The caracas package for computer algebra in R
Mikkel Meyer Andersen, Søren Højsgaard
dk (code pays fourni par la source)
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Mikkel Meyer Andersen, Søren Højsgaard
dk (code pays fourni par la source)
Søren Højsgaard, Steffen L. Lauritzen
Abstract In Gaussian graphical models, the likelihood equations must typically be solved iteratively. This paper investigates two algorithms: a version of iterative proportional scaling, which avoids inversion of large matrices, and an algorithm based on convex duality and operating on the covariance …
dk (code pays fourni par la source)
Mikkel Meyer Andersen, Søren Højsgaard
The capability of R to do symbolic mathematics is enhanced by the reticulate and caracas packages. The workhorse behind these packages is the Python computer algebra library SymPy. Via reticulate, the SymPy library can be accessed from within R. This, however, requires …
dk (code pays fourni par la source)
Mads Lindskou, Torben Tvedebrink, Søren Højsgaard, Niels Morling
A Bayesian network is a multivariate (potentially very high dimensional) probabilistic model formed by combining lower-dimensional components. In Bayesian networks, the computation of conditional probabilities is fundamental for model-based predictions. This is usually done based on message passing algorithms that utilize conditional …
dk, se (code pays fourni par la source)
Mikkel Meyer Andersen, Søren Højsgaard
The capability of R to do symbolic mathematics is enhanced by the caracas package. This package uses the Python computer algebra library SymPy as a back-end but caracas is tightly integrated in the R environment. This enables the R user with symbolic …
Mikkel Meyer Andersen, Søren Højsgaard
R's ability to do symbolic mathematics is largely restricted to finding derivatives. There are many tasks involving symbolic math that are of interest to R users, e.g. inversion of symbolic matrices, limits and solving non-linear equations. Users must resort to other computer …
dk, at, de (code pays fourni par la source)
Mads Lindskou, Torben Tvedebrink, Poul Svante Eriksen, Søren Højsgaard et autres
We propose Unity Smoothing (US) for handling inconsistencies between a Bayesian network model and new unseen observations. We show that prediction accuracy, using the junction tree algorithm with US is comparable to that of Laplace smoothing. Moreover, in applications were sparsity of …
Anne Helby Petersen, Lee McDaniel, Søren Højsgaard
Estimation of generalized linear models with correlated/clustered observations by use of generalized estimating equations (GEE). See e.g. Halekoh and Højsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details. Several types of clustering are supported, including exchangeable variance structures, AR1 structures, M-dependent, user-specified variance structures and more. …
Søren Højsgaard, Steffen L. Lauritzen
In Gaussian graphical models, the likelihood equations must typically be solved iteratively. We investigate two algorithms: A version of iterative proportional scaling which avoids inversion of large matrices, and an algorithm based on convex duality and operating on the covariance matrix by …
Daniel Lüdecke, Dominique Makowski, Mattan S. Ben‐Shachar, Indrajeet Patil et autres
Mikkel Meyer Andersen, Søren Højsgaard
Much work went into integrating caracas into R such that caracas behaves much like other R libraries and objects.
ru, dk (code pays fourni par la source)
Mikkel Meyer Andersen, Søren Højsgaard
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