diff --git a/NAMESPACE b/NAMESPACE
index b7296541..d7820f59 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -39,6 +39,7 @@ export(modGBratio)
 export(modGBtree)
 export(modMAarea)
 export(modMApop)
+export(modMAratio)
 export(modMAtree)
 export(modPB)
 export(modPBpop)
diff --git a/R/modMAratio.R b/R/modMAratio.R
new file mode 100644
index 00000000..25db2e41
--- /dev/null
+++ b/R/modMAratio.R
@@ -0,0 +1,1005 @@
+#' Model-Assisted module - Generate model-assisted tree estimates.
+#' 
+#' Generates tree estimates by estimation unit. Estimates are calculated from
+#' McConville et al. (2018)'s mase R package.
+#' 
+#' If variables are NULL, then it will prompt user to input variables.
+#' 
+#' Necessary variables:\cr \tabular{llll}{ \tab \bold{Data} \tab
+#' \bold{Variable} \tab \bold{Description}\cr \tab tree \tab tuniqueid \tab
+#' Unique identifier for each plot, to link to pltassgn (e.g. PLT_CN).\cr \tab
+#' \tab CONDID \tab Unique identifier of each condition on plot, to link to
+#' cond.  Set CONDID=1, if only 1 condition per plot.\cr \tab \tab TPA_UNADJ
+#' \tab Number of trees per acre each sample tree represents (e.g., DESIGNCD=1:
+#' TPA_UNADJ=6.018046 for trees on subplot; 74.965282 for trees on
+#' microplot).\cr \tab cond \tab cuniqueid \tab Unique identifier for each
+#' plot, to link to pltassgn (ex. PLT_CN).\cr \tab \tab CONDID \tab Unique
+#' identifier of each condition on plot.  Set CONDID=1, if only 1 condition per
+#' plot.\cr \tab \tab CONDPROP_UNADJ \tab Unadjusted proportion of condition on
+#' each plot.  Set CONDPROP_UNADJ=1, if only 1 condition per plot.\cr \tab \tab
+#' COND_STATUS_CD \tab Status of each forested condition on plot (i.e.
+#' accessible forest, nonforest, water, etc.)\cr \tab \tab NF_COND_STATUS_CD
+#' \tab If ACI=TRUE. Status of each nonforest condition on plot (i.e.
+#' accessible nonforest, nonsampled nonforest)\cr \tab \tab SITECLCD \tab If
+#' landarea=TIMBERLAND. Measure of site productivity.\cr \tab \tab RESERVCD
+#' \tab If landarea=TIMBERLAND. Reserved status.\cr \tab \tab SUBPROP_UNADJ
+#' \tab Unadjusted proportion of subplot conditions on each plot.  Set
+#' SUBPROP_UNADJ=1, if only 1 condition per subplot.\cr \tab \tab
+#' MICRPROP_UNADJ \tab If microplot tree attributes. Unadjusted proportion of
+#' microplot conditions on each plot. Set MICRPROP_UNADJ=1, if only 1 condition
+#' per microplot.\cr \tab \tab MACRPROP_UNADJ \tab If macroplot tree
+#' attributes. Unadjusted proportion of macroplot conditions on each plot. Set
+#' MACRPROP_UNADJ=1, if only 1 condition per macroplot.\cr \tab pltassgn \tab
+#' puniqueid \tab Unique identifier for each plot, to link to cond (ex. CN).\cr
+#' \tab \tab STATECD \tab Identifies state each plot is located in.\cr \tab
+#' \tab INVYR \tab Identifies inventory year of each plot.\cr \tab \tab
+#' PLOT_STATUS_CD \tab Status of each plot (i.e. sampled, nonsampled).  If not
+#' included, all plots are assumed as sampled.\cr }
+#' 
+#' Reference names are available for the following variables: \cr ADFORCD,
+#' AGENTCD, CCLCD, DECAYCD, DSTRBCD, KINDCD, OWNCD, OWNGRPCD, FORTYPCD,
+#' FLDTYPCD, FORTYPCDCALC, TYPGRPCD, FORINDCD, RESERVCD, LANDCLCD, STDSZCD,
+#' FLDSZCD, PHYSCLCD, MIST_CL_CD, PLOT_STATUS_CD, STATECD, TREECLCD, TRTCD,
+#' SPCD, SPGRPCD
+#'  
+#' @param MApopdat List. Population data objects returned from modMApop().
+#' @param ratiotype String. The type of ratio estimates ("PERACRE", "PERTREE").
+#' @param woodland String. If woodland = 'Y', include woodland tree species  
+#' where measured. If woodland = 'N', only include timber species. See 
+#' FIESTA::ref_species$WOODLAND ='Y/N'. If woodland = 'only', only include
+#' woodland species.
+#' @param landarea String. The sample area filter for estimates ("FOREST",
+#' "TIMBERLAND").  If landarea=FOREST, filtered to COND_STATUS_CD = 1; If
+#' landarea=TIMBERLAND, filtered to SITECLCD in(1:6) and RESERVCD = 0.
+#' @param estseed String. Use seedling data only or add to tree data. Seedling
+#' estimates are only for counts (estvar='TPA_UNADJ')-('none', 'only', 'add').
+#' @param pcfilter String. A filter for plot or cond attributes (including
+#' pltassgn).  Must be R logical syntax.
+#' @param estvarn String. Name of the tree estimate variable (numerator).
+#' @param estvarn.filter String. A tree filter for the estimate variable
+#' (numerator).  Must be R syntax (e.g., "STATUSCD == 1").
+#' @param estvard String. Name of the tree estimate variable (denominator).
+#' @param estvard.filter String. A tree filter for the estimate variable
+#' (denominator).  Must be R syntax (e.g., "STATUSCD == 1").
+#' @param prednames String vector. Name(s) of predictor variables to include in
+#' model.
+#' @param FIA Logical. If TRUE, the finite population term is removed from
+#' estimator to match FIA estimates.
+#' @param rowvar String. Optional. Name of domain variable to group estvarn and
+#' estvard by for rows in table output. Rowvar must be included in an input data frame
+#' (i.e., plt, cond, tree). If no rowvar is included, an estimate is returned
+#' for the total estimation unit. Include colvar for grouping by 2 variables.
+#' @param colvar String. Optional. If rowvar != NULL, name of domain variable
+#' to group estvarn and estvard by for columns in table output. Colvar must be included in
+#' an input data frame (i.e., plt, cond, tree).
+#' @param sumunits Logical. If TRUE, estimation units are summed and returned
+#' in one table.
+#' @param returntitle Logical. If TRUE, returns title(s) of the estimation
+#' table(s).
+#' @param savedata Logical. If TRUE, saves table(s) to outfolder.
+#' @param table_opts List. See help(table_options()) for a list of
+#' options.
+#' @param title_opts List. See help(title_options()) for a list of options.
+#' @param savedata_opts List. See help(savedata_options()) for a list
+#' of options. Only used when savedata = TRUE.  
+#' @param gui Logical. If gui, user is prompted for parameters.
+#' @param bootstrap Logical. If TRUE, returns bootstrap variance estimates,
+#' otherwise uses Horvitz-Thompson estimator under simple random sampling
+#' without replacement.
+#' @param modelselect Logical. If TRUE, an elastic net regression model is fit 
+#' to the entire plot level data, and the variables selected in that model are 
+#' used for the proceeding estimation.
+#' @param ...  Parameters for modMApop() if MApopdat is NULL.
+#' @return If FIA=TRUE or unitvar=NULL and colvar=NULL, one data frame is
+#' returned with tree estimates and percent sample errors. Otherwise, a list is
+#' returned with tree estimates in one data frame (est) and percent sample
+#' errors in another data frame (est.pse). If rawdata=TRUE, another list is
+#' returned including raw data used in the estimation process.  If
+#' addtitle=TRUE and returntitle=TRUE, the title for est/pse is returned. If
+#' savedata=TRUE, all data frames are written to outfolder.
+#' 
+#' \item{est}{ Data frame. Tree estimates by rowvar, colvar (and estimation
+#' unit). If FIA=TRUE or one estimation unit and colvar=NULL, estimates and
+#' percent sampling error are in one data frame. } \item{pse}{ Data frame.
