yy_control: Default Controlling Options
In snoweye/cubfits: Codon Usage Bias Fits

Description Usage Format Details Author(s) References See Also Examples

Default controls of cubfits include for models, optimizations, MCMC, plotting, global variables, etc.

  .cubfitsEnv
  .CF.CT
  .CF.CONF
  .CF.GV
  .CF.DP
  .CF.OP
  .CF.AC
  .CF.PT
  .CF.PARAM
  .CO.CT

All are in lists and contain several controlling options.

See init.function() for use cases of these objects.

.cubfitEnv is a default environment to dynamically save functions and objects.

.CF.CT is main controls of models. It currently includes

`model`	main models
`type.p`	proposal for hyper-parameters
`type.Phi`	proposal for Phi
`model.Phi`	prior of Phi
`init.Phi`	initial methods for Phi
`init.fit`	how is coefficient proposed
`parallel`	parallel functions
`adaptive`	method for adaptive MCMC

.CF.CONF controls the initial and draw scaling. It currently includes

`scale.phi.Obs`	if phi were scaled to mean 1
`init.b.Scale`	initial b scale
`init.phi.Scale`	initial phi scale
`p.nclass`	number of classes if mixture phi
`b.DrawScale`	drawing scale for b if random walk
`p.DrawScale`	drawing scale for p if random walk
`phi.DrawScale`	random walk scale for phi
`phi.pred.DrawScale`	random walk scale for phi.pred
`sigma.Phi.DrawScale`	random walk scale for sigma.Phi
`bias.Phi.DrawScale`	random walk scale for bias.Phi
`estimate.bias.Phi`	if estimate bias of phi during MCMC
`compute.logL`	if compute logL in each iteration

.CF.GV contains global variables for amino acids and codons. It currently includes

`amino.acid`	amino acids
`amino.acid.3`	amino acids
`synonymous.codon`	synonymous codons of amino acids
`amino.acid.split`	amino acid 'S' is split
`amino.acid.split.3`	amino acid 'S' is split
`synonymous.codon.split`	synonymous codons of split amino acid

.CF.OP controls optimizations. It currently includes

`optim.method`	method for `optim`()
`stable.min.exp`	minimum exponent
`stable.max.exp`	maximum exponent
`E.Phi`	expected Phi
`lower.optim`	lower of derivative of logL(x)
`upper.optim`	upper of derivative of logL(x)
`lower.integrate`	lower of integration of L(x)
`upper.integrate`	upper of integration of L(x)

.CF.DP is for dumping MCMC iterations. It currently includes

`dump`	if dumping within MCMC
`iter`	iterations per dumping
`prefix.dump`	path and file names of dumping
`verbose`	if verbose
`iterThin`	iterations to thin chain
`report`	iterations to report
`report.proc`	iterations to report `proc.time`()

.CF.AC controls adaptive MCMC. It currently includes

`renew.iter`	per renewing iterations
`target.accept.lower`	target acceptant rate lower bound
`target.accept.upper`	target acceptant rate upper bound
`scale.increase`	increase scale size
`scale.decrease`	decrease scale size
`sigma.lower`	lower bound of relative scale size
`sigma.upper`	upper bound of relative scale size

.CF.PT controls the plotting format. It currently includes

color color for codons.
.CF.PARAM controls the parameters and hyperparameters of priors. It currently includes

phi.meanlog mean of phi in loca scale

phi.sdlog standard deviation of phi in loca scale
.CO.CT controls the constrained optimization function. It currently includes

debug message printing level of debugging.

Wei-Chen Chen wccsnow@gmail.com.

https://github.com/snoweye/cubfits/

init.function(), cubfits(), cubpred(), cubappr(), and mixnormerr.optim().

## Not run: 
suppressMessages(library(cubfits, quietly = TRUE))

.CF.CT
.CF.CONF
.CF.DP
.CF.GV
.CF.OP
.CF.AC
.CF.PT
.CF.PARAM
.CO.CT

ls(.cubfitsEnv)
init.function()
ls(.cubfitsEnv)

## End(Not run)