# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' @name setcovfs
#' @title Set covariance functions
#' @description
#' \preformatted{
#' setcovfs(om, covnames)
#' }
#' Sets the covariance functions for an outermod class instance.
#' This is first thing one does when creating an outermod instance.
#' @param om an \code{\link{outermod}} instance
#' @param covnames a vector of strings of the covariance functions
#' @examples
#' om = new(outermod)
#' setcovfs(om, c("mat25", "mat25", "mat25"))
#' setcovfs(om, c("mat25", "mat25pow", "mat25", "mat25ang"))
#' @seealso \code{\link{outermod}} 
#' @returns no value is returned, \code{om} is updated
NULL

#' @name setknot
#' @title Set knot points
#' @description
#' \preformatted{
#' setknot(om, knotslist)
#' }
#' Sets the knot points of \code{om} to \code{knotslist} to estimate the 
#' eigenfunctions and eigenvalues. It will naturally check if the knot points 
#' have the same dimension as the covariance functions.  It will also check if 
#' the knot points are within reasonable bounds for the covariance functions.
#' @param om an \code{\link{outermod}} instance
#' @param knotslist a list of one dimensional vectors
#' @examples
#' om = new(outermod)
#' setcovfs(om, c("mat25", "mat25", "mat25"))
#' knotslist = list(seq(0,1,by=0.01),seq(0,1,by=0.01),seq(0,1,by=0.01))
#' setknot(om, knotslist)
#' @seealso \code{\link{outermod}}, \code{\link{setcovfs}}
#' @returns no value is returned, \code{om} is updated
NULL

#' @name gethyp
#' @title Get the hyperparameters
#' @description
#' \preformatted{
#' hyp = gethyp(om)
#' }
#' Gets the current hyperparameters from an \code{\link{outermod}} instance. It 
#' formats them in a way that makes reading in \code{R} easier. 
#' @param om an \code{\link{outermod}} instance 
#' @examples
#' om = new(outermod)
#' setcovfs(om, c("mat25", "mat25", "mat25"))
#' hyp = gethyp(om)
#' print(hyp)
#' @returns a vector of parameters
#' @seealso \code{\link{outermod}}
NULL

#' @name getpara
#' @title Get the model parameters
#' @description
#' \preformatted{
#' para = getpara(logpdf)
#' }
#' This function gets the current parameters from an \code{\link{lpdf}} class
#' instance. It formats them in a way that makes reading in \code{R} easier.
#' @returns a vector of parameters
#' @param logpdf an \code{\link{lpdf}} class instance
NULL

#' @name outermod
#' @aliases
#' Rcpp_outermod-class Rcpp_outermod
#' @title Outer product-type model
#' @description This is a class used to construct \code{\link{outerbase}}
#' class instances.  It stores key information for constructing a basis.
#' @field \link{outermod$updatehyp}(hyp) update hyperparameters
#' @field \link{outermod$selectterms}(numterms) find best \code{numterms} terms
#' @field \link{outermod$getvar}(terms) find variances of coefficients 
#' associated with  \code{terms}
#' @examples
#' om = new(outermod)
#' setcovfs(om, c("mat25", "mat25", "mat25"))
#' setknot(om,
#'          list(seq(0,1,by=0.01),seq(0,1,by=0.01),seq(0,1,by=0.01)))
#' terms = om$selectterms(40)
#' coeffvar =om$getvar(terms)
#' hyp = gethyp(om)
#' hyp[1:2] = 0.5
#' om$updatehyp(hyp)
#' coeffvar = om$getvar(terms)
#' @seealso \code{\link{outerbase}} the main product from an outermod
#' @seealso \code{\link{setcovfs}}, \code{\link{setknot}}, 
#' \code{\link{gethyp}}
#' @returns no returns, this is a class which contains methods
NULL

#' @name outermod$updatehyp
#' @title Update hyperparameters
#' @description
#' \preformatted{
#' outermod$updatehyp(hyp)
#' }
#' Updates the hyperparameters for the instance of outermod.
#' @param hyp A vector of hyperparameters
#' @returns no value is returned, the class instance is updated
#' @seealso \code{\link{outermod}}
NULL

#' @name outermod$selectterms
#' @title Select optimal terms
#' @description
#' \preformatted{
#' terms = om$selectterms(numterms)
#' }
#' Returns the best \code{numterms} given \code{outermod} currently using
#' maximum variance criteria.
#' @param numterms number of basis \code{terms} desired
#' @returns a matrix of \code{terms}
#' @seealso \code{\link{outermod}}
NULL

