bivarhr0.1.5 package

Bivariate Hurdle Regression with Bayesian Model Averaging

add_qsig

Add BH-adjusted q-values and significance stars

add_sheet

Add a worksheet to an Excel workbook with flexible content

bivarhr-package

bivarhr: Bivariate Hurdle Regression

build_design

Build Design Matrices for Bivariate Hurdle Model

contrafactual_ATE

Contrafactual Average Treatment Effects (ATE) for the Bivariate Hurdle...

disc_terciles

Discretize Numeric Vector into Terciles

dot-as_num1

Coerce to numeric and return first element

dot-build_model_with_floor

Build CmdStan model with custom FLOOR constant

dot-first_pvalue

Extract a p-value from nested test objects

dot-get_coef

Safely extract coefficient matrix from an object

dot-get_pval

Extract p-value from RTransferEntropy result

dot-get_stat

Extract TE statistic from RTransferEntropy result

dot-is_binary_like

Check if Vector is Binary-like

dot-read_te_all

Read transfer entropy results from CSV files

dot-scalar1_chr

Coerce to character scalar safely

dot-scalar1

Coerce to numeric scalar safely

dot-write_sheet

Safely write a data frame to an Excel worksheet

export_results_xlsx

Export Results to Excel

export_results

Export Analysis Results

fit_one

Fit Single Bivariate Hurdle Model

get_hurdle_model

Get Default Hurdle Model

load_saved_results

Load Saved Results from Directory

make_lags

Create Lag Matrix

normalize_names

Normalize character names by stripping BOM and NBSP

placebo_temporal

Temporal Placebo Test via Time-Index Permutations

predict_multistep

Multi-step Predictive Simulation for the Bivariate Hurdle Model

prewhiten_bin_glm

Pre-whiten binary series with logistic GLM

prewhiten_count_glm

Pre-whiten count series with GLM / NegBin model

prewhiten_rate_glm

Pre-whiten rate series with log-link Gaussian GLM

print_floor_smoketest

Print summary of FLOOR smoke test (ELPD ranking invariance)

rc_auto

Read CSV with automatic delimiter detection

read_bma_all

Read and consolidate BMA weight tables

rolling_oos

Rolling Out-of-Sample Forecast Evaluation

run_dbn

Fit a Two-Slice Dynamic Bayesian Network (DBN) for I, C, and Regime

run_eba

Extreme-Bounds Analysis (EBA) over Control-Variable Combinations

run_hmm

Hidden Markov Model (HMM) for Path Dependence (Counts I and C)

run_sensemakr

Sensitivity Analysis to Unobserved Confounding (sensemakr)

run_synth_bsts

Synthetic Control via BSTS (CausalImpact)

run_transfer_entropy

Transfer Entropy for Counts, Rates, and Binary Series

run_varx

Fit VARX model with diagnostics for I and C

select_by_bma

Select Best Model via Bayesian Model Averaging

smoketest_floor_elpd_invariance

Smoke Test for FLOOR ELPD Invariance

standardize_continuous_in_place

Standardize Continuous Columns In Place

standardize_continuous

Standardize Continuous Columns

summarise_hurdle_top3_posthoc

Summarise top-3 Hurdle-NB models across control combos

summarise_placebo_top3_posthoc

Summarise top-3 temporal placebo results

summarise_te_top3_by_type_posthoc

Summarise top-3 transfer entropy results by type

summarise_te_top3_posthoc

Summarise top-3 transfer entropy results (global)

summarise_tvarstar_posthoc

Summarise nonlinear time-series models (TVAR and LSTAR)

summarise_varx_posthoc

Summarise VARX model fit and diagnostics

Provides tools for fitting bivariate hurdle negative binomial models with horseshoe priors, Bayesian Model Averaging (BMA) via stacking, and comprehensive causal inference methods including G-computation, transfer entropy, Threshold Vector Autoregressive (TVAR) and Smooth Transition Autoregressive (STAR) models, Dynamic Bayesian Networks (DBN), Hidden Markov Models (HMM), and sensitivity analysis.

  • Maintainer: José Mauricio Gómez Julián
  • License: MIT + file LICENSE
  • Last published: 2025-12-19