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gdpar - General Dynamic Parameter Models via Reference Anchoring

Implements a unified predictive framework in which individual parameters are decomposed as theta_i equal to theta_ref plus Delta(x_i, theta_ref), with theta_ref a population reference and Delta an explicit deviation function. The decomposition follows the Additive-Multiplicative-Modulated canonical form and is estimated through three complementary paths: hierarchical Bayesian inference via 'Stan', varying-coefficient models via penalized splines, and amortized inference via hypernetworks in 'torch'. The package provides identifiability diagnostics, validity tests for the population reference, and benchmarks against canonical zero-inflated count datasets and avian abundance data from the eBird Status and Trends project. The framework and its estimation paths are described in Gomez Julian (2026) <doi:10.5281/zenodo.21046269>.

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bayesian-statisticsstan

6.00 score 20 scripts 317 downloads

RegimeChange - Comprehensive Regime Change Detection in Time Series

A unified framework for detecting regime changes (changepoints) in time series data. Implements both frequentist methods including Cumulative Sum (CUSUM, Page (1954) <doi:10.1093/biomet/41.1-2.100>), Pruned Exact Linear Time (PELT, Killick, Fearnhead, and Eckley (2012) <doi:10.1080/01621459.2012.737745>), Binary Segmentation, and Wild Binary Segmentation, as well as Bayesian methods such as Bayesian Online Changepoint Detection (BOCPD, Adams and MacKay (2007) <doi:10.48550/arXiv.0710.3742> and Shiryaev-Roberts. Supports offline analysis for retrospective detection and online monitoring for real-time surveillance. Provides rigorous uncertainty quantification through confidence intervals and posterior distributions. Handles univariate and multivariate series with detection of changes in mean, variance, trend, and distributional properties.

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changepoint-detectiontime-series

5.26 score 1 stars 2 dependents 5 scripts 359 downloads

EconCausal - Causal Analysis for Macroeconomic Time Series (ECM-MARS, BSTS, Bayesian GLM-AR(1))

Implements three complementary pipelines for causal analysis on macroeconomic time series: (1) Error-Correction Models with Multivariate Adaptive Regression Splines (ECM-MARS), (2) Bayesian Structural Time Series (BSTS), and (3) Bayesian GLM with AR(1) errors validated with Leave-Future-Out (LFO). Heavy backends (Stan) are optional and never used in examples or tests.

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causal-inferenceeconometrics

5.08 score 1 stars 223 downloads

convergenceDFM - Convergence and Dynamic Factor Models

Tests convergence in macro-financial panels combining Dynamic Factor Models (DFM) and mean-reverting, discrete-time Ornstein-Uhlenbeck/AR(1) factor processes. Provides: (i) static factor extraction with VAR stability checks, Portmanteau tests and rolling out-of-sample R^2, in the spirit of Stock and Watson (2002) <doi:10.1198/073500102317351921> and the Generalized Dynamic Factor Model of Forni, Hallin, Lippi and Reichlin (2000) <doi:10.1162/003465300559037>; (ii) cointegration analysis a la Johansen (1988) <doi:10.1016/0165-1889(88)90041-3>; (iii) Bayesian factor-OU/AR(1) estimation with convergence and half-life summaries grounded in Uhlenbeck and Ornstein (1930) <doi:10.1103/PhysRev.36.823> and Vasicek (1977) <doi:10.1016/0304-405X(77)90016-2>, with full Markov chain Monte Carlo convergence diagnostics; (iv) heteroskedasticity-consistent (HC) and, when the suggested 'sandwich' (Zeileis (2004) <doi:10.18637/jss.v011.i10>) and 'lmtest' packages are available, heteroskedasticity- and autocorrelation- consistent (HAC) robust inference, with a self-contained HC fallback; (v) coupling significance tests based on time-shift / block-bootstrap nulls that preserve marginal dynamics while breaking cross-series dependence; and (vi) optional PLS-based factor preselection (Mevik and Wehrens (2007) <doi:10.18637/jss.v018.i02>). Functions emphasize reproducibility (explicit seeds throughout) and clear, publication-ready summaries.

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dynamic-factor-modeleconometrics

4.70 score 1 stars 4 scripts 374 downloads

EmpiricalDynamics - Empirical Discovery of Differential Equations from Time Series Data

A comprehensive toolkit for discovering differential and difference equations from empirical time series data using symbolic regression. The package implements a complete workflow from data preprocessing (including Total Variation Regularized differentiation for noisy economic data), visual exploration of dynamical structure, and symbolic equation discovery via genetic algorithms. It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide industrial-grade robustness, physics-informed constraints, and rigorous out-of-sample validation. Designed for economists, physicists, and researchers studying dynamical systems from observational data.

