--- a
+++ b/data-raw/lmmSpline-method.R
@@ -0,0 +1,534 @@
+# Jasmin Straube, Queensland Facility of Advanced Bioinformatics
+# Part of this script was borrowed from the lm function from the Stats package the lme function of the nlme package
+# and functions from the lmeSpline, reshape and gdata packages
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Moleculesral Public License
+# as published by the Free Software Foundation; either version 2
+# of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Moleculesral Public License for more details.
+#
+# You should have received a copy of the GNU Moleculesral Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
+
+
+#' Data-driven linear mixed effect model spline modelling
+#' 
+#' Function that models a linear or limear mixed model depending on the best fit. Alternatively, the function can return THE derivation information of the fitted models
+#' for the fixed (original) times points and a chosen \code{basis}.
+#' 
+#' @import methods 
+#' @importFrom nlme lme lmeControl pdIdent pdDiag
+#' @importFrom parallel parLapply detectCores makeCluster clusterExport stopCluster
+#' @importFrom gdata drop.levels
+#' @importFrom lmeSplines smspline approx.Z
+#' @importFrom reshape2 melt dcast
+#' @importFrom stats lm predict.lm predict anova quantile na.exclude
+#' @usage lmmSpline(data, time, sampleID, timePredict, deri, basis, knots, keepModels,numCores)
+#' @param data \code{data.frame} or \code{matrix} containing the samples as rows and features as columns
+#' @param time \code{numeric} vector containing the sample time point information.
+#' @param sampleID \code{character}, \code{numeric} or \code{factor} vector containing information about the unique identity of each sample
+#' @param timePredict \code{numeric} vector containing the time points to be predicted.  By default set to the original time points observed in the experiment.
+#' @param deri \code{logical} value. If \code{TRUE} returns the predicted derivative information on the observed time points.By default set to \code{FALSE}.
+#' @param basis \code{character} string. What type of basis to use, matching one of \code{"cubic"}, \code{"p-spline"} or \code{"cubic p-spline"}. The \code{"cubic"} basis (\code{default}) is the cubic smoothing spline as defined by Verbyla \emph{et al.} 1999, the \code{"p-spline"} is the truncated p-spline basis as defined by Durban \emph{et al.} 2005.
+#' @param knots Alternatively an \code{integer}, the number of knots used for the \code{"p-spline"} or \code{"cubic p-spline"} basis calculation. Otherwise calculated as proposed by Ruppert 2002. Not used for the "cubic" smoothing spline basis as it used the inner design points.
+#' @param keepModels alternative \code{logical} value if you want to keep the model output. Default value is FALSE
+#' @param numCores Alternative \code{numeric} value indicating the number of CPU cores to be used. Default value is automatically estimated.
+#' @details  
+#' The first model (\code{modelsUsed}=0) assumes the response is a straight line not affected by individual variation. 
+#' 
+#' Let \eqn{y_{ij}(t_{ij})} be the expression of a feature for individual (or biological replicate) \eqn{i} at time \eqn{t_{ij}}, where \eqn{i=1,2,...,n}, \eqn{j=1,2,...,m_i}, \eqn{n} is the sample size and \eqn{m_i} is the number of observations for individual \eqn{i} for the given feature. 
+#' We fit a simple linear regression of expression \eqn{y_{ij}(t_{ij})} on time \eqn{t_{ij}}. 
