R (>= 3.1.2), stats, graphics, grDevices, utils, diagram,
methods, mice, quantreg

R (>= 3.1.2), stats, graphics, grDevices, utils, diagram,
methods, quantreg

It provides an interesting solution for handling a high number
of segmentation variables in partial least squares structural equation
modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez,
and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient
test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>)
to detect the path coefficients responsible for the identified differences),
the hybrid multi-group approach (Lamberti (2021)
<doi:10.1007/s11135-021-01096-9>) and the classical multi-group
approaches (parametric and permutation approaches). The package also
includes an extension of the "Pathmox" algorithm to the case of
linear regression models.

It provides an interesting solution for handling a high number
of segmentation variables in partial least squares structural equation
modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez,
and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient
test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>)
to detect the path coefficients responsible for the identified differences),
the hybrid multi-group approach (Lamberti (2021)
<doi:10.1007/s11135-021-01096-9>).

Pathmox Approach Segmentation Tree Analysis
It provides an interesting solution for handling a high number
of segmentation variables in partial least squares structural equation
modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez,
and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient
test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>)
to detect the path coefficients responsible for the identified differences).
The package also allows running the hybrid multi-group approach (Lamberti (2021)
<doi:10.1007/s11135-021-01096-9>).
R (>= 3.5.0), stats, cSEM, diagram, methods, graphics,
grDevices