<?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>j-magnolia.r-universe.dev</title><link>https://j-magnolia.r-universe.dev</link><description>Recent package updates in j-magnolia</description><generator>R-universe</generator><image><url>https://github.com/j-magnolia.png</url><title>R packages by j-magnolia</title><link>https://j-magnolia.r-universe.dev</link></image><lastBuildDate>Mon, 01 Dec 2025 22:31:08 GMT</lastBuildDate><item><title>[j-magnolia] datafsm 0.2.5</title><author>jonathan.gilligan@vanderbilt.edu (Jonathan M. Gilligan)</author><description>Automatic generation of finite state machine models of
dynamic decision-making that both have strong predictive power
and are interpretable in human terms. We use an efficient model
representation and a genetic algorithm-based estimation process
to generate simple deterministic approximations that explain
most of the structure of complex stochastic processes. We have
applied the software to empirical data, and demonstrated it's
ability to recover known data-generating processes by
simulating data with agent-based models and correctly deriving
the underlying decision models for multiple agent models and
degrees of stochasticity.</description><link>https://github.com/r-universe/j-magnolia/actions/runs/26562847301</link><pubDate>Mon, 01 Dec 2025 22:31:08 GMT</pubDate><r:package>datafsm</r:package><r:version>0.2.5</r:version><r:status>success</r:status><r:repository>https://j-magnolia.r-universe.dev</r:repository><r:upstream>https://github.com/j-magnolia/datafsm</r:upstream><r:article><r:source>FRD_vignette.Rmd</r:source><r:filename>FRD_vignette.html</r:filename><r:title>Example with real data</r:title><r:created>2018-08-05 00:55:35</r:created><r:modified>2021-05-25 19:26:19</r:modified></r:article><r:article><r:source>datafsm_introduction.Rmd</r:source><r:filename>datafsm_introduction.html</r:filename><r:title>Introduction to datafsm</r:title><r:created>2018-08-05 00:55:35</r:created><r:modified>2021-05-25 23:21:03</r:modified></r:article></item><item><title>[j-magnolia] kayadata 1.4.0</title><author>jonathan.gilligan@vanderbilt.edu (Jonathan Gilligan)</author><description>Provides data for Kaya identity variables (population,
gross domestic product, primary energy consumption, and
energy-related CO2 emissions) for the world and for individual
nations, and utility functions for looking up data, plotting
trends of Kaya variables, and plotting the fuel mix for a given
country or region. The Kaya identity (Yoichi Kaya and Keiichi
Yokobori, &quot;Environment, Energy, and Economy: Strategies for
Sustainability&quot; (United Nations University Press, 1998) and
&lt;https://en.wikipedia.org/wiki/Kaya_identity&gt;) expresses a
nation's or region's greenhouse gas emissions in terms of its
population, per-capita Gross Domestic Product, the energy
intensity of its economy, and the carbon-intensity of its
energy supply.</description><link>https://github.com/r-universe/j-magnolia/actions/runs/26562850973</link><pubDate>Fri, 12 Jul 2024 21:03:17 GMT</pubDate><r:package>kayadata</r:package><r:version>1.4.0</r:version><r:status>success</r:status><r:repository>https://j-magnolia.r-universe.dev</r:repository><r:upstream>https://github.com/j-magnolia/kayadata</r:upstream><r:article><r:source>policy_analysis.Rmd</r:source><r:filename>policy_analysis.html</r:filename><r:title>Example: Analysis of Emissions-Reduction Policy</r:title><r:created>2018-10-19 20:38:22</r:created><r:modified>2022-04-14 21:22:15</r:modified></r:article><r:article><r:source>kayadata.Rmd</r:source><r:filename>kayadata.html</r:filename><r:title>Getting Started with the kayadata Package</r:title><r:created>2020-06-17 17:06:50</r:created><r:modified>2022-04-14 21:22:15</r:modified></r:article></item></channel></rss>