+#' Percent sampling errors for estimates by rowvar and colvar (and estimation
+#' unit). } \item{titlelst}{ List with 1 or 2 string vectors. If
+#' returntitle=TRUE a list with table title(s). The list contains one title if
+#' est and pse are in the same table and two titles if est and pse are in
+#' separate tables. } \item{raw}{ List of data frames. If rawdata=TRUE, a list
+#' including: number of plots by plot status, if in dataset (plotsampcnt);
+#' number of conditions by condition status (condsampcnt); data used for
+#' post-stratification (stratdat); and 1-8 tables with calculated variables
+#' used for processing estimates and percent sampling error for table cell
+#' values and totals (See processing data below). }
+#' 
+#' Raw data
+#' \item{plotsampcnt}{ Table. Number of plots by plot status (ex. sampled
+#' forest on plot, sampled nonforest, nonsampled). } \item{condsampcnt}{ DF.
+#' Number of conditions by condition status (forest land, nonforest land,
+#' noncensus water, census water, nonsampled). } \item{unitarea}{ DF. Area by
+#' estimation unit. } \item{expcondtab}{ DF. Condition-level area expansion
+#' factors. } \item{tdomdat}{ DF. Final data table used for estimation. }
+#' 
+#' The data frames include the following information: \tabular{lll}{ \tab
+#' \bold{Variable} \tab \bold{Description}\cr \tab rhat \tab estimated ratio estn/estd
+#' \cr \tab rhat.var \tab variance estimate
+#' of estimated ratio estn/estd \cr \tab NBRPLT Number of plots used in estimates \cr \tab
+#' NBRPLT.gt0 \tab Number of non-zero plots used in estimates \cr \tab 
+#' ACRES \tab total area for estimation unit \cr \tab rhat.se \tab
+#' estimated standard error of ratio sqrt(rhat.var) \cr \tab rhat.cv \tab
+#' estimated coefficient of variation of ratio rhat.se/rhat \cr \tab rhat.pse
+#' \tab estimated percent standard error or ratio rhat.cv*100 \cr \tab CI99left
+#' \tab left tail of 99 percent confidence interval for estimated area \cr \tab
+#' CI99right \tab right tail of 99 percent confidence interval for estimated
+#' area \cr \tab CI95left \tab left tail of 95 percent confidence interval for
+#' estimated area \cr \tab CI95right \tab right tail of 95 percent confidence
+#' interval for estimated area \cr \tab CI67left \tab left tail of 67 percent
+#' confidence interval for estimated area \cr \tab CI67right \tab right tail of
+#' 67 percent confidence interval for estimated area \cr } 
+#' 
+#' Table(s) are also written to outfolder.
+#' @note
+#' 
+#' ADJUSTMENT FACTOR:\cr The adjustment factor is necessary to account for
+#' nonsampled conditions. It is calculated for each estimation unit by strata.
+#' by summing the unadjusted proportions of the subplot, microplot, and
+#' macroplot (i.e. *PROP_UNADJ) and dividing by the number of plots in the
+#' strata/estimation unit).
+#' 
+#' An adjustment factor is determined for each tree based on the size of the
+#' plot it was measured on. This is identified using TPA_UNADJ as follows:
+#' 
+#' \tabular{llr}{ \tab \bold{PLOT SIZE} \tab \bold{TPA_UNADJ} \cr \tab SUBPLOT
+#' \tab 6.018046 \cr \tab MICROPLOT \tab 74.965282 \cr \tab MACROPLOT \tab
+#' 0.999188 \cr }
+#' 
+#' If ACI=FALSE, only nonsampled forest conditions are accounted for in the
+#' adjustment factor. \cr If ACI=TRUE, the nonsampled nonforest conditions are
+#' removed as well and accounted for in adjustment factor.  This is if you are
+#' interested in estimates for all lands or nonforest lands in the
+#' All-Condition-Inventory.
+#' 
+#' sumunits:\cr An estimation unit is a population, or area of interest, with
+#' known area and number of plots. Individual counties or combined
+#' Super-counties are common estimation units for FIA. An estimation unit may
+#' also be a subpopulation of a larger population (e.g., Counties within a
+#' State). Subpopulations are mutually exclusive and independent within a
+#' population, therefore estimated totals and variances are additive. For
+#' example, State-level estimates are generated by summing estimates from all
+#' subpopulations within the State (Bechtold and Patterson. 2005. Chapter 2).
+#' Each plot must be assigned to only one estimation unit.
+#' 
+#' autoxreduce:\cr If MAmethod='GREG', and autoxreduce=TRUE, and there is an
+#' error because of multicolinearity, a variable reduction method is applied to
+#' remove correlated variables. The method used is based on the
+#' variance-inflation factor (vif) from a linear model. The vif estimates how
+#' much the variance of each x variable is inflated due to mulitcolinearity in
+#' the model.
+#' 
+#' rowlut/collut:\cr There are several objectives for including rowlut/collut
+#' look-up tables: 1) to include descriptive names that match row/column codes
+#' in the input table; 2) to use number codes that match row/column names in
+#' the input table for ordering rows; 3) to add rows and/or columns with 0
+#' values for consistency. No duplicate names are allowed.
+#' 
+#' Include 2 columns in the table:\cr 1-the merging variable with same name as
+#' the variable in the input merge table;\cr 2-the ordering or descriptive
+#' variable.\cr If the ordering variable is the rowvar/colvar in the input
+#' table and the descriptive variable is in rowlut/collut, set
+#' row.orderby/col.orderby equal to rowvar/colvar. If the descriptive variable
+#' is the rowvar/colvar in the input table, and the ordering code variable is
+#' in rowlut/collut, set row.orderby/col.orderby equal to the variable name of
+#' the code variable in rowlut/collut.
+#' 
+#' UNITS:\cr The following variables are converted from pounds (from FIA
+#' database) to short tons by multiplying the variable by 0.0005.  DRYBIO_AG,
+#' DRYBIO_BG, DRYBIO_WDLD_SPP, DRYBIO_SAPLING, DRYBIO_STUMP, DRYBIO_TOP,
+#' DRYBIO_BOLE, DRYBIOT, DRYBIOM, DRYBIOTB, JBIOTOT, CARBON_BG, CARBON_AG
+#' 
+#' MORTALITY:\cr For Interior-West FIA, mortality estimates are mainly based on
+#' whether a tree has died within the last 5 years of when the plot was
+#' measured. If a plot was remeasured, mortality includes trees that were alive
+#' the previous visit but were dead in the next visit. If a tree was standing
+#' the previous visit, but was not standing in the next visit, no diameter was
+#' collected (DIA = NA) but the tree is defined as mortality.
