Fix build script

Fix build script


blank basketball chart

blank basketball chart

program ddpic

local var `1'
local isbaseball `2'

run_reg `var' `isbaseball'

make_pic `var' `isbaseball'

end

program run_reg

use ${stash}master, clear

local var `1'
local isbaseball `2'

drop if isbaseball != `isbaseball'

qui xtreg `var' 1.treat##b2008.year, fe vce(robust)

qui parmest, label list(parm estimate min* max* p) saving(${stash}mypars, replace)

end

program make_pic

local var `1'
local isbaseball `2'

clear
use ${stash}mypars, clear

keep if regexm(parm, "1.*treat#20[0-9][0-9].*") == 1
gen year = regexs(1) if regexm(parm, ".*#(20[0-9][0-9])b?\..*")
destring year, replace

drop if year < 2005

qui gen xaxis = 0

if "`isbaseball'" == "0"{
   local vartitle = "Basketball Players"
   local yt = ""
 }

if "`isbaseball'" == "0" & "`var'" == "bd"{
   local vartitle = "Basketball Players (No Citations, see note below)"
   local yt = ""
 }

if "`isbaseball'" == "1"{
   local vartitle = "Baseball Players"
   local yt = "Mean value / page"
 }

if "`var'" == "img" {
    local scale "yscale(range(-0.3 0.8)) ylabel(-0.2 (0.2) 0.8)"
}

if "`var'" == "text" {
    local scale "yscale(range(-3500 3000)) ylabel(-3000 (1000) 3000)"
}

if "`var'" == "traf" {
    drop if year == 2013
    local scale "yscale(range(-100 50)) ylabel(-100 (25) 50)"
}

graph twoway (connected estimate year, msize(small) lpattern(dash) lcolor(edkblue) lwidth(thin)) (line max year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line min year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line xaxis year, lwidth(vthin) lcolor(gs8)), xtitle("") ytitle("`yt'") xlabel(2005(1)2013) legend(off) title("`vartitle'") `scale'

** ylabel(-0.2 (0.2) 0.8)

graph export "${tables}timeline_`var'_`isbaseball'.eps", replace
shell epstopdf  "${tables}timeline_`var'_`isbaseball'.eps"

end

program ddpic

local var `1'
local isbaseball `2'

run_reg `var' `isbaseball'

make_pic `var' `isbaseball'

end

program run_reg

use ${stash}master, clear

local var `1'
local isbaseball `2'

drop if isbaseball != `isbaseball'

qui xtreg `var' 1.treat##b2008.year, fe vce(robust)

qui parmest, label list(parm estimate min* max* p) saving(${stash}mypars, replace)

end

program make_pic

local var `1'
local isbaseball `2'

clear
use ${stash}mypars, clear

keep if regexm(parm, "1.*treat#20[0-9][0-9].*") == 1
gen year = regexs(1) if regexm(parm, ".*#(20[0-9][0-9])b?\..*")
destring year, replace

drop if year < 2005

qui gen xaxis = 0

if "`isbaseball'" == "0"{
   local vartitle = "Basketball"
   local yt = ""
 }

if "`isbaseball'" == "1"{
   local vartitle = "Baseball"
   local yt = "Mean value / page"
 }

if "`var'" == "img" {
    local scale "yscale(range(-0.3 0.8)) ylabel(-0.2 (0.2) 0.8)"
}

if "`var'" == "text" {
    local scale "yscale(range(-3500 3000)) ylabel(-3000 (1000) 3000)"
}

if "`var'" == "traf" {
    drop if year == 2013
    local scale "yscale(range(-100 50)) ylabel(-100 (25) 50)"
}

graph twoway (connected estimate year, msize(small) lpattern(dash) lcolor(edkblue) lwidth(thin)) (line max year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line min year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line xaxis year, lwidth(vthin) lcolor(gs8)), xtitle("") ytitle("`yt'") xlabel(2005(1)2013) legend(off) title("`vartitle' Players") `scale'

** ylabel(-0.2 (0.2) 0.8)

graph export "${tables}timeline_`var'_`isbaseball'.eps", replace
shell epstopdf  "${tables}timeline_`var'_`isbaseball'.eps"

end


change to 90 conf interval

change to 90 conf interval

program ddpic

local var `1'
local isbaseball `2'

run_reg `var' `isbaseball'

make_pic `var' `isbaseball'

end

program run_reg

use ${stash}master, clear

local var `1'
local isbaseball `2'

drop if isbaseball != `isbaseball'

qui xtreg `var' 1.treat##b2008.year, fe vce(robust)

qui parmest, label list(parm estimate min* max* p) saving(${stash}mypars, replace) level(90)

end

program make_pic

local var `1'
local isbaseball `2'

clear
use ${stash}mypars, clear

keep if regexm(parm, "1.*treat#20[0-9][0-9].*") == 1
gen year = regexs(1) if regexm(parm, ".*#(20[0-9][0-9])b?\..*")
destring year, replace

drop if year < 2005

qui gen xaxis = 0

if "`isbaseball'" == "0"{
   local vartitle = "Basketball Players"
   local yt = ""
 }

if "`isbaseball'" == "0" & "`var'" == "bd"{
   local vartitle = "Basketball Players (No Citations, see note below)"
   local yt = ""
 }

if "`isbaseball'" == "1"{
   local vartitle = "Baseball Players"
   local yt = "Mean value / page"
 }

if "`var'" == "img" {
    local scale "yscale(range(-0.3 0.8)) ylabel(-0.2 (0.2) 0.8)"
}

if "`var'" == "text" {
    local scale "yscale(range(-350 300)) ylabel(-300 (100) 300)"
}

if "`var'" == "traf" {
    drop if year == 2013
    local scale "yscale(range(-100 50)) ylabel(-100 (25) 50)"
}

graph twoway (connected estimate year, msize(small) lpattern(dash) lcolor(edkblue) lwidth(thin)) (line max year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line min year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line xaxis year, lwidth(vthin) lcolor(gs8)), xtitle("") ytitle("`yt'") xlabel(2005(1)2013) legend(off) title("`vartitle'") `scale'

** ylabel(-0.2 (0.2) 0.8)

graph export "${tables}timeline_`var'_`isbaseball'.eps", replace
shell epstopdf  "${tables}timeline_`var'_`isbaseball'.eps"

end

program ddpic

local var `1'
local isbaseball `2'

run_reg `var' `isbaseball'

make_pic `var' `isbaseball'

end

program run_reg

use ${stash}master, clear

local var `1'
local isbaseball `2'

drop if isbaseball != `isbaseball'

qui xtreg `var' 1.treat##b2008.year, fe vce(robust)

qui parmest, label list(parm estimate min* max* p) saving(${stash}mypars, replace)

end

program make_pic

local var `1'
local isbaseball `2'

clear
use ${stash}mypars, clear

keep if regexm(parm, "1.*treat#20[0-9][0-9].*") == 1
gen year = regexs(1) if regexm(parm, ".*#(20[0-9][0-9])b?\..*")
destring year, replace

drop if year < 2005

qui gen xaxis = 0

if "`isbaseball'" == "0"{
   local vartitle = "Basketball Players"
   local yt = ""
 }

if "`isbaseball'" == "0" & "`var'" == "bd"{
   local vartitle = "Basketball Players (No Citations, see note below)"
   local yt = ""
 }

if "`isbaseball'" == "1"{
   local vartitle = "Baseball Players"
   local yt = "Mean value / page"
 }

if "`var'" == "img" {
    local scale "yscale(range(-0.3 0.8)) ylabel(-0.2 (0.2) 0.8)"
}

if "`var'" == "text" {
    local scale "yscale(range(-350 300)) ylabel(-300 (100) 300)"
}

if "`var'" == "traf" {
    drop if year == 2013
    local scale "yscale(range(-100 50)) ylabel(-100 (25) 50)"
}

graph twoway (connected estimate year, msize(small) lpattern(dash) lcolor(edkblue) lwidth(thin)) (line max year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line min year, lwidth(vthin) lpattern(-) lcolor(gs8)) (line xaxis year, lwidth(vthin) lcolor(gs8)), xtitle("") ytitle("`yt'") xlabel(2005(1)2013) legend(off) title("`vartitle'") `scale'

** ylabel(-0.2 (0.2) 0.8)

graph export "${tables}timeline_`var'_`isbaseball'.eps", replace
shell epstopdf  "${tables}timeline_`var'_`isbaseball'.eps"

end


histograms done

histograms done

clear all
set more off
set matsize 11000
program drop _all

qui adopath + "/mnt/nfs6/wikipedia.proj/jmp/jmpscripts/stata/ado"
cd ${path}

// this declares global vars
declare_global

// 0. Data

// 0.1 make cleancnty, changesets data
clear
preparebasic

// 0.2 make relevant dependent variables
use ${stash}cleanchangeset1, clear
makedv fips
balancepanel fips
mergebasic
qui destring, replace
save ${stash}panelfips, replace

// 1. Summary Stats
program drop _all
use ${stash}panelfips, clear
makesummary

// 2. Treatment Control Balance
use ${stash}panelfips, clear
makehist cntypop kdensity "Population"
makehist emp_earnings kdensity "Earnings"
makehist age_median kdensity "Median Age"
makehist educ_college kdensity "Num. College Educated"

** makehist region hist

 ** Analysis

// 0. Data

// 0.1 make fips dataset
use ${stash}mergemaster1, clear

balancepanel fips
save ${stash}panelfips, replace

// 0.2 make node dataset
preparenode

use ${stash}mergemaster_node, clear
makedv fips node
balancepanel fips node
save ${stash}panelnode, replace

// 0.3 make way dataset
prepareway
makedv fips way
balancepanel fips way
save ${stash}panelway, replace

// 3. Baseline effects (xtpoisson, cluster at unit level)

//  3.1 Meanline charts (raw data)
makemeanline numchanges quarter 2011 "Changes"
makemeanline numcontrib quarter 2011 "Contributions"
makemeanline numuser quarter 2011 "Users"
makemeanline numserious90 quarter 2011 "Super Users"
makemeanline numnewusers quarter 2011 "New Users"
makemeanline numnewusers6 quarter 2011 "New Users (Stay for 6+ Months)"
makemeanline numnewusers90 quarter 2011 "New Users (who become super users)"

// 3.2 FIPS Sample -- DD

/// 3.2.1 -- contrib, users, superu
program drop _all

clear
local dv "numcontrib numuser numserious90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.1 "Contributions" "Users" "Super Users"

/// 3.2.2 -- newusers, newusers6, newuserssuper
clear
local dv "numnewusers numnewusers6 numnewusers90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.2 "New Users" "New Users(6+)" "New Super Users"

