| kalmanmain | sets defaults for library kalman |
| kalmantest | Tests the quantlets of the kalman library. |
| kanta | Supporting Quantlet for cartsplit |
| kaplanmeier | Calculation of the Kaplan-Meir (product limit) estimator of the hazard rate and the survivor function for a set of durations. The first column of the input is a censorship indicator variable, (equal to zero if the duration is censored, and to one otherwise); the second column is the duration. |
| kem |
Calculates estimates of mu, F, Q and R in a
state-space model using EM-algorithm.
The state-space model is assumed to be in the
following form: y_t = H x_t + v_t x_t = F x_t-1 + w_t x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R) Parameters Sig and H are assumed known.
|
| kemitor |
Calculates observations of a given state-space
model. The state-space model is assumed to be
in the following form: y_t = H x_t + ErrY_t x_t = F x_t-1 + ErrX_t x_0 = mu
|
| kemitor2 |
Simulates observations and states of a
given state-space-model - just as kemitor
by Petr Franek (quantlib times) - but this time also the
states are returned.
The state-space model is assumed to be
in the following form: y_t = H x_t + ErrY_t x_t = F x_t-1 + ErrX_t x_0 = mu
|
| kernelmain | generate the volume of the unit balls |
| kerneltest | kerneltest tests all the aforementioned macros of the kernel.lib |
| kfilter |
Calculates a filtered time serie (uni- or
multivariate) using the Kalman filter equations.
The state-space model is assumed to be in the
following form: y_t = H x_t + v_t x_t = F x_t-1 + w_t x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R) All parameters are assumed known.
|
| kfilter2 |
Calculates a filtered time serie (uni- or
multivariate) using the Kalman filter equations.
The state-space model is assumed to be in the
following form: y_t = H x_t + v_t x_t = F x_t-1 + w_t x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R) All parameters are assumed known.
|
| kmcont | performes a K-means cluster analysis of the rows of a contingency table including the multivariate graphic using the correspondence analysis; makes available the factorial coordinates (scores) |
| kmeans | performs cluster analysis, i.e. computes a partition of n row points into K clusters. |
| knn | computes a running mean over (2k+1) consecutive values of a given vector. To have the same length at the beginning and at the end the first and last value are repeated k times. |
| kommumat | generates a help matrix for Subset VAR models |
| kpss |
Calculation of the KPSS statistics for I(0) against long-memory
alternatives. We consider the two tests, denoted by KPSS_mu and KPSS_t,
and respectively based on a regression on a constant mu, and
on a constant and a time trend t.
The quantlet returns the value of the estimated statistic for two
type of the tests, i.e., const or trend and
the critical values for a 10, 5 and 1
percent confidence interval for I(0) (const, trend).
|
| kpssnum |
Calculation of the KPSS statistics for I(0) against long-memory
alternatives. We consider the two tests, denoted by KPSS_mu and KPSS_t,
and respectively based on a regression on a constant mu, and
on a constant and a time trend t.
The quantlet returns the value of the estimated statistic for two
type of the tests, i.e., const or trend and
the critical values for a 95 percent confidence interval for I(0) (const, trend).
|
| kron | Computes the Kronecker product of two matrices. |
| ksmoother |
Calculates a smoothed time serie (uni- or
multivariate) using the Kalman smoother equations.
The state-space model is assumed to be in the
following form: y_t = H x_t + v_t x_t = F x_t-1 + w_t x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R) All parameters are assumed known.
|
| kurtosis | Computes the kurtosis for a given vector. |
| kuva | Makes a picture of a regression tree. |