Performs the drifting Markov models (DMM) which are 
    non-homogeneous Markov models designed for modeling the heterogeneities of
    sequences in a more flexible way than homogeneous Markov chains or even 
    hidden Markov models. In this context, we developed an R package dedicated to 
    the estimation, simulation and the exact computation of associated reliability 
    of drifting Markov models. The implemented methods are described in
    Vergne, N. (2008), <doi:10.2202/1544-6115.1326> and
    Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> .

R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.

Cancer genomes contain large numbers of somatic alterations but few
    genes drive tumor development. Identifying cancer driver genes is critical 
    for precision oncology. Most of current approaches either identify driver 
    genes based on mutational recurrence or using estimated scores predicting 
    the functional consequences of mutations. 'driveR' is a tool for 
    personalized or batch analysis of genomic data for driver gene prioritization 
    by combining genomic information and prior biological knowledge. As features, 
    'driveR' uses coding impact metaprediction scores, non-coding impact scores, 
    somatic copy number alteration scores, hotspot gene/double-hit gene 
    condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG 
    pathways. It uses these features to estimate cancer-type-specific 
    probability for each gene of being a cancer driver using the related task of 
    a multi-task learning classification model. The method is described in detail 
    in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for 
    prioritizing cancer driver genes using somatic genomics data. BMC 
    Bioinformatics <doi:10.1186/s12859-021-04203-7>.