Libraries tagged by negative

A class that represents a fraction. Supports conversion to/from floats, mathematical operations, negative fractions, and Unicode stringification.

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A Laravel package that proxies and server-side caches website favicons (Google S2 by default) behind a same-origin route, so visitors' browsers never hit the third-party favicon service. Validates the upstream content-type, sends nosniff, negative-caches failures and falls back to a transparent pixel.

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A content sanitization library to filter negative content and online gambling spam.

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Create array views for easy data manipulation, select elements using Python-like slice notation, enable efficient selection of elements using index lists and boolean masks.

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The BCMathExtended library extends the PHP BCMath extension adding more features like abs, min, max, random, factorial, type comparison between integer and decimal or positive and negative, modulo and power modulo with negative modulus (see more on BCMath extendsion http://php.net/manual/en/book.bc.php).

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Polymorphic voting system for Laravel with Positive, Negative, Abstention, and Neutral vote types.

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This Module allows negative deviations for product options eg. children reductions for events

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A (very) simple URL scraper that fetches title, description and images.

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A counting bloom filter with probabilistic deletion capabilities also allowing a risky deletion which may lead to false negatives if not carefully used.

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Allows you to analyze orders placed and determine which of them are suspected frauds. You can specify the degree of personally acceptable risk or report fali positives and false negatives

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Convert numbers between any base you want, including negative numbers and floating point numbers

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Analyze travel review sentiment using AI - POSITIVE/NEGATIVE/NEUTRAL scores

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Analyze product review sentiment using AI - returns POSITIVE/NEGATIVE/NEUTRAL

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Warn your editors about the potentially negative side effects of changing slugs

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Sentiment is a sentiment classifier. It uses a model of words that are categorised as positive, negative or neutral, and a naive bayes algorithm to calculate sentiment. To improve accuracy, Sentiment removes 'noise' words.

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