In graph theory (and its applications) it is often required to model how information spreads within a given graph. This is interesting for many applications, such as attack prediction, sybil detection, and recommender systems - just to name a...

Price Earnings ratio (P/E) is one of the very popular ratios reported with all stocks. Very simply this is thought as - Current Market Price / Earning per Share. An operational definition of Earning per Share would be Total profit divided by # of Shares . I will redirect interested readers for further reading to www.investopedia.com/terms/p/price-earningsratio.asp In this post,...

I just submitted my package update (version 1.3) to CRAN. The download is already available (currently source, binaries follow). While the last two updates included new functions for table outputs (see here and here for details on these functions), the current update only provides small helper functions as new functions. The focus of this update

In this part of the tutorial, we’ll show how to load ConQuest output to make a CQmodel object and then WrightMaps. We’ll also show how to turn deltas into thresholds. All the example files here are available in the /inst/extdata folder of the github. If you download the latest version of the package, they should be in a folder...

The rNOMADS package interfaces with the NOAA Operational Model Archive and Distribution System to provide access to 55 operational (i.e. real time and prediction) models describing the state of the ocean and the atmosphere. rNOMADS has been used to get wind and wave data for a real time sailing game, to quantify solar energy available

Stochastic search variable selection (SSVS) identifies promising subsets of multiple regression covariates via Gibbs sampling (George and McCulloch 1993). Here’s a short SSVS demo with JAGS and R. Assume we have a multiple regression problem: We suspect only a subset of the elements of $\boldsymbol{\beta}$ are non-zero, i.e. some of the covariates have no effect. Assume $\boldsymbol{\beta}$...

Stochastic search variable selection (SSVS) identifies promising subsets of multiple regression covariates via Gibbs sampling (George and McCulloch 1993). Here’s a short SSVS demo with JAGS and R. Assume we have a multiple regression problem: We suspect only a subset of the elements of $boldsymbol{beta}$ are non-zero, i.e. some of the covariates have no effect. Assume $boldsymbol{beta}$ arises from...

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