/// 3.2.3 -- streetcontrib, nonstreet contrib, amenities
// TODO

use ${stash}panelnode, clear
program drop _all

diffindiff xtpoisson fips run tab_test numcontrib numuser

/// 3.2.4 -- completeness
// TODO

// 3.2.5 -- Diff in Diff charts
// TODO : make this more flexible
// TODO : this works only for fips
ddchart

// 3.3 Robustness : Drop empty counties
// TODO
//  e. Repeat (a) by dropping empty counties
//  f. Repeat (a) by dropping west
//  g. Repeat (a) for Tile Sample, MSA sample
//  h. Repeat (a) with matched estimators
//  i. Repeat (a) with spatially clustered se, Region X Time trends

// 4 Heterogenous Effects

// 1. Large vs. small
use ${stash}panelfips, clear
gen large = (cntypop > 100000)

diffindiff2 large LARGE tab_4.1 run numcontrib numuser numserious90 numnewusers6

diffindiff2 large LARGE tab_4.1 write numcontrib numuser numserious90 numnewusers6

// 2. engaged vs. non-engaged county

use ${stash}panelfips, clear

bysort fips post: egen tmp = total(numcontrib > 0)
replace tmp = . if post == 1
bysort fips: egen precontrib = max(tmp)
bysort fips: egen totcontrib = total(numcontrib)
drop tmp

drop if precontrib == 0
gen active = (prec > 1)

diffindiff2 active ACTIVE tab_4.2 run numcontrib numuser numserious90 numnewusers6

diffindiff2 active ACTIVE tab_4.2 write numcontrib numuser numserious90 numnewusers6

// 3. populated vs. not

use ${stash}panelfips, clear

gen iseast = (region==1 | region == 2)

diffindiff2 iseast EAST tab_4.3 run numcontrib numuser numserious90 numnewusers6

diffindiff2 iseast EAST tab_4.3 write numcontrib numuser numserious90 numnewusers6

// 3. urban vs rural
// 4. university vs non university
// 5. rich vs poor

// 5 Street Level Regressions

// 6 Person Level Regressions

// [A] outcome vars: County sample -- DD spec

   // a. Contributions
   // b. Street, Non Street
   // b. TIGER vs. Non Tiger
   // d. Home county, Num Counties

// [B] heterogenous

   // a. Junior vs Senior

// 7 Impacts on Craigslist

preparecl

// PENDING

// GEOID Level

// TASKS for tomorrow

// STASH

 // 1.1 Mean Charts

 // 1.2 Produce Diff in diff Latex tables
 // rundd -> batchreg.sh -> runreg.do -> diffindiff.ado
 program drop _all

 // TODO: fix manual process
 // have to do this manually

 // 1.3 Produce Diff in diff Pictures

 ** person level regressions
 // person level
 makeperson
 ddperson maketables
 ddperson makechart

// TODOs for Tuesday Jun 3
// GEOID dataset
// TILEID diff in diff
// What happened to previous people?
// What happened at the street/amenity level?
// Urban vs Rural (framework for third level diff)

// 1.4 Robust: product diff in diff for GEOID

use ${stash}panelfips_geoid10, clear

** Analysis Stage 2
//

// PROGRAMS

clear all
set more off
set matsize 11000
program drop _all

qui adopath + "/mnt/nfs6/wikipedia.proj/jmp/jmpscripts/stata/ado"
cd ${path}

// this declares global vars
declare_global

// 0. Data

// 0.1 make cleancnty, changesets data
clear
preparebasic

// 0.2 make relevant dependent variables
use ${stash}cleanchangeset1, clear
makedv fips
balancepanel fips
mergebasic
qui destring, replace
save ${stash}panelfips, replace

// 1. Summary Stats
program drop _all
use ${stash}panelfips, clear
makesummary

 ** Analysis

// 0. Data

// 0.1 make fips dataset
use ${stash}mergemaster1, clear

balancepanel fips
save ${stash}panelfips, replace

// 0.2 make node dataset
preparenode

use ${stash}mergemaster_node, clear
makedv fips node
balancepanel fips node
save ${stash}panelnode, replace

// 0.3 make way dataset
prepareway
makedv fips way
balancepanel fips way
save ${stash}panelway, replace

// 2. Treatment vs. control charts
use ${stash}panelfips, clear
makehist cntypop kdensity
makehist region hist

// 3. Baseline effects (xtpoisson, cluster at unit level)

//  3.1 Meanline charts (raw data)
makemeanline numchanges quarter 2011 "Changes"
makemeanline numcontrib quarter 2011 "Contributions"
makemeanline numuser quarter 2011 "Users"
makemeanline numserious90 quarter 2011 "Super Users"
makemeanline numnewusers quarter 2011 "New Users"
makemeanline numnewusers6 quarter 2011 "New Users (Stay for 6+ Months)"
makemeanline numnewusers90 quarter 2011 "New Users (who become super users)"

// 3.2 FIPS Sample -- DD

/// 3.2.1 -- contrib, users, superu
program drop _all

clear
local dv "numcontrib numuser numserious90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.1 "Contributions" "Users" "Super Users"

/// 3.2.2 -- newusers, newusers6, newuserssuper
clear
local dv "numnewusers numnewusers6 numnewusers90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.2 "New Users" "New Users(6+)" "New Super Users"

/// 3.2.3 -- streetcontrib, nonstreet contrib, amenities
// TODO

use ${stash}panelnode, clear
program drop _all

diffindiff xtpoisson fips run tab_test numcontrib numuser

/// 3.2.4 -- completeness
// TODO

// 3.2.5 -- Diff in Diff charts
// TODO : make this more flexible
// TODO : this works only for fips
ddchart

// 3.3 Robustness : Drop empty counties
// TODO
//  e. Repeat (a) by dropping empty counties
//  f. Repeat (a) by dropping west
//  g. Repeat (a) for Tile Sample, MSA sample
//  h. Repeat (a) with matched estimators
//  i. Repeat (a) with spatially clustered se, Region X Time trends

// 4 Heterogenous Effects

// 1. Large vs. small
use ${stash}panelfips, clear
gen large = (cntypop > 100000)

diffindiff2 large LARGE tab_4.1 run numcontrib numuser numserious90 numnewusers6

diffindiff2 large LARGE tab_4.1 write numcontrib numuser numserious90 numnewusers6

// 2. engaged vs. non-engaged county

use ${stash}panelfips, clear

bysort fips post: egen tmp = total(numcontrib > 0)
replace tmp = . if post == 1
bysort fips: egen precontrib = max(tmp)
bysort fips: egen totcontrib = total(numcontrib)
drop tmp

drop if precontrib == 0
gen active = (prec > 1)

diffindiff2 active ACTIVE tab_4.2 run numcontrib numuser numserious90 numnewusers6

diffindiff2 active ACTIVE tab_4.2 write numcontrib numuser numserious90 numnewusers6

// 3. populated vs. not

use ${stash}panelfips, clear

gen iseast = (region==1 | region == 2)

diffindiff2 iseast EAST tab_4.3 run numcontrib numuser numserious90 numnewusers6

diffindiff2 iseast EAST tab_4.3 write numcontrib numuser numserious90 numnewusers6

// 3. urban vs rural
// 4. university vs non university
// 5. rich vs poor

// 5 Street Level Regressions

// 6 Person Level Regressions

// [A] outcome vars: County sample -- DD spec

   // a. Contributions
   // b. Street, Non Street
   // b. TIGER vs. Non Tiger
   // d. Home county, Num Counties

// [B] heterogenous

   // a. Junior vs Senior

// 7 Impacts on Craigslist

preparecl

// PENDING

// GEOID Level

// TASKS for tomorrow

// STASH

 // 1.1 Mean Charts

 // 1.2 Produce Diff in diff Latex tables
 // rundd -> batchreg.sh -> runreg.do -> diffindiff.ado
 program drop _all

 // TODO: fix manual process
 // have to do this manually

 // 1.3 Produce Diff in diff Pictures

 ** person level regressions
 // person level
 makeperson
 ddperson maketables
 ddperson makechart

// TODOs for Tuesday Jun 3
// GEOID dataset
// TILEID diff in diff
// What happened to previous people?
// What happened at the street/amenity level?
// Urban vs Rural (framework for third level diff)

// 1.4 Robust: product diff in diff for GEOID

use ${stash}panelfips_geoid10, clear

** Analysis Stage 2
//

// PROGRAMS


Small code cleanup

Small code cleanup

/* load the C extension */
ensurelib_aberrance svm // check for libsvm
program _svm, plugin    // load _svm.plugin, the wrapper for libsvm

* subroutine to convert the global struct svm_model that lives in the DLL to a mixture of e() entries, variables, and matrices
* this needs to be its own subroutine because, due to limitations in the Stata C API,th
* it does an awkward dance where _svm.plugin writes to the (global!) scalar dict and then this code copies those entries to r()
* as with svm_load, the extension function is called multiple times with sub-sub-commands, because it doesn't have permission to perform all the operations needed
* if passed, SV specifies a column to create and record svm_model->sv_indecies into
program define _svm_model2stata, eclass
  version 13

  syntax [if] [in], [SV(string)] [Verbose]

  * as with loading, this has to call in and out of the plugin because chicken/egg:
  *   the plugin doesn't have permission to allocate Stata memory (in this case matrices),
  *   but we don't know how much to allocate before interrogating the svm_model

  * Phase 1

  * the total number of observations
  * this gets set by _svm.c::train(); it doesn't exist for a model loaded via import().
  * nevertheless it is in this file instead of svm_train.ado, because it is most similar here
  * but we cap { } around it so the other case is tolerable
  capture {
    ereturn scalar N = _model2stata_N
    scalar drop _model2stata_N
  }

  /*an undefined macro will inconsistently cause an eval error because `have_rho'==1 will eval to ==1 will eval to "unknown variable"*/
  /*so just define them ahead of time to be safe*/
  local have_sv_indices = 0
  local have_sv_coef = 0
  local have_rho = 0
  local labels = ""

  plugin call _svm `if' `in', `verbose' "_model2stata" 1

  * the total number of (detected?) classes
  ereturn scalar N_class = _model2stata_nr_class
  scalar drop _model2stata_nr_class

  * the number of support vectors
  ereturn scalar N_SV = _model2stata_l
  scalar drop _model2stata_l

  * Phase 2
  * Allocate Stata matrices and copy the libsvm matrices and vectors
  if(`have_sv_coef'==1 & `e(N_class)'>1 & `e(N_SV)'>0) {
    capture noisily {
      matrix sv_coef = J(e(N_class)-1,e(N_SV),.)

