Add dynamic covariates PMF cpp implementation for pnbd model #284
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- Introduced a new header file `pnbd_dyncov_pmf.h` containing the declaration of functions and classes for handling dynamic covariates in the pnbd model. - Implemented the `DynamicCovariates` class to manage and compute cumulative sums of covariate data. - Added multiple PMF functions to calculate probabilities based on dynamic covariates, including per-customer calculations and hypergeometric functions. - Ensured compatibility with Armadillo and GSL libraries for efficient mathematical computations.
- Introduced minimal working example for PNBD Dynamic Covariates PMF in `minimal_pmf_example.R`. - Created a comprehensive benchmark script `pmf_example_benchmark.R` to compare R and Rcpp implementations of PMF. - Added quick test script `quick_test_pmf.R` to verify function availability and basic functionality. - Developed a simple demonstration script `simple_pmf_demo.R` showcasing PMF functions and their usage. - Updated documentation for internal functions related to PMF calculations, ensuring clarity on parameters and outputs. - Enhanced internal functions for better performance and stability in PMF calculations.
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This pull request introduces significant enhancements to the Pareto/NBD model with dynamic covariates in the
CLVToolspackage. The changes include new functionality for calculating the probability mass function (PMF) with dynamic covariates, additional helper functions, C++ integration for computational efficiency, and improved data preparation for covariate-based modelling.New functionality for dynamic covariates:
Added a new function
pnbd_dyncov_pmf_per_customerto calculate the PMF for the Pareto/NBD model with dynamic covariates for a single customer. This function integrates dynamic transaction and lifetime covariates into the PMF computation. (R/RcppExports.R,src/RcppExports.cpp,src/pnbd_dyncov_pmf.h) [1] [2] [3]Introduced helper functions for calculating covariate effects (
Bbar,Dbar,S1,S2_ij, etc.) and time boundaries (bu_i) used in the PMF computation. These functions support detailed modelling of customer behaviour over time. (R/RcppExports.R,src/pnbd_dyncov_pmf.h) [1] [2]Data preparation and R integration:
Implemented
pnbd_dyncov_prepare_datato preprocess transaction and covariate data for PMF computation. This function handles merging, transformations, and computation of time intervals and covariate effects. (R/pnbd_dyncov_pmf.R)Added an R interface function
pnbd_dyncov_pmfwith an option to use either the R or C++ implementation for PMF calculation. This allows flexibility in balancing ease of use and computational performance. (R/pnbd_dyncov_pmf.R)C++ integration for performance:
Introduced a C++ implementation of the dynamic covariate PMF computation to improve performance for large datasets. This includes the
pnbd_dyncov_pmf_per_customerfunction and supporting components. (src/pnbd_dyncov_pmf.h,src/RcppExports.cpp) [1] [2] [3]Added a wrapper for the GSL hypergeometric function
2F1with error handling to support numerical stability in PMF calculations. (R/RcppExports.R,src/pnbd_dyncov_pmf.h) [1] [2]Refactoring and renaming:
pnbd_dyncov_pmftopnbd_dyncov_pmf_rto distinguish the R implementation from the new interface function. (R/pnbd_dyncov_pmf.R)