Comprehensive TIBCO Spotfire S+ Feature List

  1. S Programming Language
  2. S+ Workbench Development Environment
  3. Complete Application for Statistical Analysis
  4. Scale your analytics to huge data
  5. Powerful Statistical Graphics
  6. Broad array of Import and Export Options
  7. Open and flexible integration options
  8. Statistical and Numerical Techniques
S Programming Language
The award-winning S programming language is at the core of both S+ and R. The only language created specifically for exploratory data analysis and statistical modeling, the S programming language allows you to create statistical applications up to five times faster than with other languages.
  • Object-oriented, interpreted 4GL language
  • Interactive exploration and fast prototyping 
  • Rich data structures: vector, matrix, array, data frame, list and many more 
  • User-defined functions, objects, classes, methods and libraries 
  • Library of over 4000 functions for data manipulation, graphics, statistical modeling, and integration 
  • CSAN library of available packages
S+ Workbench Development Environment
Rapidly create reliable statistical applications with this integrated development environment for S programmers.
  • Based on industry-standard Eclipse framework 
  • Check-in and check-out files with source code control system integration
  • Intelligent editor for S programs with line numbering, automatic indentation, automatic completion of function names and syntax highlighting 
  • Project, file and task management 
  • Automatic syntax error detection 
  • Code outline browser 
  • Command-line console with history recall 
  • Object and search path views 
  • Analytic step-by-step debugger 
  • Analytic profiling 
  • Package system for improved porting and deployment 
  • Develop, debug and deploy scripts for TIBCO Spotfire Statistics Services directly from within the S+ Workbench.
Complete Application for Statistical Analysis
A convenient window-based user interface puts common tasks at your fingertips with easy-to-use menus and dialogs
  • File import and export dialogs
  • Database import and export dialogs 
  • Dialogs for data preparation, charting and statistical modeling 
  • Interactive command-line with history recall 
  • Manage objects with Object Explorer 
  • Script file editor 
  • Multiple data and graphics windows 
  • Cut-and-paste to Word, PowerPoint and Excel 
  • Integrated Excel spreadsheets 
  • PowerPoint Wizard: quickly create slides from charts 
  • Create custom toolbars, menus and dialogs 
  • On-line help and manuals
Scale your analytics to huge data
Using methods which scale beyond the limits of your computer's memory, apply your statistical applications to gigabytes of data without the need for additional RAM with this library of data types and functions for programming with large data sets.

  • Data types for out-of-memory vectors, data frames, and time series 
  • Use familiar S functions, operators and programming style 
  • Scalable algorithms for data manipulation, charting and modeling 
  • High-performance data preparation tools: aggregate, merge, sort, partition, filter and more 
  • Data manipulation using built-in SQL processor 
  • Hexagonal binning plots to explore structure of large data sets 
  • Scalable model estimation: univariate statistics, linear regression, analysis of variance, logistic regression, poisson regression, quasi-likelihood, K‑means clustering, principal components 
  • Scalable model scoring for more than 20 model types
Powerful Statistical Graphics
Explore data and create custom charts with this library of graphical functions in the S language
  • Scatterplots, histograms, pie charts, box plots, bar charts, dot charts, time series charts, 3-D wireframe charts, image plots and many more.
  • Brush and spin dynamic visualization 
  • Programmatic control over colors, lines, axes, annotations and layout 
  • Trellis™ graphics – create multiple charts conditioned by levels of one or more variables 
  • Create interactive, embedded web-based charts with S‑PLUS Graphlets™ 
  • Element-Specific graph arguments for plots and command-line graphics
Broad array of Import and Export Options
Data and graphics formats include:
  • Spotfire: Binary and text data files 
  • ODBC and JDBC interfaces 
  • Application data: SAS 7/8/9, SPSS, Matlab, Minitab, Sigma Plot, Systat, STATA, Gauss, Epi Info and more 
  • ASCII: fixed format, comma-separated, and tab-delimited 
  • Spreadsheets: Excel, Lotus 1-2-3, Quattro Pro 
  • Database files: Paradox, dBase, Access, FoxPro 
  • Financial data sources: LIM, Bloomberg, FAME 
  • Export graphics as PDF, PostScript, GIF, PNG, JPG, WMF, bitmap, TIFF and more
Open and flexible integration options 
  • Deployment through TIBCO Spotfire Statistics Services 
  • Local APIs for C, C++, Java and Fortran 
  • Language support for pipes, sockets, and files 
  • DDE, COM and OLE interfaces 
  • XML import and export 
  • Reporting in XML, PDF, HTML and RTF
