RDocumentation
Moon
Learn R
Search all packages and functions
codadiags (version 1.0)
Markov chain Monte Carlo burn-in based on "bridge" statistics
Description
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
Copy Link
Copy
Link to current version
Version
Version
1.0
Down Chevron
Install
install.packages('codadiags')
Monthly Downloads
12
Version
1.0
License
GPL-3
Maintainer
Yann Richet
Last Published
November 18th, 2013
Functions in codadiags (1.0)
Search functions
AR1
Generate auto-regressive order 1 sequence
null.param.cdf
Build the null CDF (cumulative density function) for a given statistic, for arbitrary length and autocorrelation sequence.
bay.cdf
Bay cumulative density function, corresponding to -B(t+)/B(t-), where B(t+) (resp. B(t-)) is the maximum (resp.minimum) of B(t)/(t*(1-t)).
transient.test
Perform a stationary test to check for an initial burn-in in a sequence
bridgestat.diag
Iterative truncation procedure based on a bridge statistic.
maxinv.bay.cdf
CDF of max(x,1/x) (=cdf(x)-cdf(1)+cdf(1)-cdf(1/x)) where x is 'Bay' distributed
null.lim.cdf
Asymptotic CDF for a given statistic
loglikbridge
Compute the so called "Log-likelihood bridge" process.
studentbridge
Compute the so called "Student bridge" process.
ad.cdf
Anderson-Darling cumulative density function, copy from ADGofTest package.
brownianbridge
Compute the so called (abusively) "Brownian bridge" process.
add.transient
Add a transient to a given mcmc sequence
ks.cdf
Kolmogorov-Smirnov cumulative density function, copy from stats::ks.test.
codadiags-package
Markov chain Monte Carlo burn-in based on "bridge" statistics.
cvm.cdf
Cramer von Mises cumulative density function, import from coda package.
autocorr1
Basic auto-correlation estimation of a given sequence