Imports: AnnotationDbi (>= 1.48.0), Biostrings (>= 2.58.0), BSgenome
(>= 1.58.0), BSgenome.Hsapiens.NCBI.GRCh38 (>= 1.3.1000), dplyr
fitdistrplus (>= 1.1-1), GenomicRanges (>= 1.42.0),
methods, org.Hs.eg.db (>= 3.10.0), plyr (>= 1.8.4), parallel,

Imports: AnnotationDbi (>= 1.48.0), Biostrings (>= 2.58.0), dplyr (>=
plyr (>= 1.8.4), parallel, rmarkdown (>= 2.8), scales (>=

Description: Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of software to investigate splicing dynamics at single-cell resolution. 'MARVEL' quantifies percent spliced-in (PSI) values for the all exon-level splicing events. Additionally, 'MARVEL' performs differential splicing analysis to identify splicing events whose PSI distribution differ between groups of cells. Finally, 'MARVEL' models the PSI distribution for each event as a beta distribution and categorises each distribution into modalities (inspired by Song (2017) <doi:10.1016/j.molcel.2017.06.003>).
Imports: ggplot2 (>= 3.3.2), methods, scales (>= 1.1.1)
Suggests: AnnotationDbi (>= 1.48.0), Biostrings (>= 2.56.0), BSgenome
clusterProfiler (>= 3.16.0), factoextra (>= 1.0.7), FactoMineR
Authors@R: person("Sean", "Wen", role = c("aut", "cre"), email = "sean.wenwx@gmail.com")

Description: Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of software to investigate splicing dynamics at single-cell resolution. 'MARVEL' enables splicing analysis of single-cell RNA-sequencing data generated from plate- and droplet-based library preparation methods.
Imports: ggplot2 (>= 3.3.2), Matrix (>= 1.3-3), methods, plyr (>=
textclean (>= 0.9.3), twosamples (>= 1.1.1), wiggleplotr (>=

Title: Simulation, Estimation and Selection of MARX Models
Authors@R: c(person("Sean", "Telg", email = "j.telg@maastrichtuniversity.nl", role = c("aut","cre","cph")), person("Alain", "Hecq", email = "a.hecq@maastrichtuniversity.nl", role = "ctb"), person("Lenard", "Lieb", email = "l.lieb@maastrichtuniversity.nl", role = "ctb"))
Author: Sean Telg [aut, cre, cph], Alain Hecq [ctb], Lenard Lieb [ctb]
Description: Simulate, estimate (by t-MLE) and select mixed causal-noncausal autoregressive models with possibly exogenous regressors, using methods proposed in Lanne and Saikkonen (2011) <doi:10.2202/1941-1928.1080> and Hecq et al. (2016) <doi:10.15609/annaeconstat2009.123-124.0307>.
Imports: matlab, fBasics, tseries, stabledist