Synthetic likelihood methods for very non-linear ecological series

Documentation for package ‘sl’ version 0.0-5

sl-package	Synthetic likelihood approach to fitting dynamically complex models.
bf	Nicholson's 1954 blowfly data
bf1	Nicholson's 1954 blowfly data
bf2	Nicholson's 1954 blowfly data
bf3	Nicholson's 1954 blowfly data
blowfly	Fit model to Nicholson's Blowfly data
blowfly.ll	Fit model to Nicholson's Blowfly data
bup.para	Fit model to data on bupalus
bupalus	Fit model to data on bupalus
bupalus.ll	Fit model to data on bupalus
chain2ll	Get likelihood asypmtotics information form MCMC output
des.bf	Fit model to Nicholson's Blowfly data
desbf.ll	Fit model to Nicholson's Blowfly data
ds.bf	Fit model to Nicholson's Blowfly data
dsbf.ll	Fit model to Nicholson's Blowfly data
get.trans	Obtain or apply a simple transformation to normality
logistic	Fit logistic and Ricker models to same data.
MVN.check	Checking the multivariate normality approximation
ng.bf	Fit model to Nicholson's Blowfly data
nlar	Turn time series into stats
not.sq	Attenuated square function
order.dist	Summarize marginal distribution of (differenced) series
ricker	Simulate multiple Ricker model replicates
ricker.fey	Simulate multiple Ricker model replicates
ricker.ll	Simulate multiple Ricker model replicates
robust.vcov	Robust covariance matrix estimation
sl.acf	Turn time series into stats
stork	Stochastic Ricker Example
trans.stat	Obtain or apply a simple transformation to normality