      // there doesn't seem to be an easy way to generate a list of strings with a prefix in Stata
      // so: the inefficient way
      local cols = ""
      forval j = 1/`e(N_SV)' {
        local cols = "`cols' SV`j'"
      }
      matrix colnames sv_coef = `cols'

      // TODO: rows
      //  there is one row per class *less one*. the rows probably represent decision boundaries, then. I'm not sure what this should be labelled.
      // matrix rownames sv_coef = class1..class`e(N_SV)'
    }
  }

  if(`have_rho'==1 & `e(N_class)'>0) {
    capture noisily matrix rho = J(e(N_class),e(N_class),.)
  }

  * TODO: also label the rows according to model->label (libsvm's "labels" are just more integers, but it helps to be consistent anyway);
  *  I can easily extract ->label with the same code, but attaching it to the rownames of the other is tricky
  capture noisily {
    plugin call _svm `if' `in', `verbose' "_model2stata" 2

    // Label the resulting matrices and vectors with the 'labels' array, if we have it
    if("`labels'"!="") {
      ereturn local levels = strtrim("`labels'")

      capture matrix rownames rho = `labels'
      capture matrix colnames rho = `labels'
    }
  }

  * Phase 3
  * Export the SVs
  if(`have_sv_indices'==1 & `e(N_SV)'>0 & "`sv'"!="") {
    capture noisily {
      // he internal libsvm format is a list of indices
      // we want indicators, which are convenient for Stata
      // so we  *start* with all 0s (rather than missings) and overwrite with 1s as we discover SVs
      quietly generate `sv' `if' `in' = 0
      plugin call _svm `sv' `if' `in', `verbose' "_model2stata" 3
    }
  }

  * Phase 4
  * Export the rest of the values to e()
  * We *cannot* export matrices to e() from the C interface, hence we have to do this very explicit thing
  * NOTE: 'ereturn matrix' erases the old name (unless you specify ,copy), which is why we don't have to explicitly drop things
  *       'ereturn scalar' doesn't do this, because Stata loves being consistent. Just go read the docs for 'syntax' and see how easy it is.
  * All of these are silenced because various things might kill any of them, and we want failures to be independent of each other

  quietly capture ereturn matrix sv_coef = sv_coef
  quietly capture ereturn matrix rho = rho
end

/* load the C extension */
ensurelib_aberrance svm // check for libsvm
program _svm, plugin    // load _svm.plugin, the wrapper for libsvm

* subroutine to convert the global struct svm_model that lives in the DLL to a mixture of e() entries, variables, and matrices
* this needs to be its own subroutine because, due to limitations in the Stata C API,th
* it does an awkward dance where _svm.plugin writes to the (global!) scalar dict and then this code copies those entries to r()
* as with svm_load, the extension function is called multiple times with sub-sub-commands, because it doesn't have permission to perform all the operations needed
* if passed, SV specifies a column to create and record svm_model->sv_indecies into
program define _svm_model2stata, eclass
  version 13

  syntax [if] [in], [SV(string)] [Verbose]

  * as with loading, this has to call in and out of the plugin because chicken/egg:
  *   the plugin doesn't have permission to allocate Stata memory (in this case matrices),
  *   but we don't know how much to allocate before interrogating the svm_model

  * Phase 1

  * the total number of observations
  * this gets set by _svm.c::train(); it doesn't exist for a model loaded via import().
  * nevertheless it is in this file instead of svm_train.ado, because it is most similar here
  * but we cap { } around it so the other case is tolerable
  capture {
    ereturn scalar N = _model2stata_N
    scalar drop _model2stata_N
  }

  /*an undefined macro will inconsistently cause an eval error because `have_rho'==1 will eval to ==1 will eval to "unknown variable"*/
  /*so just define them ahead of time to be safe*/
  local have_sv_indices = 0
  local have_sv_coef = 0
  local have_rho = 0
  local labels = ""

  plugin call _svm `if' `in', `verbose' "_model2stata" 1

  * the total number of (detected?) classes
  ereturn scalar N_class = _model2stata_nr_class
  scalar drop _model2stata_nr_class

  * the number of support vectors
  ereturn scalar N_SV = _model2stata_l
  scalar drop _model2stata_l

  * Phase 2
  * Allocate Stata matrices and copy the libsvm matrices and vectors
  if(`have_sv_coef'==1 & `e(N_class)'>1 & `e(N_SV)'>0) {
    capture noisily {
      matrix sv_coef = J(e(N_class)-1,e(N_SV),.)

      // there doesn't seem to be an easy way to generate a list of strings with a prefix in Stata
      // so: the inefficient way
      local cols = ""
      forval j = 1/`e(N_SV)' {
        local cols = "`cols' SV`j'"
      }
      matrix colnames sv_coef = `cols'

      // TODO: rows
      //  there is one row per class *less one*. the rows probably represent decision boundaries, then. I'm not sure what this should be labelled.
      // matrix rownames sv_coef = class1..class`e(N_SV)'
    }
  }

  if(`have_rho'==1 & `e(N_class)'>0) {
    capture noisily matrix rho = J(e(N_class),e(N_class),.)
  }

  * TODO: also label the rows according to model->label (libsvm's "labels" are just more integers, but it helps to be consistent anyway);
  *  I can easily extract ->label with the same code, but attaching it to the rownames of the other is tricky
  capture noisily {
    plugin call _svm `if' `in', `verbose' "_model2stata" 2

    // Label the resulting matrices and vectors with the 'labels' array, if we have it
    if("`labels'"!="") {
      ereturn local levels = strtrim("`labels'")

      capture matrix rownames rho = `labels'
      capture matrix colnames rho = `labels'
    }
  }

  * Phase 3
  * Export the SVs
  capture noisily {
    if("`sv'"!="") {
      quietly generate byte `sv' = .
      quietly replace `sv' `if' `in' = 0  //because the internal format is a list of indices, to translate to indicators we need to *start* with 0s and if we see them in the list, overwrite with 1s
      if(`have_sv_indices'==1 & `e(N_SV)'>0) {
        plugin call _svm `sv' `if' `in', `verbose' "_model2stata" 3
      }
    }
  }

  * Phase 4
  * Export the rest of the values to e()
  * We *cannot* export matrices to e() from the C interface, hence we have to do this very explicit thing
  * NOTE: 'ereturn matrix' erases the old name (unless you specify ,copy), which is why we don't have to explicitly drop things
  *       'ereturn scalar' doesn't do this, because Stata loves being consistent. Just go read the docs for 'syntax' and see how easy it is.
  * All of these are silenced because various things might kill any of them, and we want failures to be independent of each other

  quietly capture ereturn matrix sv_coef = sv_coef
  quietly capture ereturn matrix rho = rho
end


add option to set disp format

add option to set disp format

cap program drop 	ieddtable
	program define	ieddtable

	syntax varlist, ///
					///
		t(varname numeric) tmt(varname numeric) 			///
		[ 													///
		COVARiates(varlist numeric)							///
		STARLevels(numlist descending min=3 max=3 >0 <1)	///
		STARSNOadd											///
		firstdiff											///
		rowlabtype(string) 									///
		rowlabtext(string)									///
		errortype(string)									///
		diformat(string)									///
		]

	/*************

		Input handling

	*************/

	*LABELS
	*Test and prepare the row lables and test how long the longest label is.
	prepRowLabels `varlist', rowlabtype("`rowlabtype'") rowlabtext("`rowlabtext'")
	local rowlabels "`r(rowlabels)'"
	local labmaxlen "`r(rowlabel_maxlen)'"

	*ERRORTYPES
	*If error type is used test that it is only one word and that word is an allowed error type
	if "`errortype'" != "" {
		local errortype = lower("`errortype'")
		if (`: word count `errortype'' != 1) | !inlist("`errortype'", "sd", "se", "errhide") {
			noi di as error "Value in option errortype(`errortype') can only one out of the following words; se, sd, errhide."
			error 198
		}
	}
	else {
		*No errortype specified use default which is standard errors
		local errortype "se"
	}

	*DEFAULT STAR LEVELS
	*Default star levels if option not used
	if "`starlevels'" == "" local starlevels ".1 .05 .01"

	** If the format option is specified, then test if there is a valid format specified
	if "`diformat'" != "" {

			** Creating a numeric mock variable that we attempt to apply the format
			*  to. This allows us to piggy back on Stata's internal testing to be
			*  sure that the format specified is at least one of the valid numeric
			*  formats in Stata
				tempvar  formattest
				gen 	`formattest' = 1
			cap	format  `formattest' `diformat'

			** Some error with the format
			if _rc == 120 {
				di as error "{phang}The format specified in diformat(`diformat') is not a valid Stata format. See {help format} for a list of valid Stata formats. This command only accept the f, fc, g, gc and e format.{p_end}"
				error 120
			}
			else if _rc != 0 {
				di as error "{phang}Something unexpected happened related to the option diformat(`diformat'). Make sure that the format you specified is a valid format. See {help format} for a list of valid Stata formats. If this problem remains, please report this error at https://github.com/worldbank/ietoolkit.{p_end}"
				error _rc
			}

			** Format is a valid format, but is it one we allow
			else {

				local fomrmatAllowed 0
				local charLast  = substr("`diformat'", -1,.)
				local char2Last = substr("`diformat'", -2,.)

				if  "`charLast'" == "f" | "`charLast'" == "e" {
					local fomrmatAllowed 1
				}
				else if "`charLast'" == "g" {
					if "`char2Last'" == "tg" {
						*format tg not allowed. all other valid formats ending on g are allowed
						local fomrmatAllowed 0
					}
					else {

						*Formats that end in g that is not tg can only be g which is allowed.
						local fomrmatAllowed 1
					}
				}
				else if  "`charLast'" == "c" {
					if "`char2Last'" != "gc" & "`char2Last'" != "fc" {
						*format ends on c but is neither fc nor gc
						local fomrmatAllowed 0
					}
					else {

						*Formats that end in c that are either fc or gc are allowed.
						local fomrmatAllowed 1
					}
				}
				else {
					*format is neither f, fc, g, gc nor e
					local fomrmatAllowed 0
				}
				if `fomrmatAllowed' == 0 {
					di as error "{phang}The format specified in diformat(`diformat') is not allowed. Only format f, fc, g, gc and e are allowed. See {help format} for details on Stata formats.{p_end}"
					error 120
				}
			}
		}
		else {
			*Default value if fomramt not specified
			local diformat = "%9.2f"
		}

	/*************

		Initiate the result matrix

	*************/

	*Creates the template for the result matrix in a subfunction
	templateResultMatrix
	mat startRow = r(startRow)
	local colnames = "`r(colnames)'"

	*Remove this when ready for production
	//noi di "Start row to see headers, remove for production"
	//matlist startRow // See the default row with its column names

	*Initiate the result matrix with a place holder row that will not be used for anything. Matrices cannot be initiated empty
	mat ddtab_resultMap = startRow
	mat rownames ddtab_resultMap = placeholder

