{"id":15966879,"url":"https://github.com/libkriging/libkriging","last_synced_at":"2026-01-17T23:54:05.941Z","repository":{"id":37257658,"uuid":"192920489","full_name":"libKriging/libKriging","owner":"libKriging","description":"kriging library for performance and wide language support ","archived":false,"fork":false,"pushed_at":"2025-03-25T10:49:38.000Z","size":40347,"stargazers_count":41,"open_issues_count":56,"forks_count":19,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-04-01T14:31:45.986Z","etag":null,"topics":["cxx17","kriging","linux","macos","matlab","octave","python","r","windows"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/libKriging.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-06-20T12:58:55.000Z","updated_at":"2025-03-25T10:49:41.000Z","dependencies_parsed_at":"2023-02-09T15:46:12.990Z","dependency_job_id":"632a9d66-ac7b-42f0-b760-27a8817b67c7","html_url":"https://github.com/libKriging/libKriging","commit_stats":{"total_commits":1212,"total_committers":10,"mean_commits":121.2,"dds":0.3787128712871287,"last_synced_commit":"26eb5362b69e115939a99dd33785d2428184737a"},"previous_names":[],"tags_count":42,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/libKriging%2FlibKriging","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/libKriging%2FlibKriging/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/libKriging%2FlibKriging/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/libKriging%2FlibKriging/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/libKriging","download_url":"https://codeload.github.com/libKriging/libKriging/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247369954,"owners_count":20927928,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cxx17","kriging","linux","macos","matlab","octave","python","r","windows"],"created_at":"2024-10-07T18:05:55.472Z","updated_at":"2026-01-01T23:35:25.074Z","avatar_url":"https://github.com/libKriging.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Build and tests](https://github.com/libKriging/libKriging/actions/workflows/main.yml/badge.svg)](https://github.com/libKriging/libKriging/actions/workflows/main.yml)\n[![Code Analysis](https://github.com/libKriging/libKriging/actions/workflows/analysis.yml/badge.svg)](https://github.com/libKriging/libKriging/actions/workflows/analysis.yml)\n[![Coverage Status](https://coveralls.io/repos/github/libKriging/libKriging/badge.svg?branch=master)](https://coveralls.io/github/libKriging/libKriging?branch=master)\n[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n\nTable of contents\n\n- [Installation from pre-built packages](#installation-from-pre-built-packages)\n  - [pylibkriging for Python](#pylibkriging-for-python)\n  - [rlibkriging  for R](#rlibkriging--for-r)\n  - [mlibkriging for Octave and MATLAB](#mlibkriging-for-octave-and-matlab)\n  - [Expected demo results](#expected-demo-results)\n  - [Tested installation](#tested-installation)\n- [Compilation](#compilation)\n  - [Requirements (more details)](#requirements-more-details)\n  - [Get the code](#get-the-code)\n  - [Helper scripts for CI](#helper-scripts-for-ci)\n  - [Compilation and tests *manually*](#compilation-and-tests-manually)\n    - [Preamble](#preamble)\n    - [Compilation for Linux and macOS](#compilation-for-linux-and-macos)\n    - [Compilation for Windows 64bits with Visual Studio](#compilation-for-windows-64bits-with-visual-studio)\n    - [Compilation for Linux/Mac/Windows using R toolchain](#compilation-for-linuxmacwindows-using-r-toolchain)\n  - [Deployment](#deployment)\n    - [For Linux and macOS](#for-linux-and-macos)\n    - [For Windows 64bits with Visual Studio](#for-windows-64bits-with-visual-studio)\n  - [Assisted compilation and installation](#assisted-compilation-and-installation)\n    - [Using `pip install` from GitHub](#using-pip-install-from-github)\n\nIf you want to contribute read [Contribution guide](CONTRIBUTING.md).\n\n# Installation from pre-built packages\n\nFor the most common target {Python, R, Octave, Matlab} x {Linux, macOS, Windows} x { x86-64, ARM }, you can\nuse [released binaries](https://github.com/libKriging/libKriging/releases), or R CRAN or Python PyPI.