Means Packages

Enhances mean calculation efficiency in Oolong Phase 3, providing accelerated processes for random number analysis.

Tracks trending open-source projects for the upcoming phase, utilizing averages for insightful trend analysis.

Precisely calculates numerical total, averages and offering reliable mean calculation solutions for statistical analyses. Pre Jasmine version.

The simplest model of all. Submit any numbers and receiving its average.

The Beta Tea testnet ensures precise mean computations, delivering efficient solutions for data analysis queries.

This package computes the sum of five random numbers and includes functionalities for generating and summing random numbers within given ranges, useful for various analytical purposes.

Facilitates accurate tracking and computation of averages, ensuring reliable results for statistical analyses.

The Beta Tea testnet ensures precise mean computations, delivering efficient solutions for data analysis queries.

Derives totals and averages efficiently and reliably from randomly selected datasets.

Oolong, developed in JavaScript for Phase 2, offers efficient mean calculations for web applications and databases.

Provides dependable solutions for mean calculations, ensuring accuracy and effectiveness in statistical analyses.

Facilitates accurate tracking and computation of averages, ensuring reliable results for statistical analyses.

The simplest model of all. Submit any numbers and receiving its average.

The simplest model of all. Submit any numbers and receiving its average.

Oolong brewing phase 3 and accurately computes numerical averages, providing reliable solutions for mean calculation tasks.

The simplest model of all. Submit any numbers and receiving its average.

Open-source trending projects tracking for next phase. Tracks trends with averages, offering insightful mean calculations for trend analysis.

It accurately computes numerical totals, averages, and offers reliable mean calculation solutions for statistical analyses, prior to the Jasmine version.