Contains absolute-pose, relative-pose, triangulation, and point-cloud alignment methods for the calibrated case. All problems can be solved with central or non-central cameras, and embedded into a random sample consensus or nonlinear optimization context. Matlab and Python interfaces are implemented as well

Commons of Python micro-functions. This basically an out-sourced, shared utils module with a focus on functions that buffer type uncertainties in Python (e.g. "is this a list?").

Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.

An async mini ORM for Python, with support for Postgres, MySQL, and SQLite.

Expected output for test functions is stored in a YAML file.

This is the GNU Modula-2 compiler for the s390x-linux-gnu architecture, which compiles Modula-2 on platforms supported by gcc. It uses the gcc backend to generate optimised code.