seqimpute 2.2.1

Minor improvment and fixes

• Internal checks for an object having a specific class were changed from is() to inherits().

• Summary, print and plot functions added for the seqimplic object.

• Minor style change in the vignette.

• Information printed by the seqimpute() function in the console was slightly modified.

seqimpute 2.2.0

New features

• New version of the vignette.

• seqaddNA()now has apcont` argument, which allows to modify the probability that a gap with missing data will be continued.

Minor improvements and fixes

• Bug regarding the frame.radius parameter that was always set as frame.radius=0, whatever the user provided corrected.

• Bug regarding edge case with only one level in the dependent variable corrected.

• For parallel computing, the maximum number of cores is set as availableCores()-1, instead as detectCores()-1.

• Warnings now appear when the number of specified cores exceeds the number of multiple imputations, or the number of availableCores()-1.

• Explanations of seqaddNA() have been deepened.

• Explanations of seqimpute() regarding the slots of the seqimp object that is returned have been deepened.

• The colors for the seqmissIplot() and seqmissfplot plots have been changed to improve readability.

• Each variable of the imputed object now have the same levels.

• The quality and speed of the code were increased.

• Reference to new article about MICT-timing added.

seqimpute 2.1.0

New features

• seqimpute() now has an argument end.impute argument, which specifies if missing data at the end of sequences should be imputed or not.

• seqmissIplot(), seqmissfplot() and seqmissimplic now have an argument void.miss, which specifies whether the void values of a provided object of class stslist should be considered as missings or not.

• seqaddNA() now has an argument maxprop, which specifies the maximum proportion of missing data that is allowed to be simulated in a sequence.

Minor improvements and fixes

• Fixes issues with seqimpute() when more than one row only with NA’s end the dataset to impute.

• In seqmissIplot() and seqmissfplot(), the states in the plots are now labeled as ‘missing’ and ‘not missing’ instead of ‘missing’ and ‘observed’ to account for uneven sequence length.

• Fixes issues in seqimpute() related to the preparation of the data when an object of class stslist, built with the TraMineR package is provided.

seqimpute 2.0.0

Breaking changes

• seqimpute() now returns an object of class seqimp. In particular, the include and mice.return arguments are no longer relevant and have been removed.

• The OD argument has been renamed to data. The argument OD itself is deprecated.

• The CO argument has been renamed to covariates. The argument CO itself is deprecated.

• The COt argument has been renamed to time.covariates. The argument COt itself is deprecated.

• The mi argument has been renamed to m. The argument mi itself is deprecated.

• The dataset provided in the package used to be divided into three parts: the trajectories (OD), the covariates (CO), and the time-varying covariates (COt). They now appear as a single dataset, called gameadd.

• seqimpute() no longer implements linear and ordinal regressions.

• The default argument of m has been set to 5.

New features

• seqimpute() implements the MICT-timing imputation algorithm. The argument timing indicates whether to use this algorithm or the MICT algorithm, and frame.radius specifies 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 the covariates 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 a seqimp 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 class seqimp. A print, summary, and plot functions have been added for this object type.

• seqTrans() and seqQuickLook() now accept objects of class stslist built with the TraMineR package.

• A ... argument has been added to seqimpute() to pass named arguments to the imputation functions.

Bug fixes

• Fixes issues in seqimpute() related to the multinomial model when there is only one state in the dependent variable.

• Fixes issues in seqimpute() related to random forest when one state does not appear in the dependent variable.

• Fixes bug in seqimpute() when a single covariate is specified.

• Fixes bug in seqimpute() related to long internal gaps.

• Fixes bug in seqQuickLook() induced by datasets without missing data.