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<html>
<head>
<meta http-equiv="Content-Language" content="en-us">
<meta http-equiv="Content-Type" content="text/html; charset=windows-1252">
<title>Markdowns</title>
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-64480261-1', 'auto');
ga('send', 'pageview');
</script>
</head>
<body>
<h1>R Markdowns</h1>
<p><i>To access markdown code, replace any </i>.html<i> by </i>.Rmd</p>
<table border="0" width="82%">
<tr>
<td width="350" valign="top">
<h3>R Language</h3>
<ul>
<li><a href="commonFunctions.html">Common Functions</a></li>
<li><a href="basisRData.html">Data Types and Data Manipulation</a></li>
<li><a href="RClasses.html">Environments & Classes</a></li>
<li><a href="expressions.html">Expressions</a></li>
<li><a href="functionalProgramming.html">Functional Programming</a></li>
<li><a href="stringsStuff.html">Manipulating strings</a></li>
<li><a href="memoization.html">Memoization</a></li>
<li>Using with other languages:
<ul>
<li><a href="langs/cpp.html">C++</a>,
<a href="langs/python.html">Python</a>,
<a href="langs/javascript.html">JavaScript</a>,
<a href="langs/matlab.html">Matlab</a>
</li>
</ul>
</li>
</ul>
<h3><b>R Libraries</b></h3>
<ul>
<li><a href="animation/index.html">Animations</a></li>
<li><a href="benchmarking/benchmarking.html">Benchmarking</a> &
<a href="benchmarking/profiling.html">Profiling</a></li>
<li><a href="odes/index.html">Differential Equations Solving</a></li>
<li><a href="GraphicalTools/graphicalTools.html">Graphical Tools</a>
<ul>
<li><a href="GraphicalTools/colorPalette.html">Color Palettes</a></li>
<li><a href="GraphicalTools/diagrammeR.html">DiagrammeR</a></li>
<li><a href="GraphicalTools/EBImage.html">EBImage Processing</a></li>
<li><a href="GraphicalTools/ggplot2.html">ggplot2</a></li>
<li>
<a href="maps/index.html">Maps I</a>,
<a href="maps/maps2/maps.html">Maps II</a></li>
</ul></li>
<li>Manipulating tables: <a href="dplyr/dplyr.html">dplyr</a></li>
<li><a href="neuralnets/neuralnets.html">Neural Networks</a></li>
<li>Pipelining functions: <a href="magrittr.html">magrittr</a></li>
<li><a href="relationalAlgebra.html">Relational Algebra (SQL)</a></li>
<li><a href="sql/index.html">SQL tutorial using postgreSQL</a></li>
<li><a href="symbolic/index.html">Symbolic Computation</a></li>
<li><a href="web/index.html">Web Scraping</a></li>
</ul>
</td>
<td width="350" valign="top">
<h3>Math</h3>
<ul>
<li><a href="algebra/algebra.html">Algebra</a></li>
<li><a href="combinatorics/combinatorics.html">Combinatorics</a></li>
<li><a href="distributions/index.html">Distributions</a><ul>
<li><a href="distributions/distr.html">Creating</a></li>
<li><a href="distributions/fitting.html">Fitting</a></li>
</ul>
</li>
<li><a href="fourier/fourier.html">Fourier Analysis</a> (some
<a href="fourier/fourier2.html">applications</a>)</li>
<li><a href="graphs/graphs.html">Graphs</a></li>
<li><a href="fat_tails/heavy_tails.html">Heavy Tails</a></li>
<ul>
<li><a href="fat_tails/extremistan.html">Extremistan</a></li>
</ul>
<li><a href="inf_theory/informationtheory.html">Information Theory</a><ul>
<li><a href="inf_theory/empirical-entropy.html">Empirical Entropy</a></li>
</ul>
</li>
<li><a href="maxent/maxent.html">Maximum Entropy</a></li>
<li><a href="noise/noise.html">Noise</a></li>
<li><a href="optimization/optimization.html">Optimization</a>
<ul>
<li><a href="optimization/CVXR.html">CVXR</a></li>
</ul>
</li>
<li><a href="powerlaw/powerlaw.html">Power Laws</a></li>
<li>Probability
<ul>
<li><a href="prob/puzzles.html"><font size="2">Puzzles</font></a><font size="2">,
<a href="prob/range.html">Range of Uniform rv's</a>,
<a href="prob/mle_estimation.html">An estimation eg</a>.
