version 1.5.1

cran · Feb 20, 2021 · 073041c · 073041c
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -3,11 +3,11 @@ Type: Package
 Title: Inference Using Simulation
 Description: Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated (Rousset et al. 2017 <doi:10.1111/1755-0998.12627>).  
 Encoding: UTF-8
-Version: 1.4.1
-Date: 2019-08-19
-Imports: spaMM (>= 2.4.35), proxy, blackbox (>= 1.0.14), mvtnorm,
+Version: 1.5.1
+Date: 2021-02-20
+Imports: spaMM (>= 3.6.0), proxy, blackbox (>= 1.0.14), mvtnorm,
         methods, numDeriv, viridis, pbapply, foreach
-Suggests: testthat, ranger
+Suggests: testthat, ranger, Rmixmod, crayon
 Depends: R (>= 3.3.0)
 Authors@R: c(person("François", "Rousset", role = c("aut", "cre", "cph"),
                      email = "francois.rousset@umontpellier.fr",
@@ -16,10 +16,10 @@ Maintainer: François Rousset <francois.rousset@umontpellier.fr>
 License: CeCILL-2
 ByteCompile: true
 URL: https://www.R-project.org,
-        http://kimura.univ-montp2.fr/~rousset/Infusion.htm
+        https://kimura.univ-montp2.fr/~rousset/Infusion.htm
 NeedsCompilation: no
-Packaged: 2019-08-19 12:44:56 UTC; Francois.rousset
+Packaged: 2021-02-20 14:54:06 UTC; francois.rousset
 Author: François Rousset [aut, cre, cph]
     (<https://orcid.org/0000-0003-4670-0371>)
 Repository: CRAN
-Date/Publication: 2019-08-19 13:20:02 UTC
+Date/Publication: 2021-02-20 15:20:02 UTC
diff --git a/MD5 b/MD5
@@ -1,58 +1,66 @@
-db4ee460c3a2f22b952db2c4acdfbcc3 *DESCRIPTION
-971b0cac3011722d6f674863349e8bbb *NAMESPACE
+f7f10efe45cb4d6409e7351c3f3e8752 *DESCRIPTION
+b702190cc1e266caec2325ae5baf943b *NAMESPACE
 4e481e4e0a846065f17d6f628feae386 *R/Infusion_internals.R
-94586f4bea762be8174df9e323a7fc4b *R/MSL.R
-b577bcf81bf1ae1ffdb2921dcae310e3 *R/Rmixmod.R
-b4ca1f6ec27c4c0d62381faf6130f57d *R/SLik.R
-8cbafca3e57c32c63f8441cd5745bd35 *R/SLikSQuant.R
-c266f27e97083770e757df949d2a7ff9 *R/add_simulation.R
-00759c383aad4086d108fef57d48624a *R/calc_all_slices.R
+f1ea3182bf037f19af378be9018301a5 *R/MSL.R
+e91c8c331a9c5fca5c93479b28c3fe6d *R/Rmixmod.R
+d52144748bd7ffd6cf2eb0fa1ba524f3 *R/SLik.R
+296a79a5db0c96f1f4d30f411adb696c *R/SLikSQuant.R
+7dce6358161b6ef89f65e0b1d401f345 *R/add_simulation.R
+47db93e402daa056712fe4475fe8ee9b *R/calc_all_slices.R
 2852ced0beb27ba924909bfc60d1bcdd *R/check_logLs_input.R
-9be59f46ea85ab1e79ba52a72dd2e6d8 *R/confintLR.SLik.R
-ab25fd3ca976c927d0d66d1ece1e478d *R/coresWrapper.R
+9503a722442df6e033d186cb905a7eea *R/confintLR.SLik.R
+52fe74950f83a03975dbb30b2dabd7f1 *R/coresWrapper.R
+ad5a270498e6755bf56ab113651783aa *R/extractors.R
 ae5893cbe4570a7cad1759cc7789c0ad *R/generate_data.R
-1b4601da02a1fda17282220491ef384e *R/infer_logLs.R
+93ba578718b7e4bad0d7c2d19ed89913 *R/goodness.R
+81296f482cfe6ad0aac2d6b9158521f3 *R/infer_logLs.R
