You test your code. We know you do. How else are you sure that your changes don’t break the program? But after you commit, you discard those pesky scripts and throw away code. Don’t you think it’s a bit of a waste to dump all that effort that took you quite a decent chunk of your day to conjure? Well, here you are, so let’s see another way. A better way.
Introduction In this session I will focus on Bayesian inference using the integrated nested Laplace approximation (INLA) method. As described in Rue et al. (2009), INLA can be used to estimate the posterior marginal distribution of Bayesian hierarchical models. This method is implemented in the INLA package available for the R programming language. Given that the types of models that INLA can fit are quite wide, we will focus on spatial models for the analysis of lattice data.
Learning to code can be quite hard. Apart from the difficulties of learning a new language, following a book can be quite boring. From my point of view, one of the bests ways to become a good programmer is choosing small and funny experiments oriented to train specific techniques of programming. This is what I usually do in my blog Fronkonstin. In this tutorial, we will learn to combine C++ with R to create efficient loops.
Do you want to know how to make elegant and simple reproducible presentations? In this talk, we are going to explain how to do presentations in different output formats using one of the easiest and most exhaustive statistical software, R. Now, it is possible create Beamer, PowerPoint, or HTML presentations, including R code, \(\LaTeX\) equations, graphics, or interactive content.
After the tutorial, you will be able to create R presentations on your own with R Markdown in RStudio.
Network analysis offers a perspective of the data that broadens and enriches any investigation. Many times we deal with data in which the elements are related, but we have them in a tabulated format that is difficult to import into network analysis tools.
Relationship data require a definition of nodes and connections. Both parts have different structures and it is not possible to structure them in a single table, at least two would be needed.
The R language is peculiar in many ways, and its approach to object-oriented (OO) programming is just one of them. Indeed, base R supports not one, but three different OO systems: S3, S4 and RC classes. And yet, probably none of them would qualify as a fully-fledged OO system before the astonished gaze of an expert in languages such as Python, C++ or Java. In this tutorial, we will review the S3 system, the simplest yet most elegant of them.
Stan is a probabilistic programming language for specifying statistical models. Stan provides full Bayesian inference for continuous-variable models through Markov Chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan can be called through R using the rstan package, and through Python using the pystan package.
The aim of this tutorial is to show the use of TensorFlow with KERAS for classification and prediction in Time Series Analysis. The latter just implement a Long Short Term Memory (LSTM) model (an instance of a Recurrent Neural Network which avoids the vanishing gradient problem).
Introduction The code below has the aim to quick introduce Deep Learning analysis with TensorFlow using the Keras back-end in R environment. Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation and not for final products.
In this session I will try to show some utilities present in the web. One of them will help us to execute R code from the web, using an online compiler, without installing any kind of software in our computers. The other one, it can help us to solve optimization problems by a graphics way. We can draw the restrictions, the feasible region, and others elements that we can need to solve the problems.
This is our second session introducing Shiny, an R package that allows to develop interactive Apps in a familiar framework for regular R-users. During the first session we focused on the structure and workflow basics, and now, we will go further on input and output objects, reactivity, layouts and data handling.
All these functionalities will be reviewed by product of developing a Shiny App. It will provide the grades to our students and, at the same time, they will be able to explore the data set by interacting with the App.