drake VS fiery

Compare drake vs fiery and see what are their differences.

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drake fiery
1 1
1,330 238
0.1% -
6.1 5.4
2 months ago 3 months ago
R R
GNU General Public License v3.0 only GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

drake

Posts with mentions or reviews of drake. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-05-02.
  • Your impression of {targets}? (r package)
    3 projects | /r/Rlanguage | 2 May 2021
    The targets package is the official successor to Drake, and has the same primary author (Will Landau). He has explained why he created targets, which includes stronger guardrails for users and better UX.

fiery

Posts with mentions or reviews of fiery. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-06.

What are some alternatives?

When comparing drake and fiery you can also consider the following projects:

targets - Function-oriented Make-like declarative workflows for R

Iodine - iodine - HTTP / WebSockets Server for Ruby with Pub/Sub support

easystats - :milky_way: The R easystats-project

machine-learning-with-ruby - Curated list: Resources for machine learning in Ruby

tabulapdf - Bindings for Tabula PDF Table Extractor Library

gganimate - A Grammar of Animated Graphics

ncaahoopR - An R package for working with NCAA Basketball Play-by-Play Data

BeetleX - high performance dotnet core socket tcp communication components, support TLS, HTTP, HTTPS, WebSocket, RPC, Redis protocols, custom protocols and 1M connections problem solution

causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

awesome-R - A curated list of awesome R packages, frameworks and software.

droll - An R package to analyze roll distributions

patchwork - The Composer of ggplots