lxqt-panel represents the taskbar of LXQt.

lxqt-openssh-askpass is a GUI to query credentials on behalf of other programs.

LXImage-Qt is the Qt port of LXImage, a simple and fast image viewer.

PCManFM-Qt is the Qt port of PCManFM, the file manager of LXDE.

ScreenGrab is a program for fast creating screenshots, and easily publishing them on internet image hosting services.

LXQt is a lightweight Qt desktop environment.

GNU M4 is an implementation of the M4 macro language, which features some extensions over other implementations, some of which are required by GNU Autoconf. It is used as a macro processor, which means it processes text, expanding macros as it encounters them. It also has some built-in functions, for example to run shell commands or to do arithmetic.

This package provides Autograd-compatible approximations to the gamma family of functions.

This package implements a variety of persistent homology algorithms. It provides an interface for

• computing persistence cohomology of sparse and dense data sets

• visualizing persistence diagrams

• computing lowerstar filtrations on images

• computing representative cochains

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.

This is a package for hassle-free computation of shareable, comparable, and reproducible BLEU, chrF, and TER scores for natural language processing.

This package provides common Python utilities and GitHub Actions for the Lightning suite of libraries.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

The MCL algorithm is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs (also known as networks) based on simulation of (stochastic) flow in graphs.

Scikit-rebate is a scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. These algorithms excel at identifying features that are predictive of the outcome in supervised learning problems, and are especially good at identifying feature interactions that are normally overlooked by standard feature selection algorithms.

This package provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

This package provides a GStreamer plugin that wraps Kaldi's SingleUtteranceNnet2Decoder. It requires iVector-adapted DNN acoustic models. The iVectors are adapted to the current audio stream automatically.

A Python library for reading and writing GGUF & GGML format ML models.

TensorPipe provides a tensor-aware channel to transfer rich objects from one process to another while using the fastest transport for the tensors contained therein.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

This package provides provides an implementation of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for Python.

This package provides logging utilities for the SpaCy natural language processing framework.

OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs).

Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.