<a href="prob/eg_videos.html">Bayes Th. & advertising (eg)</a>
</font></li>
</ul>
</li>
<li><a href="svd/svd.html">Singular Value Decomposition</a></li>
</ul>
</td>
<td width="379" valign="top">
<h3>Machine Learning/Statistics</h3>
<ul>
<li><a href="abc/index.html">Approximate Bayesian Computing</a></li>
<li><a href="decision/index.html">Bayesian Decision Theory</a></li>
<li><a href="bayesnets/bayesnets.html">Bayesian Networks</a></li>
<li><a href="biasvariance/biasvariance.html">Bias-Variance Analysis</a></li>
<li><a href="bugs/bugs_tutorial.html">BUGS tutorial</a>
<ul>
<li>
<font size="2">
<a href="bugs/lincoln.html">Lincoln Index</a>,
<a href="bugs/hyraxes.html">Tagging Hyraxes</a>,
<a href="bugs/changepoint.html">Ln.Reg.Change Point</a>,
<a href="bugs/lighthouse.html">Lighthouse</a>
</font>
</li>
</ul>
<li><a href="censoring/censoring.html">Censored & Truncated Data</a> (with BUGS/Stan)</li>
<li><a href="circular/index.html">Circular Statistics</a> (with BUGS/Stan)</li>
<li><a href="tree/tree.html">Classification & Regression Trees</a></li>
<li><a href="clustering/clustering.html">Clustering</a></li>
<li><a href="copula/index.html">Copulas</a></li>
<li><a href="dp/dp.html">Dirichlet Processes</a></li>
<li><a href="EM/EM.html">Expectation-Maximization</a></li>
<li><a href="factoranalysis/factoranalysis.html">Factor Analysis</a></li>
<li><a href="ica/index.html">Independent Component Analysis</a></li>
<li><a href="PRML/chapter4.4.html">Laplace Approximation</a></li>
<li><a href="discriminant_analysis/discriminant_analysis.html">Linear &
Quadratic Discriminant Analysis</a></li>
<li><a href="markov/markov.html">Markov Model & HMM</a></li>
<li><a href="missing/index.html">Missing Data</a></li>
<li><a href="EM/GaussianMix.html">Mixture of Gaussians</a></li>
<li><a href="naive_bayes/naivebayes.html">Naïve Bayes</a></li>
<li><a href="pca/pca.html">Principal Component Analysis</a></li>
<li><a href="rbf/rbf.html">Radial Basis Functions</a></li>
<li><a href="regression/regression.html">Regression</a></li>
<li><a href="bootstrap/resamplings.html">Resampling & Bootstrap</a>
<ul>
<li><a href="bootstrap/stat_resampling.html">Resampling</a></li>
<li><a href="bootstrap/buckets.html">The Two Buckets Model</a></li>
</ul>
<li><a href="spectralclustering/spectralclustering.html">Spectral Clustering</a></li>
<li><a href="ECS/index.html">Statistical Computation & Simulation</a></li>
<li><a href="svm/svm.html">Support Vector Machines</a></li>
<li><a href="ts/index.html">Time Series</a></li>
<li><a href="variational_inference/variational.html">Variational Inference</a>
<li>Bishop's <i>Pattern Recognition and ML</i>
<ul><li>Chapters:
<a href="PRML/chapter3.html">3</a>,
<a href="PRML/chapter4.html">4</a>
<a href="PRML/chapter4.4.html">4.4</a>,
<a href="PRML/chapter6.html">6</a>,
<a href="PRML/chapter7.html">7</a>,
<a href="PRML/chapter8.html">8</a>,
<a href="PRML/chapter9.html">9</a>,
<a href="variational_inference/variational.html">10.1</a>,
<a href="PRML/chapter11.html">11</a>, <a href="PRML/chapter14.html">14</a></li></ul>
</li>
</td>
</tr>
</table>
<h3>Links</h3>
<ul>
<li>
<a href="https://www.youtube.com/playlist?list=PL1i2mdKutNaICujRMqCi1mPkHuMG_Ksa3">
Bayesian Tutorials & Courses</a> @ Youtube</li>
<li><a href="http://stats.stackexchange.com/questions/tagged/bayesian">Bayes</a>
QA
@ Cross Validated</li>
<li>Blogs: <a href="https://www.countbayesie.com/">Count Bayesie</a>;
<a href="http://sumsar.net/">Publishable Stuff</a>;
<a href="https://www.allendowney.com/blog/">Probably Overthinking It</a>;
<br>
<a href="https://maximum-entropy-blog.blogspot.com/">Maximum Entropy</a>;
<a href="https://plausibilitytheory.wordpress.com/">Plausibility Theory</a>;
<a href="https://jakevdp.github.io/tag/bayesianism.html">Pythonic Perambulations</a>.
</li>
<li>Related Courses:<ul>
<li>
<a href="http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/">Linear Algebra</a> (by Gilbert Strang @ MIT);
</li>
<li>Calculus (<a href="http://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/">Single
Variable</a> &
<a href="http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm">Multi-Variable</a> @ MIT);
</li>
<li>
<a href="https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1">Probability</a> (by John Tsitsiklis @ edX);
</li>
<li><a href="https://www.youtube.com/playlist?list=PLD63A284B7615313A">Machine Learning</a> (by Abu Mostafa @
Caltech); </li>
<li><a href="https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA">Machine Learning</a> (by Mathematical Monk);
</li>
<li>
<a href="https://online.stanford.edu/courses/soe-yeecvx101-convex-optimization">Convex Optimization</a> (by Stephen Boyd @ Stanford);
</li>
<li><a href="https://www.youtube.com/user/mathematicalmonk">Information Theory</a>
(by Mathematical Monk);
</li>
<li>
<a href="https://www.edx.org/course/combinatorial-mathematics-zu-he-shu-xue-tsinghuax-60240013x-1">Combinatorics</a> (by Yuchun Ma @
<span class="instructor-org">Tsinghua
University</span>)</li>
</ul></li>
<li>
<a href="https://www.amazon.co.uk/hz/wishlist/ls/37ZW3X00X7941?&sort=default">Related Books</a> @ Amazon</li>
<li>Bayesian Statistics Course, FCUL 12/13: <a href="EB-exs/solutions.html">Solutions</a>,
<a href="EB-exs/bugs_exs.html">BUGS</a> (in Portuguese)</li>
</ul>
<table border="0" width="45%"><tr><td><i>
Disclaimer: This webpage started as a private collection of tutorials
and notes until the web-crawlers made it public. Some code is mine, some
is from other people. I tried to insert all relevant references but if I
forgot someone or something, please let me know and I'll correct it.
Also, reporting bugs, typos or errors will be appreciated (jpneto AT
fc.ul.pt).
</i></td></tr></table>
</body>
</html>