R project for statistical computing
R is a free software environment for statistical computing and graphics
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
 an effective data handling and storage facility,
 a suite of operators for calculations on arrays, in particular matrices,
 a large, coherent, integrated collection of intermediate tools for data analysis,
 graphical facilities for data analysis and display either onscreen or on hardcopy, and
 a welldeveloped, simple and effective programming language which includes conditionals, loops, userdefined recursive functions and input and output facilities.
The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationallyintensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.
R has its own LaTeXlike documentation format, which is used to supply comprehensive documentation, both online in a number of formats and in hardcopy.
R project for statistical computing 2019 Projects

Gregory S Brownson
A New Package for Empirical Asset Pricing Research, or EAPRA major effort in empirical asset pricing research is the initial stage of gathering the data, cleaning and filtering it, and then formatting it in a... 
Shannon Sequeira
Add subsampling to aster modelsWe add support for sampling arrows to aster models using the theory of curved exponential families. 
Shawn Feng
Add Support for Extra Optimization Solvers to PortfolioAnalyticsPortfolioAnalytics is a popular R package designed to provide optimized solution and visualizations for portfolio allocating problems with complex... 
Ziheng Zhou
Adding plotting engine into PerformanceAnalytics packageAdding plotting engine into PerformanceAnalytics package. 
Daniel Xia
An R package for two new skewt distributionsWe will develop an R package for two families of skewt distributions that have different tail behavior for left and right tails, namely the family... 
Oliver Ford
cpVis: Interactive visualization for change point exploration and labelingA changepoint is typically defined as a point in time where the distribution of a datastream changes in a distinct manner, for example, typically... 
Ben Ubah
DataDriven Exploration of the R CommunityThis project proposes to build an infrastructure that helps the R community explore R user groups, RLadies groups and past RGSoC projects using a... 
Rahul Chauhan
Enhancing Visualizations for Biodiversity DataWe plan to incorporate into bdvis two stateoftheart elements: interactive plotting and dashboards. We plan to develop and test an interface that... 
Fahrozi Fahrozi
Exploring Election and Census Highly Informative Data Nationally for Indonesia ( Eechidna R package)In Indonesia, elections are highly anticipated, because it provides an opportunity for all people to influence the direction of their country. The... 
Vito Lestingi
Financial Transactions Analytics in blotterThe Transaction Cost Analysis (TCA) of an investment program is a critical framework to pursue its best execution, as costs minimization is a... 
Sayani Gupta
gravitas: Exploring probability distributions for bivariate temporal granularitiesgravitas aims to provide methods to operate on time in an automated way, to deconstruct it in many different ways. Deconstructions of time that... 
Marlon E. Cobos
Grinnellian ecological niches and ellipsoids in RDistributional ecology is a growing field of science dedicated to characterize species distributions based on their ecological niches. Based on early... 
Povilas Gibas
Implementing biodiversity data checks for the bdchecks packageBackground bdchecks is an infrastructure for performing, filtering and managing various biodiversity data checks using R. Data checks are a key to... 
Onno Kleen
Improving the R package highfrequencyThe R package highfrequency is the goto package for intraday financial analysis in R. In the project, I will enhance its functionalities, rework... 
Aditya Samantaray
IREGNET on CRANIregnet is the first R package to support general interval output data (no censoring as well as left, right and interval censored data) and elastic... 
Luofeng Liao
MoMA  Modern Multivariate Analysis in RMultivariate Analysis techniques are indispensable in the era of Big Data. However, a unified and userfriendly framework has been lacking to date.... 
Akshaj Verma
Neural Network Package Validation 1The purpose of this project is to verify the convergence of the training algorithms provided in 69 Neural Network R packages available on CRAN to... 
Salsabila Mahdi
Neural Network Package Validation 2The purpose of this project is to verify the convergence of the training algorithms provided in 69 Neural Network R packages available on CRAN to... 
Anuraag Srivastava
Optimal partitioning algorithm for changepoint detectionThere are several applications where we need to work with ordered data (e.g. Timeseries). This includes financial data, climate data, radio signals,... 
Yawei Ge
Parallel Coordinate Plots in ggplot2We plan to create a package for parallel coordinate plots using ggplot2 based on the existing methods. We want it to make use of larger flexibility... 
Yujia Xie
PRIMAL: An R Package for Linear Programmingbased Sparse Learning Methods in High DimensionsLinear Programming (LP) based sparse learning methods, such as the Dantzig selector (for linear regression), sparse quantile regression, sparse... 
wenyu yang
Project Proposal Translator from ggplot2 to Vega LiteProject Abstract About me Mentors Information Coding Plan and Methods Commitments Timeline 
Juan Cruz Rodriguez
R Code OptimizerR is slow compared to other popular languages. “The R interpreter is not fast and execution of large amounts of R code can be unacceptably slow”.... 
Panagiotis Repouskos
Sampling Methods for Convex OptimizationExtend VolEsti (a c++ library with an R interface) by implementing randomized algorithms for convex optimization. First, there is a need to implement... 
AndrewC1998
Second Order Structure in the Changepoint PackageDetecting changes in statistical properties of a time series is important in a large number of fields. A large amount of research has taken place... 
Andres Algaba
sentometricsThe transformation of textual data into time series variables, and their subsequent use in an econometric analysis is an important and emerging... 
Qincheng Lu
sgdnet: efficient regularized GLMs for big dataThere is not yet any way in R to fully leverage the power of stochastic gradient algorithms for fitting generalized linear models (GLM). The... 
Apostolos Chalkis
Stateoftheart geometric random walks in RSampling algorithms and volume computation of convex polytopes are very useful in many scientific fields and applications. The package volesti is a... 
Ye
Treeregularized convolutional Neural Network (tCNN) for microbiomebased predictionOne important characteristic for microbiome data is the number of microbiome is far larger than small sample size (n>>p), resulting in a... 
Avinash Barnwal1
xgboost loss functionsThe project requires implementing 2 new objective loss functions  one for survival loss and another is for binomial loss. Survival loss includes...