+#' The intercept \eqn{\beta_0} and slope \eqn{\beta_1} are estimated via ordinary least squares:
+#' \eqn{y_{ij}(t_{ij})= \beta_0 + \beta_1 t_{ij} + \epsilon_{ij}}, where \eqn{\epsilon_{ij} ~ N(0,\sigma^2_{\epsilon}).}
+#' The second model (\code{modelsUsed}=1) is nonlinear where the straight line in regression replaced with a curve modelled using here for example a spline truncated line basis (\code{basis}="p-spline") as proposed Durban \emph{et al.} 2005:
+#' 
+#' \deqn{y_{ij}(t_{ij})= f(t_{ij}) +\epsilon_{ij},} 
+#' 
+#' where \eqn{\epsilon_{ij}~ N(0,\sigma_{\epsilon}^2).}
+#' 
+#' The penalized spline is represented by \eqn{f}, which depends on a set of knot positions \eqn{\kappa_1,...,\kappa_K} in the range of \eqn{{t_{ij}}}, some unknown coefficients \eqn{u_k}, an intercept \eqn{\beta_0} and a slope \eqn{\beta_1}. The first term in the above equation can therefore be expanded as:
+#' \deqn{f(t_{ij})= \beta_0+ \beta_1t_{ij}+\sum\limits_{k=1}^{K}u_k(t_{ij}-\kappa_k)_+,}
+#' with \eqn{(t_{ij}-\kappa_k)_+=t_{ij}-\kappa_k}, if \eqn{t_{ij}-\kappa_k  > 0, 0} otherwise.
+#' 
+#' The choice of the number of knots \eqn{K} and their positions influences the flexibility of the curve. 
+#' If the argument \code{knots}=missing, we use a method proposed by Ruppert 2002 to estimate the number of knots given the measured number of time points \eqn{T}, so that the knots \eqn{\kappa_1 \ldots \kappa_K} are placed at quantiles of the time interval of interest: 
+#'
+#' \deqn{K= max(5,min(floor(\frac{T}{4}) , 40)).}
+#' 
+#' In order to account for individual variation, our third model (\code{modelsUsed}=2) adds a subject-specific random effect \eqn{U_i} to the mean response \eqn{f(t_{ij})}. 
+#' Assuming \eqn{f(t_{ij})} to be a fixed (yet unknown) population curve, \eqn{U_i} is treated as a random realization of an underlying Gaussian process with zero-mean and variance \eqn{\sigma_U^2} and is independent from the random error \eqn{\epsilon_{ij}}:
+#' 
+#' \deqn{y_{ij}(t_{ij}) = f(t_{ij}) + U_i + \epsilon_{ij}}
+#' 
+#' with \eqn{U_{i} ~ N(0,\sigma_U^2)} and \eqn{\epsilon_{ij} ~ N(0,\sigma_{\epsilon}^2)}.
+
+#' In the equation above, the individual curves are expected to be parallel to the mean curve as we assume the individual expression curves to be constant over time.
+#' A simple extension to this model is to assume individual deviations are straight lines. The fourth model (\code{modelsUsed}=3) therefore fits individual-specific random intercepts \eqn{a_{i0}} and slopes \eqn{a_{i1}}:
+#' 
+#'  \deqn{y_{ij}(t_{ij}) = f(t_{ij}) + a_{i0} + a_{i1}t_{ij} + \epsilon_{ij}}
+#'  
+#' with \eqn{\epsilon_{ij} ~ N(0,\sigma_\epsilon^2)} and \eqn{(a_{i0},a_{i1})^T} ~ \eqn{ N(0,\Sigma).}
+#' We assume independence between the random intercept and slope.
+#'  @return lmmSpline returns an object of class \code{lmmspline} containing the following components:
+#'  \itemize{
+#' \item{predSpline}{\code{data.frame} containing predicted values based on linear model object or linear mixed effect model object.}
+#' \item{modelsUsed}{\code{numeric} vector indicating the model used to fit the data. 0 = linear model, 1=linear mixed effect model spline (LMMS) with defined basis ('cubic' by default) 2 = LMMS taking subject-specific random intercept, 3 = LMMS with subject specific intercept and slope.}
+#' \item{model}{\code{list} of models used to model time profiles.}
+#' \item{derivative}{\code{logical} value indicating if the predicted values are the derivative information.}
+#'  }
+#' @references  Durban, M., Harezlak, J., Wand, M. P., & Carroll, R. J. (2005). \emph{Simple fitting of subject-specific curves for longitudinal data.} Stat. Med., 24(8), 1153-67.
+#' @references  Ruppert, D. (2002). \emph{Selecting the number of knots for penalized splines.} J. Comp. Graph. Stat. 11, 735-757
+#' @references  Verbyla, A. P., Cullis, B. R., & Kenward, M. G. (1999). \emph{The analysis of designed experiments and longitudinal data by using smoothing splines.} Appl.Statist, 18(3), 269-311.
