Start quickly with built-in navigators that deliver a seamless out-of-the-box experience.
2) Components built for iOS and Android
Platform-specific look-and-feel with smooth animations and gestures.
3) Completely customizable
4) Extensible platform
React Navigation is extensible at every layer— you can write your own navigators or even replace the user-facing API.
MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
Regression: generalized linear regression, survival regression,...
Decision trees, random forests, and gradient-boosted trees
Recommendation: alternating least squares (ALS)
Clustering: K-means, Gaussian mixtures (GMMs),...
Topic modeling: latent Dirichlet allocation (LDA)
Frequent itemsets, association rules, and sequential pattern mining
Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.
Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface.
Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries. Statmodels in free software released under the Modified BSD (3-clause) license.
Linear regression models:
Mixed Linear Model with mixed effects and variance components
GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
Bayesian Mixed GLM for Binomial and Poisson
GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
Nonparametric statistics: Univariate and multivariate kernel density estimators
Datasets: Datasets used for examples and in testing
Statistics: a wide range of statistical tests
Imputation with MICE, regression on order statistic and Gaussian imputation
Tools for reading Stata .dta files, but pandas has a more recent version
TopoJSON is an extension of GeoJSON that encodes topology. Rather than representing geometries discretely, geometries in TopoJSON files are stitched together from shared line segments called arcs. This technique is similar to Matt Bloch’s MapShaper and the Arc/Info Export format, .e00.
TopoJSON eliminates redundancy, allowing related geometries to be stored efficiently in the same file. For example, the shared boundary between California and Nevada is represented only once, rather than being duplicated for both states. A single TopoJSON file can contain multiple feature collections without duplication, such as states and counties. Or, a TopoJSON file can efficiently represent both polygons (for fill) and boundaries (for stroke) as two feature collections that share the same arc mesh.
A TopoJSON file format is a format that encodes topology. TopoJSON is an extension of geoJSON. This format contains both geospatial data (arcs) and attribute data. In contrast to other GIS formats topoJSON uses arcs. Arcs are sequences of points, while line strings and polygons are defined as sequences of arcs.
Each arc is defined only once, but can be referenced several times by different shapes, thus reducing redundancy and decreasing the file size. The topoJSON format is a format that is used by software like Microsoft PowerBI.
EOS is a blockchain platform for the development of decentralized applications (dapps), similar to Ethereum in function. It provides a complete operating system for decentralized applications focused on the web with services like user authentication, cloud storage, and server hosting.
EOSIO is a free, open-source blockchain software protocol that provides developers and entrepreneurs with a platform on which to build, deploy and run high-performing decentralized applications (DAPPs)
EOSIO based blockchains execute user-generated applications and code using WebAssembly (WASM). WASM is an emerging web standard with widespread support of Google, Microsoft, Apple, and industry leading companies.
At the moment the most mature toolchain for building applications that compile to WASM is clang/llvm with their C/C++ compiler. For best compatibility, it is recommended that you use the EOSIO toolchain.
Other toolchains in development by 3rd parties include: Rust, Python, and Solidity. While these other languages may appear simpler, their performance will likely impact the scale of application you can build. We expect that C++ will be the best language for developing high-performance and secure smart contracts and plan to use C++ for the foreseeable future.