	/*************

		Loop over all variables and prepare the data

	*************/

	foreach var of local varlist {

		//noi di "Variable `var'"

		*Each variable is a row in the result matrix
		mat `var' = startRow
		mat rownames `var' = `var'
		mat colnames `var' = `colnames'

		*Local that keeps track of which column to fill
		local colindex 0

		tempvar regsample

		/*************

			Calculating 2nd difference and restict the sample
			for this output var

		*************/

		*Run the regression to get the double difference
		qui reg `var' `tmt'#`t' `covariates'
		mat resTable = r(table)

		//noi di "Rest table 2nd diff: reg `var' `tmt'#`t' `covariates'"
		//matlist resTable

		**This is why this is done first. All other calculations
		* for this outcome var should be restricted to this sample.
		gen `regsample' = e(sample)
		local N `e(N)'

		**Test that the dummy vars creates for valid groups for each
		* combination of time and treat in the sample used for this outcome var
		testDDdums `t' `tmt' `regsample' `var'  //comment in when this is made into a command

		*Get the second differnce
		local ++colindex
		mat `var'[1,`colindex'] =  el(resTable,1,4)

		*Get the standard error of second difference
		local ++colindex
		convertErrs el(resTable,2,4) `N' "`errortype'"
		mat `var'[1,`colindex'] =  `r(converted_error)'

		*Get the number of stars using sub-command countStars
		local ++colindex
		local pvalue = el(resTable,4,4)
		countStars `pvalue' `starlevels'
		mat `var'[1,`colindex'] = `r(stars)'

		*Get the N of second difference regression
		local ++colindex
		mat `var'[1,`colindex'] = `N'

		/*************

			Calculating 1st differences

		*************/
		forvalues tmt01 = 0/1 {

			*Regress time against the outcome var one tmt group at the time
			qui reg `var' `t' `covariates' if `tmt' == `tmt01' & `regsample' == 1
			mat resTable = r(table)

			//noi di "Rest table 1st diff: reg `var' `t' `covariates' if `tmt' == `tmt01' & `regsample' == 1"
			//matlist resTable

			//Get the 1st diff
			local ++colindex
			mat `var'[1,`colindex'] =  el(resTable,1,1)

			*Get the standard error of 1st diff
			local ++colindex
			convertErrs el(resTable,2,1) `e(N)' "`errortype'"
			mat `var'[1,`colindex'] =  `r(converted_error)'

			*Get the number of stars using sub-command countStars
			local ++colindex
			local pvalue = el(resTable,4,4)
			countStars `pvalue' `starlevels'
			mat `var'[1,`colindex'] = `r(stars)'

			*Get the N of first difference regression
			local ++colindex
			mat `var'[1,`colindex'] =  `e(N)'

		}

		/*************

			Calculating standard means for all groups

		*************/
		forvalues tmt01 = 0/1 {
			forvalues t01 = 0/1 {

				*Summary stats on this group
				qui mean `var' if `tmt' == `t01' & `t' == `tmt01' & `regsample' == 1

				mat resTable = r(table)

				//Get the mean
				local ++colindex
				mat `var'[1,`colindex'] =  el(resTable,1,1)

				*Get the standard error of the mean
				local ++colindex
				convertErrs el(resTable,2,1)  `e(N)' "`errortype'"
				mat `var'[1,`colindex'] =  `r(converted_error)'

				*Get the N of the ordinary means regressions
				local ++colindex
				mat `var'[1,`colindex'] =  `e(N)'
			}
		}

		*Append this row to the result table
		mat 	ddtab_resultMap = (ddtab_resultMap \ `var')

	}

	*Remove placeholder row
	matrix ddtab_resultMap = ddtab_resultMap[2..., 1...]

	*Show the final matrix will all data needed to start building the output
	//noi di "Matlist with results"
	//matlist ddtab_resultMap

	/*************

		Output table in result window

	*************/

	outputwindow `varlist' , ddtab_resultMap(ddtab_resultMap) labmaxlen(`labmaxlen') rwlbls(`rowlabels') ///
		starlevels("`starlevels'") covariates(`covariates') `errortype' diformat(`diformat')

	/*************

		Output table in LaTeX

	*************/

	if "`savetex'" != "" {

		//outputtex `varlist' , ddtab_resultMap(ddtab_resultMap) savetex(`savetex')

	}

end

/***************************************
****************************************

	Write sub-commands for input handling

****************************************
***************************************/

**Program to prepare row labels for each outcome var, using variable name,
* variable label or manually entered labels depedning on user input. Variable
* names are used if no input.
cap program drop 	prepRowLabels
	program define	prepRowLabels, rclass

	syntax varlist, [rowlabtype(string) rowlabtext(string)]

	*First test input
	if "`rowlabtype'" == "varlab" | "`rowlabtype'" == "" | "`rowlabtype'" == "varname" {
		//All is good do nothing
	}
	else {
		noi display as error "{phang}Row label type [`rowlabtype'] is not a valid row label type. Enter either {it:varlab} or {it:varname} (the default if not specified is {it:varname}). See option {help ieddtable:rowlabtype} for details."
		error 198
	}

	/*************
		Parse through the manually entered labels
	*************/

	*Create a local with the rowlabel input to be tokenized
	local row_labels_to_tokenize `rowlabtext'

	while "`row_labels_to_tokenize'" != "" {

		*Parsing name and label pair
		gettoken nameAndLabel row_labels_to_tokenize : row_labels_to_tokenize, parse("@@")

		*Splitting name and label
		gettoken name label : nameAndLabel

		/*
			We store two locals, one with varnames and one with label. They are
			stored in the same order, so if we find varname we know we will find
			the corresponding label in the same order in the other local.
		*/

		*** Tests

		*Checking that the variables used in rowlabels() are included in the table
		local name_correct : list name in varlist
		if `name_correct' == 0 {
			noi display as error "{phang}Variable [`name'] listed in rowlabtext(`rowlabtext') is not found among the outcome variables."
			error 111
		}

		*Testing that no label is missing
		if "`label'" == "" {
			noi display as error "{phang}For variable [`name'] listed in rowlabtext(`rowlabels') you have not specified any label. Labels are requried for all variables listed in rowlabels(). Variables omitted from rowlabtext() will be assigned labels according to the rule in rowlabtype(). See also option {help ieddtable:rowlabtext}"
			error 198
		}

		*Storing the name in local to be used to get index of corresponding label in the second local
		local rowLabelNames `"`rowLabelNames' "`name'" "'

		*Removing leading or trailing spaces and store label with the same index as in the name local above
		local label = trim("`label'")
		local rowLabelLabels `"`rowLabelLabels' "`label'" "'

		*Parse char is not removed by gettoken, so remove it and start over
		local row_labels_to_tokenize = subinstr("`row_labels_to_tokenize'" ,"@@","",1)
	}

	**Set up locals to be returned. One with row labels without varname but in
	* final order, and one local with length of longest label
	local preppedRowLabels
	local maxLen 0

	*Loop over all varaibles and prepare the labels
	foreach var of local varlist {

		**Get the index of this variable in manually entered labels. Index is
		* zero if no manually entered label for this variable
		local rowLabPos : list posof "`var'" in rowLabelNames

		if `rowLabPos' == 0 {

			*No label was manually entered for this variable, use the other rules
			if "`rowlabtype'" == "varlab" {
				local this_label : variable label `var'		//Use variable label
			}
			else {

				local this_label `var'						//Default, use varname

			}
		}
		else {
			*Getting the manually defined label corresponding to this variable
			local this_label : word `rowLabPos' of `rowLabelLabels'
		}

		*Update the two locals that will be returned
		local preppedRowLabels "`preppedRowLabels' @@`this_label'"	//Prepare the final list
		local maxLen = max(strlen("`this_label'"),`maxLen')	//Store the length on the longest label, used on some outputs
	}

	*Return the locals
	return local rowlabels `preppedRowLabels'
	return local rowlabel_maxlen `maxLen'

end

** Program that test the t and tmt variables are valid dummies
*  for a Diff-in-Diff. I.e. they are both dummies and there are
*  at least 2 observations in each combination (0,0), (1,0),
*  (0,1) and (1,1)
cap program drop 	testDDdums
	program define	testDDdums

		args time treat samplevar outputvar

		*Test taht the dummies are dummies
		foreach dummy in `time' `treat' {

			cap assert inlist(`dummy',1,0) | missing(`dummy') | `samplevar' == 0
			if _rc {
				noi di as error "{phang}In the difference in differnce regression for outputvar [`outputvar'], the dummy [`dummy'] is not a real dummy as it has values differnt from 1 and 0. See tab below. The tab is restricted to the observations that are not excluded due to missing values etc. in the difference in differnce regression.{p_end}""
				noi tab `dummy' if `samplevar' == 1, missing
				error 480
			}
		}

		**Test that for only two of three dummies there are observations
		* that has only that dummy. I.e. the two that is not the
		* interaction. If the interaction is 1, all three shluld be 1.

		*Test that there is some observations in each group
		forvalues tmt01 = 0/1 {
			forvalues t01 = 0/1 {

				*Summary stats on this group
				qui count if `treat' == `t01' & `time' == `tmt01' & `samplevar' == 1

				//There got to be more than 1 observation in each group for the regression to make sense
				if `r(N)' < 2 {
					noi di as error "{phang}In the difference in differnce regression for outputvar [`outputvar'] there were not enough observations in each combination of treatment [`treat'] and time [`time']. The tab below is restricted to the observations that are not excluded due to missing values etc. in the difference in differnce regression.{p_end}""
					noi tab `treat' `time' if `samplevar' == 1, missing
					error 480

				}
			}
		}

	end

/***************************************
****************************************

	Write sub-commands for stats

****************************************
***************************************/

*Sets up template for the result matrix
cap program drop 	templateResultMatrix
	program define	templateResultMatrix, rclass

	*Set the names of the different columns in the result Matrix
	local 2ndDiff_cols 			2D 2D_err 2D_stars 2D_N

	local 1stDiff_C_cols		1DC 1DC_err 1DC_stars 1DC_N
	local 1stDiff_T_cols		1DT 1DT_err 1DT_stars 1DT_N

	local basicMean_C0_cols		C0_mean C0_err C0_N
	local basicMean_T0_cols		T0_mean T0_err T0_N
	local basicMean_C1_cols		C1_mean C1_err C1_N
	local basicMean_T1_cols		T1_mean T1_err T1_N

	local colnames `2ndDiff_cols' `1stDiff_C_cols' `1stDiff_T_cols' `basicMean_C0_cols' `basicMean_T0_cols' `basicMean_C1_cols' `basicMean_T0_cols'

	*Define default row here. The results for each var will be one row that starts with all missing vlaues
	mat startRow = (.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.)
	mat colnames startRow = `colnames'

	return matrix startRow startRow
	return local colnames `colnames'

end

*Take the significance levels and count number of starts
cap program drop 	countStars
	program define	countStars, rclass

	args pvalue star1 star2 star3

	local stars 0
	foreach star_p_level in `star1' `star2' `star3' {

		if `pvalue' < `star_p_level' local ++stars
	}

	return local stars `stars'

end

**Convert the errors to the display stats picked
* by the user. Standard errors is the default.
cap program drop 	convertErrs
	program define	convertErrs, rclass

	args se_err N errortype

	*Test if custom star levels are used
	if "`errortype'" == "sd" {
		local err = `se_err' * sqrt(`N')
	}
	else {
		//For se keep se, for errhide keep se as matrix must have some value even if it will not be used
		local err = `se_err'
	}

	return local converted_error `err'

end

/***************************************
****************************************

	Write sub-commands for outputs

****************************************
***************************************/

	/******
	The result matrix can be passed into subcommands that output in either Excel, LaTeX or in the main window.

