# Introducing H2O Lagrange (2.6.0.11) to R

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From my perspective the most important event that happened at
useR! 2014 was that I got to meet
the 0xdata team and now, long story short,
here I am introducing the latest version of H_{2}O, labeled
Lagrange (2.6.0.11),
to the R and greater data science communities. Before
joining 0xdata, I was working at a competitor on a rival project and was
repeatedly asked why my generalized linear model analytic didn’t run as fast as
H_{2}O’s GLM. The answer then as it is now is the same — because
H_{2}O has a cutting edge distributed in-memory parallel computing
architecture — but I no longer receive an electric shock every time I say so.

For those hearing about H_{2}O for the first time, it is an open-source
distributed in-memory data analysis tool designed for extremely large data sets
and the H_{2}O Lagrange (2.6.0.11) release provides scalable solutions
for the following
analysis techniques:

- Generalized Linear Model
- K-Means
- Random Forest
- Principal Components Analysis
- Summary
- Gradient Boosted Regression and Classification
- Naive Bayes
- Deep Learning

In my first blog post at 0xdata, I wanted to keep it simple and make sure R
users know how to get the `h2o`

package, which is cross-referenced on the
High-Performance and Parallel Computing
and
Machine and Statistical Learning
CRAN Task Views, up and running on their
computers. To so do, open an R console of your choice and type

# Download, install, and initialize the H2O package install.packages("h2o", repos = c("http://h2o-release.s3.amazonaws.com/h2o/rel-lagrange/11/R", getOption("repos"))) library(h2o) localH2O <- h2o.init() # List and run some demos to see H2O at work demo(package = "h2o") demo(h2o.glm) demo(h2o.deeplearning)

After you are done experimenting with the demos in R, you can open up a web
browser to http://localhost:54321/ to give the H_{2}O web interface a
once over and then hop over to
0xdata’s YouTube channel for some
in-depth talks.

Over the coming weeks we at 0xdata will continue to
blog about how to use H_{2}O
through R and other interfaces. If there is a particular use case you would like
to see addressed, join our
h2ostream Google Groups
conversation or e-mail us at [email protected]. Until then, happy analyzing.

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