Libraries tagged by usage

PHP module to geolocate the country, region, city, coordinates, zip code, ISP, domain name, timezone, connection speed, IDD code, area code, weather station code, weather station name, MCC, MNC, mobile brand name, elevation and usage type that any IP address or host name originates from.

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Statistic of code usage inspired by Laravel Stats

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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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Fluent BCMath is a PHP package designed to simplify the usage of PHP's bcmath functions in a more fluid and intuitive way. With its expressive syntax and Laravel compatibility, it aims to improve the developer experience while working with arbitrary precision arithmetic operations.

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A Symfony2 bundle providing helpers to ease usage of common patterns.

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A PHP profiler written in C. dtrace is a lightweight PHP extension which provides the full call stack of your script, with CPU and memory usage, in a plain PHP Array or JSON output.

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A Magento 2 module that configures Magento 2 for usage with Daffodil

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Oauth fde client provider for usage with oauth-server

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Reusable adapters for usage with zend-serializer.

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Additional password validators for usage in TYPO3 password policies

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A wrapper for PHPExcel usage in Silex projects

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Track and store usage information including page requests, database field changes and system errors.

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Aerospike stub file for usage in a PHP-IDE

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Including the tests and other useless information like .travis.yml in distributed packages is not a good idea. "Compify" tries to remove everything, in order to save bandwidth and disk usage, but the necessary code for the package to work in your app or library.

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Symfony2 Bundle for generating large multi-sheeted xlsx files with low memory usage. Fork of bassim/BigXlsxBundle, for compatibility with newer PHP versions.

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