+#' @references  Straube J., Gorse A.-D., Huang B.E., Le Cao K.-A. (2015).  \emph{A linear mixed model spline framework for analyzing time course 'omics' data} PLOSONE, 10(8), e0134540.
+# @seealso \code{\link[lmms]{summary.lmmspline}}, \code{\link[lmms]{plot.lmmspline}}, \code{\link[lmms]{predict.lmmspline}}, \code{\link[lmms]{deriv.lmmspline}}
+#' @examples 
+#' \dontrun{
+#' data(kidneySimTimeGroup)
+#' # running for samples in group 1
+#' G1 <- which(kidneySimTimeGroup$group=="G1")
+#' testLMMSpline<- lmmSpline(data=kidneySimTimeGroup$data[G1,],time=kidneySimTimeGroup$time[G1],
+#'                  sampleID=kidneySimTimeGroup$sampleID[G1])
+#' summary(testLMMSpline)
+#' DerivTestLMMSplineTG<- lmmSpline(data=as.data.frame(kidneySimTimeGroup$data[G1,]),
+#'                        time=kidneySimTimeGroup$time[G1],sampleID=kidneySimTimeGroup$sampleID[G1],
+#'                        deri=TRUE,basis="p-spline")
+#' summary(DerivTestLMMSplineTG)}
+# setGeneric('lmmSpline',function(data,time,sampleID,timePredict,deri,basis,knots,keepModels,numCores){standardGeneric('lmmSpline')})
+# setClassUnion("matrixOrFrame",c('matrix','data.frame'))
+# setClassUnion("missingOrnumeric", c("missing", "numeric"))
+# setClassUnion("missingOrcharacter", c("missing", "character"))
+# setClassUnion("missingOrlogical", c("missing", "logical"))
+# setClassUnion("factorOrcharacterOrnumeric", c("factor", "character","numeric"))
+# # @rdname lmmSpline-methods
+# # @aliases lmmSpline,matrixOrFrame,numeric,factorOrcharacterOrnumeric,
+# # missingOrlogical,missingOrcharacter,missingOrnumeric,missingOrlogical,missingOrnumeric-method
+# # @exportMethod lmmSpline
+# 
+# setMethod('lmmSpline',c(data="matrixOrFrame",time="numeric",sampleID="factorOrcharacterOrnumeric",timePredict="missingOrnumeric", deri="missingOrlogical", basis="missingOrcharacter",knots="missingOrnumeric",keepModels="missingOrlogical",numCores="missingOrnumeric"), function(data,time,sampleID,timePredict,deri,basis,knots,keepModels,numCores){
+#   
+#    lmmSplinePara(data=data,time=time,sampleID=sampleID,timePredict=timePredict,deri=deri,basis=basis, knots=knots,keepModels=keepModels,numCores=numCores)
+# })
+# @name lmmSpline
+
+#' @docType methods
+#' @rdname lmmSpline-methods
+#' @importFrom parallel detectCores parLapply clusterExport 
+#' @export
+lmmSpline <- function(data, time, sampleID, timePredict, deri, basis, knots,keepModels, numCores){
+    
+    if(missing(keepModels))
+        keepModels <- F
+    if(missing(timePredict))
+        timePredict <- sort(unique(time))
+    if(missing(basis))
+        basis <- "cubic"
+    
+    if(missing(deri)){
+        deri <- FALSE
+    }else{
+        deri <- deri
+    }
+    
+    basis.collection <-  c("cubic","p-spline","cubic p-spline")
+    if(!basis%in% basis.collection)
+        stop(cat("Chosen basis is not available. Choose:", paste(basis.collection,collapse=', ')))
+    if(diff(range(c(length(sampleID),length(time),nrow(data))))>0)
+        stop("Size of the input vectors rep, time and nrow(data) are not equal")
+    if(missing(knots)& (basis=="p-spline"|basis=='cubic p-spline'))
+        warning("The number of knots is automatically estimated")
+    if(deri & basis=='cubic')
+        stop('To calculate the derivative choose either "p-spline" or "cubic p-spline" as basis')
+    
+    options(show.error.messages = TRUE) 
+    
+    i <- NULL
+    fits <- NULL
+    error <- NULL
+    
+    if(missing(numCores)){
+        num.Cores <- detectCores()
+    }else{
+        num.Cores <- detectCores()
+        if(num.Cores<numCores){
+            warning(paste('The number of cores is bigger than the number of detected cores. Using the number of detected cores',num.Cores,'instead.'))
