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    <meta name="author" content="Keith O'Hara">

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        <h3 style="text-align: center;"><strong style="font-size: 130%;">Bayesian Macroeconometrics in R</strong></h3>
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<p>BMR (<b><i>B</i></b>ayesian <b><i>M</i></b>acroeconometrics in <b><i>R</i></b>) is an R interface to <a href="bmlib.html" target="_blank">BM++</a>, a templated C++ library for estimating Bayesian Vector Autoregression (BVAR) and Dynamic Stochastic General Equilibrium (DSGE) models.</p>

<hr>

<p><strong>Features</strong></p>
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        <li>Several popular prior specifications:</li>
        <ul>
            <li>normal-inverse-Wishart prior,</li>
            <li>Minnesota prior, and</li>
            <li>Mattias Villani's steady-state prior.</li>
        </ul>
        <li>BMR can estimate BVARs with time-varying parameters, as well as classical (non-Bayesian) VARs.</li>
        </ul>
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        &nbsp;
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        <ul>
        <li>For DSGE models, the library can
            <ul>
                <li>solve models using Harald Uhlig's method of undetermined coefficients and Chris Sims' canonical decomposition;</li>
                <li>estimate models using MCMC with a choice of filtering methods: the Kalman filter or Chandrasekhar recursions; and</li>
                <li>estimate a hybrid DSGE-VAR model.</li>
            </ul>
        </ul>
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The R interface:
<ul>
    <li>Provides seamless and efficient OO-based interaction using Rcpp modules.</li>
    <li>Requires no knowledge of C++ by the user.</li>
</ul>

<p><strong>Author</strong>: Keith O'Hara</p>

<p><a href="https://raw.githubusercontent.com/kthohr/BMR/master/LICENSE" target="_blank"><img src="https://img.shields.io/badge/Licence-GPL%20v2-blue.svg" alt="License"></a></p>

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<h3 style="text-align: left;"><strong style="font-size: 95%;">Download and Installation</strong></h3>

<br>

<p><strong>Package requirements</strong>: R (v3.40, or above) and Rcpp, RcppArmadillo, ggplot2, and grid (this comes pre-packaged with R). </p>

<ul>
    <li>Execute the following commands, post installation of R itself, and you should be good to go:</p>
<pre class="brush: ruby;">
install.packages("RcppArmadillo")
install.packages("ggplot2")</pre>
</ul>

<p>The source code is available on <a href="https://github.com/kthohr/BMR" target="_blank">GitHub</a>. <strong>You need to install BMR from source.</strong></p>
<ul>
    <li>Perhaps the quickest way to install from source is via the 'devtools' package in R.</li>
    <li>First, make sure you have an appropriate development environment; 
    <ul>
        <li>if you're using a Mac, get Xcode from the App Store and then check <a href="https://cran.r-project.org/bin/macosx/tools/" target="_blank">here</a>;</li>
        <li>if you're using Windows, get <a href="https://cran.r-project.org/bin/windows/Rtools/" target="_blank">Rtools</a>.</li>
    </ul>
    <li>Then simply run the following commands in R:</li>
<pre class="brush: ruby;">
install.packages("devtools")
library(devtools)
install_github("kthohr/BMR")</pre>
        <!-- <li><strong>Note:</strong> <strike>Due to Rlapack being a lobotomized distribution of LAPACK, the 'gensys' solver is not available on Windows-based machines. If you happen to have a full installation of LAPACK, then install from source by first modifying 'dsgec.cpp' appropriately, rather than using the binary file. This is not an issue for Unix-based systems (e.g., Mac and Linux distros). We're currently working on a bigger <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16482" target="_blank">Rlapack patch</a> to fix issues like this.</strike> This has been fixed in R v3.3.0.</li> -->
    </ul>
</ul>

<!-- <h3 style="text-align: left;" id="Doc"><strong style="font-size: 110%;">Documentation</strong></h3> -->

<!-- <ul> -->
 <!-- <li>Accompanying vignette: <a href="bmr/BMR.pdf" target="_blank">link</a>.</li> -->
 <!-- <li>PDF of BMR help files: <a href="bmr/BMR-manual.pdf" target="_blank">link</a>. The help files can also be accessed in the R environment using '?chosen_function'; for example, ?BVARM.</li> -->
<!-- </ul> -->

<!-- <p> The vignette (linked above) is intended to be a comprehensive guide to the routines (and what goes on under the hood, so to speak) and should be consulted first before submitting any questions to me about the package. I am more than happy to receive bug reports (and comments in general) about BMR, use the contact tab above to do so, but I am not willing to provide technical support for the package. (Technical support in the sense of 'how do I get my model into the required format', or 'what prior should I use'.)</p> -->

<p> After installing BMR, the user can check if everything is working as intended by typing:</p>
<pre class="brush: ruby;">
library(BMR)
data(BMRVARData)</pre>
<p> This should load BMR and place a dataframe called 'USMacroData' into the workspace.</p>

<hr>

<h3 style="text-align: left;" id="RepFiles"><strong style="font-size: 110%;">Replication Files</strong></h3>

<p> Replication files can be found in the <code>tests</code> folder.</p>

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