Package: catalytic 0.1.0

Dongming Huang

catalytic: Tools for Applying Catalytic Priors in Statistical Modeling

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Authors:Yitong Wu [aut], Dongming Huang [aut, cre], Weihao Li [aut], Ministry of Education, Singapore [fnd]

catalytic_0.1.0.tar.gz
catalytic_0.1.0.tar.gz(r-4.7-any)catalytic_0.1.0.tar.gz(r-4.6-any)
catalytic_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
catalytic/json (API)

# Install 'catalytic' in R:
install.packages('catalytic', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • swim - Simulated SWIM Dataset with Binary Response

On CRAN:

Conda:

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

3.18 score 4 scripts 299 downloads 15 exports 63 dependencies

Last updated from:00e3494d40. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK211
source / vignettesOK203
linux-release-x86_64OK156
wasm-releaseOK185

Exports:cat_coxcat_cox_bayescat_cox_bayes_jointcat_cox_initializationcat_cox_tunecat_glmcat_glm_bayescat_glm_bayes_jointcat_glm_bayes_joint_gibbscat_glm_initializationcat_glm_tunecat_lmmcat_lmm_initializationcat_lmm_tunetraceplot

Dependencies:abindbackportsBHbootcallrcheckmateclicodacpp11descdistributionalfarvergenericsggplot2gluegridExtragtableinlineinvgammaisobandlabelinglatticelifecyclelme4loomagrittrMASSMatrixmatrixStatsminqanlmenloptrnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpsquadformQuickJSRR6rbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreformulasrlangrstanS7scalesStanHeaderssurvivaltensorAtibbletruncnormutf8vctrsviridisLitewithr

catalytic_cox
Introduction | Data Preparation | Usage of catalytic | Step 1: Initialization | Step 2.1: Choose Method(s) - Estimation with Fixed tau | Step 2.2: Choose Method(s) - Estimation with Selective tau | Cross-validation (risk_estimate_method = "cross_validation") | Step 2.3: Bayesian Posterior Sampling with Fixed tau | 2.4 Bayesian Posterior Sampling with Adaptive Tau | References
Rendered fromcatalytic_cox.Rmdusingknitr::rmarkdown

Last update: 2024-12-18
Started: 2024-12-18

catalytic_glm_binomial
Introduction | Data Preparation | Usage of catalytic | Step 1: Initialization | Step 2.1: Choose Method(s) - Estimation with fixed tau | Step 2.2: Choose Method(s) - Estimation with Selective tau | Cross-validation (risk_estimate_method = "cross_validation") | Bootstrap (risk_estimate_method = "parametric_bootstrap") | Steinian Estimate (risk_estimate_method = "steinian_estimate") | Recommendations for Choosing risk_estimate_method and discrepancy_method | Automatic Parameter Selection | Step 2.3: Choose Method(s) - Bayesian Posterior Sampling with Fixed Tau | Step 2.4: Choose Method(s) - Bayesian Posterior Sampling with Adaptive Tau | Step 2.5: Choose Method(s) - Special Approaches for Binomial Models | Gibbs Sampling | Setting Higher Tau Lower Bound | Incorporating theta (1/tau) | Incorporating theta (1/tau) with Adaptive Alpha | References
Rendered fromcatalytic_glm_binomial.Rmdusingknitr::rmarkdown

Last update: 2024-12-18
Started: 2024-12-18

catalytic_glm_gaussian
Introduction | Data Preparation | Usage of catalytic | Step 1: Initialization | Step 2.1: Choose Method(s) - Estimation with Fixed tau | Step 2.2: Choose Method(s) - Estimation with Selective tau | Cross-validation (risk_estimate_method = "cross_validation") | Bootstrap (risk_estimate_method = "parametric_bootstrap") | Mallowian Estimate (risk_estimate_method = " mallowian_estimate") | Recommendations for Choosing risk_estimate_method and discrepancy_method | Automatic Parameter Selection | Step 2.3: Choose Method(s) - Bayesian Posterior Sampling with Fixed tau | Step 2.4: Choose Method(s) - Bayesian Posterior Sampling with Adaptive Tau | References
Rendered fromcatalytic_glm_gaussian.Rmdusingknitr::rmarkdown

