Implied risk premia

May 14, 2020 | R on OSM

In our last post, we applied machine learning to the Capital Aset Pricing Model (CAPM) to try to predict future returns for the S&P 500. This analysis was part of our overall project to analyze the various methods to set return expectations when seeking to build a satisfactory portfolio. Others ...
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R doesn’t need to throttle AWS Athena anymore

May 14, 2020 | Dyfan Jones Brain Dump HQ

RBloggers|RBloggers-feedburner I am happy to announce that RAthena-1.9.0 and noctua-1.7.0 have been released onto the cran. They both bring two key features: More stability when working with AWS Athena, focusing on AWS Rate Exceeded throttling errors New helper function to convert AWS S3 backend files to save cost NOTE: ...
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glmnet v4.0: generalizing the family parameter

May 14, 2020 | kjytay

I’ve had the privilege of working with Trevor Hastie on an extension of the glmnet package which has just been released. In essence, the glmnet() function’s family parameter can now be any object of class family. This enables the user … Continue reading →
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Overview of the yuima and yuimaGUI R packages

May 14, 2020 | R Tutorials

The YUIMA Project is an open source academic project aimed at developing a complete environment for estimation and simulation of Stochastic Differential Equations and other Stochastic Processes via the R package called yuima and its Graphical User Interface yuimaGUI. Quickstart # install the package install.packages('yuima') # load the package require(...
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Overview of the yuima and yuimaGUI R packages

May 14, 2020 | R Tutorials

The YUIMA Project is an open source academic project aimed at developing a complete environment for estimation and simulation of Stochastic Differential Equations and other Stochastic Processes via the R package called yuima and its Graphical User Interface yuimaGUI. Quickstart # install the package install.packages('yuima') # load the package require(...
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Data Privacy in the Age of COVID-19

May 14, 2020 | Ryan Sheehy

Hugo Bowne-Anderson, the host of DataFramed, the DataCamp podcast, recently interviewed Katharine Jarmul, Head of Product at Cape Privacy. Introducing Katharine Jarmul Hugo Bowne Anderson: Hey Katharine. Katharine Jarmul: Hi Hugo. Hugo Bowne Anderson: How are you? Katharine Jarmul: Good. How are you? Hugo Bowne Anderson: Pretty good. So I'm ...
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Using {drake} for Machine Learning

May 14, 2020 | Edwin Thoen

A few weeks ago, Miles McBain toke us for a tour through his project organisation in this blogpost. Not surprisingly given Miles’ frequent shoutouts about the package, it is completely centered around drake. About a year ago on twitter, he convinced me to take this package for a spin. I ... [Read more...]

RStudio Team Admin Training – Remotely

May 14, 2020 | eoda GmbH

We train you in RStudio Server Pro, RStudio Connect and RStudio Package Manager – remotely  RStudio Team is a bundle of professional software for statistical data analysis, package management and data product exchange. As a certified RStudio partner, we will train you in the proper use of RStudio Team – in German. ...
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Checking your R package on Solaris

May 13, 2020 | Posts on R-hub blog

TL;DR To check your package on Solaris, call rhub::check() as usual and choose one of our Solaris builders. Bookmark this page, in case you get an email from CRAN about your package failing on Solaris. Oracle Solaris Oracle Solaris is a non-free Unix operating system. CRAN regularly tests ... [Read more...]

Looking Normal(ly Distributed)

May 12, 2020 | R on Data & The World

Among all probability distributions, the normal distribution is probably the most well-established and well-characterized. The importance of things like the central limit theorem and the normality assumptions in linear regression highlight it well. One of the more interesting ones is the fact that you can approximate a binomial distribution with ...
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deep learning model for titanic data

May 12, 2020 | Modeling with R

Introducction Data preparation Partition the data & impute the missing values. Convert the data into a numeric matrix. Train the model. Create the model Compile the model Fit the model The model evaluation model tuning Conclusion Introducction Deep learning model belongs to the area of machine learning models which can be ... [Read more...]

Bayesian hyperparameters optimization

May 12, 2020 | Modeling with R

Introduction Bayesian optimization Acquisition functions Data preparation Random forest model The true distribution of the hyperparameters random search bayesian optimization UCB bayesian optimization PI bayesian optimization EI Contrast the results deep learning model Random search Bayesian optimization UCB Bayesian optimization PI Bayesian optimization EI Contrast the results Conclusion Session info ...
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