#' @name outermod$getvar
#' @title Get variance of coefficients
#' @description
#' \preformatted{
#' coeffvar = outermod$getvar(terms)
#' }
#' Returns the variance of the coefficients associated with \code{terms}.
#' @param terms a matrix of \code{terms}
#' @returns a vector of variances of each coefficient
#' @seealso \code{\link{outermod}}
NULL

#' @name outerbase
#' @aliases 
#' Rcpp_outerbase-class Rcpp_outerbase
#' @title Outer product-type basis
#' @description 
#' \preformatted{
#' ob = new(outerbase, om, x)
#' }
#' Class that handles the basis for a given set of points 
#' \code{x}.
#' @param x a matrix of predictors, must have as many columns as dims in 
#' \code{om}
#' @field nthreads number of threads for \code{omp} to use
#' @field \link{outerbase$getbase}(k) to get each dimensions basis 
#' functions
#' @field \link{outerbase$getmat}(terms) to get the basis matrix at 
#' \code{terms}
#' @field \link{outerbase$build}() to (re)build the basis instance
#' @field \link{outerbase$matmul}(terms,a) matrix multiply without 
#'building the basis matrix
#' @field \link{outerbase$tmatmul}(terms,a) transpose matrix multiply 
#' without building the basis matrix
#' @examples
#' om = new(outermod)
#' setcovfs(om, c("mat25", "mat25", "mat25"))
#' setknot(om,
#'          list(seq(0,1,by=0.025),seq(0,1,by=0.025),seq(0,1,by=0.025)))
#' x = matrix(runif(10*3),ncol=3)
#' ob = new(outerbase, om, x)
#' terms = om$selectterms(40)
#' basismat = ob$getmat(terms)
#' @seealso \code{\link{outermod}} the core element that controls outerbase
#' @returns no returns, this is a class which contains methods
NULL

#' @name outerbase$getbase
#' @title Get base functions
#' @description
#' \preformatted{
#' basis_func = outerbase$getbase(k)
#' }
#' Returns the basis for dimension \code{k}.   Designed mostly for 
#' visualization.
#' @param k An integer from that corresponds to the dimension.
#' @returns a matrix of evaluated basis functions
#' @seealso \code{\link{outerbase}}
NULL

#' @name outerbase$getmat
#' @title Get basis matrix
#' @description
#' \preformatted{
#' basismat = outerbase$getmat(terms)
#'  }
#' Returns the basis matrix for a given set of \code{terms}.
#' @param terms a matrix of terms
#' @returns a matrix of evaluated basis functions based on 
#' \code{terms}.
#' @seealso \code{\link{outerbase}}
NULL

#' @name outerbase$build
#' @title Builds the outerbase
#' @description
#' \preformatted{
#' outerbase$build()
#'  }
#' Build (or re-build) a basis based on the recent evaluation 
#' of \code{\link{outermod}}.
#' @seealso \code{\link{outerbase}}
#' @returns nothing is returned, the class instance is updated
NULL