 3aa6c2744f7f7d2729afebd1bf29d2c8 *R/infer_surface.R
-20712244dde5904874f8f9f645c7ae45 *R/jointDens.R
+5c3c312e9b002e71733ceea74cdd2c8c *R/jointDens.R
 44c4c5ea1d0f7bf5a3d3ce85a06b9abf *R/keras.R
-6809f3b471e27dcfe0a3223fa47b28dc *R/mclust.R
+c0d1e8dea83ba662ca71b6ba00f9c52c *R/mclust.R
 9643a88e4d07d9057edbcf9661eebdb1 *R/minImIze.R
-1500b206fbb1dcb877b7e385b49f552e *R/newParameters.R
-43c726cc31e626952e3c8c5589687617 *R/plot.SLik.R
-35600e1883f57c2b1c14246386c1bd2b *R/plot.SLik_j.R
-1696f734f7cb37f8e35b1930efde751e *R/plotProfiles.R
-b38492f54ac1119a9b3ac795dd1203d0 *R/profile.R
-7a8955ec1b2a4dc6fdc88c91b6b2659b *R/project.R
-07e5be4727ee6b373d3d7e21b4f5d7b7 *R/quantiles.R
-a6dc7947965db047f2b06bbb28100b02 *R/undocumentedFns.R
+782796dd199cef011d5ae9bdf203b91c *R/newParameters.R
+fdfc545b76459207f9bfb641a1cc9e68 *R/plot.SLik.R
+a393cc6b5c0cc872100ef05e2c0ebcf6 *R/plot.SLik_j.R
+d5b5fdc6b98ab9f39b28b6ec0a963f6e *R/plotProfiles.R
+736551e587421c1d03bb70a70520d971 *R/profile.R
+28454fd3b1f1b8bdeaee46ae838bfab4 *R/project.R
+533b5e557aaaf6e9b9dce6b3a99e54fd *R/quantiles.R
+41f581b82fa7247a6c0474ca7c6ba920 *R/safe_constrOptim.R
+118f8922d8a7d4a5c2551a2b067e6fa7 *R/undocumentedFns.R
 34fddea1d6c7f921532196783a931a65 *data/densb.RData
 88964b1928e06bc43a9981502af91c50 *data/densv.RData
 fdb5abb3d4e2119b1cd8f06e16eafd1e *data/myrnorm.RData
 e1ffc821b36641fb7b17c6bb65a4c643 *inst/CITATION
-8ceea7cdb6f9c33e228f86734df55fa2 *inst/NEWS.Rd
-e0041773c0c2d38315603ee620136a60 *man/Infusion-internal.Rd
+6b7452614030606b1f9f3923d0048812 *inst/NEWS.Rd
+fed9e6c7bf207b81adbb59b50282b783 *man/Infusion-internal.Rd
 0c7577558ea55f5de6ed55cf266e1425 *man/Infusion.Rd
-cd1a1274c5f20b4c8f9147a8b87ba2ac *man/MSL.Rd
-439a55d5072c8e422502934747257025 *man/add.simulation.Rd
+eb56d06e84da7e679114e34b694ce272 *man/MSL.Rd
+3d4ee7bc97e254fcdb24bcbb3992e544 *man/add.simulation.Rd
 28df4c638f4caf9bc6f9a0bf175c7c81 *man/confint.SLik.Rd
-4eb05cf905ed92f08bd062801513b421 *man/dMixmod.Rd
+055cdf883a274b3ef9ef07e90e32a107 *man/dMixmod.Rd
 8c3d919177eb0f3983ac3eabe7856464 *man/densv.Rd
-a3413ad8c24f7d6bf413a27d2393fbd0 *man/example_raw.Rd
-b0b17350b7cb335fba789fd55c131d42 *man/example_reftable.Rd
+6b0ece191ff20e51eb96c7f673265c49 *man/example_raw.Rd
+3c9dd234364b0ae3432d134f1268d6d5 *man/example_reftable.Rd
+18c31f96efa117b8e2cf7ddddf2801e4 *man/extractors.Rd
+8fffb74ed9dde9605559f459c641ea3b *man/get_from.Rd
+8b282d61f411f1ee801c7f9d0d72a684 *man/goftest.Rd
 4dfc80a6b2cfb254dbd14aad0698e14a *man/handling_NAs.Rd
-87044eb367194aabd0a6f5c4e5bd704e *man/infer_jointDens.Rd
-ceb4ab68b415b7a1d43c11c9ca2ca9e7 *man/infer_logLs.Rd
+343c8a5b05b118b985ae4c06e7874f38 *man/infer_jointDens.Rd
+f4bc71913b26919b85bfe1bc8878cd47 *man/infer_logLs.Rd
