libgnome-desktop-4-2t64
Utility library for the GNOME desktop - runtime files
The gnome-desktop set of libraries provides various utility functions
that are used in multiple components of the GNOME desktop, but do not
have a sufficiently stable API to be included in core libraries such
as GLib and GTK.
libgsound0t64
small library for playing system sounds
GSound is a small library for playing system sounds. It's designed to
be used via GObject Introspection, and is a thin wrapper around the
libcanberra C library.
libgulkan-0.15-0t64
GLib Vulkan helper library
GLib library for Vulkan abstraction. It provides classes for handling a
Vulkan instances, devices, shaders and initialize textures from GLib image
structures and DMA buffers.
libgxw0t64
Guitarix - GTK+ library
Guitarix is a rock guitar amplifier for the JACK Audio Connection Kit with
one input and two outputs. It is designed to produce nice
thrash/metal/rock/blues guitar sounds.
python3-cctbx
Python Toolbox for crystallography
Computational Crystallography Toolbox contains following modules:
- annlib_adaptbx:
- boost_adaptbx: wrappers for Boost functionality in CCTBX
- cbflib_adaptbx:
- ccp4io_adaptbx:
- cctbx: Libraries for general crystallographic applications,
useful for both small-molecule and macro-molecular
crystallography.
- cma_es:
- crys3d: Modules for the display of molecules, electron density,
and reciprocal space data.
- dxtbx: The Diffraction Image Toolbox, a library for handling
X-ray detector data of arbitrary complexity from a variety
of standard formats.
- fable: Fortran EMulation library for porting Fortran77 to C++.
- gltbx: Python bindings for OpenGL
- iotbx: Working with common crystallographic file formats.
- libtbx: The build system common to all other modules. This includes
a very thin wrapper around the SCons software construction tool.
It also contains many useful frameworks and utilities to simplify
application development, including tools for regression testing,
parallelization across multiprocessor systems and managed clusters,
and a flexible, modular configuration syntax called PHIL
(Python Hierarchial Interface Language) used throughout the CCTBX.
- mmtbx: Functionality specific to macromolecular crystallography.
This includes all of the machinery required for setup of
geometry restraints, bulk solvent correction and scaling,
analysis of macromolecular diffraction data, calculation of
weighted map coefficients, and most of the methods implemented
in phenix.refine. The majority of infrastructure for the MolProbity
validation server (and Phenix equivalent) is also located here.
- omptbx: OpenMP interface.
- rstbx: A reciprocal space toolbox to autoindex small molecule Bragg
diffraction, given the reciprocal space vectors.
- scitbx: General scientific calculations. his includes a family of
high-level C++ array types, a fast Fourier transform library,
and a C++ port of the popular L-BFGS quasi-Newton minimizer.
- smtbx: Small-Molecule crystallography.
- spotfinder:
- tbxx:
- wxtbx: wxPython controls used in the Phenix GUI and various
utilities
r-cran-riskregression
GNU R Risk Regression Models and Prediction Scores for Survival
Analysis with Competing Risks Implementation of the following methods
for event history analysis. Risk regression models for survival
endpoints also in the presence of competing risks are fitted using
binomial regression based on a time sequence of binary event status
variables. A formula interface for the Fine-Gray regression model and an
interface for the combination of cause-specific Cox regression models. A
toolbox for assessing and comparing performance of risk predictions
(risk markers and risk prediction models). Prediction performance is
measured by the Brier score and the area under the ROC curve for binary
possibly time-dependent outcome. Inverse probability of censoring
weighting and pseudo values are used to deal with right censored data.
Lists of risk markers and lists of risk models are assessed
simultaneously. Cross-validation repeatedly splits the data, trains the
risk prediction models on one part of each split and then summarizes and
compares the performance across splits.