#' @name outerbase$matmul
#' @title Matrix multiply
#' @description
#' \preformatted{
#'  b = outerbase$matmul(terms, a)
#' }
#' Multiplies the basis times a vector without building the basis 
#' matrix.
#' @param terms a matrix of \code{terms}
#' @param a a vector of length the same as the rows in \code{terms}
#' @returns a vector resulting from the matrix multiplication
#' @seealso \code{\link{outerbase}}
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#' @name outerbase$tmatmul
#' @title Transpose Matrix multiply
#' @description
#' \preformatted{
#'   b = outerbase$tmatmul(terms, a)
#' }
#' Multiplies the transpose of the basis times a vector without 
#'  building the basis matrix.
#' @param terms a matrix of \code{terms}
#' @param a a vector of length the same as the rows in \code{outerbase}
#' @returns a vector resulting from the matrix multiplication
#' @seealso \code{\link{outerbase}}
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#' @name lpdf
#' @aliases 
#' Rcpp_lpdf-class Rcpp_lpdf
#' @title Log probability density function class
#' @description This is a base class designed to handle the learning of 
#' the underlying coefficients, hyperparameters, and parameters associated with
#' a specific learning instance.  Polymorphism allows for the implied methods to 
#' be used across several similar classes.
#'
#' @field lpdf$val current value
#' @field lpdf$para current model parameters
#' @field lpdf$coeff current coefficients
#' @field lpdf$compute_val on calling \code{update}, compute value and store in 
#' \code{val}
#' @field lpdf$grad current gradient with respect to coefficients
#' @field lpdf$gradhyp current gradient with respect to covariance hyperparameters
#' @field lpdf$gradpara current gradient with respect to model parameters
#' @field lpdf$compute_grad on calling \code{update}, compute gradient with 
#' respect to coefficients and store in \code{grad}
#' @field lpdf$compute_gradhyp on calling \code{update}, compute gradient
#' with respect to covariance hyperparameters and store in \code{gradhyp}
#' @field lpdf$compute_gradpara on calling \code{update}, compute gradient
#' with respect to model parameters and store in \code{gradpara}
#' @field lpdf$update(coeff) update using new coefficients
#' @field \link{lpdf$optcg}(tol,epoch) do optimization with respect to coefficients 
#' via conjugate gradient
#' @field \link{lpdf$optnewton}() do optimization via matrix inversion, one Newton 
#' step
#' @field lpdf$updateom() update based on recent version of \code{\link{outermod}}
#' @field lpdf$updatepara(para) update using new model parameters
#' @field lpdf$updateterms(terms) update using new \code{terms}
#' @field lpdf$hess() returns the hessian with respect 
#' to coefficients
#' @field lpdf$hessgradhyp() returns gradient of \code{hess()} with respect to 
#' covariance hyperparameters
#' @field lpdf$hessgradpara() returns the gradient of \code{hess()} with respect to 
#' model parameters
#' @field lpdf$diaghess() returns the diagonal of the hessian with 
#' respect to coefficients
#' @field lpdf$diaghessgradhyp() returns the gradient of \code{diaghess()} with 
#' respect to  covariance hyperparameters
#' @field lpdf$diaghessgradpara() returns the gradient of \code{diaghess()} with 
#' respect to model parameters
#' @field lpdf$paralpdf(para) compute the log-prior on the parameters, useful for 
#' fitting
#' @field lpdf$paralpdf_grad(para) gradient of \code{paralpdf(para)}
#' @seealso container class: \code{\link{lpdfvec}}
#' @seealso derived classes: \code{\link{loglik_std}}, 
#' \code{\link{loglik_gauss}}, \code{\link{loglik_gda}}, 
#' \code{\link{logpr_gauss}}
#' @returns no returns, this is a class which contains methods
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#' @name lpdfvec
#' @aliases
#' Rcpp_lpdfvec-class Rcpp_lpdfvec 
#' @title Vector of \code{lpdf} instances
#' @description 
#' \preformatted{
#' logpdf = new(lpdfvec, loglik, logpr)
#' }
#' This is a class where each instance contains two \code{\link{lpdf}} 
#' instances and can be 
#' manipulated as a single instance.  It presumes both are based on the same
#' \code{\link{outermod}} instance, thus they share hyperparameters.  However
#' the model parameters are concatenated.  Currently also includes variations
#' on marginal adjustments.  
#'
#' Currently it is designed only for a pair, but the ordering is arbitrary.
#'
#' @param loglik one reference to a \code{lpdf} instance
#' @param logpr another reference to a \code{lpdf} instance that shares 
#' \code{\link{outermod}}  with \code{loglik}
#' @field lpdfvec$domarg  A boolean that controls if marginal adjustment is 
#' done
#' @seealso base class: \code{\link{lpdf}}
#' @returns no returns, this is a class which contains methods
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#' @name lpdf$optcg
#' @title Optimization via Conjugate Gradient
#' @description 
#' \preformatted{
#' lpdf$optcg(tol,epoch)
#' }
#' This optimizes the coefficient vector \code{coeff} using conjugate gradient. 
#' It currently is designed only for quadratic \code{\link{lpdf}} instances.
#' @param tol A positive double representing tolerance, default is 
#' \code{0.001}.
#' @param epoch A positive integer representing the maximum number of steps 
#' conjugate gradient will take.
#' @seealso \code{\link{lpdf}}
#' @returns nothing is returned, the class instance is updated
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#' @name lpdf$optnewton
#' @title Optimization via Newton's Method
#' @description 
#' \preformatted{
#' lpdf$optnewton()
#' }
#' This optimizes the coefficient vector \code{coeff} using Newton's Method.  
#' It currently is designed only for quadratic \code{\link{lpdf}} instances.  
#' It should take a single step.
#' @seealso \code{\link{lpdf}}  
#' @returns nothing is returned, the class instance is updated
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#' @name loglik_std
#' @aliases 
#' Rcpp_loglik_std-class Rcpp_loglik_std 
#' @title Gaussian errors
#' @description 
#' \preformatted{
#' loglik = new(loglik_std, om, terms, y, x)
#' }
#' This is a standard model which has the form
#' \deqn{y = \langle \phi(x), \theta \rangle + \varepsilon, \varepsilon \sim 
#' N(0,\sigma^2)}
#' where \eqn{\phi(x)} is the basis, \eqn{\theta} is the coefficient vector,
#' \eqn{\varepsilon} is an unseen noise vector.
#' The parameter vector is of length 1 where 
#' \code{para} \eqn{= \log(\sigma)}.  It is a slower (sometimes) 
#' version of \code{\link{loglik_gauss}} but allows for complete marginal 
#' inference.
#' @param om an \code{\link{outermod}} instance to be referred to
#' @param terms a matrix of \code{terms}, must have as many columns as dims in 
#' \code{om}
#' @param y a vector of observations
#' @param x a matrix of predictors, must have as many columns as dims in 
#' \code{om} and the same number of rows as \code{y}
#' @inherit lpdf description
#' @seealso base class: \code{\link{lpdf}}
#' @returns no returns, this is a class which contains methods
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#' @name loglik_gauss
#' @aliases 
#' Rcpp_loglik_gauss-class Rcpp_loglik_gauss 
#' @title Gaussian errors, large scale
#' @description 
#' \preformatted{
#' loglik = new(loglik_gauss, om, terms, y, x)
#' }
#' This is a standard model which has the form
#' \deqn{y = \langle \phi(x), \theta \rangle + \varepsilon, \varepsilon \sim 
#' N(0,\sigma^2)}
#' where \eqn{\phi(x)} is the basis, \eqn{\theta} is the coefficient vector,
#' \eqn{\varepsilon} is an unseen noise vector. 
#' The parameter vector is of length 1 where 
#' \code{para} \eqn{= \log(\sigma)}.  It is a faster (sometimes) version of
#' \code{\link{loglik_std}}  but can only handle diagonal variational 
#' inference.
#' @param om an \code{\link{outermod}} instance to be referred to
#' @param terms a matrix of \code{terms}, must have as many columns as dims in 
#' \code{om}
#' @param y a vector of observations
#' @param x a matrix of predictors, must have as many columns as dims in 
#' \code{om} and the same number of rows as \code{y}
#' @inherit lpdf description
#' @seealso base class: \code{\link{lpdf}}
#' @returns no returns, this is a class which contains methods
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#' @name loglik_gda
#' @aliases
#' Rcpp_loglik_gda-class Rcpp_loglik_gda 
#' @title Gaussian errors with diagonal adjustment
#' @description 
#' \preformatted{
#' loglik = new(loglik_gda, om, terms, y, x)
#' }
#' This is a standard model which has the form
#' \deqn{y = \langle \phi(x), \theta \rangle + \delta(x) + \varepsilon,
#' \delta(x) \sim N(0, \lambda g(x)), \varepsilon \sim N(0,\sigma^2)}
#' where \eqn{\phi(x)} is the basis, \eqn{\theta} is the coefficient vector,
#' \eqn{\delta(x)} is unseen vector corresponding to unmodeled 
#' variance \eqn{\lambda g(x)}, \eqn{\varepsilon} is an unseen noise vector.
#' The parameter vector is of length 2 where 
#' \eqn{\sigma=} \code{exp(para[0])} and \eqn{\lambda=}\code{exp(2*para[1])}.
#' @param om an \code{\link{outermod}} instance to be referred to
#' @param terms a matrix of \code{terms}, must have as many columns as dims in 
#' \code{om}
#' @param y a vector of observations
#' @param x a matrix of predictors, must have as many columns as dims in 
#' \code{om} and the same number of rows as \code{y}
#' @inherit lpdf description
#' @seealso base class: \code{\link{lpdf}}
#' @returns no returns, this is a class which contains methods
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#' @name logpr_gauss
#' @aliases 
#' Rcpp_logpr_gauss-class Rcpp_logpr_gauss
#' @title Gaussian prior
#' @description 
#' \preformatted{
#' logpr = new(logpr_gauss, om, terms)
#' }
#' This is a standard model of coefficients which has them as drawn 
#' independently
#' from
#' \deqn{ \theta_i \sim N(0, \rho c_i)}
#' where \eqn{c_i} is the variance supplied by \code{om} for the \eqn{i}th term. 
#' The parameter vector is of length 1 where 
#' \eqn{\rho=} \code{exp(para[0])}.
#' @param om an \code{\link{outermod}} instance to be referred to
#' @param terms a matrix of \code{terms}, must have as many columns as dims in 
#' \code{om}
#' @inherit lpdf description
#' @seealso base class: \code{\link{lpdf}}
#' @returns no returns, this is a class which contains methods
NULL