\n\n## [pylibkriging](https://pypi.org/project/pylibkriging/) for Python\n\n```shell\npip3 install pylibkriging\n```\n\nor for pre-release packages (according to your OS and Python version, see https://github.com/libKriging/libKriging/releases)\n\n```shell\npip3 install https://github.com/libKriging/libKriging/releases/download/v0.9.0/pylibkriging-0.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n```\n\n**Usage example [here](bindings/Python/tests/pylibkriging_demo.py)**\n\n\u003cdetails\u003e\n\u003csummary\u003e👆The sample code below should give you a taste. Please refer to the reference file linked above for a CI certified example.\u003c/summary\u003e\n\n```python\nimport numpy as np\n\nX = [0.0, 0.25, 0.5, 0.75, 1.0]\nf = lambda x: (1 - 1 / 2 * (np.sin(12 * x) / (1 + x) + 2 * np.cos(7 * x) * x ** 5 + 0.7))\ny = [f(xi) for xi in X]\n\nimport pylibkriging as lk\n\nk_py = lk.Kriging(y, X, \"gauss\")\nprint(k_py.summary())\n# you can also check logLikelhood using:\n# def ll(t): return k_py.logLikelihoodFun(t,False,False)[0]\n# t = np.arange(0,1,1/99); pyplot.figure(1); pyplot.plot(t, [ll(ti) for ti in t]); pyplot.show()\n\nx = np.arange(0, 1, 1 / 99)\np = k_py.predict(x, True, False)\np = {\"mean\": p[0], \"stdev\": p[1], \"cov\": p[2]}  # This should be done by predict\n\nimport matplotlib.pyplot as pyplot\n\npyplot.figure(1)\npyplot.plot(x, [f(xi) for xi in x])\npyplot.scatter(X, [f(xi) for xi in X])\n\npyplot.plot(x, p['mean'], color='blue')\npyplot.fill(np.concatenate((x, np.flip(x))),\n            np.concatenate((p['mean'] - 2 * p['stdev'], np.flip(p['mean'] + 2 * p['stdev']))), color='blue',\n            alpha=0.2)\npyplot.show()\n\ns = k_py.simulate(10, 123, x)\n\npyplot.figure(2)\npyplot.plot(x, [f(xi) for xi in x])\npyplot.scatter(X, [f(xi) for xi in X])\nfor i in range(10):\n    pyplot.plot(x, s[:, i], color='blue', alpha=0.2)\npyplot.show()\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003eNote for older versions (\u0026lt; 0.5)\u003c/summary\u003e\nNB: On Windows, it should require [extra DLL](https://github.com/libKriging/libKriging/releases/download/v0.4.2/extra_dlls_for_python_on_windows.zip) not (yet) embedded in the python package.\nTo load them into Python's search PATH, use:\n```python\nimport os\nos.environ['PATH'] = 'c:\\\\Users\\\\User\\\\Path\\\\to\\\\dlls' + os.pathsep + os.environ['PATH']\nimport pylibkriging as lk\n```\n\u003c/details\u003e\n\n## [rlibkriging](https://github.com/libKriging/rlibkriging/)  for R\n\nFrom R:\n```r\ninstall.packages('rlibkriging')\n```\n\nOr using the archive from [libKriging releases](https://github.com/libKriging/rlibKriging/releases)\n\n```R\n# in R\ninstall.packages(\"https://github.com/libKriging/rlibkriging/releases/download/0.9-0/rlibkriging_0.9-0_R_x86_64-pc-linux-gnu.tar.gz\", repos=NULL)\n```\n\n**Usage example [here](bindings/R/rlibkriging/tests/testthat/test-rlibkriging-demo.R)**\n\n\u003cdetails\u003e\n\u003csummary\u003e👆The sample code below should give you a taste. Please refer to the reference file linked above for a CI certified example.\u003c/summary\u003e\n\n```R\nX \u003c- as.matrix(c(0.0, 0.25, 0.5, 0.75, 1.0))\nf \u003c- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)\ny \u003c- f(X)\n\nlibrary(rlibkriging)\nk_R \u003c- Kriging(y, X, \"gauss\")\nprint(k_R)\n# you can also check logLikelhood using:\n# ll = function(t) logLikelihoodFun(k_R,t)$logLikelihood; plot(ll)\nx \u003c- as.matrix(seq(0, 1, , 100))\np \u003c- predict(k_R, x, TRUE, FALSE)\n\nplot(f)\npoints(X, y)\nlines(x, p$mean, col = 'blue')\npolygon(c(x, rev(x)), c(p$mean - 2 * p$stdev, rev(p$mean + 2 * p$stdev)), border = NA, col = rgb(0, 0, 1, 0.2))\n\ns \u003c- simulate(k_R,nsim = 10, seed = 123, x=x)\n\nplot(f)\npoints(X,y)\nmatplot(x,s,col=rgb(0,0,1,0.2),type='l',lty=1,add=T)\n```\n\n\u003c/details\u003e\n\n## mlibkriging for Octave and MATLAB\n\n⚠️ Matlab/Windows binary packages are done on request (GitHub Action does not support all required Operating Systems)\n\nDownload and uncompress the Octave archive from [libKriging releases](https://github.com/libKriging/libKriging/releases)\n\n```shell\n# example\ncurl -LO https://github.com/libKriging/libKriging/releases/download/v0.9.0/mLibKriging_0.9.0_Linux-x86_64.tgz\n```\n\nThen\n\n```shell\noctave --path /path/to/mLibKriging/installation\n```\n\nor inside Octave or Matlab\n\n```matlab\naddpath(\"path/to/mLibKriging\")\n```\n\n**Usage example [here](bindings/Octave/tests/mLibKriging_demo.m)**\n\n\u003cdetails\u003e\n\u003csummary\u003e👆The sample code below should give you a taste. Please refer to the reference file linked above for a CI certified example.\u003c/summary\u003e\n\n```matlab\nX = [0.0;0.25;0.5;0.75;1.0];\nf = @(x) 1-1/2.*(sin(12*x)./(1+x)+2*cos(7.*x).*x.^5+0.7)\ny = f(X);\nk_m = Kriging(y, X, \"gauss\");\ndisp(k_m.summary());\n% you can also check logLikelhood using:\n% function llt = ll (tt) global k_m; llt=k_m.logLikelihoodFun(tt); endfunction; t=0:(1/99):1; plot(t,arrayfun(@ll,t))\nx = reshape(0:(1/99):1,100,1);\n[p_mean, p_stdev] = k_m.predict(x, true, false);\n\nh = figure(1)\nhold on;\nplot(x,f(x));\nscatter(X,f(X));\nplot(x,p_mean,'b')\npoly = fill([x; flip(x)], [(p_mean-2*p_stdev); flip(p_mean+2*p_stdev)],'b');\nset( poly, 'facealpha', 0.2);\nhold off;\n\ns = k_m.simulate(int32(10),int32(123), x);\n\nh = figure(2)\nhold on;\nplot(x,f(x));\nscatter(X,f(X));\nfor i=1:10\n   plot(x,s(:,i),'b');\nend\nhold off;\n```\n\n\u003c/details\u003e\n\n## Expected demo results\n\nUsing the previous linked examples (in Python, R, Octave or Matlab), you should obtain the following results\n\n`predict` plot                               |  `simulate` plot\n:-------------------------------------------:|:---------------------------------------------:\n![](docs/images/pylibkriging_demo_plot1.png) | ![](docs/images/pylibkriging_demo_plot2.png)\n\n## Tested installation\n\nwith libKriging 0.9\n\n\u003c!-- ✔ ⌛️ ✘ --\u003e\n\n|        | Linux Ubuntu:22                             | macOS 14 (x86-64 \u0026 ARM)                     | Windows 10                                  |\n|:-------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|\n| Python | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 3.7-3.12 | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 3.7-3.12 | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 3.7-3.12 |\n| R      | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 4.0-4.4  | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 4.0-4.4  | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 4.0-4.4  |\n| Octave | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 7.2      | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 7.2      | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e 8.3      |\n| Matlab | \u003cspan style=\"color:green\"\u003e️✔\u003c/span\u003e R2022a   | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e R2022*   | \u003cspan style=\"color:green\"\u003e✔\u003c/span\u003e R2022*   |\n\n* \\* : no pre-built package or CI\n\n* \u003cspan style=\"color:orange\"\u003e\u003cb\u003e?\u003c/b\u003e\u003c/span\u003e : requires manual verification (not updated since previous release)\n\n# Compilation\n\n## Requirements ([more details](docs/dev/envs/Requirements.md))\n\n* CMake ≥ 3.13\n\n* C++ Compiler with C++17 support\n\n* Linear algebra packages providing Blas and Lapack functions.\n\n  You can use standard Blas and Lapack, OpenBlas or MKL.\n\n* Python ≥ 3.7 (optional)\n\n* Octave ≥ 6.0 (optional)\n\n* Matlab ≥ R2021 (optional)\n\n* R ≥ 4.0 (optional)\n\n## Get the code\n\nJust clone it with its submodules:\n\n```\ngit clone --recurse-submodules https://github.com/libKriging/libKriging.git\n```\n\n## Helper scripts for CI\n\nNote: calling these scripts \"by hand\" should produce the same results as following \"Compilation and unit tests\"\ninstructions (and it should be also easier).\nThey use the preset of options also used in CI workflow.