	The name of the result matrix is ddtab_resultMap. It can be refernced like this ddtab_resultMap[row, col] where
	row is the variable order where 1 is the first varaible in the varlist and col is the name of the stat.

	You always must make it first in to a 1x1 matrix, and then make that a local.

	*/

	*The results can be accessed like this, which makes the sub-commands less sensitive to changing column order in the section above.
	//mat A = ddtab_resultMap[3, "C0_Mean"] // returns a 1x1 matrix with the baseline mean for the countrol grop for the thrid outcome var
	//local a = el(A,1,1)				// returns the value

	//matlist A
	//di `a'

	/*
		Column name dictionary
			Differences:
			- 2D : Second differnce coefficient (t*tmt == 1)
			- 1DC : First  differnce coefficient control (t == 0)
			- 1DT : First  differnce coefficient treatment (tmt == 0)

			for each coefficent these stats are also provided:
				- _err : Second differnce errors (type of errors is set in command errortype)
				- _stars :  Second Differene - The number of significance stars (sig level set in command)
				- _N : Second Difference - Number of observtions in the regression

			Group means:
			- C0 - Control time 0
			- T0 - Treatment time 0
			- C1 - Control time 1
			- T1 - Treatment time 1

			for each group these stats are also provided:
				- _mean : the mean of the group
				- _err : the error in the mean (type of errors is set in command errortype)
				- _N : number of observations in baseline means

	*/

	/*************

		Output table in result window

	*************/

	cap program drop 	outputwindow
		program define	outputwindow

		syntax varlist , ddtab_resultMap(name) labmaxlen(numlist) rwlbls(string) starlevels(string) diformat(string) [covariates(string) errhide sd se]

		*Prepare lables for the erorrs to be displayed (in case any)
		if "`sd'" != "" local errlabel "SD"
		if "`se'" != "" local errlabel "SE"

		*Count numbers of variables to loop over
		local numVars = `:word count `varlist''

		*List of variabls to display and loop over when formatting
		local statlist 2D 1DT 1DC C0_mean T0_mean 2D_err 1DT_err 1DC_err C0_err T0_err 2D_N 1DT_N 1DC_N C0_N T0_N

		*************************
		* Table width for label column

		local first_hhline = 2 + `labmaxlen'
		local first_col = 4 + `labmaxlen'

		*************************
		* Table width for basline columns

		local bsln_space = 2
		local bsln_stat_left = `bsln_space' + 1
		local bsln_width = ((`bsln_space' * 2) + 8)

		*************************
		* Table width for first difference column

		local diff_space = 3
		local diff_stat_left = `diff_space' + 1
		local diff_width = ((`diff_space' * 2) + 10)

		*************************
		* Table width control column

		local ctrl_hline = `bsln_width' + 1 + `diff_width'
		local ctrl_space = (`ctrl_hline' - 7) / 2

		*************************
		* Table width treatment column

		local tmt_hline = `bsln_width' + 1 + `diff_width'
		local tmt_space = (`tmt_hline' - 9) / 2

		*************************
		* Table width diff diff column

		local didi_stat_left = 4
		local diffdiff_width  = 17

		*************************
		* Calculate stats column indexes

		local bsln_c_col	= `first_col'  + `bsln_width' + 1
		local diff_c_col	= `bsln_c_col' + `diff_width' + 1
		local bsln_t_col	= `diff_c_col' + `bsln_width' + 1
		local diff_t_col	= `bsln_t_col' + `diff_width' + 1
		local didi_col		= `diff_t_col' + `diffdiff_width' + 1

		*************************
		* Start writing table

		*Three title rows
		noi di as text "{c TLC}{hline `first_hhline'}{c TT}{hline `ctrl_hline'}{c TT}{hline `tmt_hline'}{c TT}{hline 17}{c TRC}"
		noi di as text "{c |}{col `first_col'}{c |}{dup `ctrl_space': }Control{dup `ctrl_space': }{c |}{dup `tmt_space': }Treatment{dup `tmt_space': }{c |}  Difference-in  {c |}"
		noi di as text "{c |}{col `first_col'}{c |}{dup `bsln_space': }Baseline{dup `bsln_space': }{c |}{dup `diff_space': }Difference{dup `diff_space': }{c |}{dup `bsln_space': }Baseline{dup `bsln_space': }{c |}{dup `diff_space': }Difference{dup `diff_space': }{c |}   -difference   {c |}"

		*Stats titels, show the stats displayed for each column in the order they are displayed
		noi di as text "{c |}{col `first_col'}{c |}{dup `bsln_stat_left': } Mean{col `bsln_c_col'}{c |}{dup `diff_stat_left': } Coef{col `diff_c_col'}{c |}{dup `bsln_stat_left': } Mean{col `bsln_t_col'}{c |}{dup `diff_stat_left': } Coef{col `diff_t_col'}{c |}{dup `didi_stat_left': } Coef{col `didi_col'}{c |}"
		if "`errhide'" == "" {
			noi di as text "{c |}{col `first_col'}{c |}{dup `bsln_stat_left': }(`errlabel'){col `bsln_c_col'}{c |}{dup `diff_stat_left': }(`errlabel'){col `diff_c_col'}{c |}{dup `bsln_stat_left': }(`errlabel'){col `bsln_t_col'}{c |}{dup `diff_stat_left': }(`errlabel'){col `diff_t_col'}{c |}{dup `didi_stat_left': }(`errlabel'){col `didi_col'}{c |}"
		}
		noi di as text "{c |}{col 3}Variable{col `first_col'}{c |}{dup `bsln_stat_left': } N{col `bsln_c_col'}{c |}{dup `diff_stat_left': } N{col `diff_c_col'}{c |}{dup `bsln_stat_left': } N{col `bsln_t_col'}{c |}{dup `diff_stat_left': } N{col `diff_t_col'}{c |}{dup `didi_stat_left': } N{col `didi_col'}{c |}"

		*Bottom row to table header
		noi di as text "{c LT}{hline `first_hhline'}{c +}{hline `bsln_width'}{c +}{hline `diff_width'}{c +}{hline `bsln_width'}{c +}{hline `diff_width'}{c +}{hline 17}{c RT}"

		*Loop over each variable and prepare the row
		forvalues row = 1/`numVars' {

			*Get lables from the list of lables previosly prepared
			local rwlbls = trim(subinstr("`rwlbls'", "@@","", 1))
			gettoken label rwlbls : rwlbls, parse("@@")

			*Loop over all the stats for this variable
			foreach stat of local statlist {

				**Run sub command that gets value from matrix and prepares it
				* in the format suitable for the result window table (and adding
				* stars if applicable)
				windowdiformat , statname("`stat'") row(`row') ddtab_resultMap(ddtab_resultMap) diformat("`diformat'") bslnw(`bsln_width') diffw(`diff_width') ddw(`diffdiff_width')

				*The main stat
				local `stat' `r(disp_stata)'

				*The number of spaces before the stat in the colum
				local `stat'_space = `r(disp_pre_space)'
			}

			*Disaplay each variable row at the same time
			noi di as text "{c |} `label'{col `first_col'}{c |}{dup `C0_mean_space': }`C0_mean'{dup `1DC_space': }`1DC'{dup `T0_mean_space': }`T0_mean'{dup `1DT_space': }`1DT'{dup `2D_space': }`2D'"

			*Unless error type is errhide, show the errors on a seperate row
			if "`errhide'" == "" {
				noi di as text "{c |}{col `first_col'}{c |}{dup `C0_err_space': }`C0_err'{dup `1DC_err_space': }`1DC_err'{dup `T0_err_space': }`T0_err'{dup `1DT_err_space': }`1DT_err'{dup `2D_err_space': }`2D_err'"
			}

			*The number of observations are shown on a separate row
			noi di as text "{c |}{col `first_col'}{c |}{dup `C0_N_space': }`C0_N'{dup `1DC_N_space': }`1DC_N'{dup `T0_N_space': }`T0_N'{dup `1DT_N_space': }`1DT_N'{dup `2D_N_space': }`2D_N'"

		}

		*Add bottom notes to the table
		noi di as text "{c BLC}{hline `first_hhline'}{c BT}{hline `bsln_width'}{c BT}{hline `diff_width'}{c BT}{hline `bsln_width'}{c BT}{hline `diff_width'}{c BT}{hline 17}{c BRC}"

		*************************
		* Write notes below the table

		*Show stars levels
		local star1_value : word 1 of `starlevels'
		local star2_value : word 2 of `starlevels'
		local star3_value : word 3 of `starlevels'
		noi di as text "  ***, **, and * indicate significance at the `star3_value', `star2_value', and `star1_value' percent critical level. "

		*List covariates used
		if ("`covariates'" != "") {
			noi di as text "  The following variable(s) was included as covariates [`covariates']"
		}

	end

cap program drop 	windowdiformat
	program define	windowdiformat, rclass

	syntax , statname(string) row(numlist) ddtab_resultMap(name) diformat(string)  bslnw(numlist) diffw(numlist) ddw(numlist)

		local numSpace 0

		mat temp = ddtab_resultMap[`row', "`statname'"]
		local `statname' = el(temp,1,1)

		if substr("`statname'", -2,.) == "_N" {
			local `statname'	: display %9.0f ``statname''
		}
		else {
			local `statname' 	: display `diformat' ``statname''
		}

		*Trime spaces on left from left.
		local `statname' = ltrim("``statname''")

		*For coefficients, add stars if applicable
		if inlist("`statname'", "2D", "1DT", "1DC") {

			mat starNumMat = ddtab_resultMap[`row', "`statname'_stars"]
			local starNum = el(starNumMat,1,1)

			if `starNum' == 0 local `statname' "``statname''    "
			if `starNum' == 1 local `statname' "``statname''*   "
			if `starNum' == 2 local `statname' "``statname''**  "
			if `starNum' == 3 local `statname' "``statname''*** "
		}