Statistical and Numerical Techniques
S+ is the most comprehensive statistical analysis package available, and includes all of the following capabilities:
  • Basic Statistics 
    • Summary statistics 
    • Crosstabulations 
    • Correlation and covariance 
    • Probabilities, quantiles, densities and random number generation from many distributions 
    • Durbin-Watson statistic 
  • Hypothesis Tests and Confidence Intervals 
    • One-sample and two-sample t-test and Wilcox 
    • Paired t-test 
    • Correlation: Pearson, Kendall's tau, Spearman's rho 
    • Goodness-of-Fit: Chi-square, Kolmogorov-Smirnov, Shapiro-Wilk 
    • Rank tests: Kruskal-Wallis, Friedman 
    • Proportions: exact Binomial test, Normal approximation 
    • Contingency tables and tests for independence: Chi-square, Fisher, Mantel-Haenszel, McNemar 
  • Regression 
    • Basic linear regression 
    • Polynomial regression 
    • Model diagnostics 
    • Prediction and confidence intervals 
    • Stepwise selection of models 
    • Parametric spline models 
    • Constrained regression 
    • Logistic regression 
    • Generalized linear models 
  • Analysis of Variance 
    • Univariate and multivariate ANOVA 
    • Flexible specification of variables, covariables, interactions, nesting, transformations 
    • Automatic generation of dummy variables 
    • Choice of contrasts 
    • Type III sums of squares 
    • Designed experiments: one-way, two-way, factorial, split-plot, unbalanced, fractional factorial designs, response surface methods, robust designs, taguchi methods and more 
    • Variance component estimation 
    • Multiple comparisons: Fisher, Tukey, Dunnett, Sidak, Bonferroni, Scheffé, simulation-based 
  • Nonlinear Regression and Maximum Likelihood 
    • Nonlinear regression 
    • Nonlinear maximum likelihood 
    • Quasi-likelihood 
    • Constrained nonlinear regression 
    • Nonparametric Regression 
    • Generalized additive models (GAMs) 
    • Smoothers: loess, super, kernel, spline 
    • Projection Pursuit, ACE, and AVAS 
  • Tree Models 
    • Classification trees 
    • Regression trees 
    • Pruning, shrinking, and splitting 
    • Scoring 
  • Correlated Data Analysis 
    • Longitudinal data and repeated measures analysis 
    • Linear (LME), nonlinear (NLME), and generalized mixed effects (GLMM) models 
    • Generalized Estimating Equations (GEE) 
    • Biexponential, first-order compartment, four-parameter logistic models 
    • User-defined correlation structures 
  • Resampling
    • Bootstrap 
    • Jackknife 
  • Multivariate Analysis 
    • Canonical correlation 
    • Discriminant analysis 
    • Factor analysis 
    • Multidimensional scaling 
    • Principal components 
    • Biplots 
  • Cluster Analysis 
    • K-means 
    • Hierarchical clustering 
    • Monothetic clustering 
    • Model-based clustering 
    • Crisp and fuzzy clustering 
    • Divisive and agglomerative methods 
  • Quality Control 
    • Shewhart chart 
    • Cusum chart 
    • Charts based on xbar, s, np, p, c, u 
    • Power and Sample Size 
    • Normal mean 
    • Binomial proportion 
  • Survival Analysis 
    • Kaplan-Meier curves 
    • Cox proportional hazards models with mixed effects 
    • Left, right, and interval censoring 
    • Time-dependent covariates and strata 
    • Multiple event models 
    • Competing risk models 
    • Frailty models 
    • Parametric survival 
    • Expected survival 
    • Person years analysis 
    • Aalen's Additive Regression Model 
  • Time Series Analysis 
    • Autocovariance, autocorrelation and partial autocorrelation 
    • Smoothed periodograms 
    • Box-Jenkins ARIMA models 
    • Classical and robust AR 
    • Long-memory models 
    • Seasonal decompositions 
    • Fourier transformations 
    • Classical and robust smoothers and filters 
  • Robust Statistics 
    • Robust estimation and inferences 
    • Robust MM regression 
    • Robust GLM, ANOVA, covariance, principal components, and discriminant analysis 
    • Least trimmed squares regression 
    • Minimum absolute residual regression 
    • Visually compare robust and traditional methods 
  • Missing Data 
    • Multiple imputation 
    • Gaussian, logistic, and conditional Gaussian models 
  • Date, Time, and Calendar Data 
  • Univariate and multivariate time series 
  • Aggregation, alignment, merging, and interpolation 
  • Times and dates from milliseconds to millennia 
  • Time zones with international daylight savings rules 
  • Holidays and financial market closures 
  • Custom time and date formats 
  • Relative time, time sequence, and event objects 
  • Powerful time-series charting 
  • Mathematical Computations 
    • Vector and matrix algebra 
    • Matrix decompositions 
    • Systems of linear equations 
    • Locate roots 
    • Nonlinear optimization 
    • Constrained optimization 
    • Ordinary differential equations 
    • Numerical integration
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