1242 search results for "latex"

R trends in 2015 (based on cranlogs)

January 20, 2016
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It is always fun to look back and reflect on the past year. Inspired by Christoph Safferling's post on top packages from published in 2015, I decided to have my own go at the top R trends of 2015. Contrary to Safferling's post I'll try to also (1) look at packages from previous years that hit the big...

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Heston model for Options pricing with ESGtoolkit

January 20, 2016
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Heston model for Options pricing with ESGtoolkit

Hi everyone! Best wishes for 2016! In this post, I’ll show you how to use ESGtoolkit, for the simulation of  Heston stochastic volatility model for stock prices. This is probably my last post on ESGtoolkit, before I start working on … Continue reading →

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Confidence Regions for Parameters in the Simplex

January 18, 2016
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Confidence Regions for Parameters in the Simplex

Consider here the case where, in some parametric inference problem, parameter  is a point in the Simplex, For instance, consider some regression, on compositional data, > library(compositions) > data(DiagnosticProb) > Y=DiagnosticProb-1 > X=DiagnosticProb > model = glm(Y~ilr(X),family=binomial) > b = ilrInv(coef(model),orig=X) > as.numeric(b) 0.3447106 0.2374977 0.4177917 We can visualize that estimator on the simplex, using > tripoint=function(s){ + p=s/sum(s)...

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ggtern 2.0 now available

January 16, 2016
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ggtern 2.0 now available

Recently ggplot2 received a severe makeover by releasing version 2.0, and in the spirit of improvement, I thought ggtern should also get an overhaul, so after a few-hundred hours of code review, here is what has changed: Theme elements: Previously, the nomenclature scheme for the new theme elements was a bit all over the shop, The post

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R Markdown Tutorial by RStudio and DataCamp

January 14, 2016
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In collaboration with Garrett Grolemund, RStudio's teaching specialist, DataCamp has developed a new interactive course to facilitate reproducible reporting of your R analyses. R Markdown enables you to generate reports straight from your R code, documenting your works as an HTML, pdf or Microsoft document. This course is part of DataCamp's R training path,...

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R Users Will Now Inevitably Become Bayesians

January 12, 2016
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R Users Will Now Inevitably Become Bayesians

There are several reasons why everyone isn’t using Bayesian methods for regression modeling. One reason is that Bayesian modeling requires more thought: you need pesky things like priors, and you can’t assume that if a procedure runs without throwing an … Continue reading →

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Explorable, multi-tabbed reports in R and Shiny

January 2, 2016
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Explorable, multi-tabbed reports in R and Shiny

I take a prior example of multi-tabbed reports and rework it in R and Shiny, with some data visualization improvements.

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Write in-line equations in your Shiny application with MathJax

December 30, 2015
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Write in-line equations in your Shiny application with MathJax

I've been working on a Shiny app and wanted to display some math equations. It's possible to use LaTeX to show math using MathJax, as shown in this example from the makers of Shiny. However, by default, MathJax does not allow in-line equations, because the dollar sign is used so frequently. But I needed to...

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Set up Sublime Text for light-weight all-in-one data science IDE

December 23, 2015
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Set up Sublime Text for light-weight all-in-one data science IDE

tl;dr Sublime Text is a powerful text editor. Here I introduce how to add custom REPL config for remote/local R, Python, Scala, Spark, Hive, you name it (this is only tested for OS X). The example below interprets local Python (top), R (middle) and Hive (bottom) code on remote. IDE for everything Good IDEs are everywhere. RStudio for R, Pycharm for...

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Making Sense of Logarithmic Loss

December 14, 2015
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Making Sense of Logarithmic Loss

Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in kaggle competitions. Since success in these competitions hinges on effectively minimising the Log Loss, it makes sense to have some understanding of how this metric is calculated and how it should be interpreted. Log Loss quantifies the The post

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