devtools::load_all()
models <- list(m1 = ' 
               # latent variables 
               ind60 =~ x1 + x2 + x3 
               dem60 =~ y1 + y2 + y3 + y4 
               dem65 =~ y5 + y6 + y7 + y8 
               # regressions
               dem60 ~ ind60 
               dem65 ~ ind60 + dem60 
               # residual covariances 
               y1 ~~ y5
               y2 ~~ y4 + y6 
               y3 ~~ y7 
               y4 ~~ y8
               y6 ~~ y8
               ',
               m3 = ' visual  =~ x1 + x2 + x3 
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9 ',
               m4 = 'visual  =~ x1 + start(0.8)*x2 + start(1.2)*x3
               textual =~ x4 + start(0.5)*x5 + start(1.0)*x6
               speed   =~ x7 + start(0.7)*x8 + start(1.8)*x9',
               m5 = '# three-factor model
               visual  =~ x1 + x2 + x3
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9
               # intercepts with fixed values
               x1 + x2 + x3 + x4 ~ 0.5*1',
               m6 = '# three-factor model
               visual  =~ x1 + x2 + x3
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9
               # intercepts
               x1 ~ 1
               x2 ~ 1
               x3 ~ 1
               x4 ~ 1
               x5 ~ 1
               x6 ~ 1
               x7 ~ 1
               x8 ~ 1
               x9 ~ 1', 
               m7 = ' # direct effect
               Y ~ c*X
               # mediator
               M ~ a*X
               Y ~ b*M
               # indirect effect (a*b)
               ab := a*b 
               # total effect
               total := c + (a*b)
               '
)


set.seed(1234)
X <- rnorm(100)
M <- 0.5*X + rnorm(100)
Y <- 0.7*M + rnorm(100)
d7 <- data.frame(X = X, Y = Y, M = M)

data <- list(d1 = lavaan::PoliticalDemocracy,
             d3 = lavaan::HolzingerSwineford1939, 
             d4 = lavaan::HolzingerSwineford1939, 
             d5 = lavaan::HolzingerSwineford1939, 
             d6 = lavaan::HolzingerSwineford1939, 
             d7 = d7)


estimates <- vector("list", length(models))
for (i in seq_along(estimates)) {
  estimates[[i]]$lav <- tryCatch({
    est <- lavaan::sem(models[[i]], data = data[[i]])
    est
  },
  warning = function(e) {
    warning("Warning in lav model ", i)
    warning(capturePrint(e))
  },
  error = function(e) {
    est <- NA
    warning("Error in lav model ", i)
  },
  finally = {
    est
  })
  estimates[[i]]$modsem <- tryCatch({
    est <- modsem(models[[i]], data = data[[i]], estimator = "ML")
    est
  },
  warning = function(e) {
    warning("Warning in modsem model ", i)
    est
  },
  error = function(e) {
    est <- NA
    warning("Error in modsem model ", i) 
  },
  finally = {
    est
  })
}

for (est in estimates) {
  lavaanEst <- lavaan::parameterEstimates(est$lav)
  lavaanEst[is.na(lavaanEst)] <- -999
  modsemEst <- lavaan::parameterEstimates(est$modsem$lavaan)
  modsemEst[is.na(modsemEst)] <- -999
  testthat::expect_equal(lavaanEst, modsemEst)
}


testthat::expect_error(modsemify('X =~ x1 x2 + x3'), regexp = "Unexpected token.*")
testthat::expect_error(modsemify('X =~ 1.0 1.0'),
                       regexp = "Unexpected token.*")
testthat::expect_error(modsemify('X =~ x1 + x2 + +x3'),
                       regexp = "Expected token before .*")
testthat::expect_error(modsemify('X =~ x1 + x2 +'),
                       regexp = "Expected token after .*")
testthat::expect_error(modsemify('X =~ x1 + x2 =~ x3'),
                       regexp = "Unexpected operator .*")

m <- '
X =~ x1 + x2 +
+x3
'
rebuiltLine <- paste0(getLines(m)[[2]], collapse="") # check that `+\n+` -> `+ +`, not `++`
testthat::expect_true(!any(grepl("\\+\\+", rebuiltLine)))
