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clang_impl_osx-64
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public |
clang compilers for conda-build 3
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2025-10-02 |
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clangxx_impl_osx-64
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public |
clang compilers for conda-build 3
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2025-10-02 |
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dependabot-cli
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public |
A tool for testing and debugging Dependabot update jobs.
|
2025-10-02 |
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unsloth
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public |
2-5X faster LLM finetuning
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2025-10-02 |
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unsloth_zoo
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public |
Utils for Unsloth
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2025-10-02 |
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pyaedt
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public |
Higher-Level Pythonic Ansys Electronics Desktop Framework
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2025-10-02 |
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disdrodb
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public |
disdrodb provides tools to download, standardize, share and analyze global disdrometer data.
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2025-10-02 |
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cached_path
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public |
A file utility for accessing both local and remote files through a unified interface.
|
2025-10-02 |
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nest-simulator
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public |
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural
systems rather than on the exact morphology of individual neurons.
|
2025-10-02 |
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conda-forge-pinning
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public |
The baseline versions of software for the conda-forge ecosystem
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2025-10-02 |
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tensordict
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public |
TensorDict is a pytorch dedicated tensor container.
|
2025-10-02 |
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r-lidr
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public |
Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.
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2025-10-02 |
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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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2025-10-02 |
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openscm-units
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public |
Units for use with simple climate modelling
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2025-10-02 |
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openscm-units-locked
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public |
Units for use with simple climate modelling
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2025-10-02 |
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pynmeagps
|
public |
NMEA protocol parser and generator
|
2025-10-02 |
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great-expectations
|
public |
Always know what to expect from your data.
|
2025-10-02 |
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r-leaflet
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public |
Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny applications and R Markdown documents.
|
2025-10-02 |
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textual
|
public |
Modern Text User Interface framework
|
2025-10-02 |
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r-cartography
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public |
Create and integrate maps in your R workflow. This package helps to design cartographic representations such as proportional symbols, choropleth, typology, flows or discontinuities maps. It also offers several features that improve the graphic presentation of maps, for instance, map palettes, layout elements (scale, north arrow, title...), labels or legends. See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.
|
2025-10-02 |
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pydantic-graph
|
public |
Agent Framework / shim to use Pydantic with LLMs
|
2025-10-02 |
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pydantic-ai-slim
|
public |
Agent Framework / shim to use Pydantic with LLMs
|
2025-10-02 |
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pydantic-ai
|
public |
Agent Framework / shim to use Pydantic with LLMs
|
2025-10-02 |
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pydantic-evals
|
public |
Agent Framework / shim to use Pydantic with LLMs
|
2025-10-02 |
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fastparquet
|
public |
Python interface to the parquet format
|
2025-10-02 |