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nonlinear-dynamicstime-series

4.62 score 12 scripts 335 downloads

bayesianOU - Bayesian Nonlinear Ornstein-Uhlenbeck Models with Stochastic Volatility

Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift, stochastic volatility, and Student-t innovations. The package implements hierarchical priors for sector-specific parameters and supports parallel MCMC sampling via 'Stan'. Model comparison is performed using Pareto Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation following Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. Prior specifications follow recommendations from Gelman (2006) <doi:10.1214/06-BA117A> for scale parameters.

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bayesian-statisticsstochastic-processes

4.40 score 8 scripts 395 downloads

HTDV - Hypothesis Testing for Dependent Variables with Unbalanced Data

Implements hierarchical Bayesian inference, robust frequentist inference, and distribution-free inference for dependent and unbalanced data under strong-mixing conditions. Supports triangular-array, weighted-sum and mixingale convergence regimes with Whittle and composite likelihoods, heteroskedasticity-and-autocorrelation-consistent variance estimation, block bootstrap with automatic block length, fixed-bandwidth HAR inference, adaptive conformal prediction, Bayesian decision under Region of Practical Equivalence, bridge-sampling Bayes factors, and predictive comparison via the Widely Applicable Information Criterion and leave-future-out cross-validation. Methods follow Andrews (1991) <doi:10.2307/2938229>, Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, Patton, Politis and White (2009) <doi:10.1080/07474930802459016>, Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>, Kruschke (2018) <doi:10.1177/2515245918771304>, and Gibbs and Candes (2021) <doi:10.48550/arXiv.2106.00170>.

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bayesian-statisticshypothesis-testing

4.30 score 7 scripts 310 downloads

BayesianDisaggregation - Evidence-Based Bayesian Disaggregation of Aggregate Indices

Disaggregates an observed aggregate price index into sectoral components with a Bayesian state-space model in which the aggregate enters as a genuine observation density rather than as a renormalization identity. A random-walk-with-drift transition in log space (with partial pooling on the drift and the innovation scale) and an estimable cross-sectional concentration produce posterior draws of the sectoral indices with credible intervals, suitable as multiple-imputation input for downstream dynamic models. The Hamiltonian Monte Carlo engine follows Stan (Carpenter et al., 2017) <doi:10.18637/jss.v076.i01>; model comparison uses Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (Vehtari, Gelman and Gabry, 2017) <doi:10.1007/s11222-016-9696-4>. A closed-form linear-Gaussian Kalman/RTS smoother provides an exact, MCMC-free Bayesian alternative for the same aggregate evidence.

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bayesian-statisticseconometrics

4.08 score 1 stars 1 dependents 2 scripts 385 downloads

valueprhr - Value-Price Analysis with Bayesian and Panel Data Methods

Provides tools for analyzing the relationship between direct prices (based on labor values) and prices of production using Bayesian generalized linear models, panel data methods, partial least squares regression, canonical correlation analysis, and panel vector autoregression. Includes functions for model comparison, out-of-sample validation, and structural break detection. Here, methods use raw accounting data with explicit temporal structure, following Gomez Julian (2023) <doi:10.17605/OSF.IO/7J8KF> and standard econometric techniques for panel data analysis.

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econometricspolitical-economy

4.00 score 5 scripts 303 downloads

SignalY - Signal Extraction from Panel Data via Bayesian Sparse Regression and Spectral Decomposition

Provides a comprehensive toolkit for extracting latent signals from panel data through multivariate time series analysis. Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) <doi:10.1017/CBO9780511841040>, empirical mode decomposition for non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology.

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signal-processingtime-series

3.60 score 3 scripts 245 downloads

topologyR - Topological Connectivity Analysis for Numeric Data

Builds topologies on the vertex set of a graph from the closed neighbourhoods of its vertices, a closed-neighbourhood variant of the construction of Nada, El Atik and Atef (2018) <doi:10.1002/mma.4726>, and computes their connected components exactly through the specialization preorder, without enumerating the topology. For time series it builds the horizontal and natural visibility graphs (Luque et al., 2009, <doi:10.1103/PhysRevE.80.046103>; Lacasa et al., 2008, <doi:10.1073/pnas.0709247105>) at equally or unequally spaced instants, deciding the natural criterion exactly under IEEE 754 double arithmetic, and from their directed versions the forward and backward topologies, a bitopological space in the sense of Kelly (1963) <doi:10.1112/plms/s3-13.1.71>, together with the Alexandrov topology of reachability. Reading a series backwards, with its instants reflected, exchanges the two topologies, so the difference of their numbers of components changes sign.

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persistent-homologytopological-data-analysiscpp

2.60 score 8 scripts 217 downloads

bivarhr - Bivariate Hurdle Regression with Bayesian Model Averaging

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.

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bayesian-networkshidden-markov-model

2.48 score 2 scripts 414 downloads