nlme, mvtnorm, KFAS (>= 1.0.1), stats, utils, graphics

c(person("Pankaj", "Das", role = c("aut","cre"),email="pankaj.das2@icar.gov.in"),person("Achal", "Lama", role = "aut",email="achal.lama@icar.gov.in"),
person("Girish", "Jha", role = "ths",email="grish.stat@gmail.com"))
Pankaj Das [aut, cre],
Achal Lama [aut],
Girish Jha [ths]
Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.

Revealing Splicing Dynamics at Single-Cell Resolution
Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of softwares to investigate splicing dynamics at single-cell resolution. 'MARVEL' quantifies percent spliced-in (PSI) values for the five primary exon-level splicing events, namely skipped-exon (SE), mutually exclusive exons (MXE), retained intron (RI), alternative 5' and 3' splice sites (A5SS and A3SS, respectively). 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>).

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>).
person("Sean", "Wen", role = c("aut", "cre"), email = "sean.wenwx@gmail.com")