libqgis-core3.28.8

QGIS - shared core library

QGIS is a Geographic Information System (GIS) which manages, analyzes and display databases of geographic information.

libqgis-native3.28.8

QGIS - shared native gui library

QGIS is a Geographic Information System (GIS) which manages, analyzes and display databases of geographic information.

python3-django-pyscss

makes it easier to use PySCSS in Django - Python 3.x

Django-pyscss is a collection of tools for making it easier to use pyScss within Django. It overwrites the import system to use Django's staticfiles app. This way you can import SCSS files from any app (or any file that's findable by the STATICFILES_FINDERS) with no hassle. It provides a django-compressor precompile filter class so that you can easily use pyScss with django-compressor without having to bust out to the shell. This has the added benefit of removing the need to configure pyScss through its command-line arguments AND makes it possible for the exceptions and warnings that pyScss emits to bubble up to your process so that you can actually know what's going on.

libqgisgrass8-3.28.8

QGIS - shared grass library

QGIS is a Geographic Information System (GIS) which manages, analyzes and display databases of geographic information.

r-cran-diagram

GNU R functions for visualising simple graphs (networks), plotting

Flow Diagrams Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualising webs, electrical networks, etc. Support for the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter M.J. Herman (2009), Springer. and the book "Solving Differential Equations in R" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer. Includes demo(flowchart), demo(plotmat), demo(plotweb).

r-cran-mclogit

GNU R multinomial logit models, with or without random effects

Multinomial Logit Models, with or without Random Effects or Overdispersion Provides estimators for multinomial logit models in their conditional logit and baseline logit variants, with or without random effects, with or without overdispersion. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon- Cox approximation). Estimates should be treated with caution if the group sizes are small.