Libraries tagged by data analysis

Autochartist API integration with Laravel

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regression-php is a Php component containing a collection of linear least-squares fitting methods for simple data analysis.

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Database views to aggregate EAV entity data for easier data analysis.

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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The PHP IPA ISO Parser is a robust tool tailored for extracting and interpreting data from IPA (iOS App Store Package) builds. Seamlessly dive into the core details of any IPA file, retrieve essential metadata, and streamline your iOS app analysis and deployment processes with this efficient package.

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Data manipulaton tool

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Determine optimal number of bins 𝒌 for histogram creation and optimal bin width 𝒉 using various statistical methods.

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Datakit is a library that assists with data analysis and research. It includes classes for working with tables of data and deriving statistical information, importing those tables from file formats such as CSV, a class wrapper with statistical methods for PHP arrays, as well as memory efficient packed arrays.

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Run fast SQL queries on massive datasets (CSV, JSON, and Parquet) powered by DuckDB.

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Official PHP client for SlicingDice, Data Warehouse and Analytics Database as a Service.

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A Laravel package for seamless integration with DeepSeek AI services, enabling efficient and scalable AI-powered functionalities such as natural language processing, data analysis, and automation through a simple and intuitive API wrapper.

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Genlytics is a powerful Google Analytics Composer package that allows for easy data collection, analysis, and visualization. Optimized for SEO, it helps improve website performance and drive more organic traffic.

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Tools for data mining, analysis and predictions in PHP.

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一个高性能、自动化的微信账单解析器，支持压缩包密码自动破解与账单数据智能提取，适用于账单分析、账单自动化入账、个人理财工具开发等场景 | A fast, automated Wechat bill parser that cracks compressed file passwords and extracts bill data. Perfect for bill analysis, automatic bookkeeping, and personal finance tools

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Analysis and generation of bookkeeping data according to Swedish standards

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