| Package Name | Access | Summary | Updated |
|---|---|---|---|
| dagster-papertrail | public | Package for papertrail Dagster framework components. | 2025-10-10 |
| dagster-tableau | public | Build assets representing Tableau workbooks and sheets. | 2025-10-10 |
| dagster-k8s | public | A Dagster integration for k8s | 2025-10-10 |
| dagster-wandb | public | Package for wandb Dagster components. | 2025-10-10 |
| dagster-ge | public | Package for GE-specific Dagster framework solid and resource components. | 2025-10-10 |
| dagstermill | public | A Dagster integration for papermill | 2025-10-10 |
| dagster-managed-elements | public | Package for Managed elements with Dagster. | 2025-10-10 |
| dagster-celery-docker | public | A Dagster integration for celery-docker | 2025-10-10 |
| dagster-ssh | public | Package for ssh Dagster framework components. | 2025-10-10 |
| dagster-mysql | public | A Dagster integration for MySQL | 2025-10-10 |
| dagster-twilio | public | A Dagster integration for twilio | 2025-10-10 |
| dagster-dask | public | Package for using Dask as Dagster's execution engine. | 2025-10-10 |
| causal-conv1d | public | Causal depthwise conv1d in CUDA, with a PyTorch interface | 2025-10-10 |
| pyerrors | public | Error propagation and statistical analysis for Monte Carlo simulations | 2025-10-10 |
| pipefunc | public | A Python library for defining, managing, and executing function pipelines. | 2025-10-10 |
| pipefunc-extras | public | A Python library for defining, managing, and executing function pipelines. | 2025-10-10 |
| cue | public | The home of the CUE language! Validate and define text-based and dynamic configuration | 2025-10-10 |
| zppy | public | E3SM post-processing toolchain | 2025-10-10 |
| prisma-language-server | public | Implements language-server-protocol for Prisma | 2025-10-10 |
| warp-lang | public | A Python framework for high-performance simulation and graphics programming. | 2025-10-10 |
| swmmanywhere | public | SWMManywhere software | 2025-10-10 |
| r-rfast | public | A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. Reference: Tsagris M, Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. | 2025-10-10 |
| nicegui | public | Create web-based interfaces with Python. The nice way. | 2025-10-10 |
| prek | public | Better `pre-commit`, re-engineered in Rust | 2025-10-10 |
| rr_cache | public | RetroRules Cache | 2025-10-10 |
