R-project is a stand-alone software application and programming environment supporting the R statistical language. R is a general purpose programming language and free BSD-licensed software environment for visual basic for application development and statistical computation. It is extensively used by data miners and statistical researchers for developing dynamic data analysis and statistical applications. The statistical packages used by R help in analyzing large sets of data with the assistance of several statistical and computing methods.
R is a free software application and therefore can be downloaded from the internet for downloading the required components and running the statistical computing software. The main advantages of the R-project software over other free statistical computing packages are that it can be run on all the major operating systems, it has a large database covering almost all areas of statistical computing, it is cross-platform with a number of implementation options available, it can handle real time data analysis and can even implement time into the statistical algorithms. Another advantage of R-project over other similar free software packages is its being fast, with the fastest performance amongst all the statistical computing packages. It is an open source software and so can be used easily by people from various backgrounds with no prior experience in programming. It is written in a high-level language (R) that makes it easy to manipulate and extend.
R package can be used to analyze, manipulate and forecast the financial markets. To analyze and visualize data sets you must have some knowledge about statistics such as probability, interval function, tree ring growth rate, mean value, deviation data set, maximum likelihood estimate, t-value, chi-square value, independence, and significance tests. You must also have some knowledge of linear algebra and have good mathematical skills. The best approach to statistical inference is to use the R packages lattice and lasso. Lasso and lattice packages provide a powerful method of logistic regression, decision trees and neural networks for high-dimensional data.