+        }else{
+            num.Cores <- numCores
+        }
+        
+    }
+    Molecule <- ''
+    
+    derivLme <- function(fit){ 
+        #random slopes
+        
+        if(class(fit)=='lm'){
+            beta.hat <- rep(fit$coefficients[2],length(unique(fit$model$time)))
+            return(beta.hat)
+            
+        }else if(class(fit)=='lme'){
+            u <- unlist(fit$coefficients$random$all)
+            beta.hat <- fit$coefficients$fixed[2]
+            Zt <-  fit$data$Zt[!duplicated(fit$data$Zt),]>0
+            deriv.all <-    beta.hat + rowSums(Zt%*%t(u)) 
+            return(deriv.all)
+        }
+    }
+    
+    #penalized cubic
+    
+    derivLmeCubic <- function(fit){ 
+        #random slopes
+        if(class(fit)=='lm'){
+            beta.hat <- rep(fit$coefficients[2],length(unique(fit$model$time)))
+            return(beta.hat)
+            
+        }else if(class(fit)=='lme'){
+            u <- unlist(fit$coefficients$random$all)
+            beta.hat <- fit$coefficients$fixed[2]
+            PZ <-  fit$data$Zt[!duplicated(fit$data$Zt),]
+            PZ <-PZ^(1/3)
+            deriv.all <-    beta.hat + rowSums((PZ*PZ)%*%(t(u)*3)) 
+            return(deriv.all)
+        }
+        
+    }
+    
+    if(missing(knots))
+        knots <-NULL
+    nMolecules <- NULL
+    nMolecules <- ncol(data)
+    
+    
+    lme <- nlme::lme
+    cl <- makeCluster(num.Cores,"SOCK")
+    clusterExport(cl, list('data','lm','try','class','unique','anova','drop.levels','pdDiag','pdIdent','time','sampleID','melt','dcast','predict','derivLme','knots','derivLmeCubic','lme','keepModels','basis','data','other.reshape'),envir=environment())
+    
+    models <-list()
+    
+    
+    new.data <- parLapply(cl,1:nMolecules,fun = function(i){
+    # new.data <- list()
+    # for(i in 1:nMolecules){
+
+        expr <- data[,i]
+        
+        dataM <- as.data.frame(other.reshape(Rep=sampleID,Time=time,Data=unlist(expr)))
+        dataM$all = rep(1, nrow(dataM))
+        dataM$time = as.numeric(as.character(dataM$Time))
+        dataM$Expr = as.numeric(as.character(dataM$Expr))
+        
+        
+        #### CUBIC SPLINE BASIS ####
+        if(basis=="cubic"){
+            dataM$Zt <- lmeSplines::smspline(~ time, data=dataM)
+            knots <- sort(unique(time))[2:(length(unique(time))-1)]
+        }
+        #### PENALIZED SPLINE BASIS#####
+        if(basis%in%c("p-spline","cubic p-spline")){
+            
+            if(is.null(knots)){
+                K <- max(6,min(floor(length(unique(dataM$time))/4),40))
+            }else{
+                K <- max(knots,6)
+            }
+            knots <- quantile(unique(dataM$time),seq(0,1,length=K+2))[-c(1,K+2)]
+            if(min(knots)<=min(dataM$time) | max(knots)>=max(dataM$time))
+                stop(cat('Make sure the knots are within the time range',range(dataM$time)[1],'to',range(dataM$time)[2]))
+            PZ <- outer(dataM$time,knots,"-")
+            if(basis=="cubic p-spline")
+                PZ <- PZ^3
+            PZ <- PZ *(PZ>0)
+            dataM$Zt <- PZ 
+            
+        }
+        
+        
+        
+        if(deri){
+            pred.spline = rep(NA,length(timePredict))
+        }else{
+            pred.spline =rep(NA,length(timePredict))
+            pred.df <- data.frame(all=rep(1,length(timePredict)), time=timePredict)
+            pred.df$Zt = lmeSplines::approx.Z(dataM$Zt, dataM$time, timePredict)
+            
+        }
+        
+        
+        
+        #library(nlme)
+        fit0 <- NULL
+        fit0  <- try(lm(Expr ~ time, data=dataM ))
+        if(class(fit0) == 'try-error') {
+            models <- list()
+            error <- i
+            pred.spline <- rep(NA,length(timePredict))
