libegl1-nvidia-tesla-440

NVIDIA binary EGL library (non-GLVND variant) (Tesla 440 version)

EGL provides a platform-agnostic mechanism for creating rendering surfaces for use with other graphics libraries, such as OpenGL|ES.

python3-django-ical

iCalendar feeds for Django (Python 3)

This module provides iCalendar feeds support in Django applications.

libgcc-s1-mips64r6el-cross

GCC support library (mips64r6el)

Shared version of the support library, a library of internal subroutines that GCC uses to overcome shortcomings of particular machines, or special needs for some languages.

r-bioc-degreport

BioConductor report of DEG analysis

Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.

libxnnpack0

High-efficiency floating-point neural network inference operators (libs)

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.

r-bioc-fmcsr

GNU R mismatch tolerant maximum common substructure searching

The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering.