camel-internal: Internal camel functions
In camel: Calibrated Machine Learning

Description Usage Arguments Details Author(s) See Also

Internal camel functions

tiger.likelihood(Sigma, Omega)
tiger.tracel2(Sigma, Omega)
camel.tiger.cv(obj, loss=c("likelihood", "tracel2"), fold=5)
part.cv(n, fold)
camel.tiger.clime.mfista(Sigma, d, maxdf, mu, lambda, shrink, prec, max.ite)
camel.tiger.slasso.mfista(data, n, d, maxdf, mu, lambda, shrink, prec, max.ite)
camel.slim.lad.mfista(Y, X, lambda, nlambda, n, d, maxdf, mu, max.ite, prec, 
                      intercept, verbose)
camel.slim.sqrt.mfista(Y, X, lambda, nlambda, n, d, maxdf, mu, max.ite, prec, 
                       intercept, verbose)
camel.slim.dantzig.mfista(Y, X, lambda, nlambda, n, d, maxdf, mu, max.ite, prec, 
                          intercept, verbose)
camel.cmr.mfista(Y, X, lambda, nlambda, n, d, m, mu, max.ite, prec)

`Sigma`	Covariance matrix.
`Omega`	Inverse covariance matrix.
`obj`	An object with S3 class returned from `"tiger"`.
`loss`	Type of loss function for cross validation.
`fold`	The number of fold for cross validatio.
`n`	The number of observations (sample size).
`d`	Dimension of data.
`m`	Columns of parameters in multivariate regression.
`maxdf`	Maximal degree of freedom.
`lambda`	Grid of non-negative values for the regularization parameter lambda.
`nlambda`	The number of the regularization parameter lambda.
`shrink`	Shrinkage of regularization parameter based on precision of estimation.
`mu`	The smooth surrogate parameter.
`prec`	Stopping criterion.
`max.ite`	Maximal value of iterations.
`data`	`n` by `d` data matrix.
`Y`	Dependent variables in linear regression.
`X`	Design matrix in linear regression.
`intercept`	Whether the intercept is included in the model.
`verbose`	Tracing information printing is disabled if `verbose = FALSE`.