Last update: 2024-12-18
Started: 2024-12-18

Readme and manuals

Help Manual

Help pageTopics
Catalytic Cox Proportional Hazards Model (COX) Fitting Function with Fixed Taucat_cox
Bayesian Estimation for Catalytic Cox Proportional-Hazards Model (COX) with Fixed taucat_cox_bayes
Bayesian Estimation for Catalytic Cox Proportional-Hazards Model (COX) with adaptive taucat_cox_bayes_joint
Initialization for Catalytic Cox proportional hazards model (COX)cat_cox_initialization
Catalytic Cox Proportional-Hazards Model (COX) Fitting Function by Tuning tau from a Sequence of tau Valuescat_cox_tune
Catalytic Generalized Linear Models (GLMs) Fitting Function with Fixed Taucat_glm
Bayesian Estimation for Catalytic Generalized Linear Models (GLMs) with Fixed taucat_glm_bayes
Bayesian Estimation for Catalytic Generalized Linear Models (GLMs) with adaptive taucat_glm_bayes_joint
Bayesian Estimation with Gibbs Sampling for Catalytic Generalized Linear Models (GLMs) Binomial Family for Coefficients and taucat_glm_bayes_joint_gibbs
Initialization for Catalytic Generalized Linear Models (GLMs)cat_glm_initialization
Catalytic Generalized Linear Models (GLMs) Fitting Function by Tuning tau from a Sequence of tau Valuescat_glm_tune
Catalytic Linear Mixed Model (LMM) Fitting Function with fixed taucat_lmm
Initialization for Catalytic Linear Mixed Model (LMM)cat_lmm_initialization
Catalytic Linear Mixed Model (LMM) Fitting Function by Tuning tau from a Sequence of tau Valuescat_lmm_tune
Perform Cross-Validation for Model Estimationcross_validation
Perform Cross-Validation for Catalytic Cox Proportional-Hazards Model (COX) to Select Optimal taucross_validation_cox
Perform Cross-Validation for Catalytic Linear Mixed Model (LMM) to Select Optimal taucross_validation_lmm
Extract and Format Model Coefficientsextract_coefs
Extract Dimension Information from Model Initializationextract_dim
Extract and Format Summary of Stan Model Resultsextract_stan_summary
Extract and Format Sequence of Tau Valuesextract_tau_seq
Adjusted Cat Initializationget_adjusted_cat_init
Compute the Gradient for Cox Proportional Hazards Modelget_cox_gradient
Compute the Hessian Matrix for Cox Proportional Hazards Modelget_cox_hessian
Estimate the kappa value for the synthetic Cox proportional hazards modelget_cox_kappa
Compute the Partial Likelihood for the Cox Proportional Hazards Modelget_cox_partial_likelihood
Solve Linear System using QR Decompositionget_cox_qr_solve
Calculate Risk and Failure Sets for Cox Proportional Hazards Modelget_cox_risk_and_failure_sets
Identify the risk set indices for Cox proportional hazards modelget_cox_risk_set_idx
Compute the gradient of the synthetic Cox proportional hazards modelget_cox_syn_gradient
Compute the Synthetic Hessian Matrix for Cox Proportional Hazards Modelget_cox_syn_hessian
Compute Discrepancy Measuresget_discrepancy
Extract Left-Hand Side of Formula as Stringget_formula_lhs
Extract the Right-Hand Side of a Formulaget_formula_rhs
Convert Formula to Stringget_formula_string
Get Custom Variance for Generalized Linear Model (GLM)get_glm_custom_var
Compute Diagonal Approximate Covariance Matrixget_glm_diag_approx_cov
Retrieve GLM Family Name or Name with Link Functionget_glm_family_string
Compute Lambda Based on Discrepancy Methodget_glm_lambda
Compute Log Density Based on GLM Familyget_glm_log_density
Compute Gradient of Log Density for GLM Familiesget_glm_log_density_grad
Compute Mean Based on GLM Familyget_glm_mean
Generate Sample Data for GLMget_glm_sample_data
Run Hamiltonian Monte Carlo to Get MCMC Sample Resultget_hmc_mcmc_result
Compute Linear Predictorget_linear_predictor
Resampling Methods for Data Processingget_resampled_df
Generate Stan Model Based on Specified Parametersget_stan_model
Standardize Dataget_standardized_data
Hamiltonian Monte Carlo (HMC) Implementationhmc_neal_2010
Check if a Variable is Continuousis.continuous
Perform Mallowian Estimate for Model Risk (Only Applicable for Gaussian Family)mallowian_estimate
Perform Parametric Bootstrap for Model Risk Estimationparametric_bootstrap
Plot Likelihood or Risk Estimate vs. Tau for Tuning Modelplot.cat_tune
Predict Linear Predictor for New Data Using a Fitted Cox Modelpredict.cat_cox
Predict Outcome for New Data Using a Fitted GLM Modelpredict.cat_glm
Predict Linear Predictor for New Data Using a Fitted Linear Mixed Modelpredict.cat_lmm
Print Data Frame with Head and Tail Rowsprint_df_head_tail
Generate Suggestions for Bayesian Joint Binomial GLM Parameter Estimationprint_glm_bayes_joint_binomial_suggestion
Print Method for 'cat' Objectprint.cat
Print Summary of 'cat_bayes' Modelprint.cat_bayes
Print Summary of 'cat_gibbs' Modelprint.cat_gibbs
Print Summary for Catalytic Initialization Modelprint.cat_initialization
Print Method for 'cat_tune' Objectprint.cat_tune
Perform Steinian Estimate for Model Risk (Only Applicable for Binomial Family)steinian_estimate
Simulated SWIM Dataset with Binary Responseswim
Traceplot for Bayesian Model Samplingtraceplot
Traceplot for Bayesian Sampling Modeltraceplot.cat_bayes
Traceplot for Gibbs Sampling Modeltraceplot.cat_gibbs
Calculates the log-likelihood for linear mixed models (LMMs) by combining observed and synthetic log-likelihoods based on the variance parameters.update_lmm_variance
Validate Inputs for Catalytic Cox proportional hazards model (COX) Initializationvalidate_cox_initialization_input
Validate Inputs for Catalytic Cox Modelvalidate_cox_input
Validate Inputs for Catalytic Generalized Linear Models (GLMs) Initializationvalidate_glm_initialization_input
Validate Inputs for Catalytic Generalized Linear Models (GLMs)validate_glm_input
Validate Inputs for Catalytic Linear Mixed Model (LMM) Initializationvalidate_lmm_initialization_input
Validate Inputs for Catalytic Linear Mixed Model (LMM)validate_lmm_input
Validate Positive or Non-negative Parametervalidate_positive