+            fits <- NA
+        }else{
+            fit1 <- NULL
+            fit1 <- try(lme(Expr ~ time, data=dataM, random=list(all=pdIdent(~Zt - 1)),
+                            na.action=na.exclude, control=lmeControl(opt = "optim"))) 
+            pvalue <-1
+            if(class(fit1) != 'try-error') { 
+                
+                pvalue <- anova(fit1, fit0)$'p-value'[2]
+                
+            }
+            
+            if(pvalue <= 0.05){  
+                
+                fit2 <- NULL
+                fit2 <- try(lme(Expr ~ time, data=dataM, 
+                                random=list(all=pdIdent(~Zt - 1), Rep=pdIdent(~1)), 
+                                na.action=na.exclude, control=lmeControl(opt = "optim")))
+                
+                if(class(fit2) != 'try-error') {  # to prevent errors stopping the loop
+                    
+                    pvalue = anova(fit1, fit2)$'p-value'[2]
+                }else{ 
+                    pvalue=1
+                }
+                
+                if(pvalue <= 0.05){  
+                    fit3 <-NULL
+                    fit3 <- try(lme(Expr ~ time, data=dataM, 
+                                    random=list(all=pdIdent(~Zt - 1), Rep=pdDiag(~time)), 
+                                    na.action=na.exclude, control=lmeControl(opt = "optim")))  
+                    
+                    if(class(fit3) != 'try-error') {  # to prevent errors stopping the loop
+                        pvalue = anova(fit2, fit3)$'p-value'[2]
+                        
+                    }else{
+                        pvalue=1
+                    }
+                    if(pvalue <= 0.05){
+                        fits <- 3
+                        models<- fit3
+                        if(deri){
+                            if(basis=='p-spline')
+                                pred.spline = derivLme(fit3)
+                            if(basis=='cubic p-spline')
+                                pred.spline = derivLmeCubic(fit3)
+                            
+                        }else{
+                            pred.spline = predict(fit3, newdata=pred.df, level=1, na.action=na.exclude)
+                        }
+                    }else{ # choose simpler model: fit2
+                        fits <- 2
+                        models <- fit2
+                        if(deri){
+                            if(basis=='p-spline')
+                                pred.spline = derivLme(fit2)
+                            if(basis=='cubic p-spline')
+                                pred.spline = derivLmeCubic(fit2)
+                        }else{
+                            pred.spline = predict(fit2, newdata=pred.df, level=1, na.action=na.exclude)
+                        }
+                    } 
+                    
+                }else{ 
+                    models <- fit1
+                    fits <- 1
+                    if(deri){
+                        if(basis=='p-spline')
+                            pred.spline = derivLme(fit1)
+                        if(basis=='cubic p-spline')
+                            pred.spline = derivLmeCubic(fit1)
+                    }else{
+                        pred.spline = predict(fit1, newdata=pred.df, level=1, na.action=na.exclude)
+                    }
+                }
+            }else{ 
+                
+                models <- fit0
+                fits <-0
+                if(deri){
+                    pred.spline = rep(fit0$coefficients[2],length(unique(dataM$time)))    
+                }else{
+                    
+                    pred.spline = predict(fit0, newdata=pred.df, level=1, na.action=na.exclude)