#' @name covf
#' @aliases 
#' Rcpp_covf-class Rcpp_covf 
#' @title covariance function class
#' @description This is a base class designed to handle the specific features of 
#' covariances needed for outerbase.  Polymorphism allows for the implied 
#' methods to be used across several similar classes.
#' @field covf$hyp hyperparameters for this specific correlation function
#' @field covf$lowbnd,covf$uppbnd upper and lower bounds for the inputs to the 
#' covariance function.
#' @field covf$cov(x1,x2) returns the covariance matrix between two vectors of 
#' inputs \code{x1} and \code{x2}
#' @field covf$covdiag(x1) returns the diagonal of the covariance matrix between 
#' \code{x1} and itself
#' @field covf$cov_gradhyp(x1,x2) returns a cube of the gradient the \code{cov} 
#' with respect to the covariance hyperparameters
#' @seealso derived class: \code{\link{covf_mat25}}, 
#' \code{\link{covf_mat25pow}}, 
#' \code{\link{covf_mat25ang}}
#' @returns no returns, this is a class which contains methods
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#' @name covf_mat25
#' @aliases 
#' Rcpp_covf_mat25-class Rcpp_covf_mat25 
#' @title Matern covariance function
#' @description 
#' \preformatted{
#' covf = new(covf_mat25)
#' }
#' This is the standard Matern covariance function which has form
#' \deqn{c(x_1,x_2) = (1+ |h| + h^2/3) \exp(-|h|) }
#' where \eqn{h = (x_1-x_2)/\rho} and \eqn{\rho}=\code{exp(2*hyp[0])}.
#' @seealso base class: \code{\link{covf}}
#' @returns no returns, this is a class which contains methods
NULL