 f94d16ce86a5a3978a74b487e65ecefa *man/infer_surface.Rd
 ca5af1babe4982be52f8cfa29d72a153 *man/init_grid.Rd
 7e196bf5012b1fd547ca8ec71b44da90 *man/magic.binning.Rd
-a4e27a2c816e8f90ab2ce9a331671dc4 *man/options.Rd
-e068210c20cd3f2efa6d8e76555de6bf *man/plot.SLik.Rd
-9a28ededdfe79e826916cd22b51c6ebd *man/plot1Dprof.Rd
-d164cc8ff6a078b7558f5af07c5ffb5d *man/profile.SLik.Rd
-64b27eab6e55a02277959abab234bdd0 *man/project.Rd
-d0bffe2ffffbb264e852e60890ac6919 *man/refine.SLik.Rd
+92cc9a9b5e5fbfdc0ded4392028afd8e *man/options.Rd
+b3ae0d999b8e683e86e0ff2fa84d4fe0 *man/plot.SLik.Rd
+fb60f7192ebc309f4fc9bca2036ab5b3 *man/plot1Dprof.Rd
+b48374fbeeae9215f69344612ea267a6 *man/profile.SLik.Rd
+756e0bce59cffe143a0b80404b746f52 *man/project.Rd
+10e5d78c0b7dcabbb492c695dc63e827 *man/refine.SLik.Rd
 b69d077e06bde675d1202e327ef9b8f6 *man/sample_volume.Rd
-d6cebc9855a45ca2bb1e2a2fa5239cfb *tests/test-all.R
-428869ad1374d693382df2088bf2b9db *tests/testthat/test-3par.R
-d19035ada3a28cdc5372fbeb2e452a3a *tests/testthat/test-Infusion.R
-aa4cd6f58873fca8eb4dbe7dd7cf770a *tests/testthat/test-reftable.R
+8b714ec3e3de11c12ba673bbbe0a5cbe *tests/test-all.R
+d13c12a1189e8feb74551b6f35bd9166 *tests/testthat/test-3par.R
+820d6f9848aafd9b72abcb984f4b96b1 *tests/testthat/test-Infusion.R
+731ba44c2e9c51d59e8bcaab456af507 *tests/testthat/test-MSL_updating.R
+2df4144b0bf3ea84f9a7f62693c23430 *tests/testthat/test-localmax.R
+093a9eccd763fc4378aef0dc718795b4 *tests/testthat/test-reftable.R
diff --git a/NAMESPACE b/NAMESPACE
@@ -12,9 +12,9 @@ importFrom("proxy", "dist")
 #importFrom("Rmixmod", "mixmodCluster", "mixmodStrategy", "mixmodGaussianModel", "plotCluster")
 importFrom("spaMM", "corrHLfit", "MaternCorr", "mapMM", "fitme", "spaMM.colors", "fixef", "filled.mapMM", 
            "spaMM.filled.contour", "HLCor", "HLfit", "spaMMplot2D", "get_residVar") ## doc'ed fns
-importFrom("spaMM", "get_ranPars") 
-importFrom("spaMM", "makeTicks") ## undoc fns
-importFrom("stats", "deviance", "glm", "var", "as.formula", "cov", "dnorm", "fitted", "optim", "pchisq", "pnorm", 
+importFrom("spaMM", "get_ranPars", "spaMM.getOption") 
+importFrom("spaMM", "makeTicks", ".safe_opt") ## undoc fns
+importFrom("stats", "deviance", "glm", "var", "as.formula", "cov", "dnorm", "fitted", "optim", "pchisq", "pnorm", "quantile",
            "predict", "qchisq", "runif", "uniroot", "binomial", "poisson", "constrOptim", "dchisq", "profile", "confint", "rnorm")
 importFrom("utils", "packageVersion", "setTxtProgressBar", "txtProgressBar", "packageDescription", "getS3method")
 importFrom("viridis", "viridis")
@@ -35,8 +35,11 @@ S3method(summary, logLs)
 S3method(plot, SLikp)
 S3method(plot, SLik_j)
 S3method(plot,MixmodResults)
+S3method(plot, dMixmod)
 S3method(print, SLikp)
 S3method(summary, SLikp)
+S3method(logLik,SLik)
