R: Online Change Point Detection for Matrix-Valued Time Series

# Online Change Point Detection for Matrix-Valued Time Series

## Documentation for package ‘OLCPM’ version 0.1.2

- DESCRIPTION file

## Help Pages

cv.table | cv.table
gen.data | generate data
gen.psi.tau.flat | calculate eigenvalue series by "flat" method
gen.psi.tau.proj | calculate eigenvalue series by projected method
getcv | calculate critical values
impute.linear | impute missing entries by linear interpolation
ITP_noproj | testing the number of row factors- without projection
ITP_proj | testing the number of row factors- with projection
kpe | determine factor number - projected
KSTP | determine row factor number - test
moment.determine | determine the moment (largest) of the data samples
moment.test | test whether the k-th moment exists
outlier.remove | remove outliers
test.multiple.robust | robust test of multiple change point for matrix-valued online time series
test.once.flat | test single change point for matrix-valued online time series -"flat" version
test.once.flat.robust | robust test of single change point for matrix-valued online time series -"flat" version
test.once.proj | test single change point for matrix-valued online time series-projected version
test.once.proj.robust | robust test of single change point for matrix-valued online time series-projected version
test.once.psi | test single change point for matrix-valued online data given rolling eigenvalue series
test.once.psi.robust | robust test of single change point for matrix-valued online data given rolling eigenvalue series
var.exp | explanatory power of factors