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 # DeltaPy⁠⁠ — Tabular Data Augmentation & Feature Engineering
 
+[![Downloads](https://pepy.tech/badge/deltapy)](https://pepy.tech/project/deltapy)
+
+[![DOI](https://zenodo.org/badge/253993655.svg)](https://zenodo.org/badge/latestdoi/253993655)
+
+---------
+
+Finance Quant Machine Learning
+------------------
+- [ML-Quant.com](https://www.ml-quant.com/)  -  Automated Research Repository 
+
 ### Introduction
 
 Tabular augmentation is a new experimental space that makes use of novel and traditional data generation and synthesisation techniques to improve model prediction success. It is in essence a process of modular feature engineering and observation engineering while emphasising the order of augmentation to achieve the best predicted outcome from a given information set. DeltaPy was created with finance applications in mind, but it can be broadly applied to any data-rich environment.
@@ -60,8 +70,6 @@ df_out = transform.fft_feat(df.copy(), ["Close"]); df_out.head()
 df_out = transform.harmonicradar_cw(df.copy(), ["Close"],0.3,0.2); df_out.head()
 df_out = transform.saw(df.copy(),["Close","Open"]); df_out.head()
 df_out = transform.modify(df.copy(),["Close"]); df_out.head()
-df_out = transform.multiple_rolling(df, columns=["Close"]); df_out.head()
-df_out = transform.multiple_lags(df, start=1, end=3, columns=["Close"]); df_out.head()
 df_out  = transform.prophet_feat(df.copy().reset_index(),["Close","Open"],"Date", "D"); df_out.head()
 ```
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