Metadata-Version: 2.1
Name: pycaltransfer
Version: 0.1.1
Summary: Calibration transfer for chemometrics and spectral data applications
Home-page: https://gitlab.com/chemosoftware/python/pycaltransfer
Author: Valeria Fonseca Diaz
Author-email: valeria.fonseca.diaz@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# Calibration transfer for chemometrics and spectral data applications

This package contains methods to perform calibration transfer based on bilinear models, mainly Partial Least Squares Regression.
Numpy and Sci-Kit Learn are mandatory dependencies

The methods included are:

(Piecewise) Direct Standardization (PDS, DS) (Wang 1991, Bouveresse1996)

Orthogonal projection (EPO transfer) (Zeaiter 2006, Roger 2003)

Domain invariant PLS (Nikzad-Langerodi 2018, 2020)

Joint Y PLS (Folch-Fortuny 2017, Garcia Munoz 2005)

## Installation options

### Option 1. Install via pip

```python
pip install pycaltransfer
```

### Option 2. Clone repository

```git
git clone https://gitlab.com/chemosoftware/python/pycaltransfer.git
```

To start using this package and get the documentation of the methods, do:

```python
import pycaltransfer.caltransfer as caltransfer
help(caltransfer.ds_pc_transfer_fit)
help(caltransfer.pds_pls_transfer_fit)
help(caltransfer.epo_fit)
help(caltransfer.jointypls_regression)
help(caltransfer.slope_bias_correction)
help(caltransfer.dipals)
```


