110 need getting started documentation for users

.github/workflows/markdown-check.yml

@@ -11,4 +11,4 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
-    - uses: gaurav-nelson/github-action-markdown-link-check@v1
+    - uses: gaurav-nelson/github-action-markdown-link-check@v1

CITATION.bib

@@ -2,6 +2,6 @@ @misc{pyttb
 	author = {Dunlavy, Daniel M. and Johnson, Nicholas T. and others},
 	month = oct,
 	title = {{pyttb: Python Tensor Toolbox, v1.8.0}},
-	url = {https://github.com/sandialab/pyttb},
+	url = {https://github.com/sandialabs/pyttb},
 	year = {2024}
 }

CONTRIBUTING.md

@@ -1,3 +1,9 @@
+```
+Copyright 2024 National Technology & Engineering Solutions of Sandia,
+LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the
+U.S. Government retains certain rights in this software.
+```
+
 # Python Tensor Toolbox Contributor Guide
 
 ## Issues

README.md

@@ -1,5 +1,5 @@
 ```
-Copyright 2022 National Technology & Engineering Solutions of Sandia,
+Copyright 2024 National Technology & Engineering Solutions of Sandia,
 LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the
 U.S. Government retains certain rights in this software.
 ```
@@ -12,32 +12,73 @@ U.S. Government retains certain rights in this software.
 
 # pyttb: Python Tensor Toolbox
 
-Welcome to `pyttb`, a set of Python classes and methods functions for 
-manipulating dense, sparse, and structured tensors, along with algorithms 
-for computing low-rank tensor models.
+Welcome to `pyttb`, a refactor of the 
+[Tensor Toolbox for MATLAB](https://www.tensortoolbox.org) in Python.
 
-**Tensor Classes:**
-* `tensor`: dense tensors
-* `sptensor`: sparse tensors
-* `ktensor`: Kruskal tensors
-* `tenmat`: matricized tensors
-* `ttensor`: Tucker tensors
+This package contains data classes and methods for manipulating dense, 
+sparse, and structured tensors, along with algorithms for computing 
+low-rank tensor decompositions:
 
-**Tensor Algorithms:**
-* `cp_als`, `cp_apr`: Canonical Polyadic (CP) decompositions
-* `tucker_als`: Tucker decompostions
+- Data Classes: 
+[`tensor`](https://pyttb.readthedocs.io/en/stable/tensor.html "dense tensors"), 
+[`sptensor`](https://pyttb.readthedocs.io/en/stable/sptensor.html "sparse tensors"), 
+[`ktensor`](https://pyttb.readthedocs.io/en/stable/ktensor.html "Kruskal tensors"), 
+[`ttensor`](https://pyttb.readthedocs.io/en/stable/ttensor.html "Tucker tensors"), 
+[`tenmat`](https://pyttb.readthedocs.io/en/stable/tenmat.html "matricized dense tensors"), 
+[`sptenmat`](https://pyttb.readthedocs.io/en/stable/sptenmat.html "matricized sparse tensors"), 
+[`sumtensor`](https://pyttb.readthedocs.io/en/stable/sumtensor.html "implicit sum of tensors")
+- Algorithms:
+[`cp_als`](https://pyttb.readthedocs.io/en/stable/cpals.html "CP decomposition via Alternating Least Squares"),
+[`cp_apr`](https://pyttb.readthedocs.io/en/stable/cpapr.html "CP decomposition via Alternating Poisson Regression"), 
+[`gcp_opt`](https://pyttb.readthedocs.io/en/stable/gcpopt.html "Generalized CP decomposition"), 
+[`hosvd`](https://pyttb.readthedocs.io/en/stable/hosvd.html "Tucker decomposition via Higher Order Singular Value Decomposition"),
+[`tucker_als`](https://pyttb.readthedocs.io/en/stable/tuckerals.html "Tucker decompostion via Alternating Least Squares")
 
-# Getting Started
-For full details see our [documentation](https://pyttb.readthedocs.io).
 ## Quick Start
-We are on pypi
-```commandline
-pip install pyttb
-```
-or install from source
+
+### Installation
 ```commandline
-pip install .
+python3 -m pip install pyttb
 ```
 
-# Contributing
-Check out our [contributing guide](CONTRIBUTING.md).
+### Example
+```python
+>>> import pyttb as ttb
+>>> X = ttb.tenrand((2,2,2))
+>>> type(X)
+<class 'pyttb.tensor.tensor'>
+>>> M = ttb.cp_als(X, rank=1)
+CP_ALS:
+ Iter 0: f = 7.367245e-01 f-delta = 7.4e-01
+ Iter 1: f = 7.503069e-01 f-delta = 1.4e-02
+ Iter 2: f = 7.508240e-01 f-delta = 5.2e-04
+ Iter 3: f = 7.508253e-01 f-delta = 1.3e-06
+ Final f = 7.508253e-01
+ ```
+
+### Getting Help
+- [Documentation](https://pyttb.readthedocs.io)
+- [Tutorials](https://pyttb.readthedocs.io/en/stable/tutorials.html)
+- [Info for users coming from MATLAB](https://pyttb.readthedocs.io/en/stable/for_matlab_users.html)
+- Learn about tensor decompositions: 
+<!-- markdown-link-check-disable -->
+[tensor paper](https://doi.org/10.1137/07070111X "Tensor Decompositions and Applications by Tamara G. Kolda, Brett W. Bader"), 
+<!-- markdown-link-check-enable -->
+[tensor book](https://www.mathsci.ai/post/tensor-textbook/ "Tensor Decompositions for Data Science by Grey Balard and Tamara G. Kolda") 
+
+### Contributing
+- [Report a bug](https://github.com/sandialabs/pyttb/issues/new)
+- [Guide for contributors](CONTRIBUTING.md)
+- [List of contributors](CONTRIBUTORS.md)
+
+### Citing pyttb in your work 
+If you use pyttb in your work, please cite it as follows:
+```bibtex
+@misc{pyttb,
+	author = {Dunlavy, Daniel M. and Johnson, Nicholas T. and others},
+	month = oct,
+	title = {{pyttb: Python Tensor Toolbox, v1.8.0}},
+	url = {https://github.com/sandialabs/pyttb},
+	year = {2024}
+}
+```