+S3method(logLik,SLik_j)
 S3method(confint,SLik)
 S3method(confint,SLik_j)
 S3method(confint,SLikp)
@@ -57,3 +60,7 @@ S3method(infer_surface,tailp)
 S3method(calc.lrthreshold,SLik)
 S3method(calc.lrthreshold,SLikp)
 S3method(calc.lrthreshold,default)
+S3method(get_from, SLik)
+S3method(get_from, SLik_j)
+S3method(get_from, default)
+S3method("[", reftable)
diff --git a/R/MSL.R b/R/MSL.R
@@ -42,9 +42,9 @@
 
 # creates <slik>$par_RMSEs 
 .par_RMSEwrapper <- function(object,verbose=interactive()) {
-  RMSEs <- object$RMSEs
-  if( (lenRMSEs <- length(RMSEs))>1L)  {
-    CIs <- object$CIobject$CIs
+  RMSEs <- object$RMSEs$RMSEs
+  CIs <- object$CIobject$CIs
+  if (( ! is.null(CIs)) && (lenRMSEs <- length(RMSEs))>1L)  {
     pars <- names(CIs)
     par_RMSEs <- rep(NA,lenRMSEs-1L) ## (over)size as the MSEs have no NAs
     par_ests <- rep(NA,lenRMSEs-1L) ## (over)size as the MSEs have no NAs
@@ -75,63 +75,12 @@
   } else return(NULL)
 }
 
-.par_wrapper <- function(object,verbose=interactive()) {
-  CIs <- object$CIobject$CIs
-  lenCIs <- length(CIs)    
-  pars <- names(CIs)
-  resu <- rep(NA_real_,NROW(object$CIobject$bounds)) ## (over)size as the MSEs have no NAs
-  names(resu) <- rownames(object$CIobject$bounds)
-  map <- c(low=1L,up=2L)
-  for (nam in rownames(object$CIobject$bounds)) {
-    stterms <- regmatches(nam, regexpr("\\.", nam), invert = TRUE)[[1]] ## regmatches(<...>) splits at the first '.'
-    parm <- stterms[2L]
-    interval <- CIs[[parm]]$interval
-    th <- interval[map[stterms[1L]]]
-    resu[nam] <- th
-  }
-  if (verbose && length(resu)>0L) {
-    par_headline <- "*** Interval estimates ***\n"
-    cat(par_headline)
-    print(resu)
-    return(invisible(resu))
-  } else return(resu)
-}
-
-## summary likelihood ratio (with uncertainty measures) extractor. Unfinished, in particular, need to separate residVar an to handle prior.weights
-.SLR <- function(object,newdata=NULL,variance="predVar",df=NULL) {
-  fittedPars <- object$colTypes$fittedPars
-  if (is.null(newdata)) newdata <- unique(object$logLs[,fittedPars])
-  locdata <- rbind(MSLE=object$MSL$MSLE, ## name needed for spaMM::calcNewCorrs -> newuniqueGeo
-                   newdata)
-  if (variance=="respVar") {
-    logls <- predict(object$fit,newdata=locdata,variances=list(respVar=TRUE,cov=TRUE))
-    covmat <- attr(logls,"respVar")
-    # but problem with prior weights
-  } else {
-    logls <- predict(object$fit,newdata=locdata,variances=list(linPred=TRUE,dispVar=TRUE,cov=TRUE))
-    covmat <- attr(logls,"predVar")
-  }
-  slr <- object$MSL$maxlogL -logls[]
-  MSEs <- c(diag(covmat[-1,-1,drop=FALSE])+covmat[1,1]-2*covmat[1,-1])
-  if ( any(MSEs<0) ) {
-    message("Inaccurate MSE computation, presumably from nearly-singular covariance matrices.")
