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r-gdistance
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public |
Calculate distances and routes on geographic grids.
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2025-09-27 |
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r-n2r
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public |
Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <arXiv:1603.09320>.
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2025-09-27 |
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r-paralleldist
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public |
A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods.
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2025-09-27 |
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wsidata
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public |
Data structures and I/O functions for whole-slide image (WSI).
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2025-09-27 |
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pycrdt
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public |
CRDTs based on Yrs
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2025-09-27 |
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highfive
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public |
Header-only C++ HDF5 interface
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2025-09-27 |
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dscribe
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public |
DScribe is a python package for creating machine learning descriptors for atomistic systems.
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2025-09-27 |
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r-asbio
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public |
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
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2025-09-27 |
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wcurl
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public |
A simple wrapper around curl to easily download files
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2025-09-27 |
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pyiron_atomistics
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public |
pyiron - an integrated development environment (IDE) for computational materials science.
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2025-09-27 |
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r-tkrplot
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public |
Simple mechanism for placing R graphics in a Tk widget.
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2025-09-27 |
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legend-pygeom-l200
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public |
Geometry model of the LEGEND-200 experimental setup
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2025-09-27 |
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k9s
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public |
🐶 Kubernetes CLI To Manage Your Clusters In Style!
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2025-09-27 |
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drjit
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public |
Dr.Jit — A Just-In-Time-Compiler for Differentiable Rendering
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2025-09-27 |
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r-divemove
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public |
Utilities to represent, visualize, filter, analyse, and summarize time-depth recorder (TDR) data. Miscellaneous functions for handling location data are also provided.
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2025-09-27 |
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torchsort
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public |
Fast, differentiable sorting and ranking in PyTorch
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2025-09-27 |
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r-cnorm
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public |
Conventional methods for producing standard scores in psychometrics or biometrics are often plagued with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores. The continuous norming method introduced by A. Lenhard et al. (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>) and generates continuous test norm scores on the basis of the raw data from standardization samples, without requiring assumptions about the distribution of the raw data: Norm scores are directly established from raw data by modeling the latter ones as a function of both percentile scores and an explanatory variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost all of the variance in the original norm data sample. An online demonstration is available via <https://cnorm.shinyapps.io/cNORM/>.
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2025-09-27 |
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r-poolfstat
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public |
Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix, BayPass and SelEstim format).
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2025-09-27 |
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felupe
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public |
Finite Element Analysis
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2025-09-27 |
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drjit-cpp
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public |
Dr.Jit — A Just-In-Time-Compiler for Differentiable Rendering
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2025-09-27 |
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python-telegram-bot
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public |
We have made you a wrapper you can't refuse
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2025-09-27 |
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gersemi
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public |
A formatter to make your CMake code the real treasure
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2025-09-27 |
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dvc-objects
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public |
Library for generic objects used in DVC
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2025-09-27 |
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foamlib
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public |
A Python interface for interacting with OpenFOAM
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2025-09-27 |
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r-rdimtools
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public |
We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.
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2025-09-27 |
