Visualising Splicing at Single-Cell Resolution
Alternative splicing produces a variety of different protein products from a given gene. 'VALERIE' enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. 'VALERIE' computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) <doi:10.1038/s41467-018-06063-x>. PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) <doi:10.1371/journal.pcbi.1003118>. Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the 'pheatmap' package by Kolde (2010) <https://CRAN.R-project.org/package=pheatmap>. Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the 'ggplot2' package by Wickham (2007) <https://CRAN.R-project.org/package=ggplot2>.

Contains logic for cell-specific gene set scoring of single cell RNA sequencing data.

Seurat (>= 4.0.0), SeuratObject (>= 4.0.0), sctransform (>=
0.3.2)

VAR modelling: estimation, testing, and prediction
Estimation, Hypothesis Testing, Prediction for Stationary Vector Autoregressive Models

VAR Modelling: Estimation, Testing, and Prediction
A collection of the functions for estimation, hypothesis testing, prediction for stationary vector autoregressive models.

Multiple Change Point Detection in Structural VAR Models
Yue Bai [aut, cre],
Peiliang Bai [aut],
Abolfazl Safikhani [aut],
George Michailidis [aut]
c(person("Yue", "Bai", email = "baiyue@ufl.edu", role = c("aut", "cre")),
person("Peiliang", "Bai", email = "baipl92@ufl.edu", role = c("aut")),
person("Abolfazl", "Safikhani", email = "a.safikhani@ufl.edu", role = c("aut")),
person("George", "Michailidis", email = "gmichail@ufl.edu", role = c("aut")))
Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) <doi:10.1080/01621459.2020.1770097> and Bai, Safikhani and Michailidis (2020) <doi:10.1109/TSP.2020.2993145>.
stats, igraph, pracma, graphics, mvtnorm, ggplot2, glmnet,
sparsevar, lattice, Rcpp (>= 1.0.6)