-    MSEs[MSEs<0] <- NA ## quick patch
-  }
-  RMSEs <- sqrt(MSEs)
-  resu <- rbind(LRstat=2*slr[-1],RMSE=RMSEs)
-  if (!is.null(df)) {
-    resu <- rbind(resu,p_RMSE=RMSEs*dchisq(resu["LRstat",],df=df))
-  }
-  return(resu)
-}
-
-#.SLR(slik)
 
 # both SLik and SLikp, with different methods used in -> allCIs -> confint
 MSL <- function (object,CIs=TRUE,level=0.95, verbose=interactive(),
                  eval_RMSEs=TRUE, #inherits(object,"SLik"), 
+                 cluster_args=list(),
                  ...) { ##
-  # Maximization ## revised 13/07/2016
   fittedPars <- object$colTypes$fittedPars
   if (inherits(object,"SLik")) {
     vertices <- object$fit$data[,fittedPars,drop=FALSE]
@@ -143,33 +92,59 @@ MSL <- function (object,CIs=TRUE,level=0.95, verbose=interactive(),
     lower <- object$lower
     upper <- object$upper
   }
-  # (a constrOptim woul be even better)
-  stat.obs <- object$stat.obs
-  # coeffs <- object$coeffs
+  # (a constrOptim would be even better)
   parscale <- (upper-lower) 
-  if ( is.null(init <- object$optrEDF$par)) { ## ...if inherits(object,"SLik_j")
-    # SLik object:
+  if (inherits(object,"SLik_j")) {
+    pred_data <- object$logLs[,fittedPars, drop=FALSE]
+    if (is.null(object$thr_dpar)) {
+      pardensv <- predict(object,newdata = pred_data, which="parvaldens")
+      object$thr_dpar <- min(max(pardensv)- qchisq(1-(1-level)/2,df=1)/2 , ## __F I X M E__ rethink
+                             quantile(pardensv,probs=1/sqrt(length(pardensv)))
+      )
+      #print(object$thr_dpar)
+    } 
+    predsafe <- predict(object,newdata=pred_data, which="safe") 
+    init <- unlist(pred_data[which.max(predsafe), ])
+  } else {
     init <- unlist(object$obspred[which.max(object$obspred[,attr(object$obspred,"fittedName")]),fittedPars]) 
     init <- init*0.999+ colMeans(vertices)*0.001
   }
-  method <- "L-BFGS-B" ## works also in 1Dand does not ignore the init value (while Brent would)
+  prev_init <- object$MSL$init_from_prof ## provided by plot1Dprof() -> profil(). 
+  ## prev_init is not NULL if MSL called by .MSL_update() or by [refine() after a plot], but then object$MSL$MSLE should not be used ;
+  ## in other calls by refine(), the object is being reconstructed from scratch, and then object$MSL$MSLE is NULL too ;
+  ## in neither case the next line is useful
+  # if (is.null(prev_init)) prev_init <- object$MSL$MSLE 
+  ##   which="safe" is effective only for SLik_j objects. Otherwise, it is ignored.
+  if ( (! is.null(prev_init)) &&
+    predict(object,newdata=prev_init, which="safe") > predict(object,newdata=init, which="safe")) {init <- prev_init}
   time1 <- Sys.time()
-  msl <- optim(init, function(v) {as.numeric(predict(object,newdata=v))},
-               ## as numeric because otherwise in 1D, optim -> minimize -> returns a max 
-               ##   of same type as predict(object$fit,newdata=v), i.e. matrix... 
-               ## FR->FR with spaMM>1.7.12, predict(object,newdata=v)[] should be OK  
-               lower=lower,upper=upper,control=list(fnscale=-1,parscale=parscale),method=method)
+  msl <- .safe_opt(init, objfn=function(v) { - predict(object,newdata=v, which="safe")}, 
+                   lower=lower,upper=upper, LowUp=list(), verbose=FALSE)
+  msl$value <- - msl$objective 
+  msl$par <- msl$solution
   optim_time <- round(as.numeric(difftime(Sys.time(), time1, units = "secs")), 1) ## spaMM:::.timerraw(time1)
-  if (inherits(object,"SLik") && length(fittedPars)>1L) {
+  if (inherits(object,"SLik") && length(fittedPars)>1L) { # for SLik_j objects, this should not be run
     locchull <- resetCHull(vertices, formats=c("constraints"))
     if( ! (isPointInCHull(msl$par, constraints=locchull[c("a", "b")]))) { ## if simple optim result not in convex hull
+      # this block can be tested by test-3par, with mixmodGaussianModel="Gaussian_pk_Lk_Ck"
       ui <- -locchull$a
       ci <- -locchull$b
-      objectivefn <- function(v) {as.numeric(predict(object,newdata=v))}
+      candttes_in_hull <- 0.99*vertices + 0.01*colMeans(vertices)
+      best_candidate <- unlist(candttes_in_hull[which.max(predict(object,newdata=candttes_in_hull, which="safe")),])
+      objectivefn <- function(v) { - as.numeric(predict(object,newdata=v, which="safe"))}
       objectivefn.grad <- function(x) {grad(func=objectivefn, x=x)} ## no need to specify fixedlist here
-      msl <- constrOptim(theta=init,f=objectivefn,grad=objectivefn.grad, ui=ui, ci=ci , 
-                         mu=1e-08, ## a low mu appear important
-                         method = "BFGS",control=list(fnscale=-1,parscale=parscale))
+      if (TRUE || .Infusion.data$options$constrOptim) {
+        msl <- constrOptim(theta=best_candidate,f=objectivefn,grad=objectivefn.grad, ui=ui, ci=ci , 
+                           mu=1e-08, ## a low mu appear important
+                           method = "BFGS",control=list(parscale=parscale))
+        msl$value <- - msl$value
+      } else { ## more or less works, but the result is not as accurate as by constrOptim. Presumably related to the  method used, with bound constraints.
