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.