python3-pyproject-api

API to interact with Python pyproject.toml-based projects

pyproject-api aims to abstract away interaction with pyproject.toml style projects in a flexible way.

libiv-unidraw2t64

Application Frameworks layered on Unidraw

InterViews is written in C++ and portable to several different Unix platforms. Applications written with the InterViews library can be configured to provide a special "Look and Feel", such as SGI-Motif and normal Motif. The package includes the unidraw dynamic library, along with extensions for frame and graph editing needed by the ivtools vector graphics editors.

python3-ubelt

Python Utility Belt Library

Ubelt is a small library of robust, tested, documented, and simple functions that extend the Python standard library. It has a flat API that all behaves similarly on Windows, Mac, and Linux (up to some small unavoidable differences). Almost every function in ubelt was written with a doctest.

r-cran-spatstat.explore

GNU R exploratory data analysis for the 'spatstat' family

Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore- Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

r-cran-spatstat.linnet

linear networks functionality of the 'spatstat' family of GNU R

Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K- functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov- Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two- stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller- Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.

libstd-rust-1.66

Rust standard libraries

Rust is a curly-brace, block-structured expression language. It visually resembles the C language family, but differs significantly in syntactic and semantic details. Its design is oriented toward concerns of "programming in the large", that is, of creating and maintaining boundaries - both abstract and operational - that preserve large-system integrity, availability and concurrency.