		*Add brackets to errors
		if inlist("`statname'", "2D_err", "1DT_err", "1DC_err", "C0_err", "T0_err") {
			local `statname' "(``statname'')"
		}

		*Get the lengt of the characters to display
		local len = strlen("``statname''")

		*Which column for this stat
		local colName = substr("`statname'", 1, 2)

		*Get corresponding width for that col
		if "`colName'" == "C0" local colw = `bslnw'
		if "`colName'" == "T0" local colw = `bslnw'
		if "`colName'" == "1D" local colw = `diffw'
		if "`colName'" == "2D" local colw = `ddw'

		* Calculate the number of spaces needed bofore
		* the value and add spaces after it
		if inlist("`statname'", "C0_mean", "T0_mean", "C0_N", "T0_N") {
			*Baseline mean values
			return local disp_stata "``statname''  {c |}"
			local numSpace = `colw' - `len' - 2
		}
		else if inlist("`statname'", "C0_err", "T0_err") {
			*Baseline mean values errors
			return local disp_stata "``statname'' {c |}"
			local numSpace = `colw' - `len' - 1
		}
		else if inlist("`statname'", "1DT", "1DC", "2D") {
			*First difference coefficent
			return local disp_stata "``statname''{c |}"
			local numSpace = `colw' - `len'
		}
		else if inlist("`statname'", "1DT_N", "1DC_N", "2D_N") {
			*First difference coefficent
			return local disp_stata "``statname''    {c |}"
			local numSpace = `colw' - `len' - 4
		}
		else if inlist("`statname'", "1DT_err", "1DC_err", "2D_err")  {
			*First difference coefficient error
			return local disp_stata "``statname''   {c |}"
			local numSpace = `colw' - `len' - 3
		}

		**If numbers are so big that numSapce is negative, set it to 0 as
		* numSpace cannot be negative. Table will show incorrectly but
		* will display.
		if `numSpace' < 0 local numSpace 0

		return local disp_pre_space = `numSpace'
		return local disp_len = `len'

	end

cap program drop 	ieddtable
	program define	ieddtable

	syntax varlist, ///
					///
		t(varname numeric) tmt(varname numeric) 			///
		[ 													///
		COVARiates(varlist numeric)							///
		STARLevels(numlist descending min=3 max=3 >0 <1)	///
		STARSNOadd											///
		firstdiff											///
		rowlabtype(string) 									///
		rowlabtext(string)									///
		errortype(string)									///
		]

	/*************

		Input handling

	*************/

	*LABELS
	*Test and prepare the row lables and test how long the longest label is.
	prepRowLabels `varlist', rowlabtype("`rowlabtype'") rowlabtext("`rowlabtext'")
	local rowlabels "`r(rowlabels)'"
	local labmaxlen "`r(rowlabel_maxlen)'"

	*ERRORTYPES
	*If error type is used test that it is only one word and that word is an allowed error type
	if "`errortype'" != "" {
		local errortype = lower("`errortype'")
		if (`: word count `errortype'' != 1) | !inlist("`errortype'", "sd", "se", "errhide") {
			noi di as error "Value in option errortype(`errortype') can only one out of the following words; se, sd, errhide."
			error 198
		}
	}
	else {
		*No errortype specified use default which is standard errors
		local errortype "se"
	}

	*DEFAULT STAR LEVELS
	*Default star levels if option not used
	if "`starlevels'" == "" local starlevels ".1 .05 .01"

	/*************

		Initiate the result matrix

	*************/

	*Creates the template for the result matrix in a subfunction
	templateResultMatrix
	mat startRow = r(startRow)
	local colnames = "`r(colnames)'"

	*Remove this when ready for production
	//noi di "Start row to see headers, remove for production"
	//matlist startRow // See the default row with its column names

	*Initiate the result matrix with a place holder row that will not be used for anything. Matrices cannot be initiated empty
	mat ddtab_resultMap = startRow
	mat rownames ddtab_resultMap = placeholder

	/*************

		Loop over all variables and prepare the data

	*************/

	foreach var of local varlist {

		//noi di "Variable `var'"

		*Each variable is a row in the result matrix
		mat `var' = startRow
		mat rownames `var' = `var'
		mat colnames `var' = `colnames'

		*Local that keeps track of which column to fill
		local colindex 0

		tempvar regsample

		/*************

			Calculating 2nd difference and restict the sample
			for this output var

		*************/

		*Run the regression to get the double difference
		qui reg `var' `tmt'#`t' `covariates'
		mat resTable = r(table)

		//noi di "Rest table 2nd diff: reg `var' `tmt'#`t' `covariates'"
		//matlist resTable

		**This is why this is done first. All other calculations
		* for this outcome var should be restricted to this sample.
		gen `regsample' = e(sample)
		local N `e(N)'

		**Test that the dummy vars creates for valid groups for each
		* combination of time and treat in the sample used for this outcome var
		testDDdums `t' `tmt' `regsample' `var'  //comment in when this is made into a command

		*Get the second differnce
		local ++colindex
		mat `var'[1,`colindex'] =  el(resTable,1,4)

		*Get the standard error of second difference
		local ++colindex
		convertErrs el(resTable,2,4) `N' "`errortype'"
		mat `var'[1,`colindex'] =  `r(converted_error)'

		*Get the number of stars using sub-command countStars
		local ++colindex
		local pvalue = el(resTable,4,4)
		countStars `pvalue' `starlevels'
		mat `var'[1,`colindex'] = `r(stars)'

		*Get the N of second difference regression
		local ++colindex
		mat `var'[1,`colindex'] = `N'

		/*************

			Calculating 1st differences

		*************/
		forvalues tmt01 = 0/1 {

			*Regress time against the outcome var one tmt group at the time
			qui reg `var' `t' `covariates' if `tmt' == `tmt01' & `regsample' == 1
			mat resTable = r(table)

			//noi di "Rest table 1st diff: reg `var' `t' `covariates' if `tmt' == `tmt01' & `regsample' == 1"
			//matlist resTable

			//Get the 1st diff
			local ++colindex
			mat `var'[1,`colindex'] =  el(resTable,1,1)

			*Get the standard error of 1st diff
			local ++colindex
			convertErrs el(resTable,2,1) `e(N)' "`errortype'"
			mat `var'[1,`colindex'] =  `r(converted_error)'

			*Get the number of stars using sub-command countStars
			local ++colindex
			local pvalue = el(resTable,4,4)
			countStars `pvalue' `starlevels'
			mat `var'[1,`colindex'] = `r(stars)'

			*Get the N of first difference regression
			local ++colindex
			mat `var'[1,`colindex'] =  `e(N)'

		}

		/*************

			Calculating standard means for all groups

		*************/
		forvalues tmt01 = 0/1 {
			forvalues t01 = 0/1 {

				*Summary stats on this group
				qui mean `var' if `tmt' == `t01' & `t' == `tmt01' & `regsample' == 1

				mat resTable = r(table)

				//Get the mean
				local ++colindex
				mat `var'[1,`colindex'] =  el(resTable,1,1)

				*Get the standard error of the mean
				local ++colindex
				convertErrs el(resTable,2,1)  `e(N)' "`errortype'"
				mat `var'[1,`colindex'] =  `r(converted_error)'

				*Get the N of the ordinary means regressions
				local ++colindex
				mat `var'[1,`colindex'] =  `e(N)'
			}
		}

		*Append this row to the result table
		mat 	ddtab_resultMap = (ddtab_resultMap \ `var')

	}

	*Remove placeholder row
	matrix ddtab_resultMap = ddtab_resultMap[2..., 1...]

	*Show the final matrix will all data needed to start building the output
	//noi di "Matlist with results"
	//matlist ddtab_resultMap

	/*************

		Output table in result window

	*************/

	outputwindow `varlist' , ddtab_resultMap(ddtab_resultMap) labmaxlen(`labmaxlen') rwlbls(`rowlabels') ///
		starlevels("`starlevels'") covariates(`covariates') `errortype'

	/*************

		Output table in LaTeX

	*************/

	if "`savetex'" != "" {

		//outputtex `varlist' , ddtab_resultMap(ddtab_resultMap) savetex(`savetex')

	}

end

/***************************************
****************************************

	Write sub-commands for input handling

****************************************
***************************************/

**Program to prepare row labels for each outcome var, using variable name,
* variable label or manually entered labels depedning on user input. Variable
* names are used if no input.
cap program drop 	prepRowLabels
	program define	prepRowLabels, rclass

	syntax varlist, [rowlabtype(string) rowlabtext(string)]

	*First test input
	if "`rowlabtype'" == "varlab" | "`rowlabtype'" == "" | "`rowlabtype'" == "varname" {
		//All is good do nothing
	}
	else {
		noi display as error "{phang}Row label type [`rowlabtype'] is not a valid row label type. Enter either {it:varlab} or {it:varname} (the default if not specified is {it:varname}). See option {help ieddtable:rowlabtype} for details."
		error 198
	}

	/*************
		Parse through the manually entered labels
	*************/

	*Create a local with the rowlabel input to be tokenized
	local row_labels_to_tokenize `rowlabtext'

	while "`row_labels_to_tokenize'" != "" {

		*Parsing name and label pair
		gettoken nameAndLabel row_labels_to_tokenize : row_labels_to_tokenize, parse("@@")

		*Splitting name and label
		gettoken name label : nameAndLabel

		/*
			We store two locals, one with varnames and one with label. They are
			stored in the same order, so if we find varname we know we will find
			the corresponding label in the same order in the other local.
		*/

		*** Tests

		*Checking that the variables used in rowlabels() are included in the table
		local name_correct : list name in varlist
		if `name_correct' == 0 {
			noi display as error "{phang}Variable [`name'] listed in rowlabtext(`rowlabtext') is not found among the outcome variables."
			error 111
		}

		*Testing that no label is missing
		if "`label'" == "" {
			noi display as error "{phang}For variable [`name'] listed in rowlabtext(`rowlabels') you have not specified any label. Labels are requried for all variables listed in rowlabels(). Variables omitted from rowlabtext() will be assigned labels according to the rule in rowlabtype(). See also option {help ieddtable:rowlabtext}"
			error 198
		}

		*Storing the name in local to be used to get index of corresponding label in the second local
		local rowLabelNames `"`rowLabelNames' "`name'" "'

		*Removing leading or trailing spaces and store label with the same index as in the name local above
		local label = trim("`label'")
		local rowLabelLabels `"`rowLabelLabels' "`label'" "'

