Features

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Parametric Time to Event Analysis (Temporal)Performs likelihood-based estimation and inference on time to event data, possibly subject to non-informative right censoring. fitParaSurv() provides m …

Today after over a decade of programming, I was reminded that programming is hard.

For those who will laugh at seeing deep learning with one hidden layer and the Iris data set of 150 records, I will say: you’re perfectly right The goal at this stage is simply to take the first steps

You can read a number of books to increase your knowledge about Deep Learning

One of the differences between statistical data scientists and machine learning engineers is that while the latter group are concerned primarily with the predictive performance of a model, the former group are also concerned with the fit of the model. A model that misses important structures in the data — for example, seasonal trends, or a poor fit to specific subgroups — is likely to be lacking important variables or features in the source data. You can try different machine learning techniques or adjust hyperparameters to your heart’s content, but you’re unlikely to discover problems like this without evaluating the model fit.

A new version of our RcppTOML package just arrived on CRAN. It wraps an updated version of the cpptoml parser which, after a correction or two, now brings support for TOML v0.5.0 – which is still rather rare.