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 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.
  2. functional data sets This package contains a number of data sets for testing the performance of forecasting methods.
  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.

My R packages scheduled updates

  1. The dynupdate function in ftsa package needs to be updated to incorporate three ways of computing functional principal components.

R packages

  1. SemiPar
  2. rggobi: Interface between R and GGobi
  3. xtable: Export tables to LaTex or HTML
  4. ks: Kernel smoothing
  5. KernSmooth: functions for kernel smoothing for Wand & Jones (1995)
  6. nlme: linear and nonlinear mixed effects models
  7. R2WinBUGS
  8. mgcv: GAMs with GCV smoothness estimation and GAMMs by REML/PQL
  9. Generalized additive models
  10. glmnet: Lasso and elastic-net regularized generalized linear models
  11. animation
  12. mFilter The package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series
  13. BARS
  14. MortalitySmooth
  15. LanguageR
  16. K-Means for Longitudinal Data
  17. Date
  18. Bootstrap
  19. Boot
  20. Cluster
  21. lars
  22. tree
  23. sos
  24. Lattice
  25. rgl

Functional data analysis R packages

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

Functional data analysis MATLAB packages

  1. PACE package: Principal analysis by conditional expectation

R books

  1. Murrell, P (2006) R graphics, Chapman & Hall, Web link
  2. Venables, W. N. and Ripley, B. D. (2002) Modern applied statistics with S, Springer, 4th nd, New York.

Some R books on CRAN

Some functionality of R

  1. Color codes
  2. Integrate function in stats package
  3. Rd files with unknown encoding problem
  4. Derivative of nonparametric curve
  5. rcmd INSTALL --build
  6. Lattice plots
  7. data
  8. Animation website of Yihui Xie
  9. Data analysis software
  10. cleversearch() in svcm package performs a greedy grid search with lookup-table
  11. optim and optimize for one-dimensional optimization and constrOptim for constrained optimization
  12. y <- c("1,200","20,000","100","12,111") 
 as.numeric(gsub(",","", y)) to correct the quotes
  1. rgl package for 3D real-time plotting
  2. Assign a list of strings
  3. switch function in R
  4. Controlling figure and table placement in Latex
  5. A (Not So) Short Introduction to S4
  6. R Wiki
  7. S4 classes
  8. S4 classes in 15 pages, more or less
  9. S4 class examples
  10. Showing graphs one by one allowing full control by users
  11. Removing columns from a matrix
  12. Remove a pattern from R remove(list=ls(pattern = "^r..$"))
  13. Cluster analysis in R
  14. Calinski and Harabasz stopping rule
  15. index.G1 in clusterSim package calculates Calinski and Harabasz stopping rule
  16. ignore error in for-loop
  17. Jonathan Baron's R help page
  18. Finding a function in any package
  19. connectivity between MATLAB and R
  20. unique function in R
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