Depends: R (>= 2.0.0), ConvergenceConcepts, dynlm, kinship, survival,
Suggests: boot, car, geepack, hdrcde, lmtest, strucchange, tcltk
Packaged: Thu Nov 19 20:43:51 2009; javlacalle

Packaged: 2009-12-04 19:29:37 UTC; javlacalle

Packaged: 2011-07-26 18:46:27 UTC; javlacalle

Packaged: 2012-01-06 10:04:14 UTC; javlacalle

Depends: R (>= 2.0.0), ConvergenceConcepts, dynlm, survival, splines,
Packaged: 2012-03-04 10:07:11 UTC; javlacalle

Packaged: 2012-09-13 14:48:29 UTC; javlacalle

Depends: R (>= 2.0.0), dynlm, nlme, plm, zoo
Suggests: boot, car, ConvergenceConcepts, geepack, lmtest, strucchange,
Packaged: 2013-07-23 17:05:37 UTC; javlacalle

Packaged: 2014-03-26 19:58:08 UTC; javlacalle

Packaged: 2014-09-28 09:18:49 UTC; javlacalle

Packaged: 2015-06-09 20:52:30 UTC; javlacalle

Description: Maximum entropy density based dependent data bootstrap.
An algorithm is provided to create a population of time series (ensemble)
without assuming stationarity. The reference paper (Vinod, H.D., 2004) explains
how the algorithm satisfies the ergodic theorem and the central limit theorem.
Packaged: 2016-11-16 20:02:41 UTC; javlacalle

Author: Hrishikesh D. Vinod <Vinod@fordham.edu>, Javier
López-de-Lacalle <javlacalle@yahoo.es>, and Fred Viole
Depends: R (>= 3.0.0), dynlm, nlme, tdigest

Depends: R (>= 3.5.0), dynlm, nlme, tdigest, hdrcde
without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains

Depends: R (>= 3.0.0), dynlm, nlme, tdigest, hdrcde

Description: Univariate and multivariate ECDFs. Currently this produces
step functions (which is what one would expect). Methods for
producing smooth(ish) distribution functions are being
considered. Note, this package is still experimental.

Description: Univariate and multivariate ECDFs. This produces step
functions, methods for producing smooth(ish) distribution

Description: Univariate and multivariate empirical cumulative
distribution functions (ECDFs), including both step functions
(true ECDFs) and continuous functions (via interpolation). Plus
special purpose functions for plotting univariate and bivariate