		*Parse char is not removed by gettoken, so remove it and start over
		local row_labels_to_tokenize = subinstr("`row_labels_to_tokenize'" ,"@@","",1)
	}

	**Set up locals to be returned. One with row labels without varname but in
	* final order, and one local with length of longest label
	local preppedRowLabels
	local maxLen 0

	*Loop over all varaibles and prepare the labels
	foreach var of local varlist {

		**Get the index of this variable in manually entered labels. Index is
		* zero if no manually entered label for this variable
		local rowLabPos : list posof "`var'" in rowLabelNames

		if `rowLabPos' == 0 {

			*No label was manually entered for this variable, use the other rules
			if "`rowlabtype'" == "varlab" {
				local this_label : variable label `var'		//Use variable label
			}
			else {

				local this_label `var'						//Default, use varname

			}
		}
		else {
			*Getting the manually defined label corresponding to this variable
			local this_label : word `rowLabPos' of `rowLabelLabels'
		}

		*Update the two locals that will be returned
		local preppedRowLabels "`preppedRowLabels' @@`this_label'"	//Prepare the final list
		local maxLen = max(strlen("`this_label'"),`maxLen')	//Store the length on the longest label, used on some outputs
	}

	*Return the locals
	return local rowlabels `preppedRowLabels'
	return local rowlabel_maxlen `maxLen'

end

** Program that test the t and tmt variables are valid dummies
*  for a Diff-in-Diff. I.e. they are both dummies and there are
*  at least 2 observations in each combination (0,0), (1,0),
*  (0,1) and (1,1)
cap program drop 	testDDdums
	program define	testDDdums

		args time treat samplevar outputvar

		*Test taht the dummies are dummies
		foreach dummy in `time' `treat' {

			cap assert inlist(`dummy',1,0) | missing(`dummy') | `samplevar' == 0
			if _rc {
				noi di as error "{phang}In the difference in differnce regression for outputvar [`outputvar'], the dummy [`dummy'] is not a real dummy as it has values differnt from 1 and 0. See tab below. The tab is restricted to the observations that are not excluded due to missing values etc. in the difference in differnce regression.{p_end}""
				noi tab `dummy' if `samplevar' == 1, missing
				error 480
			}
		}

		**Test that for only two of three dummies there are observations
		* that has only that dummy. I.e. the two that is not the
		* interaction. If the interaction is 1, all three shluld be 1.

		*Test that there is some observations in each group
		forvalues tmt01 = 0/1 {
			forvalues t01 = 0/1 {

				*Summary stats on this group
				qui count if `treat' == `t01' & `time' == `tmt01' & `samplevar' == 1

				//There got to be more than 1 observation in each group for the regression to make sense
				if `r(N)' < 2 {
					noi di as error "{phang}In the difference in differnce regression for outputvar [`outputvar'] there were not enough observations in each combination of treatment [`treat'] and time [`time']. The tab below is restricted to the observations that are not excluded due to missing values etc. in the difference in differnce regression.{p_end}""
					noi tab `treat' `time' if `samplevar' == 1, missing
					error 480

				}
			}
		}

	end

/***************************************
****************************************

	Write sub-commands for stats

****************************************
***************************************/

*Sets up template for the result matrix
cap program drop 	templateResultMatrix
	program define	templateResultMatrix, rclass

	*Set the names of the different columns in the result Matrix
	local 2ndDiff_cols 			2D 2D_err 2D_stars 2D_N

	local 1stDiff_C_cols		1DC 1DC_err 1DC_stars 1DC_N
	local 1stDiff_T_cols		1DT 1DT_err 1DT_stars 1DT_N

	local basicMean_C0_cols		C0_mean C0_err C0_N
	local basicMean_T0_cols		T0_mean T0_err T0_N
	local basicMean_C1_cols		C1_mean C1_err C1_N
	local basicMean_T1_cols		T1_mean T1_err T1_N

	local colnames `2ndDiff_cols' `1stDiff_C_cols' `1stDiff_T_cols' `basicMean_C0_cols' `basicMean_T0_cols' `basicMean_C1_cols' `basicMean_T0_cols'

	*Define default row here. The results for each var will be one row that starts with all missing vlaues
	mat startRow = (.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.)
	mat colnames startRow = `colnames'

	return matrix startRow startRow
	return local colnames `colnames'

end

*Take the significance levels and count number of starts
cap program drop 	countStars
	program define	countStars, rclass

	args pvalue star1 star2 star3

	local stars 0
	foreach star_p_level in `star1' `star2' `star3' {

		if `pvalue' < `star_p_level' local ++stars
	}

	return local stars `stars'

end

**Convert the errors to the display stats picked
* by the user. Standard errors is the default.
cap program drop 	convertErrs
	program define	convertErrs, rclass

	args se_err N errortype

	*Test if custom star levels are used
	if "`errortype'" == "sd" {
		local err = `se_err' * sqrt(`N')
	}
	else {
		//For se keep se, for errhide keep se as matrix must have some value even if it will not be used
		local err = `se_err'
	}

	return local converted_error `err'

end

/***************************************
****************************************

	Write sub-commands for outputs

****************************************
***************************************/

	******
	*Then the result matrix can be passed into subcommands that output in either Excel, LaTeX or in the main window.

	*The results can be accessed like this, which makes the sub-commands less sensitive to changing column order in the section above.
	//mat A = ddtab_resultMap[3, "C0_Mean"] // returns a 1x1 matrix with the baseline mean for the countrol grop for the thrid outcome var
	//local a = el(A,1,1)				// returns the value

	//matlist A
	//di `a'

	/*
		Column name dictionary
			Differences:
			- 2D : Second differnce coefficient (t*tmt == 1)
			- 1DC : First  differnce coefficient control (t == 0)
			- 1DT : First  differnce coefficient treatment (tmt == 0)

			for each coefficent these stats are also provided:
				- _err : Second differnce errors (type of errors is set in command errortype)
				- _stars :  Second Differene - The number of significance stars (sig level set in command)
				- _N : Second Difference - Number of observtions in the regression

			Group means:
			- C0 - Control time 0
			- T0 - Treatment time 0
			- C1 - Control time 1
			- T1 - Treatment time 1

			for each group these stats are also provided:
				- _mean : the mean of the group
				- _err : the error in the mean (type of errors is set in command errortype)
				- _N : number of observations in baseline means

	*/

	/*************

		Output table in result window

	*************/

	cap program drop 	outputwindow
		program define	outputwindow

		/*
		Todo:
			Add N somewhere
			Make format specifiable by user

		*/

		syntax varlist , ddtab_resultMap(name) labmaxlen(numlist) rwlbls(string) starlevels(string) [covariates(string) errhide sd se]

		*Prepare lables for the erorrs to be displayed (in case any)
		if "`sd'" != "" local errlabel "SD"
		if "`se'" != "" local errlabel "SE"

		*Count numbers of variables to loop over
		local numVars = `:word count `varlist''

		*List of variabls to display and loop over when formatting
		local statlist 2D 1DT 1DC C0_mean T0_mean 2D_err 1DT_err 1DC_err C0_err T0_err 2D_N 1DT_N 1DC_N C0_N T0_N
		local diformat = "%9.2f"

		*************************
		* Table width for label column

		local first_hhline = 2 + `labmaxlen'
		local first_col = 4 + `labmaxlen'

		*************************
		* Table width for basline columns

		local bsln_space = 2
		local bsln_stat_left = `bsln_space' + 1
		local bsln_width = ((`bsln_space' * 2) + 8)

		*************************
		* Table width for first difference column

		local diff_space = 3
		local diff_stat_left = `diff_space' + 1
		local diff_width = ((`diff_space' * 2) + 10)

		*************************
		* Table width control column

		local ctrl_hline = `bsln_width' + 1 + `diff_width'
		local ctrl_space = (`ctrl_hline' - 7) / 2

		*************************
		* Table width treatment column

		local tmt_hline = `bsln_width' + 1 + `diff_width'
		local tmt_space = (`tmt_hline' - 9) / 2

		*************************
		* Table width diff diff column

		local didi_stat_left = 4
		local diffdiff_width  = 17

		*************************
		* Calculate stats column indexes

		local bsln_c_col	= `first_col'  + `bsln_width' + 1
		local diff_c_col	= `bsln_c_col' + `diff_width' + 1
		local bsln_t_col	= `diff_c_col' + `bsln_width' + 1
		local diff_t_col	= `bsln_t_col' + `diff_width' + 1
		local didi_col		= `diff_t_col' + `diffdiff_width' + 1

		*************************
		* Start writing table

		noi di as text "{c TLC}{hline `first_hhline'}{c TT}{hline `ctrl_hline'}{c TT}{hline `tmt_hline'}{c TT}{hline 17}{c TRC}"
		noi di as text "{c |}{col `first_col'}{c |}{dup `ctrl_space': }Control{dup `ctrl_space': }{c |}{dup `tmt_space': }Treatment{dup `tmt_space': }{c |}  Difference-in  {c |}"
		noi di as text "{c |}{col `first_col'}{c |}{dup `bsln_space': }Baseline{dup `bsln_space': }{c |}{dup `diff_space': }Difference{dup `diff_space': }{c |}{dup `bsln_space': }Baseline{dup `bsln_space': }{c |}{dup `diff_space': }Difference{dup `diff_space': }{c |}   -difference   {c |}"

		*Stat lable row different if errors are shown or not
		noi di as text "{c |}{col `first_col'}{c |}{dup `bsln_stat_left': } Mean{col `bsln_c_col'}{c |}{dup `diff_stat_left': } Coef{col `diff_c_col'}{c |}{dup `bsln_stat_left': } Mean{col `bsln_t_col'}{c |}{dup `diff_stat_left': } Coef{col `diff_t_col'}{c |}{dup `didi_stat_left': } Coef{col `didi_col'}{c |}"
		if "`errhide'" == "" {
			noi di as text "{c |}{col `first_col'}{c |}{dup `bsln_stat_left': }(`errlabel'){col `bsln_c_col'}{c |}{dup `diff_stat_left': }(`errlabel'){col `diff_c_col'}{c |}{dup `bsln_stat_left': }(`errlabel'){col `bsln_t_col'}{c |}{dup `diff_stat_left': }(`errlabel'){col `diff_t_col'}{c |}{dup `didi_stat_left': }(`errlabel'){col `didi_col'}{c |}"
		}
		noi di as text "{c |}{col 3}Variable{col `first_col'}{c |}{dup `bsln_stat_left': } N{col `bsln_c_col'}{c |}{dup `diff_stat_left': } N{col `diff_c_col'}{c |}{dup `bsln_stat_left': } N{col `bsln_t_col'}{c |}{dup `diff_stat_left': } N{col `diff_t_col'}{c |}{dup `didi_stat_left': } N{col `didi_col'}{c |}"

