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dataframely
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public |
A declarative, polars-native data frame validation library.
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2025-10-09 |
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r-grf
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public |
A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables).
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2025-10-09 |
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plottr
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public |
A tool for live plotting and processing data
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2025-10-09 |
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terraform-ls
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public |
Terraform Language Server
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2025-10-09 |
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py-wake
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public |
PyWake a collection of wake models
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2025-10-09 |
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atlasopenmagic
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public |
A utility package for retrieving ATLAS open data URLs and metadata.
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2025-10-09 |
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pyvinecopulib
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public |
A python interface to vinecopulib
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2025-10-09 |
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cytoplay
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public |
Cytosim: Langevin dynamics of active polymer networks
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2025-10-09 |
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rubin-scheduler
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public |
Scheduling algorithms for Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST).
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2025-10-09 |
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r-nlmixr2
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public |
Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
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2025-10-09 |
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pytango
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public |
Python binding for the TANGO control system
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2025-10-09 |
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pyqgc
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public |
QGC protocol parser and generator
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2025-10-09 |
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nessai-bilby
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public |
Interface and plugin for using nessai in bilby
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2025-10-09 |
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gto
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public |
Version and deploy your models following GitOps principles
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2025-10-09 |
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minipcn
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public |
A minimal preconditioned Crank-Nicolson MCMC sampler
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2025-10-09 |
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r-sits
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public |
The sits R package provides a set of tools for analysis, visualization and classification of satellite image time series. The main aim of SITS is to support land cover and land change classification of image data cubes using machine learning methods. The basic workflow in SITS is:
1. Create a data cube using image collections available in the cloud or in local machines.
2. Extract time series from the data cube which are used as training data.
3. Perform quality control and filtering on the samples.
4. Train a machine learning model using the extracted samples.
5. Classify the data cube using the trained model.
6. Post-process the classified images.
7. Evaluate the accuracy of the classification using best practices.
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2025-10-09 |
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collada-dom
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public |
The COLLADA Document Object Model (DOM) is an application programming interface (API) that provides a C++ object representation of a COLLADA XML instance document.
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2025-10-09 |
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rubin_sim
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public |
Scheduler, survey strategy analysis, and other simulation tools for Rubin Observatory.
|
2025-10-09 |
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rubin-sim
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public |
Scheduler, survey strategy analysis, and other simulation tools for Rubin Observatory.
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2025-10-09 |
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blpapi
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public |
Python SDK for Bloomberg BLPAPI
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2025-10-09 |
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aws-lambda-powertools
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public |
Python utilities for AWS Lambda functions including but not limited to tracing, logging and custom metric
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2025-10-09 |
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publicsuffixlist
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public |
publicsuffixlist implement
|
2025-10-09 |
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types-jsonschema
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public |
Typing stubs for jsonschema
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2025-10-09 |
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imath
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public |
IMath libraries required for OpenEXR.
|
2025-10-09 |
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types-six
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public |
Typing stubs for six
|
2025-10-09 |
