Below are my “Top 40” picks from the approximately 212 new packages that made it to CRAN in April. They are organized into ten categories: Computational Methods, Data, Data Science, Machine Learning, Music, Science, Statistics, Time Series, Utilities, and Visualizations. Computational Methods diffeqr v0.1.1: Provides an interface to DifferentialEquations.jl which offers high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more.

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This is a second post in a series on enterprise dashboards. See our previous post, Enterprise-ready dashboards with Shiny Databases. We have been living with spreadsheets for so long that most office workers think it is obvious that spreadsheets generated with programs like Microsoft Excel make it easy to understand data and communicate insights. Everyone in a business, from the newest intern to the CEO, has had some experience with spreadsheets.

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rstudio::conf 2018 and the New York R Conference are both behind us, but we are rushing headlong into the season for conferences focused on the R Language and its applications. The European R Users Meeting (eRum) begins this coming Monday, May 14th, in Budapest with three days of workshops and talks. Headlined by R Core member Martin Mächler and fellow keynote speakers Achim Zeileis, Nathalie Villa-Vialaneix, Stefano Maria Iacus, and Roger Bivand, the program features an outstanding array of accomplished speakers including RStudio’s own Barbara Borges Ribeiro, Andrie de Vries, and Lionel Henry.

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In a previous post, we reviewed how to import the Fama French 3-Factor data, wrangle that data, and then regress our portfolio returns on the factors. Please have a look at that previous post, as the following work builds upon it.

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By my count, just over 200 new packages made it to CRAN and stuck during March. The trend for specialized, and sometimes downright esoteric science packages continues. I counted 40 new packages in this class. Most, but not all of these, are focused on bio-science applications. For example, the foreSIGHT package profiled below focuses on climate science. I was also pleased to see two new packages (not from RStudio) in the Data Science category, h2o4gpu and onnx, built on the reticulate package for interfacing with Python.

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I was surprised by greta. I had assumed that the tensorflow and reticulate packages would eventually enable R developers to look beyond deep learning applications and exploit the TensorFlow platform to create all manner of production-grade statistical applications. But I wasn’t thinking Bayesian. After all, Stan is probably everything a Bayesian modeler could want. Stan is a powerful, production-level probability distribution modeling engine with a slick R interface, deep documentation, and a dedicated development team.

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RStudio recently announced the reticulate package, which is designed to help R users inter-operate with Python code. I was immediately excited by this announcement. In a past life, I worked with a team at the National Renewable Energy Lab (NREL) on vehicle simulations. Their models could predict MPG for vehicles based on driving routes. At the time, I had wanted to build a web app that would allow users to predict MPG for different vehicles based on their daily commutes.

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In two previous posts, we calculated and then visualized the CAPM beta of a portfolio by fitting a simple linear model. Today, we move beyond CAPM’s simple linear regression and explore the Fama French (FF) multi-factor model of equity risk/return. For more background, have a look at the original article published in The Journal Financial Economics, Common risk factors in the returns on stocks and bonds. The FF model extends CAPM by regressing portfolio returns on several variables, in addition to market returns.

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In early March, the Bay Area useR Group was able to hold an R and TensorFlow mini-conference on Google’s new Sunnyvale campus. Pete Mohanty, a Stanford researcher and frequent BARUG speaker, lead off with a talk on his recent kerasformula package, which allows R users to call a keras-based neural net with R formula objects. Pete’s slides show an example of using using a regression-style formula with the kerasformula::kms() function to fit a sequential TensorFlow model.

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Here are my picks for the “Top 40” packages of the 171 new packages that made it to CRAN (and stuck) in February, organized into the following categories: Computational Methods, Data, Finance, Science, Statistics, Time Series, and Utilities. Computational Methods adnuts v1.0.0: Provides an implementation of the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) for ADMB and TMB models. The vignette will get you started. CholWishart v0.9.2: Provides functions to sample from the Cholesky factorization of a Wishart random variable, the inverse Wishart distribution and the Cholesky factorization of an inverse Wishart random variable.

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