+#' 
+#' Common tree filters: \cr
+#' 
+#' \tabular{llr}{ \tab \bold{FILTER} \tab \bold{DESCRIPTION} \cr \tab "STATUSCD
+#' == 1" \tab Live trees \cr \tab "STATUSCD == 2" \tab Dead trees \cr \tab
+#' "TPAMORT_UNADJ > 0" \tab Mortality trees \cr \tab "STATUSCD == 2 & DIA >=
+#' 5.0" \tab Dead trees >= 5.0 inches diameter \cr \tab "STATUSCD == 2 &
+#' AGENTCD == 30" \tab Dead trees from fire \cr }
+#' @author Josh Yamamoto
+#' @references Kelly McConville, Becky Tang, George Zhu, Shirley Cheung, and
+#' Sida Li (2018). mase: Model-Assisted Survey Estimation. R package version
+#' 0.1.4 https://cran.r-project.org/package=mase
+#' @keywords data
+#' @export modMAratio          
+modMAratio <- function(MApopdat, 
+                       ratiotype = "PERACRE",
+                       woodland = "Y",
+                       landarea = "FOREST",
+                       estseed = "none",
+                       pcfilter = NULL, 
+                       estvarn = NULL, 
+                       estvarn.filter = NULL, 
+                       estvard = NULL, 
+                       estvard.filter = NULL, 
+                       prednames = NULL,
+                       FIA = TRUE,
+                       rowvar = NULL, 
+                       colvar = NULL, 
+                       sumunits = TRUE, 
+                       returntitle = FALSE, 
+                       savedata = FALSE, 
+                       table_opts = NULL, 
+                       title_opts = NULL, 
+                       savedata_opts = NULL, 
+                       gui = FALSE, 
+                       bootstrap = FALSE,
+                       modelselect = FALSE,
+                       ...){
+  
+  if (nargs() == 0 && is.null(MApopdat)) {
+    gui <- TRUE
+  } 
+  
+  ## If gui.. set variables to NULL
+  if (gui) { 
+    tree=landarea=areavar=sumunits=adj=getwt=cuniqueid=ACI=
+      tuniqueid=savedata=addtitle=returntitle=rawdata=unitvar <- NULL
+  }
+  
+  ## Set parameters
+  rowcol.total <- TRUE
+  esttype <- "RATIO"
+  parameters <- FALSE
+  returnlst <- list()
+  rawdata <- TRUE
+  
+  ## Set global variables
+  ONEUNIT=n.total=TOTAL=tdom=estvar.name=
+    variable=estvard.name=rhat=rhat.se=rhat.var=rhat.cv=pse <- NULL
+  
+  ## Check input parameters
+  input.params <- names(as.list(match.call()))[-1]
+  formallst <- c(names(formals(modMAratio)),
+                 names(formals(modMApop))) 
+  if (!all(input.params %in% formallst)) {
+    miss <- input.params[!input.params %in% formallst]
+    stop("invalid parameter: ", toString(miss))
+  }
+  
+  ## Check parameter lists
+  pcheck.params(input.params, table_opts=table_opts, title_opts=title_opts, 
+                savedata_opts=savedata_opts)
+  
+  ## Set savedata defaults
+  savedata_defaults_list <- formals(savedata_options)[-length(formals(savedata_options))]
+  
+  for (i in 1:length(savedata_defaults_list)) {
+    assign(names(savedata_defaults_list)[[i]], savedata_defaults_list[[i]])
+  }
+  
+  ## Set user-supplied savedata values
+  if (length(savedata_opts) > 0) {
+    if (!savedata) {
+      message("savedata=FALSE with savedata parameters... no data are saved")
+    }
+    for (i in 1:length(savedata_opts)) {
+      if (names(savedata_opts)[[i]] %in% names(savedata_defaults_list)) {
+        assign(names(savedata_opts)[[i]], savedata_opts[[i]])
+      } else {
+        stop(paste("Invalid parameter: ", names(savedata_opts)[[i]]))
+      }
+    }
+  }
+  
+  table_defaults_list <- formals(table_options)[-length(formals(table_options))]
+  
+  for (i in 1:length(table_defaults_list)) {
+    assign(names(table_defaults_list)[[i]], table_defaults_list[[i]])
+  }
+  
+  ## Set user-supplied table values
+  if (length(table_opts) > 0) {
+    for (i in 1:length(table_opts)) {
+      if (names(table_opts)[[i]] %in% names(table_defaults_list)) {
+        assign(names(table_opts)[[i]], table_opts[[i]])
+      } else {
+        stop(paste("Invalid parameter: ", names(table_opts)[[i]]))
+      }
+    }
+  }
+  
+  ## Set title defaults
+  title_defaults_list <- formals(title_options)[-length(formals(title_options))]
+  
+  for (i in 1:length(title_defaults_list)) {
+    assign(names(title_defaults_list)[[i]], title_defaults_list[[i]])
+  }
+  
+  ## Set user-supplied title values
+  if (length(title_opts) > 0) {
+    for (i in 1:length(title_opts)) {
+      if (names(title_opts)[[i]] %in% names(title_defaults_list)) {
+        assign(names(title_opts)[[i]], title_opts[[i]])
+      } else {
+        stop(paste("Invalid parameter: ", names(title_opts)[[i]]))
+      }
+    }
+  }
+  
+  # only option for now
+  MAmethod <- "gregRatio"
+  var_method <- "LinHTSRS"
+  
+  list.items <- c("condx", "pltcondx", "cuniqueid", "condid", 
+                  "ACI.filter", "unitarea", "unitvar", "unitlut", "npixels",
+                  "npixelvar", "plotsampcnt", "condsampcnt")
+  
+  
+  MApopdat <- pcheck.object(MApopdat, "MApopdat", list.items=list.items)
+  
+  if (is.null(MApopdat)) return(NULL)
+  condx <- MApopdat$condx
+  pltcondx <- MApopdat$pltcondx	
+  treex <- MApopdat$treex
+  seedx <- MApopdat$seedx
+  if (is.null(treex) && is.null(seedx)) {
+    stop("must include tree data for ratio estimates")
+  }
+  
+  cuniqueid <- MApopdat$cuniqueid
+  npixels <- MApopdat$npixels
+  npixelvar <- MApopdat$npixelvar
+  condid <- MApopdat$condid
+  tuniqueid <- MApopdat$tuniqueid
+  ACI.filter <- MApopdat$ACI.filter
+  unitarea <- MApopdat$unitarea
+  areavar <- MApopdat$areavar
+  areaunits <- MApopdat$areaunits
+  unitvar <- MApopdat$unitvar
+  unitvars <- MApopdat$unitvars
+  unitlut <- MApopdat$unitlut
+  unitvars <- MApopdat$unitvars
+  expcondtab <- MApopdat$expcondtab
+  plotsampcnt <- MApopdat$plotsampcnt
+  condsampcnt <- MApopdat$condsampcnt
+  predfac <- MApopdat$predfac
+  states <- MApopdat$states
+  invyrs <- MApopdat$invyrs
+  estvar.area <- MApopdat$estvar.area
+  adj <- MApopdat$adj
+  expcondtab <- MApopdat$expcondtab
+  
+  
+  if (is.null(prednames)) {
+    prednames <- MApopdat$prednames
+  } else {
+    if (!all(prednames %in% MApopdat$prednames)) {
+      if (any(prednames %in% MApopdat$predfac)) {
+        predfacnames <- prednames[prednames %in% MApopdat$predfac]
+        for (nm in predfacnames) {           
+          prednames[prednames == nm] <- MApopdat$prednames[grepl(nm, MApopdat$prednames)]
+        }
+      } else {
+        stop("invalid prednames... must be in: ", toString(MApopdat$prednames))
+      }
+    }
+  }
+  
+  
+  unitchk <- pcheck.areaunits(unitarea = unitarea, areavar = areavar, 
+                              areaunits = areaunits, metric = metric)
+  unitarea <- unitchk$unitarea
+  areavar <- unitchk$areavar
+  areaunits <- unitchk$outunits
+  
+  if (is.null(key(unitarea))) {
+    setkeyv(unitarea, unitvar)
+  }
+  
+  estdat <- check.estdata(esttype=esttype, pltcondf=pltcondx, 
+                          cuniqueid=cuniqueid, condid=condid, 
+                          treex=treex, seedx=seedx, estseed=estseed, woodland=woodland,
+                          sumunits=sumunits, landarea=landarea, 
+                          ACI.filter=ACI.filter, pcfilter=pcfilter, 
+                          allin1=allin1, estround=estround, pseround=pseround, 
+                          divideby=divideby, addtitle=addtitle, returntitle=returntitle, 
+                          rawdata=rawdata, rawonly=rawonly, savedata=savedata, 
+                          outfolder=outfolder, overwrite_dsn=overwrite_dsn, 
+                          overwrite_layer=overwrite_layer, outfn.pre=outfn.pre, 
+                          outfn.date=outfn.date, append_layer=append_layer, 
+                          raw_fmt=raw_fmt, raw_dsn=raw_dsn, gui=gui)
+  
+  
+  if (is.null(estdat)) return(NULL)
+  pltcondf <- estdat$pltcondf
+  cuniqueid <- estdat$cuniqueid
+  treef <- estdat$treef
+  seedf <- estdat$seedf
+  tuniqueid <- estdat$tuniqueid
+  estseed <- estdat$estseed
+  woodland <- estdat$woodland
+  sumunits <- estdat$sumunits
+  landarea <- estdat$landarea
+  allin1 <- estdat$allin1
+  estround <- estdat$estround
+  pseround <- estdat$pseround
+  divideby <- estdat$divideby
+  addtitle <- estdat$addtitle
+  returntitle <- estdat$returntitle