\n\nTo configure it, you can define following environment variables ([more details](docs/dev/AllCMakeOptions.md)):\n\n| Variable name          | Default value | Useful values                      | Comment                                         |\n|:-----------------------|:--------------|:-----------------------------------|:------------------------------------------------|\n|`MODE`                  | `Debug`       | `Debug`, `Release`                 |                                                 |\n|`ENABLE_OCTAVE_BINDING` | `AUTO`        | `ON`, `OFF`, `AUTO` (if available) | Exclusive with Matlab binding build             |\n|`ENABLE_MATLAB_BINDING` | `AUTO`        | `ON`, `OFF`, `AUTO` (if available) | Exclusive with Octave binding build             |\n|`ENABLE_PYTHON_BINDING` | `AUTO`        | `ON`, `OFF`, `AUTO` (if available) |                                                 |\n\nThen choose your `BUILD_NAME` using the following rule (stops a rule matches)\n\n| `BUILD_NAME`     | when you want to build         | available bindings             |\n|:-----------------|:-------------------------------|:-------------------------------|\n| `r-windows`      | a R binding for windows        | C++, rlibkriging               | \n| `r-linux-macos`  | a R binding for Linux or macOS | C++, rlibkriging               |  \n| `octave-windows` | an Octave for windows          | C++, mlibkriging               | \n| `windows`        | for windows                    | C++, mlibkriging, pylibkriging |\n| `linux-macos`    | for Linux or macOS             | C++, mlibkriging, pylibkriging |\n\nThen:\n\n* Go into `libKriging` root directory\n    ```shell\n    cd libKriging\n    ```\n* Prepare your environment (Once, for your first compilation)\n    ```shell\n    .travis-ci/${BUILD_NAME}/install.sh\n    ```\n* Build\n  ```shell\n  .travis-ci/${BUILD_NAME}/build.sh\n  ```\n  NB: It will create a `build` directory.\n\n* Test\n  ```shell\n  .travis-ci/${BUILD_NAME}/test.sh\n  ```\n\n## Compilation and tests *manually*\n\n### Preamble\n\nWe assume that:\n\n* [libKriging](https://github.com/libKriging/libKriging.git) code is available locally in directory *`${LIBKRIGING}`*\n  (could be a relative path like `..`)\n* you have built a fresh new directory *`${BUILD}`*\n  (should be an absolute path)\n* following commands are executed in *`${BUILD}`* directory\n\nPS: *`${NAME}`* syntax represents a word or an absolute path of your choice\n\nSelect your compilation *`${MODE}`* between:\n\n* `Release` : produce an optimized code\n* `Debug` (default) : produce a debug code\n* `Coverage` : for code coverage analysis (not yet tested with Windows)\n\nFollowing commands are made for Unix shell. To use them with Windows use [git-bash](https://gitforwindows.org)\nor [Mingw](http://www.mingw.org) environments.\n\n### Compilation for Linux and macOS\n\n\u003cdetails\u003e\n\u003csummary\u003e👆 expand the details\u003c/summary\u003e\n\n* Configure\n    ```shell\n    cmake -DCMAKE_BUILD_TYPE=${MODE} ${LIBKRIGING}\n    ```\n* Build\n    ```shell\n    cmake --build .\n    ```\n* Run tests\n    ```shell\n    ctest\n    ```\n* Build documentation (requires doxygen)\n    ```shell\n    cmake --build . --target doc\n    ```\n* if you have selected `MODE=Coverage` mode, you can generate code coverage analysis over all tests using\n    ```shell\n    cmake --build . --target coverage --config Coverage\n    ```\n  or\n    ```shell\n    cmake --build . --target coverage-report --config Coverage\n    ```\n  to produce a html report located in `${BUILD}/coverage/index.html`\n\n\u003c/details\u003e\n\n### Compilation for Windows 64bits with Visual Studio\n\n\u003cdetails\u003e\n\u003csummary\u003e👆 expand the details\u003c/summary\u003e\n\n* Configure\n    ```shell\n    cmake -DCMAKE_GENERATOR_PLATFORM=x64 -DEXTRA_SYSTEM_LIBRARY_PATH=${EXTRA_SYSTEM_LIBRARY_PATH} ${LIBKRIGING}\n    ```\n  where `EXTRA_SYSTEM_LIBRARY_PATH` is an extra path where libraries (e.g. OpenBLAS) can be found.