+        msl <- .safe_constrOptim(theta=best_candidate,f=objectivefn,grad=objectivefn.grad, ui=ui, ci=ci , 
+                                 lower=lower,upper=upper,
+                           mu=1e-08)
+        msl$value <- - msl$objective
+        msl$par <- msl$solution
+      }
       ## then we will optimize in the convex hull using constroptim(R). But we need a good starting point
     }
   }
@@ -185,46 +160,49 @@ MSL <- function (object,CIs=TRUE,level=0.95, verbose=interactive(),
       print(c(msl$par,"tailp"=msl$value))
     }
   }
-  object$MSL$MSLE <- msl$par
-  if (length(lower)==1) names(object$MSL$MSLE) <- names(lower)
-  object$MSL$maxlogL <- msl$value
+  if (length(lower)==1) names(msl$par) <- names(lower)
+  object$MSL <- list2env(list(MSLE=msl$par, maxlogL=msl$value))
   if (inherits(object$fit,"HLfit")) object$MSL$predVar <- attr(predict(object,newdata=msl$par,variances=list(linPred=TRUE,dispVar=TRUE)),"predVar")
   # CIs
+  prevmsglength <- 0L
   if(CIs) {
     locverbose <- (verbose && ! inherits(object$fit,"HLfit")## for HLfit object, printing is later
                    && optim_time>3) # guess when it is useful to be verbose from the time to find the maximum 
     if (locverbose) {
       prevmsglength <- .overcat("Computing confidence intervals...\n", 0L) 
-    } else {
-      prevmsglength <- 0L
-    }
-    object$CIobject <- .allCIs(object,verbose=locverbose, level=level)  ## may be NULL
-  } else object$CIobject <- NULL
+    } 
+    object$CIobject <- list2env(.allCIs(object,verbose=locverbose, level=level))  ## may be NULL
+  } 
   # MSEs computation
   if (inherits(object$fit,"HLfit")) {
-    object$RMSEs <- .RMSEwrapper(object,verbose=FALSE)
+    object$RMSEs <- list2env(list(RMSEs=.RMSEwrapper(object,verbose=FALSE),
+                                  warn=NULL))
   } else {
     if (eval_RMSEs) {
       if  (verbose) .overcat("Computing RMSEs... (may be slow)\n",prevmsglength)
-      object$RMSEs <- .RMSEwrapper.SLik_j(object,verbose=FALSE)
-    } else {
-      object$pars <- .par_wrapper(object,verbose=FALSE) ## quick patch b/c no RMSEs
+      object$RMSEs <- list2env(list(RMSEs=.RMSEwrapper.SLik_j(object,cluster_args=cluster_args,verbose=FALSE),
+                                    warn=NULL))
     } 
   }
-  object$par_RMSEs <- .par_RMSEwrapper(object,verbose=FALSE)
+  # needs $CIobject information and $RMSEs information:
+  object$par_RMSEs <- list2env(list(par_RMSEs=.par_RMSEwrapper(object,verbose=FALSE)))
   if  (verbose) {
-    if ( ! is.null(object$par_RMSEs)) {
+    if ( ! is.null(object$par_RMSEs$par_RMSEs)) {
       .overcat("*** Interval estimates and RMSEs ***\n",prevmsglength)
-      print(object$par_RMSEs)
+      print(object$par_RMSEs$par_RMSEs)
     } else {
-      .overcat("*** Interval estimates ***\n",prevmsglength)
-      print(object$pars)
+      bounds <- .extract_intervals(object,verbose=FALSE) 
+      if (length(bounds)) {
+        .overcat("*** Interval estimates ***\n",prevmsglength)
+        print(bounds)
+      }
     }
   }
   if (is.null(object$logLs$cumul_iter)) {
     object$logLs$cumul_iter <- 1L ## adds a column to the data frame
     attr(object$logLs,"n_first_iter") <- nrow(object$logLs)
   }
+  object$`Infusion.version` <- packageVersion("Infusion")
   invisible(object)
 }