dplyr, nlme, mvtnorm
Shanpeng Li [aut, cre],
Ning Li [ctb],
Hong Wang [ctb],
Jin Zhou [ctb],
Hua Zhou [ctb],
Gang Li [ctb]