+  rawdata <- estdat$rawdata
+  rawonly <- estdat$rawonly
+  savedata <- estdat$savedata
+  outfolder <- estdat$outfolder
+  overwrite_layer <- estdat$overwrite_layer
+  raw_fmt <- estdat$raw_fmt
+  raw_dsn <- estdat$raw_dsn
+  rawfolder <- estdat$rawfolder
+  
+  if ("STATECD" %in% names(pltcondf)) {
+    states <- pcheck.states(sort(unique(pltcondf$STATECD)))
+  }
+  if ("INVYR" %in% names(pltcondf)) {
+    invyr <- sort(unique(pltcondf$INVYR))
+  }
+  
+  rowcolinfo <- check.rowcol(gui=gui, esttype=esttype, treef=treef, seedf=seedf, 
+                             condf=pltcondf, cuniqueid=cuniqueid, 
+                             tuniqueid=tuniqueid, estseed=estseed,
+                             rowvar=rowvar, colvar=colvar, 
+                             row.FIAname=row.FIAname, col.FIAname=col.FIAname, 
+                             row.orderby=row.orderby, col.orderby=col.orderby, 
+                             row.add0=row.add0, col.add0=col.add0, 
+                             title.rowvar=title.rowvar, title.colvar=title.colvar, 
+                             rowlut=rowlut, collut=collut, rowgrp=rowgrp, 
+                             rowgrpnm=rowgrpnm, rowgrpord=rowgrpord, landarea=landarea) 
+  treef <- rowcolinfo$treef
+  seedf <- rowcolinfo$seedf
+  condf <- rowcolinfo$condf
+  uniquerow <- rowcolinfo$uniquerow
+  uniquecol <- rowcolinfo$uniquecol
+  domainlst <- rowcolinfo$domainlst
+  rowvar <- rowcolinfo$rowvar
+  colvar <- rowcolinfo$colvar
+  row.orderby <- rowcolinfo$row.orderby
+  col.orderby <- rowcolinfo$col.orderby
+  row.add0 <- rowcolinfo$row.add0
+  col.add0 <- rowcolinfo$col.add0
+  title.rowvar <- rowcolinfo$title.rowvar
+  title.colvar <- rowcolinfo$title.colvar
+  rowgrpnm <- rowcolinfo$rowgrpnm
+  title.rowgrp <- rowcolinfo$title.rowgrp
+  bytdom <- rowcolinfo$bytdom
+  tdomvar <- rowcolinfo$tdomvar
+  tdomvar2 <- rowcolinfo$tdomvar2
+  grpvar <- rowcolinfo$grpvar
+  
+  
+  if (rowvar == "TOTAL") rowcol.total <- TRUE
+  
+  ## Generate a uniquecol for estimation units
+  if (!sumunits & colvar == "NONE") {
+    uniquecol <- data.table(unitarea[[unitvar]])
+    setnames(uniquecol, unitvar)
+    uniquecol[[unitvar]] <- factor(uniquecol[[unitvar]])
+  }
+  
+  #####################################################################################
+  ### Get estimation data from tree table
+  #####################################################################################
+  adjtree <- ifelse(adj %in% c("samp", "plot"), TRUE, FALSE)
+  treedat <- check.tree(gui=gui, treef=treef, seedf=seedf, estseed=estseed,
+                        bycond=TRUE, condf=condf, bytdom=bytdom, 
+                        tuniqueid=tuniqueid, cuniqueid=cuniqueid, 
+                        esttype=esttype, ratiotype=ratiotype,
+                        estvarn=estvarn, estvarn.filter=estvarn.filter, 
+                        estvard=estvard, estvard.filter=estvard.filter, 
+                        esttotn=TRUE, esttotd=TRUE, 
+                        tdomvar=tdomvar, tdomvar2=tdomvar2, 
+                        adjtree=adjtree, metric=metric, woodland=woodland)
+  
+  if (is.null(treedat)) return(NULL)
+  tdomdat <- treedat$tdomdat
+  
+  if (rowvar != "TOTAL") {
+    if (!row.add0) {
+      if (any(is.na(tdomdat[[rowvar]]))) {
+        tdomdat <- tdomdat[!is.na(tdomdat[[rowvar]]), ]
+      }
+    }
+    if (colvar != "NONE") {
+      if (!col.add0) {
+        if (any(is.na(tdomdat[[colvar]]))) {
+          tdomdat <- tdomdat[!is.na(tdomdat[[colvar]]), ]
+        }
+      }
+    }
+  }
+  
+  ## Merge tdomdat with condx
+  xchk <- check.matchclass(condx, tdomdat, c(cuniqueid, condid))
+  condx <- xchk$tab1
+  tdomdat <- xchk$tab2  
+  tdomdat <- merge(condx, tdomdat, by=c(cuniqueid, condid))
+  
+  if (!is.null(tdomvar)) {
+    ## Merge condf with condx
+    xchk <- check.matchclass(condx, condf, c(cuniqueid, condid))
+    condx <- xchk$tab1
+    condf <- xchk$tab2   
+    cdomdat <- merge(condx, condf, by=c(cuniqueid, condid))
+  }
+  estvarn <- treedat$estvarn
+  estvarn.name <- treedat$estvarn.name
+  estvarn.filter <- treedat$estvarn.filter
+  tdomvarlstn <- treedat$tdomvarlstn
+  estunitsn <- treedat$estunitsn
+  estunitsd <- treedat$estunitsd
+  
+  if (ratiotype == "PERTREE") {
+    estvard <- treedat$estvard
+    estvard.name <- treedat$estvard.name
+    tdomvarlstd <- treedat$tdomvarlstd
+  } else {
+    estvard.name <- estvar.area
+    tdomvarlstd <- NULL
+    estunitsd <- areaunits
+  }  
+  
+  #####################################################################################
+  ### Get titles for output tables
+  #####################################################################################
+  alltitlelst <- check.titles(dat=tdomdat, esttype=esttype, estseed=estseed, 
+                              ratiotype=ratiotype, sumunits=sumunits, 
+                              title.main=title.main, title.ref=title.ref,
+                              title.rowvar=title.rowvar, title.rowgrp=title.rowgrp, 
+                              title.colvar=title.colvar, title.unitvar=title.unitvar, 
+                              title.filter=title.filter, title.unitsn=estunitsn,
+                              title.unitsd=estunitsd, title.estvarn=title.estvarn,
+                              unitvar=unitvar, rowvar=rowvar, colvar=colvar,
+                              estvarn=estvarn, estvarn.filter=estvarn.filter, 
+                              estvard=estvard, estvard.filter=estvard.filter, 
+                              addtitle=addtitle, returntitle=returntitle,
+                              rawdata=rawdata, states=states, invyrs=invyrs, 
+                              landarea=landarea, pcfilter=pcfilter, 
+                              allin1=allin1, divideby=divideby, outfn.pre=outfn.pre)
+  title.unitvar <- alltitlelst$title.unitvar
+  title.est <- alltitlelst$title.est
+  title.pse <- alltitlelst$title.pse
+  title.estpse <- alltitlelst$title.estpse
+  title.ref <- alltitlelst$title.ref
+  outfn.estpse <- alltitlelst$outfn.estpse
+  outfn.param <- alltitlelst$outfn.param
+  if(rawdata) {
+    outfn.rawdat <- alltitlelst$outfn.rawdat
+  }
+  
+  #####################################################################################
+  ## GENERATE ESTIMATES
+  #####################################################################################
+  unit_totest=unit_grpest=unit_rowest=unit_colest=unit_grpest=rowunit=totunit <- NULL
+  addtotal <- ifelse(((rowvar == "TOTAL" || length(unique(tdomdat[[rowvar]])) > 1) ||
+                        (!is.null(tdomvarlstn) && length(tdomvarlstn) > 1)), TRUE, FALSE)
+  
+  if (modelselect == TRUE) {
+    
+    # want to do variable selection on plot level data...
+    pltlvl <- tdomdat[ , lapply(.SD, sum, na.rm = TRUE), 
+                       by=c(unitvar, cuniqueid, "TOTAL", prednames),
+                       .SDcols=estvarn.name]
+    
+    y <- pltlvl[[estvarn.name]]
+    xsample <- pltlvl[ , prednames, with = F, drop = F]
+    xpop <- unitlut[ , prednames, with = F, drop = F]
+    N <- sum(npixels[["npixels"]])
+    
+    preds.selected <- gregEN.select(y = y,
+                                    x_sample = xsample,
+                                    x_pop = xpop,
+                                    N = N,
+                                    alpha = 0.5)
+    
+    if (length(preds.selected) == 0 || is.null(preds.selected)) {
+      
+      warning("No variables selected in model selection, proceeding with all possible predictors. \n")
+      
+    } else {
+      
+      prednames <- preds.selected 
+      message(paste0("Predictors ", "[", paste0(prednames, collapse = ", "), "]", " were chosen in model selection.\n"))
+      
+    }
+    
+    
+  }
+  
+  # can do modelselect above this if we want
+  message("using the following predictors...", toString(prednames))
+  
+  response <- estvarn.name
+  response_d <- estvard.name
+  # use this for now since estunitsn is NULL...
+  estunits <- unitarea[[unitvar]]
+  # estunits <- treedat$estunits
+  
+  if (bootstrap) {
+    var_method <- "bootstrapSRS"
+  } else {
+    var_method <- "LinHTSRS"
+  }
+  
+  # not sure what this part does...
