Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.

Provides functions to conduct title and abstract screening in systematic reviews using large language models, such as the Generative Pre-trained Transformer (GPT) models from 'OpenAI' <https://developers.openai.com/>. These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2025) <DOI:10.1037/met0000769>.

A production-grade AI toolkit for R featuring a layered
    architecture (Specification, Utilities, Providers, Core), request
    interception support, robust error handling with exponential retry
    delays, support for multiple AI model providers ('OpenAI',
    'Anthropic', etc.), local small language model inference,
    distributed 'MCP' ecosystem, multi-agent orchestration, progressive
    knowledge loading through skills, and a global skill store for
    sharing AI capabilities.

Additional AI model provider adapters for the 'aisdk' toolkit,
    covering OpenAI-compatible and Anthropic-compatible services such as
    'DeepSeek', 'Moonshot'/'Kimi', 'Stepfun', 'Volcengine', 'AiHubMix',
    'xAI', 'OpenRouter', 'Bailian', and 'NVIDIA'. Providers register themselves
    with the core 'aisdk' provider registry on load.

Nonparametric estimation of additive isotonic covariate effects for proportional hazards model.

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>, and Tong Lin, Jinseok Lee, Matt Helgeson, Megan T Valentine, Yimin Luo, Mengyang Gu (2026), <doi:10.48550/arXiv.2605.29424>.