elpa-transient
Emacs key and popup interface for complex keybindings
Taking inspiration from prefix keys and prefix arguments, Transient
implements a similar abstraction involving a prefix command, infix
arguments and suffix commands. This abstraction could be called
"transient command", but because it always involves at least two
commands (a prefix and a suffix) it's preferable to call it just a
"transient".
libopencv-apps2d
opencv_apps Robot OS package - runtime files
This package is part of Robot OS (ROS). It contains several ROS
packages for working providing OpenCV functionalities in a simplest
manner in ROS, i.e., running a launch file that corresponds to
the functionality.
libvisp-visual-features3.3
visual servoing platform visual-features library
This package contains the ViSP (Visual Servoing Platform) visual features
runtime library.
lib32stdc++-10-dev-mips64r6el-cross
GNU Standard C++ Library v3 (development files) (mips64r6el
This package contains the headers and static library files necessary for
building C++ programs which use libstdc++.
libstdc++-10-dev-mips64r6el-cross
GNU Standard C++ Library v3 (development files) (mips64r6el)
This package contains the headers and static library files necessary for
building C++ programs which use libstdc++.
r-cran-metafor
Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in
R. The package includes functions to calculate various effect sizes or
outcome measures, fit fixed-, random-, and mixed-effects models to such
data, carry out moderator and meta-regression analyses, and create
various types of meta-analytical plots (e.g., forest, funnel, radial,
L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-
time data, the package also provides functions that implement
specialized methods, including the Mantel-Haenszel method, Peto's
method, and a variety of suitable generalized linear (mixed-effects)
models (i.e., mixed-effects logistic and Poisson regression models).
Finally, the package provides functionality for fitting meta-analytic
multivariate/multilevel models that account for non-independent sampling
errors and/or true effects (e.g., due to the inclusion of multiple
treatment studies, multiple endpoints, or other forms of clustering).
Network meta-analyses and meta-analyses accounting for known correlation
structures (e.g., due to phylogenetic relatedness) can also be
conducted.