{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Carolina is a Python wrapper around Dakota, a design optimization and uncertainty quantification toolkit, enabling Python developers to use Dakota's capabilities without MPI support.","skillfed_tags":["optimization","uncertainty-quantification","dakota-wrapper"],"use_cases":["Run design optimization studies where Dakota explores parameter spaces to find optimal designs","Perform uncertainty quantification and sensitivity analysis on computational models","Build surrogate models or metamodels from expensive simulation runs","Conduct parameter studies to understand how model outputs vary across input ranges","Integrate Dakota-based workflows into larger Python data science or engineering pipelines"],"what_it_does":"Carolina provides a Python interface to Dakota, a Sandia National Laboratories toolkit for design optimization, parameter studies, and uncertainty quantification. It is a maintained fork of pyDAKOTA by Equinor, designed to simplify wrapping Dakota in Python without requiring MPI support. The package depends on numpy and requires Dakota and Boost libraries to be pre-installed on your system.\n\nYou use Carolina when you need to run Dakota-based workflows\u2014such as design space exploration, sensitivity analysis, or surrogate model construction\u2014from within Python code. The installation requires careful setup of system dependencies (Boost, Dakota, CMake, C/C++ compiler), but once configured, it allows direct Python access to Dakota's optimization and UQ algorithms. It supports Python 3.12, 3.13, and 3.14.","worth_installing":"Yes, if you already use Dakota and want a Python interface without MPI overhead, and you are willing to manage system dependencies (Boost, Dakota, compilers). No, if you need a lightweight, pure-Python optimization library or lack the system-level build infrastructure. The package is actively maintained and has no known vulnerabilities, but installation complexity is the primary barrier."},"id":"carolina","links":{"html":"https://skillfed.io/packages/carolina","md":"https://skillfed.io/packages/carolina.md","pypi":"https://pypi.org/project/carolina/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-25","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"carolina","python_support":"supports_current","summary":"Python wrapper around Dakota"},"popularity":{"monthly_downloads":95353,"position":13267,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.0.5"}