#' @name covf_mat25pow
#' @aliases 
#' Rcpp_covf_mat25pow-class Rcpp_covf_mat25pow 
#' @title Matern covariance function with power transform
#' @description 
#' \preformatted{
#' covf = new(covf_mat25pow)
#' }
#' This is the standard Matern covariance function with a power transformation
#' which has form
#' \deqn{c(x_1,x_2) = (1+ |h| + h^2/3) \exp(-|h|) }
#' where \eqn{h = (x_1^\alpha-x_2^\alpha)/\rho} and \code{hyp} is a two 
#' dimensional vector with \eqn{\rho}=\code{exp(2*hyp[0]+0.25*hyp[1])}
#' and \eqn{\alpha}=\code{exp(0.25*hyp[1])}.
#' @seealso base class: \code{\link{covf}}
#' @returns no returns, this is a class which contains methods
NULL

#' @name covf_mat25ang
#' @aliases 
#' Rcpp_covf_mat25ang-class Rcpp_covf_mat25ang 
#' @title Matern covariance function with angular transform
#' @description 
#' \preformatted{
#' covf = new(covf_mat25ang)
#' }
#' This is the standard Matern covariance function with a power transformation
#' which has form
#' \deqn{c(x_1,x_2) = (1+ |h| + h^2/3) \exp(-|h|) }
#' where \deqn{h = \sqrt{(\sin(x_1)-\sin(x_2))^2/\rho_s + 
#' (\cos(x_1)-\cos(x_2))^2/\rho_c}.}
#' \code{hyp} is a two dimensional vector with 
#' \eqn{\rho_s}=\code{exp(2*hyp[0])} and \eqn{\rho_c}=\code{exp(2*hyp[1])}.
#' @seealso base class: \code{\link{covf}}
#' @returns no returns, this is a class which contains methods
NULL

#' @name predictor
#' @aliases 
#' Rcpp_predictor-class Rcpp_predictor 
#' @title prediction class
#' @description 
#' \preformatted{
#' pred = new(predictor, loglik)
#' }
#' This is a base class design to allow for coherent building of
#' predictions across multiple models.  Unlike many base classes in this 
#' package, it is meant to be directly used.
#'
#' @param loglik An \code{\link{lpdf}} instance, specifically that starts with 
#' \code{loglik}, to build the predictor
#' @field predictor$update(x) update the current input to \code{x} for prediction
#' @field predictor$mean() return the vector of means for the prediction
#' @field predictor$var() return the vector of variances for the prediction
#' @field predictor$setnthreads(k) specifics \code{k} as the number of threads
#' to use
#' @returns no returns, this is a class which contains methods
NULL