# 使用 Homebrew, MacPorts 安装 gibbslda

查看 gibbslda 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

## 安装

```sh
sudo av install brew:gibbslda
```

其他安装命令:

### macOS

- Homebrew (100%):

```sh
brew install gibbslda
```

  证据: local Homebrew formula metadata

- MacPorts (94%):

```sh
sudo port install gibbslda
```

  证据: MacPorts ports tree: math/gibbslda/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1

## 软件包事实

- **软件包键:** brew:gibbslda
- **软件包管理器:** Homebrew
- **版本:** 0.2
- **来源摘要:** Library wrapping imlib2's context API
- **主页:** <https://gibbslda.sourceforge.net/>
- **已生成:** 2026-08-03T19:37:03+00:00

## 可执行文件

- lda (别名)

## 安装行为

- Bottle: 不可用

## 版本和新鲜度

- 页面生成时间: 2026-08-03
- 管理器版本: 0.2
## 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

- <https://gibbslda.sourceforge.net/>
- <https://sourceforge.net/projects/gibbslda/>
- <https://sourceforge.net/projects/gibbslda/files/>
- source_facts.package-manager


## 安全说明

library-like package without higher-risk signals.

- **Geiger 风险:** 绿色 / 低
- library-like package without higher-risk signals

## 其他软件包管理器记录

- MacPorts - gibbslda: normalized package name match | MacPorts ports tree: math/gibbslda/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1


## Combined YAML source

View the package source record on GitHub. [combined/gibbslda.yml](https://github.com/mxcl/pkgdb/blob/main/combined/gibbslda.yml)


## 来源

- pkg.so package database
- Geiger risk classifier
- curated package history
- pkgdb category and tag curation
- external package-manager database matches
- cross-ecosystem install command graph