+  # generally seems like tdomvar is rowvar and tdomvar2 is colvar (if they exist)
+  if (!is.null(tdomvar2)) {
+    ddomvar <- "TOTAL"
+    tdomdat <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                       by=c(unitvar, cuniqueid, ddomvar), .SDcols=tdomvarlstn]
+    tdomdat <- transpose2row(tdomdat, uniqueid=c(unitvar, cuniqueid, ddomvar),
+                             tvars=tdomvarlstn)
+    setnames(tdomdat, "value", estvarn.name)
+    suppressWarnings(tdomdat[, (grpvar) := tstrsplit(variable, "#")])[, variable := NULL]
+    
+    cdomdattot <- cdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                          by=c(unitvar, cuniqueid, "TOTAL"), .SDcols=estvard.name]   
+    tdomdat <- merge(tdomdat, cdomdattot, by=c(unitvar, cuniqueid, "TOTAL"))
+  }
+  
+  if (addtotal) {
+    ## Get estimate for total
+    if (!is.null(tdomvar)) {
+      tdomdattot <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                            by=c(unitvar, cuniqueid, "TOTAL", prednames), .SDcols=estvarn.name]
+      cdomdattot <- cdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                            by=c(cuniqueid, "TOTAL"), .SDcols=estvard.name]   
+      tdomdattot <- merge(tdomdattot, cdomdattot, by=c(cuniqueid, "TOTAL"))
+    } else {
+      tdomdattot <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                            by=c(unitvar, cuniqueid, "TOTAL", prednames), .SDcols=c(estvarn.name, estvard.name)]
+    }
+    
+    # unitarea should get passed as it includes the acres for each estimation unit
+    # npixels should also get passed 
+    
+    unit_totestlst <- lapply(estunits, MAest.unit, 
+                             dat=tdomdattot, cuniqueid=cuniqueid, 
+                             unitlut=unitlut, unitvar=unitvar, esttype=esttype, 
+                             MAmethod=MAmethod, prednames=prednames, strvar = NULL,
+                             domain="TOTAL", response=response, response_d=response_d,
+                             npixels=npixels, unitarea=unitarea, FIA=FIA,
+                             var_method=var_method)
+    
+  }
+  
+  unit_totest <- do.call(rbind, sapply(unit_totestlst, '[', "unitest"))
+  
+  
+  tabs <- check.matchclass(unitarea, unit_totest, unitvar)
+  unitarea <- tabs$tab1
+  unit_totest <- tabs$tab2
+  setkeyv(unit_totest, unitvar)
+  unit_totest <- unit_totest[unitarea, nomatch=0]
+  
+  suppressWarnings(
+    unit_totest[, rhat.se := sqrt(rhat.var)][,
+                                             rhat.cv := rhat.se/rhat][,
+                                                                      pse := rhat.cv*100]
+  )
+  
+  if (rowvar != "TOTAL") {
+    if (!is.null(tdomvar)) {
+      if (!is.null(tdomvar2)) {
+        tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                              by=c(unitvar, cuniqueid, rowvar, prednames), .SDcols=estvarn.name]    
+      } else {
+        if (tdomvar == rowvar) {
+          tdomdatsum <- transpose2row(tdomdat, uniqueid=c(unitvar, cuniqueid),
+                                      tvars=tdomvarlstn)
+          setnames(tdomdatsum, c("variable", "value"), c(rowvar, estvarn.name))
+          tdomdatsum <- tdomdatsum[, lapply(.SD, sum, na.rm=TRUE), 
+                                   by=c(unitvar, cuniqueid, rowvar, prednames), .SDcols=estvarn.name]
+        } else {  
+          tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                                by=c(unitvar, cuniqueid, rowvar, prednames), .SDcols=estvarn.name]
+        }
+      }
+      if (rowvar %in% names(cdomdat)) {
+        cdomdatsum <- cdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                              by=c(unitvar, cuniqueid, rowvar, prednames), .SDcols=estvard.name]
+        tdomdatsum <- merge(tdomdatsum, cdomdatsum, 
+                            by=c(unitvar, cuniqueid, rowvar))
+      } else {
+        cdomdatsum <- cdomdat[, lapply(.SD, sum, na.rm=TRUE),
+                              by=c(unitvar, cuniqueid, prednames), .SDcols=estvard.name]
+        tdomdatsum <- merge(tdomdatsum, cdomdatsum, by=c(unitvar, cuniqueid))
+      }
+    } else {
+      tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                            by=c(unitvar, cuniqueid, rowvar, prednames), .SDcols=c(estvarn.name, estvard.name)]
+    }
+    
+    unit_rowestlst <- lapply(estunits, MAest.unit, 
+                             dat=tdomdatsum, cuniqueid=cuniqueid, 
+                             unitlut=unitlut, unitvar=unitvar, esttype=esttype, 
+                             MAmethod=MAmethod, prednames=prednames, strvar = NULL,
+                             domain=rowvar, response=response, response_d=response_d,
+                             npixels=npixels, unitarea=unitarea, FIA=FIA,
+                             var_method=var_method)
+    
+    unit_rowest <- do.call(rbind, sapply(unit_rowestlst, '[', "unitest"))
+    
+    if (colvar != "NONE") {
+      if (!is.null(tdomvar)) {
+        if (!is.null(tdomvar2)) {
+          tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                                by=c(unitvar, cuniqueid, colvar, prednames), .SDcols=estvarn.name]    
+        } else {
+          if (tdomvar == colvar) {
+            tdomdatsum <- transpose2row(tdomdat, uniqueid=c(unitvar, cuniqueid),
+                                        tvars=tdomvarlstn)
+            setnames(tdomdatsum, c("variable", "value"), c(colvar, estvarn.name))
+            tdomdatsum <- tdomdatsum[, lapply(.SD, sum, na.rm=TRUE), 
+                                     by=c(unitvar, cuniqueid, colvar, prednames), .SDcols=estvarn.name]
+          } else {     
+            tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE),
+                                  by=c(unitvar, cuniqueid, colvar, prednames), .SDcols=estvarn.name]
+          }
+        }
+        if (colvar %in% names(cdomdat)) {
+          cdomdatsum <- cdomdat[, lapply(.SD, sum, na.rm=TRUE),
+                                by=c(unitvar, cuniqueid, colvar, prednames), .SDcols=estvard.name]
+          tdomdatsum <- merge(tdomdatsum, cdomdatsum, by=c(unitvar, cuniqueid, colvar))
+        } else {
+          cdomdatsum <- cdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                                by=c(unitvar, cuniqueid, prednames), .SDcols=estvard.name]
+          tdomdatsum <- merge(tdomdatsum, cdomdatsum, by=c(unitvar, cuniqueid))
+        }
+      } else {
+        tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                              by=c(unitvar, cuniqueid, colvar, prednames), .SDcols=c(estvarn.name, estvard.name)]
+      }
+      
+      unit_colestlst <- lapply(estunits, MAest.unit, 
+                               dat=tdomdatsum, cuniqueid=cuniqueid, 
+                               unitlut=unitlut, unitvar=unitvar, esttype=esttype, 
+                               MAmethod=MAmethod, prednames=prednames, strvar = NULL,
+                               domain=colvar, response=response, response_d=response_d,
+                               npixels=npixels, unitarea=unitarea, FIA=FIA,
+                               var_method=var_method)
+      
+      unit_colest <- do.call(rbind, sapply(unit_colestlst, '[', "unitest"))
+      
+      if (!is.null(tdomvar)) {
+        if (!is.null(tdomvar2)) {
+          tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                                by=c(unitvar, cuniqueid, grpvar, prednames), .SDcols=c(estvarn.name, estvard.name)]
+        } else {
+          ddomvar <- grpvar[grpvar != tdomvar]
+          tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                                by=c(unitvar, cuniqueid, ddomvar, prednames), .SDcols=tdomvarlstn]
+          tdomdatsum <- transpose2row(tdomdatsum, 
+                                      uniqueid=c(unitvar, cuniqueid, ddomvar), 
+                                      tvars=tdomvarlstn)
+          setnames(tdomdatsum, c("variable", "value"), c(tdomvar, estvarn.name))      
+          
+          if (any(grpvar %in% names(cdomdat))) {
+            mergevar <- grpvar[grpvar %in% names(cdomdat)]
+            cdomdatsum <- cdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                                  by=c(unitvar, cuniqueid, mergevar, prednames), .SDcols=estvard.name]
+            tdomdatsum <- merge(tdomdatsum, cdomdatsum, by=c(unitvar, cuniqueid, mergevar))
+          } else {
+            cdomdatsum <- cdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                                  by=c(unitvar, cuniqueid, prednames), .SDcols=estvard.name]
+            tdomdatsum <- merge(tdomdatsum, cdomdatsum, by=c(unitvar, cuniqueid))
+          }
+        }
+      } else {
+        tdomdatsum <- tdomdat[, lapply(.SD, sum, na.rm=TRUE), 
