GD is a library for the dynamic creation of images by programmers. GD is written in C, and "wrappers" are available for Perl, PHP and other languages. GD creates PNG, JPEG, GIF, WebP, XPM, BMP images, among other formats. GD is commonly used to generate charts, graphics, thumbnails, and most anything else, on the fly. While not restricted to use on the web, the most common applications of GD involve website development.

This library provides docking features for gtk+.

GNOME Display Manager is a system service that is responsible for providing graphical log-ins and managing local and remote displays.

GCC is the GNU Compiler Collection. It provides compiler front-ends for several languages, including C, C++, Objective-C, Fortran, Ada, and Go. It also includes runtime support libraries for these languages.

GCC is the GNU Compiler Collection. It provides compiler front-ends for several languages, including C, C++, Objective-C, Fortran, Ada, and Go. It also includes runtime support libraries for these languages.

GCC is the GNU Compiler Collection. It provides compiler front-ends for several languages, including C, C++, Objective-C, Fortran, Ada, and Go. It also includes runtime support libraries for these languages.

GDB is the GNU debugger. With it, you can monitor what a program is doing while it runs or what it was doing just before a crash. It allows you to specify the runtime conditions, to define breakpoints, and to change how the program is running to try to fix bugs. It can be used to debug programs written in C, C++, Ada, Objective-C, Pascal and more.

GDB is the GNU debugger. With it, you can monitor what a program is doing while it runs or what it was doing just before a crash. It allows you to specify the runtime conditions, to define breakpoints, and to change how the program is running to try to fix bugs. It can be used to debug programs written in C, C++, Ada, Objective-C, Pascal and more.

GDB is the GNU debugger. With it, you can monitor what a program is doing while it runs or what it was doing just before a crash. It allows you to specify the runtime conditions, to define breakpoints, and to change how the program is running to try to fix bugs. It can be used to debug programs written in C, C++, Ada, Objective-C, Pascal and more.

GDB is the GNU debugger. With it, you can monitor what a program is doing while it runs or what it was doing just before a crash. It allows you to specify the runtime conditions, to define breakpoints, and to change how the program is running to try to fix bugs. It can be used to debug programs written in C, C++, Ada, Objective-C, Pascal and more.

GDB is the GNU debugger. With it, you can monitor what a program is doing while it runs or what it was doing just before a crash. It allows you to specify the runtime conditions, to define breakpoints, and to change how the program is running to try to fix bugs. It can be used to debug programs written in C, C++, Ada, Objective-C, Pascal and more.

This package provides a DMD-like wrapper for the GNU D Compiler.

GDBM is a library for manipulating hashed databases. It is used to store key/value pairs in a file in a manner similar to the Unix dbm library and provides interfaces to the traditional file format.

GDAL is a translator library for raster and vector geospatial data formats. As a library, it presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats. It also comes with a variety of useful command line utilities for data translation and processing.

Grassroots DICOM (GDCM) is an implementation of the DICOM standard designed to be open source so that researchers may access clinical data directly. GDCM includes a file format definition and a network communications protocol, both of which should be extended to provide a full set of tools for a researcher or small medical imaging vendor to interface with an existing medical database.

The Generic Data Structures Library (GDSL) is a collection of routines for generic data structures manipulation. It is a re-entrant library fully written from scratch in pure ANSI C. It is designed to offer for C programmers common data structures with powerful algorithms, and hidden implementation. Available structures are lists, queues, stacks, hash tables, binary trees, binary search trees, red-black trees, 2D arrays, permutations and heaps.

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

This package provides functions to explore datasets from the Global Biodiversity Information Facility (GBIF - <https://www.gbif.org/>) using a Shiny interface.

CL-GD is a library for Common Lisp which provides an interface to the GD Graphics Library for the dynamic creation of images. It is based on UFFI and should thus be portable to all CL implementations supported by UFFI.

Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers.

This package contains a function called gds() which accepts three input parameters like lower limits, upper limits and the frequencies of the corresponding classes. The gds() function calculate and return the values of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', gq2', gq3'), inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate effective data analysis. For skewness and kurtosis calculations we use moments.

This package provides tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. GDI was first employed by Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999) <doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass estimation of (extinct) animals. The package gdi aims to make this technique as convenient and versatile as possible. The core functions of gdi provide utilities for automatically measuring diameters from digital silhouettes provided as image files and calculating volume via graphic double integration with simple elliptical, superelliptical (following Motani 2001 <doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex cross-sectional models. Additionally, the package provides functions for estimating the center of mass position (COM), the moment of inertia (I) for 3D shapes and the second moment of area (Ix, Iy, Iz) of 2D cross-sections, as well as for visualization of results.

This package provides functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02> for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy.

Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. The training graph is used to estimate eigenvectors, and the test graph is used to evaluate the correlation between the training eigenvectors and the eigenvectors of the test graph. The correlations follow a simple central limit theorem that can be used to estimate graph dimension via hypothesis testing, see Chen et al. (2021) <arXiv:2108.03336> for details.