pylspm is a Python package dedicated to Partial Least Squares Path Modeling (PLS-PM) analysis.

• SciPy
• Numpy
• Pandas
• Matplotlib
• Gurobi (for fuzzy regression)

# Instantiating the PyPLS class
plspm = PyLSpm(data, LVcsv, MVcsv, scheme, regression, h, maxit, stopCriterion)

Where data, LVcsv and MVcsv are CSV files;

scheme allows the following options:

• path
• centroid
• factor

regression allows the following options:

• ols
• fuzzy

h is the h-certain factor used for fuzzy regression only;

maxit (default = 300) is the maximum number of iterations allowed until convergence;

and stopCriterion (default = 10^-7) is the desired error.

Must contain all the indicators with their respective values (comma-separated values).

The LVcsv must contain two collums in the following format (comma-separated values):

Defining how the latent variables are influencing each other.

The MVcsv must contain three collums in the following format (comma-separated values):

Defining the indicators connected with each latent variables and the connection mode (A or B).

# Instantiating the PyPLSboot class
boot = PyLSboot(br, cores, data, LVcsv, MVcsv, scheme, regression, h, maxit, stopCriterion, method, boolen-stine)

Where br is the number of replications desired;

cores is the numbers of cores to use;

method allows the following options:

• percentile
• BCa

and bollen-stine enables or disables boolen-stine boostraping.

Other methods are also available:

A missForest (Stekhoven and Bulhmann, 2012) adaptation is available in imputation.py.

Multicore is available in bootstraping methods and segmentation methods, also a parallel processing MPI method is implemented in boot_mpi.py.

Library inspired by Juan Manuel Velasquez Estrada's simplePLS, Gaston Sanchez's plspm and Mikko Rönkkö's matrixpls made in R.

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