+                              by=c(unitvar, cuniqueid, grpvar, prednames), .SDcols=c(estvarn.name, estvard.name)]
+      }
+      tdomdatsum[, grpvar := do.call(paste, c(.SD, sep="#")), .SDcols=grpvar]
+      
+      unit_grpestlst <- lapply(estunits, MAest.unit, 
+                               dat=tdomdatsum, cuniqueid=cuniqueid, 
+                               unitlut=unitlut, unitvar=unitvar, esttype=esttype, 
+                               MAmethod=MAmethod, prednames=prednames, strvar = NULL,
+                               domain="grpvar", response=response, response_d=response_d,
+                               npixels=npixels, unitarea=unitarea, FIA=FIA,
+                               var_method=var_method)
+      
+      unit_grpest <- do.call(rbind, sapply(unit_grpestlst, '[', "unitest"))
+      
+      if (any(unit_grpest$grpvar == "NA#NA")) {
+        unit_grpest <- unit_grpest[unit_grpest$grpvar != "NA#NA", ]
+      }
+      
+      unit_grpest[, c(rowvar, colvar) := tstrsplit(grpvar, "#", fixed=TRUE)]
+      
+    }
+    
+  }
+  
+  if (!sumunits && nrow(unitarea) > 1) col.add0 <- TRUE
+  if (!is.null(unit_rowest)) {
+    unit_rowest <- add0unit(x=unit_rowest, xvar=rowvar, uniquex=uniquerow, 
+                            unitvar=unitvar, xvar.add0=row.add0)
+    tabs <- check.matchclass(unitarea, unit_rowest, unitvar)
+    unitarea <- tabs$tab1
+    unit_rowest <- tabs$tab2
+    
+    if (!is.null(row.orderby) && row.orderby != "NONE") {
+      setorderv(unit_rowest, c(row.orderby))
+    }
+    setkeyv(unit_rowest, unitvar)
+    unit_rowest <- unit_rowest[unitarea, nomatch=0]
+    
+    suppressWarnings(
+      unit_rowest[, rhat.se := sqrt(rhat.var)][,
+                                               rhat.cv := rhat.se/rhat][,
+                                                                        pse := rhat.cv*100]
+    )
+    
+    setkeyv(unit_rowest, c(unitvar, rowvar))
+  }
+  
+  if (!is.null(unit_colest)) {
+    unit_colest <- add0unit(x=unit_colest, xvar=colvar, 
+                            uniquex=uniquecol, unitvar=unitvar, 
+                            xvar.add0=col.add0)
+    tabs <- check.matchclass(unitarea, unit_colest, unitvar)
+    unitarea <- tabs$tab1
+    unit_colest <- tabs$tab2
+    
+    if (!is.null(col.orderby) && col.orderby != "NONE") {
+      setorderv(unit_colest, c(col.orderby))
+    }
+    setkeyv(unit_colest, unitvar)
+    unit_colest <- unit_colest[unitarea, nomatch=0]
+    
+    suppressWarnings(
+      unit_colest[, rhat.se := sqrt(rhat.var)][,
+                                               rhat.cv := rhat.se/rhat][,
+                                                                        pse := rhat.cv*100]
+    )
+    
+    setkeyv(unit_colest, c(unitvar, colvar))
+  }
+  if (!is.null(unit_grpest)) {
+    unit_grpest <- add0unit(x=unit_grpest, xvar=rowvar, 
+                            uniquex=uniquerow, unitvar=unitvar, 
+                            xvar.add0=row.add0, xvar2=colvar, 
+                            uniquex2=uniquecol,
+                            xvar2.add0=col.add0)
+    tabs <- check.matchclass(unitarea, unit_grpest, unitvar)
+    unitarea <- tabs$tab1
+    unit_grpest <- tabs$tab2
+    
+    if (!is.null(row.orderby) && row.orderby != "NONE") {
+      if (!is.null(col.orderby) && col.orderby != "NONE") {
+        setorderv(unit_grpest, c(row.orderby, col.orderby))
+      } else {
+        setorderv(unit_grpest, c(row.orderby))
+      }         
+    } else if (!is.null(col.orderby) && col.orderby != "NONE") {
+      setorderv(unit_grpest, c(col.orderby))
+    }         
+    setkeyv(unit_grpest, unitvar)
+    unit_grpest <- unit_grpest[unitarea, nomatch=0]
+    
+    suppressWarnings(
+      unit_grpest[, rhat.se := sqrt(rhat.var)][,
+                                               rhat.cv := rhat.se/rhat][,
+                                                                        pse := rhat.cv*100]
+    )
+    
+    setkeyv(unit_grpest, c(unitvar, rowvar, colvar))
+  }
+  
+  message("getting output...")
+  estnm <- "rhat"
+  
+  # use esttype = "TREE" because this ratio format is different than in GB
+  
+  tabs <- est.outtabs(esttype="TREE", sumunits=sumunits, areavar=areavar, 
+                      unitvar=unitvar, unitvars=unitvars, unit_totest=unit_totest, 
+                      unit_rowest=unit_rowest, unit_colest=unit_colest, unit_grpest=unit_grpest, 
+                      rowvar=rowvar, colvar=colvar, uniquerow=uniquerow, uniquecol=uniquecol, 
+                      rowgrp=rowgrp, rowgrpnm=rowgrpnm, rowunit=rowunit, totunit=totunit, 
+                      allin1=allin1, savedata=savedata, addtitle=addtitle, 
+                      title.ref=title.ref, title.colvar=title.colvar, 
+                      title.rowvar=title.rowvar, title.rowgrp=title.rowgrp, 
+                      title.unitvar=title.unitvar, title.estpse=title.estpse, 
+                      title.est=title.est, title.pse=title.pse, rawdata=rawdata, 
+                      outfn.estpse=outfn.estpse, outfolder=outfolder, outfn.date=outfn.date, 
+                      overwrite=overwrite_layer, estnm=estnm, psenm = "pse",
+                      estround=estround, pseround, divideby=divideby, 
+                      returntitle=returntitle, estnull=estnull, psenull=psenull) 
+  
+  est2return <- tabs$tabest
+  pse2return <- tabs$tabpse
+  
+  if (!is.null(est2return)) {
+    returnlst$est <- est2return 
+  }
+  if (!is.null(pse2return)) {
+    returnlst$pse <- pse2return 
+  }
+  if (returntitle) {
+    returnlst$titlelst <- alltitlelst
+  }
+  
+  if (rawdata) {
+    rawdat <- tabs$rawdat
+    rawdat$domdat <- setDF(tdomdat)
+    rawdat$estvarn <- estvarn.name
+    rawdat$estvarn.filter <- estvarn.filter
+    
+    if (ratiotype == "PERACRE") {
+      rawdat$estvard <- estvard.name
+      rawdat$estvard.filter <- estvard.filter
+    }
+    
+    if (savedata) {
+      if (!is.null(title.estpse)) {
+        title.raw <- paste(title.estpse, title.ref)
+      } else {
+        title.raw <- title.est
+      }
+      
+      for (i in 1:length(rawdat)) {
+        tabnm <- names(rawdat[i])
+        if (!tabnm %in% c(estvarn, prednames)) {
+          rawtab <- rawdat[[i]]
+          outfn.rawtab <- paste0(outfn.rawdat, "_", tabnm) 
+          if (tabnm %in% c("plotsampcnt", "condsampcnt")) {
+            write2csv(rawtab, outfolder=rawfolder, outfilenm=outfn.rawtab, 
+                      outfn.date=outfn.date, overwrite=overwrite_layer)
+          } else if (is.data.frame(rawtab)) {
+            if (raw_fmt != "csv") {
+              out_layer <- tabnm 
+            } else {
+              out_layer <- outfn.rawtab
+            }
+            datExportData(rawtab, 
+                          savedata_opts=list(outfolder=rawfolder, 
+                                             out_fmt=raw_fmt, 
+                                             out_dsn=raw_dsn, 
+                                             out_layer=out_layer,
+                                             overwrite_layer=overwrite_layer,
+                                             append_layer=append_layer,
+                                             add_layer=TRUE))
+          }
+        }
+      }
+    }
+    rawdat$module <- "MA"
+    rawdat$esttype <- "RATIO"
+    rawdat$MAmethod <- MAmethod
+    rawdat$predselect <- prednames
+    rawdat$estvarn <- estvarn
+    rawdat$estvarn.filter <- estvarn.filter
+    if (!is.null(estvard)) rawdat$estvard <- estvard
+    if (!is.null(estvard.filter)) rawdat$estvard.filter <- estvard.filter
+    if (!is.null(rowvar)) rawdat$rowvar <- rowvar
+    if (!is.null(colvar)) rawdat$colvar <- colvar
+    if (ratiotype == "PERACRE") {
+      rawdat$areaunits <- areaunits
+    }
+    rawdat$estunitsn <- estunitsn
+    returnlst$raw <- rawdat
+  }
+  
+  if ("STATECD" %in% names(pltcondf)) {
+    returnlst$statecd <- sort(unique(pltcondf$STATECD))
+  }
+  if ("INVYR" %in% names(pltcondf)) {
+    returnlst$invyr <- sort(unique(pltcondf$INVYR))
+  }
+  
+  return(returnlst)
+  
+  
+}
diff --git a/man/modMAratio.Rd b/man/modMAratio.Rd
new file mode 100644
index 00000000..c8f156c6
--- /dev/null
+++ b/man/modMAratio.Rd
@@ -0,0 +1,281 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/modMAratio.R
+\name{modMAratio}
+\alias{modMAratio}
+\title{Model-Assisted module - Generate model-assisted tree estimates.}
+\usage{
+modMAratio(
+  MApopdat,
+  ratiotype = "PERACRE",
+  woodland = "Y",
+  landarea = "FOREST",
+  estseed = "none",
+  pcfilter = NULL,
+  estvarn = NULL,
+  estvarn.filter = NULL,
+  estvard = NULL,
+  estvard.filter = NULL,
+  prednames = NULL,
+  FIA = TRUE,
+  rowvar = NULL,
+  colvar = NULL,
+  sumunits = TRUE,
+  returntitle = FALSE,
+  savedata = FALSE,
+  table_opts = NULL,
+  title_opts = NULL,
+  savedata_opts = NULL,
+  gui = FALSE,
+  bootstrap = FALSE,
+  modelselect = FALSE,
+  ...
