Search Results for "diagnostics"

ArviZ.jl (pronounced "AR-vees") is a Julia package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, model checking, comparison and diagnostics.

...It allows R users to specify complex Bayesian models using formula syntax similar to lme4 but with far more flexibility (distributions, link functions, hierarchical structure, nonlinear terms, etc.). It supports model diagnostics, posterior predictive checking, model comparison, custom priors, and advanced features such as distributional regression.

Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines.

...It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. Residual checks, model accuracy, plots, forecast error measures etc.

see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It includes themes, scales, geoms for ggplot2, and custom color palettes to make visual summaries more informative and attractive.

...Dagster as a unified control plane: The ‘single plane of glass’ data teams love to use. Rein in the chaos and maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities.

...It offers a unified dashboard, rich device tiles, and automation hooks so users can monitor and control temperature, modes, and alerts alongside other smart home devices. The project emphasizes usability with guided setup flows, status summaries, and in-app diagnostics to help troubleshoot connectivity or permission issues. It exposes detailed attributes and commands, enabling powerful rules and scenes that coordinate Nest with sensors, presence, and schedules in SmartThings. Historical and environmental data can be surfaced to support energy-aware automations and notifications. For advanced users, it provides granular preferences to tune polling, event verbosity, and safety behaviors, turning SmartThings into a capable hub for Nest-centric homes.

ActiveGEM contains a set of perl/Tk graphical interfaces for the GEMPAK meteorological diagnostics and plotting package. These research-oriented interfaces provide easy access to low-level GEMPAK functions and allow for the maximum possible flexibility.