{"id":23100638,"url":"https://github.com/benkeser/slapnap","last_synced_at":"2025-08-16T14:31:54.376Z","repository":{"id":46677165,"uuid":"186173232","full_name":"benkeser/slapnap","owner":"benkeser","description":"Super LeArner Predictions using NAb Panels","archived":false,"fork":false,"pushed_at":"2024-10-07T17:48:42.000Z","size":18261,"stargazers_count":0,"open_issues_count":2,"forks_count":5,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-04T12:52:46.891Z","etag":null,"topics":["docker-image","genetic-algorithm","hiv","machine-learning","superlearner"],"latest_commit_sha":null,"homepage":"https://benkeser.github.io/slapnap/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/benkeser.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2019-05-11T19:23:35.000Z","updated_at":"2024-10-07T17:48:46.000Z","dependencies_parsed_at":"2024-12-16T23:34:04.635Z","dependency_job_id":"ed0579bd-aebf-474c-8997-c279fcffacde","html_url":"https://github.com/benkeser/slapnap","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/benkeser/slapnap","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benkeser%2Fslapnap","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benkeser%2Fslapnap/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benkeser%2Fslapnap/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benkeser%2Fslapnap/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/benkeser","download_url":"https://codeload.github.com/benkeser/slapnap/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benkeser%2Fslapnap/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270723209,"owners_count":24634339,"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","status":"online","status_checked_at":"2025-08-16T02:00:11.002Z","response_time":91,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["docker-image","genetic-algorithm","hiv","machine-learning","superlearner"],"created_at":"2024-12-16T23:33:29.003Z","updated_at":"2025-08-16T14:31:53.765Z","avatar_url":"https://github.com/benkeser.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"README-\"\n)\n```\n\n# `slapnap`\n\n\u003e Super LeArner Predictions using NAb Panels\n\n__Authors:__ [David Benkeser](https://www.github.com/benkeser/), [Brian D. Williamson](https://www.github.com/bdwilliamson/), Craig A. Magaret, Courtney Simmons, Sohail Nizam, Peter B. Gilbert\n\n[![Build Status](https://travis-ci.com/benkeser/slapnap.svg?token=WgmsWkd2hyf88ZxhK8bp\u0026branch=master)](https://travis-ci.com/benkeser/slapnap)\n[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)\n[![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](http://opensource.org/licenses/MIT)\n\u003c!-- [![DOI](https://zenodo.org/badge/75324341.svg)](https://zenodo.org/badge/latestdoi/75324341) --\u003e\n\n---\n\n## Description\n\n`slapnap` is a Docker image for developing cross-validation-based ensemble predictors of neutralization sensitivity/resistance using HIV sequences from the [CATNAP database](http://www.hiv.lanl.gov/). The image provides an automated tool for reading the data from the online database, compiling analytic data sets, developing prediction models, and summarizing results.\n\n---\n\n## Usage\n\nThis GitHub repository contains the source code needed to build the `slapnap` docker image. The repository is also set up for continuous integration via Travis-CI, with built images found on [DockerHub](https://cloud.docker.com/u/slapnap/repository/docker/slapnap/slapnap). See the [Docker website](https://docs.docker.com/docker-for-windows/install/) for installation instructions.\n\nFrom a terminal the image can be downloaded from DockerHub via the command line.\n\n```{bash, eval = FALSE}\ndocker pull slapnap/slapnap\n```\n\n`slapnap` is executed using the docker run command. For example, the following code will instruct `slapnap` to create and evaluate a neutralization predictor for the bnAb combination VRC07-523-LS and PGT121:\n\n```{bash, eval = FALSE}\ndocker run \\\n  -v path/to/local/save/directory:/home/output/ \\\n  -e nab=\"VRC07-523-LS;PGT121\" \\\n  -e outcomes=”ic50;estsens” \\\n  -e combination_method=\"additive\" \\\n  -e learners=”rf;lasso” \\\n  -e importance_grp=”marg” \\\n  -e importance_ind=”pred” \\\n  slapnap/slapnap:latest\n```\n\nThe `–v` tag specifies the directory on the user’s computer where the report will be saved, and `path/to/local/save/directory` should be replaced with the desired target directory.  Options for the analysis are passed to the container via the `-e` tag; these options include the bnAbs to include in the analysis (`nab`), the neutralization outcomes of interest (`outcomes`), the method for predicting combination neutralization (`combination_method`), the learners to use in the analysis (`learners`), and the types of variable importance to compute (`importance_grp`, for groups of variables; `importance_ind`, for individual variables). Other output (e.g., the formatted analysis dataset and the fitted learners) can be requested via the `return` option. A full list of options and their syntax are available in the [`slapnap` documentation](https://benkeser.github.io/slapnap/3-sec-runningcontainer.html).\n\nComplete documentation is available [here](https://benkeser.github.io/slapnap/).\n\n## Issues\n\nIf you encounter any bugs or have any specific feature requests, please [file an\nissue](https://github.com/benkeser/slapnap/issues).\n\n---\n\n## License\n\n\u0026copy; 2019--present David Benkeser\n\nThe contents of this repository are distributed under the MIT license:\n```\nThe MIT License (MIT)\n\nCopyright (c) 2019--present David Benkeser\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenkeser%2Fslapnap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbenkeser%2Fslapnap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenkeser%2Fslapnap/lists"}