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icc_linux-64
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public |
Intel OneAPI classic compilers (icc, icpc, ifort).
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2025-03-25 |
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oneapi
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public |
Intel OneAPI classic compilers (icc, icpc, ifort).
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2025-03-25 |
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xmlf90
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public |
xmlf90 is a suite of libraries to handle XML in Fortran.
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2025-03-25 |
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jupyterlmod
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public |
jupyterlmod: notebook server extension to interact with Lmod system
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2025-03-25 |
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wannier90
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public |
The Maximally-Localised Generalised Wannier Functions Code
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2025-03-25 |
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netlogo
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public |
NetLogo is a multi-agent programmable modeling environment
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2025-03-25 |
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libgeotrans
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public |
MSP GEOTRANS is the NGA and DOD approved coordinate converter and datum translator.
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2025-03-25 |
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3dslicer
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public |
Multi-platform, free open source software for visualization and image computing.
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2025-03-25 |
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cbor2
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public |
CBOR (de)serializer with extensive tag support
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2025-03-25 |
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plumed
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public |
Free energy calculations in molecular systems
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2025-03-25 |
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dokdo
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public |
A Python package for microbiome sequencing analysis with QIIME 2.
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2025-03-25 |
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hcc-python-stack
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public |
Meta-package for standard python packages included in envs.
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2025-03-25 |
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gym-2048
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public |
OpenAI Gym Environment for 2048.
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2025-03-25 |
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anvio
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public |
A platform for integrated multi-omics
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2025-03-25 |
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r-srs
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public |
Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.
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2025-03-25 |
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shared-ndarray
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public |
A pickleable wrapper for sharing NumPy ndarrays between processes using POSIX shared memory.
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2025-03-25 |
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posix_ipc
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public |
POSIX IPC primitives (semaphores, shared memory and message queues) for Python
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2025-03-25 |
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free-mujoco-py
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public |
free-mujoco-py
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2025-03-25 |
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pytorch-pretrained-biggan
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public |
PyTorch version of DeepMind's BigGAN model with pre-trained models
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2025-03-25 |
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microbiome-helper
|
public |
A repository of bioinformatic scripts, SOPs, and tutorials for analyzing microbiome data.
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2025-03-25 |
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epivia
|
public |
Virial Integration Analysis with epigenetic data
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2025-03-25 |
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unikmer
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public |
unikmer: toolkit for nucleic acid k-mer analysis, including set operations on k-mers (sketch) optional with TaxIDs but without count information.
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2025-03-25 |
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perl-moosex-params-validate
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public |
an extension of Params::Validate using Moose's types
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2025-03-25 |
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jupyterlab
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public |
An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.
|
2025-03-25 |
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docloud
|
public |
The IBM Decision Optimization on Cloud Python client
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2025-03-25 |
