The Spliced Transcripts Alignment to a Reference (STAR) software is based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences.

The Spliced Transcripts Alignment to a Reference (STAR) software is based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences.

The Spliced Transcripts Alignment to a Reference (STAR) software is based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

StarPU is a run-time system that offers support for heterogeneous multicore machines. While many efforts are devoted to design efficient computation kernels for those architectures (e.g. to implement BLAS kernels on GPUs), StarPU not only takes care of offloading such kernels (and implementing data coherency across the machine), but it also makes sure the kernels are executed as efficiently as possible.

Starman is a PSGI perl web server that has unique features such as high performance, preforking, signal support, superdaemon awareness, and UNIX socket support.

This is a package for reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in R, using GDAL bindings provided by sf, and NetCDF bindings by ncmeta and RNetCDF.

Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. <arXiv:1811.06782>, using a grid of binary variables to estimate the spread of a disease on the grid over the years.

Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.

Stargate is a digital audio workstation with built-in instrument and effect plugins and wave editor, providing innovative features, especially for EDM production.

This package provides modular functions and applications for quickly generating plots and tables. Each modular function opens a graphical user interface providing the user with options to create and customise plots and tables.

This package contains functions for estimating the STARTS model of Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>. Penalized maximum likelihood estimation and Markov Chain Monte Carlo estimation are also provided, see Luedtke, Robitzsch and Wagner (2018) <DOI:10.1037/met0000155>.

The Spliced Transcripts Alignment to a Reference (STAR) software is based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences.

This package provides a robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2024), <doi:10.1371/journal.pgen.1011423>].

This package provides The minimal, blazing-fast, and infinitely customizable prompt for any shell!

• Fast: it's fast - *really really* fast :rocket:

• Customizable: configure every aspect of your prompt

• Universal: works on any shell, on any operating system

• Intelligent: shows relevant information at a glance

• Feature rich: support for all your favorite tools

• Easy: quick to install - start using it in minutes

Note: users must have a nerd font installed and enabled in their terminal

Performance analysis workflow that combines the power of the R language (and the tidyverse realm) and many auxiliary tools to provide a consistent, flexible, extensible, fast, and versatile framework for the performance analysis of task-based applications that run on top of the StarPU runtime (with its MPI (Message Passing Interface) layer for multi-node support). Its goal is to provide a fruitful prototypical environment to conduct performance analysis hypothesis-checking for task-based applications that run on heterogeneous (multi-GPU, multi-core) multi-node HPC (High-performance computing) platforms.

Performance analysis workflow that combines the power of the R language (and the tidyverse realm) and many auxiliary tools to provide a consistent, flexible, extensible, fast, and versatile framework for the performance analysis of task-based applications that run on top of the StarPU runtime (with its MPI (Message Passing Interface) layer for multi-node support). Its goal is to provide a fruitful prototypical environment to conduct performance analysis hypothesis-checking for task-based applications that run on heterogeneous (multi-GPU, multi-core) multi-node HPC (High-performance computing) platforms.

Automatically fetch, transform and arrange subsets of multidimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. This tool provides an interface to perceive a collection of data sets as a single large multidimensional data array, and enables the user to request for automatic retrieval, processing and arrangement of subsets of the large array. Wrapper functions to add support for custom file formats can be plugged in/out, making the tool suitable for any research field where large multidimensional data sets are involved.

Adds support for R startup configuration via .Renviron.d and .Rprofile.d directories in addition to .Renviron and .Rprofile files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.

Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

This package implements stacked elastic net regression (Rauschenberger 2021 <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005 <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).