Reverse Dependencies of mkl

The following projects have a declared dependency on mkl:

• amicrt — Model-based conditional independence tests
• biolearn — Machine learning for biomarkers computing
• bmiselect — Bayesian MI-LASSO for variable selection on multiply-imputed data.
• boms — Cell Segmentation for Spatial Transcriptomics Data using BOMS
• chompack — Library for chordal matrix computations
• codpydll — An RKHS based module for machine learning and data mining
• dpnp — NumPy-like API accelerated with SYCL
• fast-tts — no summary
• feapack — A finite element analysis package for solids using Python.
• intel-numpy — NumPy optimized with Intel(R) MKL library
• keras_attention_block — simple tools
• mastapy — A package for integrating scripts with Masta
• MCEq — Numerical cascade equation solver
• mkl-devel — Intel® oneAPI Math Kernel Library
• mkl-dpcpp — Intel® oneAPI Math Kernel Library
• mkl-fft — MKL-based FFT transforms for NumPy arrays
• mkl-random — NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
• mkl-service — MKL Support Functions
• mkl-umath — MKL-based universal functions for NumPy arrays
• ngstrefftz — NGSTrefftz is an add-on to NGSolve for Trefftz methods.
• nutils — Numerical Utilities for Finite Element Analysis
• oboe — An AutoML pipeline selection system to quickly select a promising pipeline for a new dataset.
• onemkl-sycl-datafitting — Intel® oneAPI Math Kernel Library
• pegasuspy — Pegasus is a Python package for analyzing sc/snRNA-seq data of millions of cells
• pyblock3-general — An efficient python block-sparse tensor and MPS/DMRG library.
• pynibs — A python toolbox to conduct non-invasive brain stimulation experiments (NIBS).
• pypardiso — Python interface to the Intel MKL Pardiso library to solve large sparse linear systems of equations
• scCloud — scRNA-Seq analysis tools that scale to millions of cells
• scooby — A Great Dane turned Python environment detective
• sectionproperties — A python package for the analysis of arbitrary cross-sections using the finite element method.
• simpeg-octree-mt — SimPeg package with modification to incorporate octree meshes with MT
• smote-variants — Variants of the synthetic minority oversampling technique (SMOTE) for imbalanced learning
• tacco — TACCO: Transfer of Annotations to Cells and their COmbinations
• tensorpowerflow — Ultra fast power flow based in Laurent series expansion.
• tinyrl — A Python wrapper for RLtools
• torch-dreams — Making neural networks more interpretable, for research and art
• trialtracker — Methods to extract and transform clinical trial data
• yaib — Yet Another ICU Benchmark is a holistic framework for the automation of the development of clinical prediction models on ICU data. Users can create custom datasets, cohorts, prediction tasks, endpoints, and models.