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.