macOS
Homebrew已验证 · 100%
brew install gibbsldalocal Homebrew formula metadata
MacPorts已验证 · 94%
sudo port install gibbsldaMacPorts ports tree · math/gibbslda/Portfile · 来源: api.github.com
安装
其他安装命令
概览
软件包摘要
Library wrapping imlib2's context API
历史
项目历史与用法
GibbsLDA++ is a C/C++ implementation of Latent Dirichlet Allocation using Gibbs sampling for parameter estimation and inference. It belongs to the late-2000s generation of compact research toolkits for topic modeling.
项目历史
The official SourceForge project identifies GibbsLDA++ as a beta C/C++ Gibbs-sampling LDA implementation by pxhieu. The project was registered in July 2007, and its file area shows the manual, case-study material, and 0.2 source distribution published in July and August 2007.
采用历史
GibbsLDA++ was useful because LDA had become a common model for hidden topic structures while many researchers still wanted a small command-line implementation. The supplied package metadata shows it carried by Homebrew and MacPorts, reflecting a preservation-style package footprint for older scientific workflows.
使用方式
Practitioners use the `lda` executable to estimate LDA models and infer topic distributions from text corpora. The official project description emphasizes large-scale text data collections, Gibbs sampling, and console/terminal use.
为什么软件包爱好者会关心
For package maintainers, GibbsLDA++ is a small legacy scientific CLI whose main value is reproducibility. Keeping it packaged lets old scripts and papers continue to depend on a stable `lda` executable rather than a broad machine-learning framework.
时间线
- 2007: SourceForge project registered.
- 2007: Documents published on SourceForge.
- 2007: GibbsLDA++ 0.2 source and case-study files published.
- 2013: SourceForge project page records a later project update.
Related projects
- GibbsLDA++ is related to JGibbLDA from the same topic-modeling lineage, LDA research implementations, and other C/C++, Java, Matlab, and Python topic-modeling packages.
来源
安全态势
风险级别：绿色
library-like package without higher-risk signals.
风险分类器
绿色 风险 · 低 置信度 · appliance
原因
- library-like package without higher-risk signals
信号
- metadata:library-like
安装行为
- formula 元数据中未记录 Homebrew post-install 钩子。
- Homebrew bottle 元数据适用于 13 个平台目标。
建议审查
在无人值守的代理使用前，请检查该工具是否读取明文凭据、写入远程状态、发布制品或调用插件。
可执行文件
已安装的可执行文件
| 命令 | 类型 | 暴露范围 | 备注 |
|---|---|---|---|
lda | cli | 全局可执行文件 |
新鲜度
版本和新鲜度
这些信号区分页生成时间、软件包管理器活动和上游发布比较。只有存在证据 URL 和可比较版本时，才会提示版本落后。
页面生成时间2026-08-04
管理器版本0.2
管理器更新时间
本地数据OK
上游not checked
检测到的最新版本未检测到
https://gibbslda.sourceforge.net/
- 信息No package-manager update timestamp was available.低 置信度
- 信息Release/tag comparison is only available for GitHub repositories.https://gibbslda.sourceforge.net/none 置信度
安装元数据
软件包元数据
| 软件包键 | brew:gibbslda |
|---|---|
| 版本 | 0.2 |
| 软件包管理器 | Homebrew |
| 软件包管理器页面 | https://formulae.brew.sh/formula/gibbslda |
| 主页 | https://gibbslda.sourceforge.net/ |
| 上游文档 | https://gibbslda.sourceforge.net/ |
| 许可证 | GPL-2.0-or-later |
| 源码归档 | https://downloads.sourceforge.net/project/gibbslda/GibbsLDA%2B%2B/0.2/GibbsLDA%2B%2B-0.2.tar.gz |
| Bottle | 可用 (于 arm64_big_sur, arm64_linux, arm64_monterey, arm64_sequoia, arm64_sonoma, arm64_tahoe, arm64_ventura, big_sur, catalina, monterey, sonoma, ventura, x86_64_linux) |
| Homebrew post-install | 未定义 |
| 服务 | 未声明 |
注册表事实
源数据库详情
| Source Database | Homebrew formula API |
|---|---|
| Tap | homebrew/core |
| Full Name | gibbslda |
| Version Scheme | 0 |
| Revision | 0 |
| Bottle Stable Root URL | https://ghcr.io/v2/homebrew/core |
| Deprecated | no |
| Disabled | no |
| Keg Only | no |
| URL Keys |
|
源数据库匹配
其他软件包管理器记录
匹配项来自外部软件包管理器索引，并与本地 Automic Vault 软件包链接分开显示。
MacPorts95%
gibbslda
sudo port install gibbslda- normalized package name match
- 匹配方式：Gibbslda
来源线索
由仓库数据生成
此页面由 av-web 从 scripts/generate-pkg-sqlite.py 生成的私有软件包 SQLite 工件提供。
Combined YAML source
View the package source record on GitHub.
使用的来源
- Geiger risk classifier
- cross-ecosystem install command graph
- curated package history
- external package-manager database matches
- package relationship graph
- package version freshness
- package-page enrichment
- pkg.so package database
- pkgdb category and tag curation