The R Project SVN R

Time-series package for R
=========================

Functions in base R:
-------------------

ts      Create a (univariate or multivariate) ts object
[.ts        Subsetting method for ts objects.
as.ts, is.ts    Coercion and membership functions
plot, lines,  print methods

cbind.ts    cbind method for time series (aligns time bases)
na.omit.ts  na.omit method for time series: omits at ends only
Ops.ts      arithmetic (such as + - * /) for time series

aggregate   Computes summaries (e.g. sum) over disjoint time intervals
diff        Lagged differences of a time series
end     Time of last observation
frequency   Number of observations per unit of time
deltat      Return time interval between observations
time        Create time series giving the times of observations
cycle       Create time series giving the positions in a cycle 
           of a time series
start       Time of first observation
tsp, tsp<-  Get and set time-series attributes
window      Subset to a time window


Functions in package ts:
-----------------------

acf     Auto- and Cross- Covariance and -Correlation Function
                   Estimation
acf2AR          Compute an AR Process Exactly Fitting an ACF
ar      Wrapper for autoregression estimation functions
ar.burg     Estimate autoregression model by Burg's method
ar.ols      Estimate autoregression model by ordinary least squares
ar.mle      Estimate autoregression model by maximum likelihood
ar.yw       Estimate autoregression model by solving Yule-Walker equations
arima           ARIMA Modelling of Time Series
arima0      ARIMA Modelling of Time Series -- Preliminary Version
arima.sim       Simulate from an ARIMA Model
ARMAacf         Compute Theoretical ACF for an ARMA Process
ARMAtoMA        Convert ARMA Process to Infinite MA Process
Box.test    Box-Pierce and Ljung-Box tests
ccf     Cross-covariance and cross-correlations for two series
cpgram      Plot Cumulative Periodogram
decompose   Classical Seasonal Decomposition by Moving Averages
diffinv     Discrete Integration: Inverse of Differencing
embed       Embedding a Time Series
filter      Linear Filtering on a Time Series
HoltWinters     Holt-Winters Filtering
kernapply   Apply Smoothing Kernel
kernel      Smoothing kernel Objects (and (modified) Daniell, Fejer and 
           Dirichlet kernels)
lag     Lag a Time Series
lag.plot        Time Series Lag Plots
monthplot   Plot a Seasonal or other Subseries
na.contiguous   Find Longest Contiguous Stretch of non-NAs
pacf        Partial autocorrelation function
plot.acf    Plot Autocovariance and Autocorrelation Functions
plot.spec   Plot Spectral Densities
PP.test     Phillips-Perron Test for Unit Roots
predict     methods for ar, arima, arima0 and StructTS
spec.ar     Estimate Spectral Density of a Time Series from AR Fit
spec.pgram  Estimate Spectral Density of a Time Series by a Smoothed
           Periodogram
spec.taper  Taper a Time Series by a Cosine Bell
spectrum    Wrapper for spectral density estimation functions
stl     Seasonal Decomposition of Time Series by Loess
stlmethods      Methods for STL Objects
StructTS        Fit Structural Time Series
toeplitz    Form Symmetric Toeplitz Matrix
ts.intersect    Bind time series as multivariate ts over the common time base
ts.plot     Plot Multiple Time Series
ts.union    Bind time series as multivariate ts over their total time base
tsdiag          Diagnostic Plots for Time-Series Fits
tsSmooth        Use Fixed-Interval Smoothing on Time Series

In some cases the visual output will closer to that of S(-PLUS) if
options(ts.S.compat=TRUE) has been set.


Datasets in base R:
------------------

airmiles    Passenger-Miles on US Airlines 1937-1960
co2     Moana Loa Atmospheric CO2 Concentrations
nhtemp      Yearly Average Temperatures in New Haven CT
presidents  Quarterly Approval Ratings for US Presidents
sunspots    Monthly Mean Relative Sunspot Numbers 1749-1983
uspop       Populations Recorded by the US Census


Datasets in package ts:
----------------------

See data(package="ts").