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 ## skpcp
 
-Robust principal component analysis via Principal Component Pursuit (PCP)
+Robust principal component analysis via Principal Component Pursuit (PCP) with scikit-learn transformer interface. 
 
+[![codecov](https://codecov.io/gh/quantfinlib/skpcp/graph/badge.svg?token=ZUZEM2WENL)](https://codecov.io/gh/quantfinlib/skpcp)
+[![Tests](https://github.com/quantfinlib/skpcp/actions/workflows/test.yml/badge.svg)](https://github.com/quantfinlib/skpcp/actions/workflows/test.yml)
+[![Doc Build](https://github.com/quantfinlib/skpcp/actions/workflows/gh-pages.yml/badge.svg)](https://github.com/quantfinlib/skpcp/actions/workflows/docs.yml)
+
+
+### Installation
+
+```bash
+pip install skpcp
+```
+
+### Getting Started
+
+Principal Component Pursuit (PCP) is a method for decomposing a data matrix `X` into a low-rank component `L` and a sparse component `S`, i.e., `X = L + S`. The `skpcp` package provides an implementation of PCP with a scikit-learn compatible transformer interface.
+
+At its core the algorithm solves the following optimization problem $$ \min_{L,S} \|L\|_* + \lambda \|S\|_1 \quad \text{s.t.} \quad X = L + S $$ where $\|L\|_*$ is the nuclear norm (sum of singular values) of `L`, $\|S\|_1$ is the element-wise $\ell_1$ norm of `S`, and $\lambda > 0$ is a regularization parameter that controls the trade-off between the low-rank and sparse components. In practice, the user does not need to set the value of $\lambda$, as it is automatically chosen based on the dimensions of the input data matrix `X`.
+We refer the users to the original paper by Candes et al. (2011) for more details: [Robust Principal Component Analysis?](https://www.microsoft.com/en-us/research/wp-content/uploads/2009/12/RobustPCA.pdf).
+
+
+```python
+
+import numpy as np
+from skpcp import PCP
+
+# Generate synthetic data with low-rank and sparse components
+RNG = np.random.default_rng(42)
+n_samples, n_features, rank = 100, 50, 5
+L = np.dot(RNG.normal(size=(n_samples, rank)), RNG.normal(size=(rank, n_features)))  # Low rank component
+S = RNG.binomial(1, 0.1, size=(n_samples, n_features)) * RNG.normal(loc=0, scale=10, size=(n_samples, n_features))  # Sparse component
+X = L + S
+
+# Fit PCP model
+pcp = PCP()
+pcp.fit(X)
+L_est = pcp.low_rank_  # Estimated low-rank component
+S_est = pcp.sparse_  # Estimated sparse component
+
+```
+Alternatively you can use the `fit_transform` method to fit the model and obtain the low-rank component in one step:
+
+```python
+L_est = pcp.fit_transform(X)
+```
+
+Note that the `fit` method decomposes the input data matrix `X` into its low-rank component `L_est` and sparse component `S_est`.
+The behavior of the `transform`method of `PCP` differs from that of a typical scikit-learn transformer, in that it accepts the same data matrix `X` that was used in `fit`. You cannot pass a new data matrix to `transform`, as the decomposition is specific to the input data used in `fit`.
+
+Please see the [examples](https://quantfinlib.github.io/skpcp/examples/examples.html) and the [API reference](https://quantfinlib.github.io/skpcp/api/pcp.html) for more details.
+
+
+### [Documentation](https://quantfinlib.github.io/skpcp/)
+
+The documentation is supported by [Sphinx](https://www.sphinx-doc.org/en/master/) and it is hosted on [GitHub pages](https://quantfinlib.github.io/skpcp/). 
+
+To build the HTML pages locally, first make sure you have installed the package with its documentation dependencies:
+
+```bash
+uv pip install -e .[docs]
+```
+
+then run the following:
+
+```bash
+sphinx-build docs docs/_build
+```
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