+)
+}
+\arguments{
+\item{MApopdat}{List. Population data objects returned from modMApop().}
+
+\item{ratiotype}{String. The type of ratio estimates ("PERACRE", "PERTREE").}
+
+\item{woodland}{String. If woodland = 'Y', include woodland tree species
+where measured. If woodland = 'N', only include timber species. See 
+FIESTA::ref_species$WOODLAND ='Y/N'. If woodland = 'only', only include
+woodland species.}
+
+\item{landarea}{String. The sample area filter for estimates ("FOREST",
+"TIMBERLAND").  If landarea=FOREST, filtered to COND_STATUS_CD = 1; If
+landarea=TIMBERLAND, filtered to SITECLCD in(1:6) and RESERVCD = 0.}
+
+\item{estseed}{String. Use seedling data only or add to tree data. Seedling
+estimates are only for counts (estvar='TPA_UNADJ')-('none', 'only', 'add').}
+
+\item{pcfilter}{String. A filter for plot or cond attributes (including
+pltassgn).  Must be R logical syntax.}
+
+\item{estvarn}{String. Name of the tree estimate variable (numerator).}
+
+\item{estvarn.filter}{String. A tree filter for the estimate variable
+(numerator).  Must be R syntax (e.g., "STATUSCD == 1").}
+
+\item{estvard}{String. Name of the tree estimate variable (denominator).}
+
+\item{estvard.filter}{String. A tree filter for the estimate variable
+(denominator).  Must be R syntax (e.g., "STATUSCD == 1").}
+
+\item{prednames}{String vector. Name(s) of predictor variables to include in
+model.}
+
+\item{FIA}{Logical. If TRUE, the finite population term is removed from
+estimator to match FIA estimates.}
+
+\item{rowvar}{String. Optional. Name of domain variable to group estvarn and
+estvard by for rows in table output. Rowvar must be included in an input data frame
+(i.e., plt, cond, tree). If no rowvar is included, an estimate is returned
+for the total estimation unit. Include colvar for grouping by 2 variables.}
+
+\item{colvar}{String. Optional. If rowvar != NULL, name of domain variable
+to group estvarn and estvard by for columns in table output. Colvar must be included in
+an input data frame (i.e., plt, cond, tree).}
+
+\item{sumunits}{Logical. If TRUE, estimation units are summed and returned
+in one table.}
+
+\item{returntitle}{Logical. If TRUE, returns title(s) of the estimation
+table(s).}
+
+\item{savedata}{Logical. If TRUE, saves table(s) to outfolder.}
+
+\item{table_opts}{List. See help(table_options()) for a list of
+options.}
+
+\item{title_opts}{List. See help(title_options()) for a list of options.}
+
+\item{savedata_opts}{List. See help(savedata_options()) for a list
+of options. Only used when savedata = TRUE.}
+
+\item{gui}{Logical. If gui, user is prompted for parameters.}
+
+\item{bootstrap}{Logical. If TRUE, returns bootstrap variance estimates,
+otherwise uses Horvitz-Thompson estimator under simple random sampling
+without replacement.}
+
+\item{modelselect}{Logical. If TRUE, an elastic net regression model is fit 
+to the entire plot level data, and the variables selected in that model are 
+used for the proceeding estimation.}
+
+\item{...}{Parameters for modMApop() if MApopdat is NULL.}
+}
+\value{
+If FIA=TRUE or unitvar=NULL and colvar=NULL, one data frame is
+returned with tree estimates and percent sample errors. Otherwise, a list is
+returned with tree estimates in one data frame (est) and percent sample
+errors in another data frame (est.pse). If rawdata=TRUE, another list is
+returned including raw data used in the estimation process.  If
+addtitle=TRUE and returntitle=TRUE, the title for est/pse is returned. If
+savedata=TRUE, all data frames are written to outfolder.
+
+\item{est}{ Data frame. Tree estimates by rowvar, colvar (and estimation
+unit). If FIA=TRUE or one estimation unit and colvar=NULL, estimates and
+percent sampling error are in one data frame. } \item{pse}{ Data frame.
+Percent sampling errors for estimates by rowvar and colvar (and estimation
+unit). } \item{titlelst}{ List with 1 or 2 string vectors. If
+returntitle=TRUE a list with table title(s). The list contains one title if
+est and pse are in the same table and two titles if est and pse are in
+separate tables. } \item{raw}{ List of data frames. If rawdata=TRUE, a list
+including: number of plots by plot status, if in dataset (plotsampcnt);
+number of conditions by condition status (condsampcnt); data used for
+post-stratification (stratdat); and 1-8 tables with calculated variables
+used for processing estimates and percent sampling error for table cell
+values and totals (See processing data below). }
+
+Raw data
+\item{plotsampcnt}{ Table. Number of plots by plot status (ex. sampled
+forest on plot, sampled nonforest, nonsampled). } \item{condsampcnt}{ DF.
+Number of conditions by condition status (forest land, nonforest land,
+noncensus water, census water, nonsampled). } \item{unitarea}{ DF. Area by
+estimation unit. } \item{expcondtab}{ DF. Condition-level area expansion
+factors. } \item{tdomdat}{ DF. Final data table used for estimation. }
+
+The data frames include the following information: \tabular{lll}{ \tab
+\bold{Variable} \tab \bold{Description}\cr \tab rhat \tab estimated ratio estn/estd
+\cr \tab rhat.var \tab variance estimate
+of estimated ratio estn/estd \cr \tab NBRPLT Number of plots used in estimates \cr \tab
+NBRPLT.gt0 \tab Number of non-zero plots used in estimates \cr \tab 
+ACRES \tab total area for estimation unit \cr \tab rhat.se \tab
+estimated standard error of ratio sqrt(rhat.var) \cr \tab rhat.cv \tab
+estimated coefficient of variation of ratio rhat.se/rhat \cr \tab rhat.pse
+\tab estimated percent standard error or ratio rhat.cv*100 \cr \tab CI99left
+\tab left tail of 99 percent confidence interval for estimated area \cr \tab
+CI99right \tab right tail of 99 percent confidence interval for estimated
+area \cr \tab CI95left \tab left tail of 95 percent confidence interval for
+estimated area \cr \tab CI95right \tab right tail of 95 percent confidence
+interval for estimated area \cr \tab CI67left \tab left tail of 67 percent
+confidence interval for estimated area \cr \tab CI67right \tab right tail of
+67 percent confidence interval for estimated area \cr } 
+
+Table(s) are also written to outfolder.
