<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>isadorenabi.r-universe.dev</title><link>https://isadorenabi.r-universe.dev</link><description>Recent package updates in isadorenabi</description><generator>R-universe</generator><image><url>https://github.com/isadorenabi.png</url><title>R packages by isadorenabi</title><link>https://isadorenabi.r-universe.dev</link></image><lastBuildDate>Sun, 05 Apr 2026 00:10:46 GMT</lastBuildDate><item><title>[isadorenabi] topologyR 0.2.0</title><author>isadore.nabi@pm.me (José Mauricio Gómez Julián)</author><description>Topological data analysis methods based on graph-theoretic
approaches for discovering topological structures in data.
Constructs topological spaces from graphs following Nada et al.
(2018) &lt;doi:10.1002/mma.4726&gt;, with visibility graph
construction for time series following Lacasa et al. (2008)
&lt;doi:10.1073/pnas.0709247105&gt;. Supports directed visibility
graphs for bitopological analysis of temporal irreversibility
(Kelly, 1963), and Alexandrov topology construction from
reachability preorders.</description><link>https://github.com/r-universe/isadorenabi/actions/runs/26657741879</link><pubDate>Sun, 05 Apr 2026 00:10:46 GMT</pubDate><r:package>topologyR</r:package><r:version>0.2.0</r:version><r:status>success</r:status><r:repository>https://isadorenabi.r-universe.dev</r:repository><r:upstream>https://github.com/IsadoreNabi/topologyR</r:upstream></item><item><title>[isadorenabi] EconCausal 1.0.2</title><author>isadore.nabi@pm.me (José Mauricio Gómez Julián)</author><description>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.</description><link>https://github.com/r-universe/isadorenabi/actions/runs/26811817567</link><pubDate>Fri, 24 Oct 2025 03:14:37 GMT</pubDate><r:package>EconCausal</r:package><r:version>1.0.2</r:version><r:status>success</r:status><r:repository>https://isadorenabi.r-universe.dev</r:repository><r:upstream>https://github.com/IsadoreNabi/EconCausal</r:upstream><r:article><r:source>bsts-esp.Rmd</r:source><r:filename>bsts-esp.html</r:filename><r:title>Detalles Metodológicos de Modelos de Estado-Espacio Bayesianos con Selección de Variables</r:title><r:created>2025-09-20 00:33:36</r:created><r:modified>2025-10-24 03:14:37</r:modified></r:article><r:article><r:source>ecm-mars-esp.Rmd</r:source><r:filename>ecm-mars-esp.html</r:filename><r:title>Detalles Metodológicos del Modelo de Corrección de Errores con MARS</r:title><r:created>2025-09-20 00:33:36</r:created><r:modified>2025-10-24 03:14:37</r:modified></r:article><r:article><r:source>bglmar-esp.Rmd</r:source><r:filename>bglmar-esp.html</r:filename><r:title>Detalles Metodológicos del Modelo GLM Bayesiano con Estructura AR(1)</r:title><r:created>2025-09-20 00:33:36</r:created><r:modified>2025-10-24 03:14:37</r:modified></r:article><r:article><r:source>bsts-eng.Rmd</r:source><r:filename>bsts-eng.html</r:filename><r:title>Methodological Details of Bayesian State-Space Models with Variable Selection</r:title><r:created>2025-09-20 00:33:36</r:created><r:modified>2025-10-24 03:14:37</r:modified></r:article><r:article><r:source>bglmar1-eng.Rmd</r:source><r:filename>bglmar1-eng.html</r:filename><r:title>Methodological Details of the Bayesian GLM Model with AR(1) Structure</r:title><r:created>2025-09-20 00:33:36</r:created><r:modified>2025-10-24 03:14:37</r:modified></r:article><r:article><r:source>ecm-mars-eng.Rmd</r:source><r:filename>ecm-mars-eng.html</r:filename><r:title>Methodological Details of the Error Correction Model with MARS</r:title><r:created>2025-09-20 00:33:36</r:created><r:modified>2025-10-24 03:14:37</r:modified></r:article></item><item><title>[isadorenabi] BayesianDisaggregation 0.1.2</title><author>isadore.nabi@pm.me (José Mauricio Gómez Julián)</author><description>Implements a novel Bayesian disaggregation framework that
combines Principal Component Analysis (PCA) and Singular Value
Decomposition (SVD) dimension reduction of prior weight
matrices with deterministic Bayesian updating rules. The method
provides Markov Chain Monte Carlo (MCMC) free posterior
estimation with built-in diagnostic metrics. While based on
established PCA (Jolliffe, 2002) &lt;doi:10.1007/b98835&gt; and
Bayesian principles (Gelman et al., 2013) &lt;doi:10.1201/b16018&gt;,
the specific integration for economic disaggregation represents
an original methodological contribution.</description><link>https://github.com/r-universe/isadorenabi/actions/runs/26271968634</link><pubDate>Fri, 24 Oct 2025 03:10:57 GMT</pubDate><r:package>BayesianDisaggregation</r:package><r:version>0.1.2</r:version><r:status>success</r:status><r:repository>https://isadorenabi.r-universe.dev</r:repository><r:upstream>https://github.com/IsadoreNabi/BayesianDisaggregation</r:upstream><r:article><r:source>MANUALUSUARIO-ESP.Rmd</r:source><r:filename>MANUALUSUARIO-ESP.html</r:filename><r:title>Marco de Análisis de Sensibilidad para la Desagregación Económica Bayesiana</r:title><r:created>2025-10-24 03:10:57</r:created><r:modified>2025-10-24 03:10:57</r:modified></r:article><r:article><r:source>USERMANUAL-ENG.Rmd</r:source><r:filename>USERMANUAL-ENG.html</r:filename><r:title>Sensitivity Analysis Framework for Bayesian Economic Disaggregation</r:title><r:created>2025-10-24 03:10:57</r:created><r:modified>2025-10-24 03:10:57</r:modified></r:article></item></channel></rss>