Package: DSDRM 0.1.3

Guangbao Guo

DSDRM: Distributed Sampling for Dynamic Regression Models

A toolbox for distributed dynamic regression modeling, parallel estimation, multiple distributed sampling algorithms (Metropolis-Hastings, block bootstrap, adaptive, hypergeometric), sparse matrix optimization, model visualization, prediction and performance evaluation. The philosophy of the package is described in Guo (2025) <doi:10.1038/s41598-025-93333-6>.

Authors:Guangbao Guo [aut, cre]

DSDRM_0.1.3.tar.gz
DSDRM_0.1.3.tar.gz(r-4.7-any)DSDRM_0.1.3.tar.gz(r-4.6-any)
DSDRM_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DSDRM/json (API)

# Install 'DSDRM' in R:
install.packages('DSDRM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 32 exports 2 dependencies

Last updated from:4d80006cb4. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK116
source / vignettesOK161
linux-release-x86_64OK112
wasm-releaseOK95

Exports:adaptive_block_lenblock_data_splitcalc_metricsdist_parallel_estimatedist_qn_algorithmdist_qn_with_timedsdrm_fitdsdrm_generate_datadsdrm_metricsdsdrm_predictdsdrm_samplingdynamic_coef_plotdynamic_opt_omegagamma_convergegamma_fusionglobal_info_matglobal_mcmc_estimatorglobal_mcmc_with_timeglobal_posterior_gammaglobal_quasi_llglobal_scoreinit_gammalocal_marginal_likelihoodlocal_quasi_llmh_gamma_updateparallel_estimatepenalized_quasi_llrun_batch_simulationrun_batch_simulation_with_timesparse_matrix_optimstatic_dist_with_timestatic_distributed_estimator

Dependencies:latticeMatrix

Readme and manuals

Help Manual

Help pageTopics
Compute Adaptive Block Lengthadaptive_block_len
Split Time Series Data into Distributed Blocksblock_data_split
Simulation Evaluation Metrics: MSE, MAE, R2, and Model Selection Accuracycalc_metrics
Fully Distributed Parallel Estimationdist_parallel_estimate
Distributed Quasi-Newton Iteration Main Algorithmdist_qn_algorithm
DSDRM Algorithm with Computation Time Trackingdist_qn_with_time
DSDRM Model Fitting (Main Interface)dsdrm_fit
Generate Time-Varying Distributed Dynamic Regression Datadsdrm_generate_data
DSDRM Comprehensive Performance Metricsdsdrm_metrics
Out-of-Sample Prediction for DSDRMdsdrm_predict
DSDRM with Posterior Sampling (MCMC-based)dsdrm_sampling
Dynamic Coefficient Path Plotdynamic_coef_plot
Compute Dynamic Optimal Sampling Weightsdynamic_opt_omega
Convergence Check for Model Structuregamma_converge
Fusion of Block-wise Model Selection Results (Weighted Voting)gamma_fusion
Global Weighted Fisher Information Matrixglobal_info_mat
Global MCMC Estimator (Benchmark Method)global_mcmc_estimator
Global MCMC Estimator with Computation Time Trackingglobal_mcmc_with_time
Global Posterior Probability for Model Indicatorglobal_posterior_gamma
Global Weighted Quasi Log-Likelihoodglobal_quasi_ll
Global Score Vectorglobal_score
Initialize Binary Model Indicator Vector Creates a binary (0/1) indicator vector for model selection. Sets 1 for active (true) covariates and 0 for irrelevant covariates.init_gamma
Local Marginal Likelihood for One Blocklocal_marginal_likelihood
Local Quasi Log-Likelihood for One Blocklocal_quasi_ll
Dynamic Metropolis-Hastings Update for Model Structuremh_gamma_update
Parallel Block-wise Estimation Helperparallel_estimate
L2 Penalized Quasi Log-Likelihoodpenalized_quasi_ll
Batch Simulation Main Function (Multivariate Grid Search)run_batch_simulation
Batch Simulation with Runtime and Parallel Accelerationrun_batch_simulation_with_time
Sparse Matrix Optimization for DSDRMsparse_matrix_optim
Static Distributed Estimator with Computation Time Trackingstatic_dist_with_time
Static Distributed Estimator (Benchmark Method)static_distributed_estimator