CRAN
Crane Harbor Acquisition Corp. II Class A
stockNASDAQ

Entropic Scree... a new R package in CRAN. It can be used to identify independent clusters of variables and estimate rank and other data topology metrics. Run "install.packages("Entropic.Scree")" to get started. \# Resources \* \*\*Methodology & Empirical Tests:\*\* \[https://zenodo.org/records/22028087\](https://zenodo.org/records/22028087) \* \*\*Source Code (C++ / R):\*\* \[https://github.com/tjleestjohn/Entropic-Scree\](https://github.com/tjleestjohn/Entropic-Scree) Happy to answer questions about the underlying math. Note: I use these same Entropic Scree results to actively size the bottleneck layers within a prediction platform I designed to handle high-D, error-prone data. I am always open to discussing full-scale implementation or collaborating with others tackling similar structural modeling challenges. Ps: This is not AI quant mods!

Many quant pipelines require dimensionality reduction to isolate independent market drivers. Standard PCA is common, but it has restrictive structural limits: it assumes linearity and penalizes mixed data types. Distance-based methods are also limited, particularly in high-dimensional spaces where distance concentration systematically degenerates. I recently published **Entropic.Scree (v1.0.1)** on CRAN. It is an R package with a C++ OpenMP-accelerated backend designed for unsupervised topological manifold unmixing. By using Information-Theoretic Jaccard Similarity, it evaluates structural entropy, aiming to find the true intrinsic generative rank of your data. Here is how it upgrades a standard quant pipeline: * **Isolate Low-Dependency Cleavages / Subnetworks:** It extracts feature clusters that have high degrees of within-cluster dependency, but exhibit relatively low between-cluster mutual information. For example, in a 125-variable empirical stress test, it estranged undirected volatility (ATR) from directional trend persistence (ADX), suggesting that they operate as relatively independent generative engines. * **Identify Divergent Generative Roots:** It maps topologies that challenge standard autoregressive models. In that same 125-variable test, it isolated temporal decay as an active causal engine, suggesting that raw temporal progression shares little mutual information with parabolic trend continuation. * **Quantify Signal-to-Noise & Structural Gravity:** The framework maps abstract matrix properties into actionable footprints via Average Informational Gravity (AIG). It also calculates total signal-to-idiosyncratic volume, measuring if the signal is robust enough to model. For example, it estimated that the aforementioned dataset contained about 49.5% shared signal volume against 50.5% idiosyncratic noise. * **Integrate Alternative Data:** It projects continuous price feeds and discrete categorical data onto a unified, dimension-free probability scale, mitigating the artificial deflation caused by marginal shape mismatches. * **Test Linear Sufficiency:** The extracted non-linear generative rank (K\_{roots}) can be compared against a standard PCA scree plot to derive the Dimensional Inflation Index (Delta\_K). This quantifies the severity of linear fragmentation, providing evidence of whether standard PCA is sufficient or if you genuinely need non-linear methods for downstream modeling. # Implementation The package relies on its multi-threaded C++ backend for performance. The R version is currently live on CRAN, and a Python version is actively under development. install.packages("Entropic.Scree") library(Entropic.Scree) library(data.table) # The input dataset must be a raw data.table object (not a correlation matrix) financial_dt <- as.data.table(your_high_dimensional_data) # Run the unsupervised manifold unmixing (interactive dashboards print by default) results <- Entropic.Scree(financial_dt, extract_bipolar_modules = TRUE) # View bipolar clusters / sub-networks results$bipolar_modules # Resources * **Methodology & Empirical Tests:** [https://zenodo.org/records/22028087](https://zenodo.org/records/22028087) * **Source Code (C++ / R):** [https://github.com/tjleestjohn/Entropic-Scree](https://github.com/tjleestjohn/Entropic-Scree) Happy to answer questions about the underlying math! Note: I use these same Entropic Scree results to actively size the bottleneck layers within a prediction platform I designed to handle high-D, error-prone data. I am always open to discussing full-scale implementation or collaborating with others tackling similar structural modeling challenges.