| Package Name | Access | Summary | Updated |
|---|---|---|---|
| dagster-deltalake | public | Package for Deltalake-specific Dagster framework op and resource components. | 2025-10-02 |
| dagster-pandas | public | Utilities and examples for working with pandas and dagster, an opinionated framework for expressing data pipelines | 2025-10-02 |
| dagster-ge | public | Package for GE-specific Dagster framework solid and resource components. | 2025-10-02 |
| dagster-datadog | public | Package for datadog Dagster framework components. | 2025-10-02 |
| dagster-powerbi | public | Build assets representing Power BI dashboards and reports. | 2025-10-02 |
| dagster-msteams | public | A Microsoft Teams client resource for posting to Microsoft Teams | 2025-10-02 |
| dagster-dlt | public | This library provides a Dagster integration with dlt | 2025-10-02 |
| dagster-fivetran | public | Package for integrating Fivetran with Dagster. | 2025-10-02 |
| dagster-looker | public | Looker with Dagster. | 2025-10-02 |
| dagster-deltalake-pandas | public | Package for storing Pandas DataFrames in Delta tables | 2025-10-02 |
| dagster-snowflake-pandas | public | Package for integrating Snowflake and Pandas with Dagster. | 2025-10-02 |
| dagster-twilio | public | A Dagster integration for twilio | 2025-10-02 |
| dagster-docker | public | A Dagster integration for docker | 2025-10-02 |
| dagster-snowflake | public | Package for Snowflake Dagster framework components. | 2025-10-02 |
| dagster-dask | public | Package for using Dask as Dagster's execution engine. | 2025-10-02 |
| dagster-pandera | public | Integration layer for dagster and pandera. | 2025-10-02 |
| clang-format-19 | public | Development headers and libraries for Clang | 2025-10-02 |
| libclang-cpp19.1 | public | Development headers and libraries for Clang | 2025-10-02 |
| r-penalized | public | Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters. | 2025-10-02 |
| dingo-gw | public | Deep inference for gravitational-wave observations | 2025-10-02 |
| momentum | public | A library for human kinematic motion and numerical optimization solvers to apply human motion | 2025-10-02 |
| pymomentum-cpu | public | A library for human kinematic motion and numerical optimization solvers to apply human motion | 2025-10-02 |
| momentum-cpp | public | A library for human kinematic motion and numerical optimization solvers to apply human motion | 2025-10-02 |
| pymomentum | public | A library for human kinematic motion and numerical optimization solvers to apply human motion | 2025-10-02 |
| power-grid-model | public | Python/C++ library for distribution power system analysis | 2025-10-02 |
