This natural language processing toolkit provides language-agnostic tokenization, parts of speech tagging, lemmatization and dependency parsing of raw text. Next to text parsing, the package also allows you to train annotation models based on data of treebanks in CoNLL-U format as provided at https://universaldependencies.org/format.html. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at doi:10.18653/v1/K17-3009. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.

This package provides R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.

This package provides an R wrapper around the argon library.

The xlsxjars package collects all the external jars required for the xlxs package. This release corresponds to POI 3.10.1.

This package provides a collection of custom input controls and user interface components for Shiny applications.

This package plots path diagrams from models in lavaan using the plotting functionality from the DiagrammeR package. DiagrammeR provides nice path diagrams via Graphviz, and these functions make it easy to generate these diagrams from a lavaan path model without having to write the DOT language graph specification.

This package lets you create Bootstrap 4 dashboards powered by Argon.

With the DiagrammeR package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into R Markdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files.

This package implements piecewise structural equation modeling from a single list of structural equations, with new methods for non-linear, latent, and composite variables, standardized coefficients, query-based prediction and indirect effects.

Redox is a C++ interface to the Redis key-value store that makes it easy to write applications that are both elegant and high-performance. Communication should be a means to an end, not something we spend a lot of time worrying about. Redox takes care of the details so you can move on to the interesting part of your project.

GHDL Language Server Protocol (LSP) is a server for VHDL based on GHDL.

UVVM Light is a low threshold version of UVVM and is intended for developers who want to start using UVVM Utilty library and Bus Functional Models.

This package gathers GNAT binaries from FSF GCC releases of the Alire Project.

This plugin provides a shared library module for Yosys to implement logical synthesis of VHDL designs.

pyVHDLModel provides an unified abstract language model for VHDL written in Python.

GHDL analyses, elaborates and simulates VHDL sources. It may also be used as an experimental synthesizer backend.

This package provides tools for calculating smoothed 2D position, speed, head direction.

Python library for reading, writing, and validating SNIRF files

mne-denoise provides powerful signal denoising techniques for the MNE-Python ecosystem, including Denoising Source Separation (DSS) and ZapLine algorithms. These methods excel at extracting signals of interest by exploiting data structure rather than just variance.

Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats.

This package provides tools for finding sharp-wave ripple events (150-250 Hz) from local field potentials.

This package implements both parametric and permutation-based ARI, and is meant to be compatible with the MNE-Python ecosystem.

This library implements a simple lossless compression scheme adapted to time-dependent high-frequency, high-dimensional signals. It is being developed within the International Brain Laboratory with the aim of being the compression library used for all large-scale electrophysiological recordings based on Neuropixels. The signals are typically recorded at 30 kHz and 10 bit depth, and contain several hundreds of channels.

mne-icalabel is a Python package for labeling independent components that stem from an Independent Component Analysis (ICA).