+                }
+            }
+        }
+        if(!keepModels)
+            keepModels <- list()
+        
+        return(list(pred.spl=pred.spline,fit=fits,models=models,error=error,knots=knots))
+        #new.data[[i]] <- list(pred.spl=pred.spline,fit=fits,models=models,error=error,knots=knots)
+        
+    })
+    #}
+    stopCluster(cl)
+    knots <- sort(unique(as.vector((sapply(new.data,'[[','knots')))))
+    pred.spl <- matrix(sapply(new.data,'[[','pred.spl'),nrow=nMolecules,byrow=T)
+    fits <-  unlist(sapply(new.data,'[[','fit'))
+    error <-  unlist(sapply(new.data,'[[','error'))
+    models <-list()
+    if(keepModels){
+        models <- sapply(new.data,'[[','models')
+        
+        if(is.matrix(models))
+            models <- sapply(new.data,'[','models')
+    }
+    
+    pred.spl = as.data.frame(pred.spl)
+    MolNames <- as.character(unlist(colnames(data)))
+    
+    if(is.null(MolNames)| sum(is.na(MolNames))>0)
+        MolNames <- 1:nrow(pred.spl)
+    if(nrow(pred.spl)==length(MolNames))
+        rownames(pred.spl)<-MolNames
+    if(ncol(pred.spl)==length(timePredict))
+        colnames(pred.spl) <- timePredict
+    error2 <- "All features were modelled"
+    if(length(error)>0){
+        warning('The following features could not be fitted ',paste(MolNames[error],' ',sep='\n'))
+        error2 <- ''
+        error2 <- MolNames[error]
+        
+    }
+    
+    l <-new('lmmspline',predSpline=pred.spl,modelsUsed=fits,basis=basis,knots=knots,errorMolecules=error2,models=models, derivative=deri)
+    return(l)
+    
+}
+
+
+other.reshape <- function(Rep, Time, Data){
+    lme.data<-NULL
+    lme.data <- data.frame(Time=Time,Rep=Rep,as.matrix(Data))
+    lme.data$Time = factor(drop.levels(lme.data$Time))
+    lme.data$Rep = factor(drop.levels(lme.data$Rep))
+    melt.lme.data <-NULL
+    melt.lme.data <- melt(lme.data)
+    cast.lme.data  <- NULL
+    cast.lme.data <- dcast(melt.lme.data, variable+ Rep ~ Time)
+    melt.lme.data2 <- NULL
+    melt.lme.data2 <-  melt(data.frame(cast.lme.data))
+    names(melt.lme.data2) <- c("Molecule",  "Rep", "Time", "Expr")
+    melt.lme.data2$Time <- factor(gsub("^X", "", as.character(melt.lme.data2$Time)))
+    return(as.data.frame(melt.lme.data2))
+}
+
+
+
+#' \code{lmms} class a S4 superclass to extend \code{lmmspline}  and \code{lmmsde} class.
+#'
+#' \code{lmms} class is a superclass for classes \code{lmmspline}  and \code{lmmsde}. These classes inherit common slots.
+#'
+#' @slot basis  An object of class \code{character} describing the basis used for modelling.
+#' @slot knots An object of class \code{numeric}, describing the boundaries of the splines. If not defined or if basis='cubic' knots are automatically estimated using Ruppert 2002 or are the design points when using 'cubic'. 
+#' @slot errorMolecules Vector of class \code{character}, describing the molecules that could not be modelled.
+#'
+#' @name lmms-class
+#' @rdname lmms-class
+#' @exportClass lmms
+
+
+setClass('lmms',slots=c(basis="character", knots="numeric",errorMolecules="character"))
+
+#' \code{lmmspline} class a S4 class that extends \code{lmms} class.
+#'
+#' \code{lmmspline} class inherits from class \code{lmms} and extends it with three further slots: \code{predSpline}, \code{modelsUsed}, \code{models}. The class is returned when applying \code{\link{lmmSpline}} method.
+#'
+#' @slot predSpline A \code{data.frame} returning the fitted values for the time points of interest.
+#' @slot models  A \code{list} of class \code{\link{lm}} or  \code{\link{lme}} containing the models for every molecule
+#' @slot modelsUsed A \code{list} of class \code{lm} or \code{lme}, containing the models used to model the particular feature of interest. 