		*Bottom row to table header
		noi di as text "{c LT}{hline `first_hhline'}{c +}{hline `bsln_width'}{c +}{hline `diff_width'}{c +}{hline `bsln_width'}{c +}{hline `diff_width'}{c +}{hline 17}{c RT}"

		*Loop over each variable and prepare the row
		forvalues row = 1/`numVars' {

			*Get lables from the list of lables previosly prepared
			local rwlbls = trim(subinstr("`rwlbls'", "@@","", 1))
			gettoken label rwlbls : rwlbls, parse("@@")

			*Loop over all the stats for this variable
			foreach stat of local statlist {

				**Run sub command that gets value from matrix and prepares it
				* in the format suitable for the result window table (and adding
				* stars if applicable)
				displayformatter , statname("`stat'") row(`row') ddtab_resultMap(ddtab_resultMap) diformat("`diformat'") bslnw(`bsln_width') diffw(`diff_width') ddw(`diffdiff_width')

				*The main stat
				local `stat' `r(disp_stata)'

				*The number of spaces before the stat in the colum
				local `stat'_space = `r(disp_pre_space)'
			}

			*Disaplay each variable row at the same time
			noi di as text "{c |} `label'{col `first_col'}{c |}{dup `C0_mean_space': }`C0_mean'{dup `1DC_space': }`1DC'{dup `T0_mean_space': }`T0_mean'{dup `1DT_space': }`1DT'{dup `2D_space': }`2D'"

			*Unless error type is errhide, show the errors on a seperate row
			if "`errhide'" == "" {
				noi di as text "{c |}{col `first_col'}{c |}{dup `C0_err_space': }`C0_err'{dup `1DC_err_space': }`1DC_err'{dup `T0_err_space': }`T0_err'{dup `1DT_err_space': }`1DT_err'{dup `2D_err_space': }`2D_err'"
			}

			noi di as text "{c |}{col `first_col'}{c |}{dup `C0_N_space': }`C0_N'{dup `1DC_N_space': }`1DC_N'{dup `T0_N_space': }`T0_N'{dup `1DT_N_space': }`1DT_N'{dup `2D_N_space': }`2D_N'"

		}

		*Add bottom notes to the table
		noi di as text "{c BLC}{hline `first_hhline'}{c BT}{hline `bsln_width'}{c BT}{hline `diff_width'}{c BT}{hline `bsln_width'}{c BT}{hline `diff_width'}{c BT}{hline 17}{c BRC}"

		*************************
		* Write notes below the table

		*Show stars levels
		local star1_value : word 1 of `starlevels'
		local star2_value : word 2 of `starlevels'
		local star3_value : word 3 of `starlevels'

		noi di as text "  ***, **, and * indicate significance at the `star3_value', `star2_value', and `star1_value' percent critical level. "

		*List covariates used
		if ("`covariates'" != "") {
			noi di as text "  The following variables was included as covariates [`covariates']"
		}

	end

cap program drop 	displayformatter
	program define	displayformatter, rclass

	syntax , statname(string) row(numlist) ddtab_resultMap(name) diformat(string)  bslnw(numlist) diffw(numlist) ddw(numlist)

		local numSpace 0

		mat temp = ddtab_resultMap[`row', "`statname'"]
		local `statname' = el(temp,1,1)

		if substr("`statname'", -2,.) == "_N" {
			local `statname'	: display %9.0f ``statname''
		}
		else {
			local `statname' 	: display `diformat' ``statname''
		}

		*Trime spaces on left from left.
		local `statname' = ltrim("``statname''")

		*For coefficients, add stars if applicable
		if inlist("`statname'", "2D", "1DT", "1DC") {

			mat starNumMat = ddtab_resultMap[`row', "`statname'_stars"]
			local starNum = el(starNumMat,1,1)

			if `starNum' == 0 local `statname' "``statname''    "
			if `starNum' == 1 local `statname' "``statname''*   "
			if `starNum' == 2 local `statname' "``statname''**  "
			if `starNum' == 3 local `statname' "``statname''*** "
		}

		*Add brackets to errors
		if inlist("`statname'", "2D_err", "1DT_err", "1DC_err", "C0_err", "T0_err") {
			local `statname' "(``statname'')"
		}

		*Get the lengt of the characters to display
		local len = strlen("``statname''")

		*Which column for this stat
		local colName = substr("`statname'", 1, 2)

		*Get corresponding width for that col
		if "`colName'" == "C0" local colw = `bslnw'
		if "`colName'" == "T0" local colw = `bslnw'
		if "`colName'" == "1D" local colw = `diffw'
		if "`colName'" == "2D" local colw = `ddw'

		* Calculate the number of spaces needed bofore
		* the value and add spaces after it
		if inlist("`statname'", "C0_mean", "T0_mean", "C0_N", "T0_N") {
			*Baseline mean values
			return local disp_stata "``statname''  {c |}"
			local numSpace = `colw' - `len' - 2
		}
		else if inlist("`statname'", "C0_err", "T0_err") {
			*Baseline mean values errors
			return local disp_stata "``statname'' {c |}"
			local numSpace = `colw' - `len' - 1
		}
		else if inlist("`statname'", "1DT", "1DC", "2D") {
			*First difference coefficent
			return local disp_stata "``statname''{c |}"
			local numSpace = `colw' - `len'
		}
		else if inlist("`statname'", "1DT_N", "1DC_N", "2D_N") {
			*First difference coefficent
			return local disp_stata "``statname''    {c |}"
			local numSpace = `colw' - `len' - 4
		}
		else if inlist("`statname'", "1DT_err", "1DC_err", "2D_err")  {
			*First difference coefficient error
			return local disp_stata "``statname''   {c |}"
			local numSpace = `colw' - `len' - 3
		}

		**If numbers are so big that numSapce is negative, set it to 0 as
		* numSpace cannot be negative. Table will show incorrectly but
		* will display.
		if `numSpace' < 0 local numSpace 0

		return local disp_pre_space = `numSpace'
		return local disp_len = `len'

	end


iegitaddmd : small fix to #101

iegitaddmd : small fix to #101

cap program drop   iegitaddmd
	program define iegitaddmd

	qui {

		syntax , folder(string) [all]

		*Test that folder exist
		mata : st_numscalar("r(dirExist)", direxists("`folder'"))

		if `r(dirExist)' == 0 {

			noi di as error `"{phang}The "`folder'" folder does not exist. You must enter the full path. For example, on most Windows computers it starts with {it:C:} and on most Mac computers with {it:/user/}. Important: Specify the whole file path to the repository folder, not just {it:C:} or {it:/user/} as that would creaet the .txt file in every empty folder on your computer.{p_end}"'
			error 693
			exit
		}

		*List files, directories and other files
		local flist : dir `"`folder'"' files "*"
		local dlist : dir `"`folder'"' dirs "*"
		local olist : dir `"`folder'"' other "*"

		*Test if all of those lists are empty.
		if `"`flist'`dlist'`olist'"' == "" | ("`all'" == "all") {

			** If all those lists are empty then we are in an
			*  empty folder and should write README.md
			writeGitKeep `"`folder'"'

		}

		*Use the command on each subfolder to this folder (if any)
		foreach dir of local dlist {

			*Recursive call on each subfolder
			iegitaddmd , folder(`"`folder'//`dir'"')
		}
	}

end

*Write a README.md file iegitaddmd have found an empty folder
cap program drop   writeGitKeep
	program define writeGitKeep

		args folder

		* Do not create a README.md file if one already exist. This issue could
		* happen if the option [all] is used on a repository that already has at
		* least one README.md file
		cap confirm file `"`folder'/README.md"'
		if _rc {

			*Create file
			tempname 	newHandle
			cap file close 	`newHandle'
				file open  	`newHandle' using "`folder'/README.md", text write replace

			*Add some text to the file
			file write  `newHandle' ///
				_col(4)"*******************************************************************************" _n ///
				_col(4)"*******************************************************************************" _n _n ///
				_col(8) "This file is only a placeholder file so that GitHub syncs empty folders!" _n _n ///
				_col(8) "If files have been added to the folder of this file so that this" _n ///
				_col(8) "file is no longer the only file in this folder, then this file" _n ///
				_col(8) "may be deleted. " _n _n ///
				_col(4)"*******************************************************************************" _n ///
				_col(4)"*******************************************************************************" _n ///
				_n

			*Closing the file
			file close 		`newHandle'

		}

end

cap program drop   iegitaddmd
	program define iegitaddmd

	qui {

		syntax , folder(string) [all]

		*Test that folder exist
		mata : st_numscalar("r(dirExist)", direxists("`folder'"))

		if `r(dirExist)' == 0 {

			noi di as error `"{phang}The "`folder'" folder does not exist. You must enter the full path. For example, on most Windows computers it starts with {it:C:} and on most Mac computers with {it:/user/}. Important: Specify the whole file path to the repository folder, not just {it:C:} or {it:/user/} as that would creaet the .txt file in every empty folder on your computer.{p_end}"'
			error 693
			exit
		}

		*List files, directories and other files
		local flist : dir `"`folder'"' files "*"
		local dlist : dir `"`folder'"' dirs "*"
		local olist : dir `"`folder'"' other "*"

		*Test if all of those lists are empty.
		if `"`flist'`dlist'`olist'"' == "" | ("`all'" == "all") {

			** If all those lists are empty then we are in an
			*  empty folder and should write README.md
			writeGitKeep `"`folder'"'

		}

		*Use the command on each subfolder to this folder (if any)
		foreach dir of local dlist {

			*Recursive call on each subfolder
			iegitaddmd , folder(`"`folder'//`dir'"')
		}
	}

end

*Write a README.md file iegitaddmd have found an empty folder
cap program drop   writeGitKeep
	program define writeGitKeep

		args folder

		* Do not create a README.md file if one already exist. This issue could
		* happen if the option [all] is used on a repository that already has at
		* least one README.md file
		cap confirm file `"`folder'/README.md"'
		if !_rc {

			*Create file
			tempname 	newHandle
			cap file close 	`newHandle'
				file open  	`newHandle' using "`folder'/README.md", text write replace

			*Add some text to the file
			file write  `newHandle' ///
				_col(4)"*******************************************************************************" _n ///
				_col(4)"*******************************************************************************" _n _n ///
				_col(8) "This file is only a placeholder file so that GitHub syncs empty folders!" _n _n ///
				_col(8) "If files have been added to the folder of this file so that this" _n ///
				_col(8) "file is no longer the only file in this folder, then this file" _n ///
				_col(8) "may be deleted. " _n _n ///
				_col(4)"*******************************************************************************" _n ///
				_col(4)"*******************************************************************************" _n ///
				_n

			*Closing the file
			file close 		`newHandle'

		}

end