libopenh264-8

OpenH264 Video Codec

OpenH264 is a codec library which supports H.264 encoding and decoding. It is suitable for use in real time applications such as WebRTC.

python3-pyopenuv

Library to access UV and ozone data from OpenUV

This library allows access to UV and ozone data from the OpenUV API. It provides functions to retrieve current UV index, forecasted UV information, and details about safe exposure times for different skin types. Additionally, it includes features to get the maximum UV index for the day, the current ozone level, and a protection window indicating when sunblock protection should be used.

debian-edu-router-fai

FAI config space (et al.) for the Debian Edu Router system

The Debian Edu Router project provides installation routines for setting up a network router for the Debian Edu system (or for other school networks).

debian-edu-router-plugin.content-filter

Debian Edu Router Plugin for content filtering

The Debian Edu Router project provides installation routines for setting up a network router for the Debian Edu system (or for other school networks).

r-cran-gap.datasets

Datasets for 'gap'

Datasets associated with the 'gap' package. Currently, it includes an example data for regional association plot (CDKN), an example data for a genomewide association meta-analysis (OPG), data in studies of Parkinson's diease (PD), ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers and Schizophrenia (apoeapoc), cystic fibrosis (cf), a Olink/INF panel (inf1), Manhattan plots with (hr1420, mhtdata) and without (w4) gene annotations.

r-cran-qgam

Smooth Additive Quantile Regression Models

Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>. See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.