+#' @slot derivative A \code{logical} value indicating if the derivative was calculated.
+#' 
+#'
+#' @name lmmspline-class
+#' @rdname lmmspline-class
+#' @exportClass lmmspline
+
+setClass("lmmspline",slots= c(predSpline="data.frame", modelsUsed="numeric",models="list",derivative='logical'),contains='lmms')
+
+
+#' Predicts fitted values of an \code{lmmspline} Object
+#' 
+#' Predicts the fitted values of an \code{lmmspline} object for time points of interest.
+#' 
+#' @importFrom parallel parLapply
+#' @importFrom lmeSplines approx.Z
+#' @param object an object inheriting from class \code{lmmspline}.
+#' @param timePredict an optional \code{numeric} vector. Vector of time points to predict fitted values. If \code{missing} uses design points. 
+#' @param numCores alternative \code{numeric} value indicating the number of CPU cores to be used for parallelization. By default estimated automatically.
+#' @param ... ignored.
+#' @return \code{matrix} containing predicted values for the requested time points from argument \code{timePredict}. 
+#' @examples
+#' \dontrun{
+#' data(kidneySimTimeGroup)
+#' G1 <- which(kidneySimTimeGroup$group=="G1")
+#' testLMMSpline<- lmmSpline(data=kidneySimTimeGroup$data[G1,],
+#'                  time=kidneySimTimeGroup$time[G1],
+#'                  sampleID=kidneySimTimeGroup$sampleID[G1],keepModels=T)
+#' mat.predict <- predict(testLMMSpline, timePredict=c(seq(1,4, by=0.5)))}
+
+#' @export
+predict.lmmspline<- function(object, timePredict, numCores, ...){
+    
+    if(missing(timePredict)){
+        return(object@pred.spline)
+    }else{
+        
+        
+        
+        models <- object@models
+        
+        if(length(models)==0)
+            stop('You will need to keep the models to predict time points.')
+        cl <-sapply(models,class)
+        i <- which(cl=="lme")[1]
+        if(length(i)>0){
+            lme.model <- models[[i]]
+            t <- na.omit(lme.model$data$time)
+            
+            pred.spline <- rep(NA,length(timePredict))
+            pred.df <- data.frame(all=rep(1,length(timePredict)), time=timePredict)
+            pred.df$Zt = approx.Z(lme.model$data$Zt, lme.model$data$time, timePredict)
+            
+        }else{
+            lme.model <- models[[i]]
+            i <- which(cl=="lm")[1]
+            t <- na.omit(lme.model$model$time)
+            pred.df <- data.frame(x=timePredict)
+        }
+        if(min(timePredict)<min(t) | max(timePredict)>max(t))
+            stop(cat('Can only predict values within the time range',range(t)[1],'to',range(t)[2]))
+        
+        
+        if(missing(numCores)){
+            num.Cores <- detectCores()
+        }else{
+            num.Cores <- detectCores()
+            if(num.Cores<numCores){
+                warning(paste('The number of cores is bigger than the number of detected cores. Using the number of detected cores',num.Cores,'instead.'))
+            }else{
+                num.Cores <- numCores
+            }
+            
+        }
+        lme <- nlme::lme
+        cl <- makeCluster(num.Cores,"SOCK")
+        clusterExport(cl, list('models','pred.spline','pred.df','predict'),envir=environment())
+        
+        new.data <- parLapply(cl,1:length(models),fun = function(i){
+            # library(nlme)
+            cl <- class(models[[i]])
+            pred.spline <- switch(cl,
+                                  lm=predict.lm(models[[i]], newdata=pred.df, level=1, na.action=na.exclude),
+                                  lme=predict(models[[i]], newdata=pred.df, level=1, na.action=na.exclude)          
+            )
+            return(pred.spline)
+            
+        })
+        
+        stopCluster(cl)
+        pred.spl <- matrix(unlist(new.data),nrow=length(models),ncol=length(timePredict),byrow=T)
+        return(pred.spl)}
+}
\ No newline at end of file