\n* Build\n    ```shell\n    cmake --build . --target ALL_BUILD --config ${MODE}\n    ```\n* Run tests\n    ```shell\n    export PATH=${BUILD}/src/lib/${MODE}:$PATH\n    ctest -C ${MODE}\n    ```\n\n\u003c/details\u003e\n\n### Compilation for Linux/Mac/Windows using R toolchain\n\n\u003cdetails\u003e\n\u003csummary\u003e👆 expand the details\u003c/summary\u003e\n\nWith this method, you need [R](https://cran.r-project.org) (\nand [R-tools](https://cran.r-project.org/bin/windows/Rtools/) if you are on Windows).\n\nWe assume you have previous requirements and also `make` command available in your `PATH`.\n\n* Configure\n    ```shell\n    CC=$(R CMD config CC) CXX=$(R CMD config CXX) cmake -G \"Unix Makefiles\" -DCMAKE_BUILD_TYPE=${MODE} ${LIBKRIGING}\n    ```\n* Build\n    ```shell\n    cmake --build .\n    ```\n* Run tests\n    ```shell\n    ctest\n    ```\n\n\u003c/details\u003e\n\n## Deployment\n\nTo deploy libKriging as an installed library, you have to add `-DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX}` option to\nfirst `cmake` configuration command.\n\nIf `CMAKE_INSTALL_PREFIX` variable is not set with CMake, default installation directory is `${BUILD}/installed`.\n\n### For Linux and macOS\n\n\u003cdetails\u003e\n\u003csummary\u003e👆 expand the details\u003c/summary\u003e\n\n```shell\ncmake -DCMAKE_BUILD_TYPE=${MODE} -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} ${LIBKRIGING}\n```\n\nand then\n\n```shell\ncmake --build . --target install\n```\n\naka with classical makefiles\n\n```shell\nmake install\n```\n\n\u003c/details\u003e\n\n### For Windows 64bits with Visual Studio\n\n\u003cdetails\u003e\n\u003csummary\u003e👆 expand the details\u003c/summary\u003e\n\n```shell\ncmake -DCMAKE_GENERATOR_PLATFORM=x64 -DEXTRA_SYSTEM_LIBRARY_PATH=${EXTRA_SYSTEM_LIBRARY_PATH} -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} ${LIBKRIGING} \n```\n\nand then\n\n```shell\ncmake --build . --target install --config ${MODE}\n```\n\n\u003c/details\u003e\n\n## Assisted compilation and installation\n\n### Using `pip install` from GitHub\n\nYou don't need to download libKriging. `pip install` will do everything. To do that, you need\nthe [Compilation requirements](#requirements-more-details).\n\n```shell\npython3 -m pip install \"git+https://github.com/libKriging/libKriging.git\"\n```\n\nwill download, compile and install pylibkriging from *master* branch.\n\n\u003cdetails\u003e\n\u003csummary\u003eExample of build process output (~2mn)\u003c/summary\u003e\n\n```\nCollecting git+https://github.com/libKriging/libKriging.git\n  Cloning https://github.com/libKriging/libKriging.git to /private/var/folders/g0/56fnffpn6tjd5kplh140ysrh0000gn/T/pip-req-build-2xhzyw9g\n  Running command git clone --filter=blob:none -q https://github.com/libKriging/libKriging.git /private/var/folders/g0/56fnffpn6tjd5kplh140ysrh0000gn/T/pip-req-build-2xhzyw9g\n  Resolved https://github.com/libKriging/libKriging.git to commit 1a86dd69cf1f60b6dcd2b5e5b876cafc97d616e9\n  Running command git submodule update --init --recursive -q\n  Preparing metadata (setup.py) ... done\nBuilding wheels for collected packages: pylibkriging\n  Building wheel for pylibkriging (setup.py) ... done\n  Created wheel for pylibkriging: filename=pylibkriging-0.4.8-cp39-cp39-macosx_12_0_x86_64.whl size=712572 sha256=7963a78f16628c5a7d877b368fdd73452e5acf01784cd6931d48dcdc08e62a5b\n  Stored in directory: /private/var/folders/g0/56fnffpn6tjd5kplh140ysrh0000gn/T/pip-ephem-wheel-cache-o7kxij3t/wheels/52/71/b0/e534f2249e9180c596a5b785cf0bfa5471fcfd38d2987318f8\nSuccessfully built pylibkriging\nInstalling collected packages: pylibkriging\nSuccessfully installed pylibkriging-0.4.8\n```\n\n\u003c/details\u003e\n\nTo get a particular version (branch or tag ≥v0.4.9), you can use:\n\n```shell\npython3 -m pip install \"git+https://github.com/libKriging/libKriging.git@tag\"\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flibkriging%2Flibkriging","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flibkriging%2Flibkriging","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flibkriging%2Flibkriging/lists"}