seqimpute() implements the MICT-timing imputation algorithm. The argument timing indicates whether to use this algorithm or the MICT algorithm, and frame.radiusspecifies the radius of the time frame.- The user can now pass a dataset to the
seqimpute()function and specify which columns correspond to the trajectories with the var argument, to the covariates with thecovariates argument, and the time-varying covariates with the time.covariates argument.
- A vignette has been added.
- New
seqmissfplot() plot function, which plots the most frequent patterns of missing data.
- New
seqmissIplot() plot function, which plots all patterns of missing data.
- New
seqmissimplic() function for identifying and visualizing the states that best characterize sequences with missing data.
- New
fromseqimp() function, which converts aseqimp object into a specified format.
- New
addcluster() function, which adds a clustering result to a seqimp object
- New
seqaddNA() function to simulate missing data.
- New
seqcomplete() function, which extracts all trajectories without missing data.
- New
seqwithmiss() function, which extracts all the trajectories with at least one missing value.
seqimpute() now returns an object of classseqimp. A print, summary, and plot functions have been added for this object type.seqTrans() and seqQuickLook() now accept objects of class stslist built with theTraMineR package.- A
... argument has been added toseqimpute() to pass named arguments to the imputation functions.