showSummaryTable(summaryStats, keepCols=c("Title", "LL", "AIC", "BIC", "CFI"), sortBy="AIC")
showSummaryTable(summaryStats, dropCols=c("InputInstructions", "Observations", "Parameters"), sortBy="CFI")

standardizedResults <- modelResults[["stdyx.standardized"]]
unstandardizedResults <- modelResults$unstandardized

allModelParameters <- extractModelParameters("C:/Data_Analysis/ComparingLCAvCFA", recursive=TRUE)
The names of the returned list elements would be based on the directory and ?le names of each ?le
 (note that spaces, slashes, and the minus sign have been replaced by periods to be compatible with R naming conventions):
> names(allModelParameters)
ComparingLCAvCFA.LCA.1.class.LCA.out
ComparingLCAvCFA.LCA.2.class.LCA.out
ComparingLCAvCFA.LCA.3.class.LCA.out 
ComparingLCAvCFA.CFA.1.factor.CFA.out 
ComparingLCAvCFA.CFA.2.factor.CFA.out
ComparingLCAvCFA.CFA.3.factor.CFA.out
So, to extract the STDYX standardized results for the 2-factor CFA, one would access that data.frame as follows:
TwoFacCFA.STDYX <- allModelParameters$ComparingLCAvCFA.CFA.2.factor.CFA.out$stdyx.standardized

 parallelModels <- readModels("10_14_Harsh_SelfCon_Impul")
> compareModels(parallelModels[["backport.from.grand.model.out"]], 
   parallelModels[["backport.from.grand.model.slopesonw1.out"]], 
   show=c("diff", "pdiff", "summaries", "unique"), equalityMargin=c(param=.05, pvalue=http://www.mamicode.com/.02), 
   sort="type", diffTest=TRUE, showNS=FALSE)

 A Complete Example
Here, we provide a complex complete example of a template ?le that generates 480 input ?les in numerous subfolders. 
This is probably overkill for most applications, but it gives a sense of what the language is capable of. 
The application here is growth mixture modeling for symptoms of DSM-IV personality disorders over time. 
There are 10 personality disorders and two groups of participants: high risk and low risk. 
In addition, one question of interest is whether a class constrained to 0 at the ?rst measurement occasion with 0 growth captures a subgroup of participants. 
In addition, one might be interested in comparing whether these count data are better modeled by a continuous normal model versus a Poisson model, and whether Poisson GMM provides a better ?t than Poission latent class growth analysis (LCGA). 
All of these considerations are re?ected in this single template ?le. As a sane default, we chose to generate models that vary between 1 and 4 latent trajectory classes.
[[init]] iterators = outcome group model classes zeroclass; 
outcome = 1:10;
 group = 2 5; 
model = 1:3; 
classes = 1:4; 
zeroclass = 1:2;
outcomenames#outcome = Paranoid Schizoid Schizotypal Antisocial Borderline Histrionic Narcissistic Avoidant Dependent OCPD;
groupnames#group = "Low Risk" "High Risk";
modelnames#model = "Normal LGCM" "Poisson GMM" "Poisson LCGA";
zeroclassnames#zeroclass = "" " with zero class";
#wave names are with respect to the outcome iterator w1name#outcome = Paran1 Szoid1 Sztyp1 Anti1 Border1 Hist1 Narc1 Avoid1 Depend1 OCPD1; 
w2name#outcome = Paran2 Szoid2 Sztyp2 Anti2 Border2 Hist2 Narc2 Avoid2 Depend2 OCPD2; 
w3name#outcome = Paran3 Szoid3 Sztyp3 Anti3 Border3 Hist3 Narc3 Avoid3 Depend3 OCPD3;
filename = "[[classes]]-class [[groupnames#group]] [[outcomenames#outcome]] [[modelnames#model]][[zeroclassnames#zeroclass]].inp"; 
outputDirectory = "PD GMM/[[outcomenames#outcome]]/[[groupnames#group]]/Unconditional_Models/[[modelnames#model]]"; 
[[/init]]
TITLE: [[classes]]-class [[outcomenames#outcome]] [[groupnames#group]] [[modelnames#model]] Unconditional Model[[zeroclassnames#zeroclass]] 
DATA: FILE = "personality_mplus.dat"; VARIABLE: NAMES ARE id group sex age Paran1 Szoid1 Sztyp1 Anti1 Border1 Hist1 Narc1 Avoid1 Depend1
OCPD1 PaAg1 Sadist1 SelfDef1 Paran2 Szoid2 Sztyp2 Anti2 Border2 Hist2 Narc2 Avoid2 Depend2 OCPD2 Paran3 Szoid3 Sztyp3 Anti3 Border3 Hist3 Narc3 Avoid3 Depend3 OCPD3;
MISSING ARE .; 
USEVARIABLES ARE [[w1name#outcome]] [[w2name#outcome]] [[w3name#outcome]];
 USEOBSERVATIONS ARE group EQ [[group]]; ![[groupnames#group]] Only
[[model > 1]] COUNT ARE [[w1name#outcome]] [[w2name#outcome]] [[w3name#outcome]];
 [[/model > 1]] CLASSES = c ([[classes]]);
ANALYSIS: TYPE = MIXTURE; STARTS = 1000 10;
 K-1STARTS = 750 6; PROCESSORS = 4;
 [[model = 2]] ALGORITHM = INTEGRATION; 
[[/model = 2]] MODEL:
%OVERALL% Int Slope | [[w1name#outcome]]@0 [[w2name#outcome]]@0.97 [[w3name#outcome]]@2.77;
[[classes > 1]] [[zeroclass = 2]] !creates a class with all zeros at all time points [[model=1]] %c#2% [Int@0 Slope@0]; 
Int@0 Slope@0; 
[[/model=1]] [[model>1]] %c#2% [Int@-15 Slope@-15]; Int@0 Slope@0; [[/model>1]] [[/zeroclass = 2]] [[/classes > 1]]
PLOT: Type = PLOT3; 
Series = [[w1name#outcome]] (0) [[w2name#outcome]] (0.97) [[w3name#outcome]] (2.77); 
OUTPUT: TECH1 TECH4 [[model != 1]]TECH10 [[/model != 1]][[classes > 1]]TECH11 TECH14 [[/classes > 1]]STANDARDIZED RESIDUAL;