Search Results for "git:/git.code.sf.net/p/docfetcher/code"

broom is part of the tidymodels ecosystem that converts statistical model outputs (e.g. from lm, glm, t.test, lme4, etc.) into tidy tibbles — standardized data frames — using functions tidy(), glance(), and augment(). These are easier to manipulate, visualize, and report programmatically.

No-code system is for the visual creation of structural-functional models and the automatic generation of R language simulation models. The program can be used to describe information, production, organizational, and other processes. For graphical representation, the EdPM/EPM notation is used, which allowed us to implement: - structural-functional modeling using graphical methods; - the study of the efficiency of structural-functional models using simulation methods, that allow (e.g. unlike Petri nets) to process queries in groups, which is important for the study of the efficiency of using such methods as volumetric calendar planning and AI methods in process activities, since the operating time of these methods depends on the number of parameters and changes nonlinearly; - the study of multiprocess systems; - the results were obtained, that allow you to find efficient topologies of structural-functional models.

This repository hosts the source for Geocomputation with R, an open-source book covering spatial data analysis, visualization, and modeling using R. It teaches how to work with vector and raster data, coordinate systems, mapping, and geocomputation techniques using packages like sf, terra, tmap, and more. Actively maintained and updated for real-world geospatial workflows.

...The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.