+}
+\description{
+Generates tree estimates by estimation unit. Estimates are calculated from
+McConville et al. (2018)'s mase R package.
+}
+\details{
+If variables are NULL, then it will prompt user to input variables.
+
+Necessary variables:\cr \tabular{llll}{ \tab \bold{Data} \tab
+\bold{Variable} \tab \bold{Description}\cr \tab tree \tab tuniqueid \tab
+Unique identifier for each plot, to link to pltassgn (e.g. PLT_CN).\cr \tab
+\tab CONDID \tab Unique identifier of each condition on plot, to link to
+cond.  Set CONDID=1, if only 1 condition per plot.\cr \tab \tab TPA_UNADJ
+\tab Number of trees per acre each sample tree represents (e.g., DESIGNCD=1:
+TPA_UNADJ=6.018046 for trees on subplot; 74.965282 for trees on
+microplot).\cr \tab cond \tab cuniqueid \tab Unique identifier for each
+plot, to link to pltassgn (ex. PLT_CN).\cr \tab \tab CONDID \tab Unique
+identifier of each condition on plot.  Set CONDID=1, if only 1 condition per
+plot.\cr \tab \tab CONDPROP_UNADJ \tab Unadjusted proportion of condition on
+each plot.  Set CONDPROP_UNADJ=1, if only 1 condition per plot.\cr \tab \tab
+COND_STATUS_CD \tab Status of each forested condition on plot (i.e.
+accessible forest, nonforest, water, etc.)\cr \tab \tab NF_COND_STATUS_CD
+\tab If ACI=TRUE. Status of each nonforest condition on plot (i.e.
+accessible nonforest, nonsampled nonforest)\cr \tab \tab SITECLCD \tab If
+landarea=TIMBERLAND. Measure of site productivity.\cr \tab \tab RESERVCD
+\tab If landarea=TIMBERLAND. Reserved status.\cr \tab \tab SUBPROP_UNADJ
+\tab Unadjusted proportion of subplot conditions on each plot.  Set
+SUBPROP_UNADJ=1, if only 1 condition per subplot.\cr \tab \tab
+MICRPROP_UNADJ \tab If microplot tree attributes. Unadjusted proportion of
+microplot conditions on each plot. Set MICRPROP_UNADJ=1, if only 1 condition
+per microplot.\cr \tab \tab MACRPROP_UNADJ \tab If macroplot tree
+attributes. Unadjusted proportion of macroplot conditions on each plot. Set
+MACRPROP_UNADJ=1, if only 1 condition per macroplot.\cr \tab pltassgn \tab
+puniqueid \tab Unique identifier for each plot, to link to cond (ex. CN).\cr
+\tab \tab STATECD \tab Identifies state each plot is located in.\cr \tab
+\tab INVYR \tab Identifies inventory year of each plot.\cr \tab \tab
+PLOT_STATUS_CD \tab Status of each plot (i.e. sampled, nonsampled).  If not
+included, all plots are assumed as sampled.\cr }
+
+Reference names are available for the following variables: \cr ADFORCD,
+AGENTCD, CCLCD, DECAYCD, DSTRBCD, KINDCD, OWNCD, OWNGRPCD, FORTYPCD,
+FLDTYPCD, FORTYPCDCALC, TYPGRPCD, FORINDCD, RESERVCD, LANDCLCD, STDSZCD,
+FLDSZCD, PHYSCLCD, MIST_CL_CD, PLOT_STATUS_CD, STATECD, TREECLCD, TRTCD,
+SPCD, SPGRPCD
+}
+\note{
+ADJUSTMENT FACTOR:\cr The adjustment factor is necessary to account for
+nonsampled conditions. It is calculated for each estimation unit by strata.
+by summing the unadjusted proportions of the subplot, microplot, and
+macroplot (i.e. *PROP_UNADJ) and dividing by the number of plots in the
+strata/estimation unit).
+
+An adjustment factor is determined for each tree based on the size of the
+plot it was measured on. This is identified using TPA_UNADJ as follows:
+
+\tabular{llr}{ \tab \bold{PLOT SIZE} \tab \bold{TPA_UNADJ} \cr \tab SUBPLOT
+\tab 6.018046 \cr \tab MICROPLOT \tab 74.965282 \cr \tab MACROPLOT \tab
+0.999188 \cr }
+
+If ACI=FALSE, only nonsampled forest conditions are accounted for in the
+adjustment factor. \cr If ACI=TRUE, the nonsampled nonforest conditions are
+removed as well and accounted for in adjustment factor.  This is if you are
+interested in estimates for all lands or nonforest lands in the
+All-Condition-Inventory.
+
+sumunits:\cr An estimation unit is a population, or area of interest, with
+known area and number of plots. Individual counties or combined
+Super-counties are common estimation units for FIA. An estimation unit may
+also be a subpopulation of a larger population (e.g., Counties within a
+State). Subpopulations are mutually exclusive and independent within a
+population, therefore estimated totals and variances are additive. For
+example, State-level estimates are generated by summing estimates from all
+subpopulations within the State (Bechtold and Patterson. 2005. Chapter 2).
+Each plot must be assigned to only one estimation unit.
+
+autoxreduce:\cr If MAmethod='GREG', and autoxreduce=TRUE, and there is an
+error because of multicolinearity, a variable reduction method is applied to
+remove correlated variables. The method used is based on the
+variance-inflation factor (vif) from a linear model. The vif estimates how
+much the variance of each x variable is inflated due to mulitcolinearity in
+the model.
+
+rowlut/collut:\cr There are several objectives for including rowlut/collut
+look-up tables: 1) to include descriptive names that match row/column codes
+in the input table; 2) to use number codes that match row/column names in
+the input table for ordering rows; 3) to add rows and/or columns with 0
+values for consistency. No duplicate names are allowed.
+
+Include 2 columns in the table:\cr 1-the merging variable with same name as
+the variable in the input merge table;\cr 2-the ordering or descriptive
+variable.\cr If the ordering variable is the rowvar/colvar in the input
+table and the descriptive variable is in rowlut/collut, set
+row.orderby/col.orderby equal to rowvar/colvar. If the descriptive variable
+is the rowvar/colvar in the input table, and the ordering code variable is
+in rowlut/collut, set row.orderby/col.orderby equal to the variable name of
+the code variable in rowlut/collut.
+
+UNITS:\cr The following variables are converted from pounds (from FIA
+database) to short tons by multiplying the variable by 0.0005.  DRYBIO_AG,
+DRYBIO_BG, DRYBIO_WDLD_SPP, DRYBIO_SAPLING, DRYBIO_STUMP, DRYBIO_TOP,
+DRYBIO_BOLE, DRYBIOT, DRYBIOM, DRYBIOTB, JBIOTOT, CARBON_BG, CARBON_AG
+
+MORTALITY:\cr For Interior-West FIA, mortality estimates are mainly based on
+whether a tree has died within the last 5 years of when the plot was
+measured. If a plot was remeasured, mortality includes trees that were alive
+the previous visit but were dead in the next visit. If a tree was standing
+the previous visit, but was not standing in the next visit, no diameter was
+collected (DIA = NA) but the tree is defined as mortality.
+
+Common tree filters: \cr
+
+\tabular{llr}{ \tab \bold{FILTER} \tab \bold{DESCRIPTION} \cr \tab "STATUSCD
+== 1" \tab Live trees \cr \tab "STATUSCD == 2" \tab Dead trees \cr \tab
+"TPAMORT_UNADJ > 0" \tab Mortality trees \cr \tab "STATUSCD == 2 & DIA >=
+5.0" \tab Dead trees >= 5.0 inches diameter \cr \tab "STATUSCD == 2 &
+AGENTCD == 30" \tab Dead trees from fire \cr }
+}
+\references{
+Kelly McConville, Becky Tang, George Zhu, Shirley Cheung, and
+Sida Li (2018). mase: Model-Assisted Survey Estimation. R package version
+0.1.4 https://cran.r-project.org/package=mase
+}
+\author{
+Josh Yamamoto
+}
+\keyword{data}