# From CRAN 
install.packages("modsem")

# Latest version from Github
install.packages("devtools")
devtools::install_github("kss2k/modsem", build_vignettes = TRUE)

library(modsem)
m1 <- '
  # Outer Model
  X =~ x1 + x2 +x3
  Y =~ y1 + y2 + y3
  Z =~ z1 + z2 + z3
  
  # Inner model
  Y ~ X + Z + X:Z 
'

# Double centering approach
est1_dca <- modsem(m1, oneInt)
summary(est1_dca)

# Constrained approach
est1_ca <- modsem(m1, oneInt, method = "ca")
summary(est1_ca)

# QML approach 
est1_qml <- modsem(m1, oneInt, method = "qml")
summary(est1_qml, standardized = TRUE) 

# LMS approach 
est1_lms <- modsem(m1, oneInt, method = "lms") 
summary(est1_lms)

tpb <- "
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  # Causal Relationsships
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC
  BEH ~ PBC:INT
"

# double centering approach
est_tpb_dca <- modsem(tpb, data = TPB, method = "dblcent")
summary(est_tpb_dca)

# Constrained approach using Wrigths path tracing rules for generating
# the appropriate constraints
est_tpb_ca <- modsem(tpb, data = TPB, method = "ca") 
summary(est_tpb_ca)

# LMS approach 
est_tpb_lms <- modsem(tpb, data = TPB, method = "lms")
summary(est_tpb_lms, standardized = TRUE) 

# QML approach 
est_tpb_qml <- modsem(tpb, data = TPB, method = "qml") 
summary(est_tpb_qml, standardized = TRUE)

est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "pind")
summary(est2)

## Interaction between an obsereved and a latent variable 
m3 <- '
  # Outer Model
  X =~ x1 + x2 +x3
  Y =~ y1 + y2 + y3
  
  # Inner model
  Y ~ X + z1 + X:z1 
'

est3 <- modsem(m3, oneInt, method = "pind")
summary(est3)