# Yuan Tang

#### autoplotly

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Functionalities to automatically generate interactive visualizations for statistical results supported by 'ggfortify', such as time series, PCA, clustering and survival analysis, with 'plotly.js' <https://plot.ly/> and 'ggplot2' style. The generated visualizations can also be easily extended using 'ggplot2' and 'plotly' syntax while staying interactive.

#### dml

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The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.

#### ggfortify

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Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using 'ggplot2'.

#### h2o4gpu

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Interface to 'H2O4GPU' <https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.

#### lfda

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Functions for performing and visualizing Local Fisher Discriminant Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised Local Fisher Discriminant Analysis(SELF).

#### onnx

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R Interface to 'ONNX' - Open Neural Network Exchange <https://onnx.ai/>. 'ONNX' provides an open source format for machine learning models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

#### scaffolder

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Comprehensive set of tools for scaffolding R interfaces to modules, classes, functions, and documentations written in other programming languages, such as 'Python'.

#### rflow

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This package provides a simple pipelined interface for Google TensorFlow in R.

#### tfio

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Interface to 'TensorFlow IO', Datasets and filesystem extensions maintained by TensorFlow SIG-IO <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.

#### caret

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Misc functions for training and plotting classification and regression models.

#### forecast

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Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

#### h2o

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R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models, Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (AutoML).

#### keras

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Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

#### reticulate

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Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7.

#### rgee

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Earth Engine <https://earthengine.google.com/> client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of R (Earth Engine) spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See <https://r-spatial.github.io/rgee/> for further details.

#### tensorflow

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Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

#### tfdatasets

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Interface to 'TensorFlow' Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <https://www.tensorflow.org/programmers_guide/datasets> for additional details.

#### tfestimators

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Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/programmers_guide/estimators>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.

#### xgboost

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Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.