R packages

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Background on R

R is a language and environment for statistical computing and graphics. It is a GNU object which is similar to the S language and environment which was developed by the Bell laboratories by John Chamber and colleagues.

R provides a wide variety of statistical (linear and non-linear modelling, time series analysis, classification and discrimination and etc...) and graphical techniques (low level and high level), and is highly extensible.

R is an integreated suite of software facilities for data collection, data modelling, and data forecasting with the assistance of graphical display. It consists:

  1. matrix-based calculation.
  2. an effective handing and storage facilities.
  3. integrated collection of intermediate tools for data analysis.
  4. a well-developed, simple and effective programming language which includes loops, conditions, user-defined recursive functions.

My R packages

This page provides information about software on forecasting functional data. The R packages and code may be freely copied and used. No guarantee of their reliability is given. If any errors are found, please let me know.

  1. functional data sets This package contains a number of data sets for testing the performance of forecasting methods.
  2. functional time serie analysis This package contains a number of functions that can plot, model and forecast functional data, functional time series and sliced functional time series.
  3. Rainbow plot, functional bagplot, and functional HDR boxplot This package contains a number of functions that can plot functional data ordered by time, depth, or density, along with a number of real data sets and toy examples.
  4. Hierarchical time series analysis This package contains methods for analysing and forecasting hierarchical time series.


My R packages scheduled updates

Building R packages

  1. Tools for building R packages
  2. FileZilla to distribute to cran

R packages

  1. alr3: Methods and data to accompany Applied Linear Regression 3rd edition
  2. animation: Demonstrate Animations in Statistics
  3. Acinonyx - Next-generation interactive graphics
  4. Boot: Bootstrap R (S-Plus) Functions (Canty)
  5. Bootstrap: Functions for the Book "An Introduction to the Bootstrap"
  6. Cluster: Cluster Analysis Extended Rousseeuw et al
  7. Date: Functions for handling dates
  8. fBasics: Rmetrics - Markets and Basic Statistics
  9. gam: Generalized additive models
  10. glmnet: Lasso and elastic-net regularized generalized linear models
  11. ks: Kernel smoothing
  12. KernSmooth: functions for kernel smoothing for Wand & Jones (1995)
  13. kml: K-Means for Longitudinal Data
  14. LanguageR: Data sets and functions with "Analyzing Linguistic Data: A practical introduction to statistics"
  15. Lattice: Lattice Graphics
  16. lars: Least Angle Regression, Lasso and Forward Stagewise
  17. mFilter: The package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series
  18. mgcv: GAMs with GCV smoothness estimation and GAMMs by REML/PQL
  19. mnormt: The multivariate normal and t distributions
  20. MortalitySmooth: Smoothing Poisson counts with P-splines
  21. nlme: linear and nonlinear mixed effects models
  22. nortest: Tests for Normality, such as shapiro.test, ks.test, tsdiag
  23. R2WinBUGS: Running WinBUGS and OpenBUGS from R/S-PLUS
  24. RExcelInstaller (Integration of R and Excel, (use R in Excel, read/write XLS files))
  25. rggobi: Interface between R and GGobi
  26. rgl: 3D visualization device system (OpenGL)
  27. Rpad: Workbook-style, web-based interface to R
  28. SemiPar: Semiparametic Regression
  29. sos: sos
  30. tree: Classification and regression trees
  31. vars: VAR modeling
  32. xtable: Export tables to LaTex or HTML

Machine Learning R packages

  1. nnet: Forward Neural Networks and Multinomial Log-Linear Models
  2. gbm: Generalized Boosted Regression Models
  3. mlbench: Machine Learning Benchmark Methods
  4. Polymars: Multivariate Adaptive Polynomial Spline Regression
  5. MARS: Multivariate Adaptive Regression Splines

Functional data analysis R packages

  1. pendensity: Density estimation with a penalized mixture approach
  2. fda: Functional data analysis
  3. Modeling functional data in finance
  4. far: Modelization for functional autoregressive processes
  5. mar1s: Multiplicative AR(1) with seasonal processes
  6. fpca: Restricted MLE for functional principal component analysis

Functional data analysis MATLAB packages

  1. fda: Functional data analysis using R and MATLAB
  2. PACE package: Principal analysis by conditional expectation

CRAN Task View

  1. Bayesian


R books

  1. Albert, J. (2009) Bayesian computation with R, Springer.
  2. Cook, D., and Swayne, D. F. (2007)Interactive and dynamic graphics for data analysis, Springer.
  3. Cowpertwait, P.S.P. and Metcalfe, A. V. (2009)Introductory time series with R, Springer.
  4. Murrell, P (2006) R graphics, Chapman & Hall.
  5. Ramsay, J., Hooker, G., and Graves, S. (2009) Functional data analysis using R and MATLAB, Springer.
  6. Robert, C. and Casella, G. (2010)Introducing Monte Carlo methods with R, Springer.
  7. Sarkar, D. (2008) Lattice, Springer.
  8. Spector, P. (2008) Data Manipulation with R, Springer.
  9. Venables, W. N. and Ripley, B. D. (2002) Modern applied statistics with S, Springer, 4th nd, New York.
  10. Wickham, H. (2009) ggplot2, Springer.

Some R books on CRAN

Some functionality of R

  1. A (Not So) Short Introduction to S4
  2. Add noise to numbers
  3. Animation website of Yihui Xie
  4. Assign a list of strings
  5. Calinski and Harabasz stopping rule
  6. Check positive definite
  7. cleversearch() in svcm package performs a greedy grid search with lookup-table
  8. Cluster analysis in R
  9. Color codes
  10. Connectivity between MATLAB and R
  11. Controlling figure and table placement in Latex
  12. Data analysis software
  13. Derivative of nonparametric curve
  14. Finding a function in any package
  15. Finding keywords
  16. index.G1 in clusterSim package calculates Calinski and Harabasz stopping rule
  17. Integrate function in stats package
  18. Ignore error in for-loop
  19. Jonathan Baron's R help page
  20. Lattice plots
  21. optim and optimize for one-dimensional optimization and constrOptim for constrained optimization
  22. Plot area under a curve
  23. mvtsplot
  24. R Wiki
  25. R workshop in 2008
  26. rcmd INSTALL --build
  27. Rd files with unknown encoding problem
  28. Remove a pattern from R remove(list=ls(pattern = "^r..$"))
  29. Removing columns from a matrix
  30. rgl package for 3D real-time plotting
  31. S4 classes
  32. S4 class examples
  33. S4 classes in 15 pages, more or less
  34. Showing graphs one by one allowing full control by users
  35. Swedish mortality data
  36. Switch function in R
  37. unique function in R
  38. uniroot
  39. y <- c("1,200","20,000","100","12,111") as.numeric(gsub(",","", y)) to correct the quotes
  40. Creating Identity Matrices
  41. Maximum likelihood estimation
  42. format a string
  43. Version number of a package
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