1956 search results for "time series"

Bayes models from SAS PROC MIXED in R, post 2

October 5, 2014
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This is my second post in converting SAS's PROC MCMC examples in R. The task in his week is determining the transformation parameter in a Box-Cox transformation. SAS only determines Lambda, but I am not so sure about that. What I used to do was get an ...

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Consumer Preference Driven by Benefits and Affordances, Yet Management Sees Only Products and Features

October 2, 2014
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Consumer Preference Driven by Benefits and Affordances, Yet Management Sees Only Products and Features

Return on Investment (ROI) is management's bottom line. Consequently, everything must be separated and assigned a row with associated costs and profits. Will we make more by adding another product to our line? Will we lose sales by limiting the feature...

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Building a DGA Classifer: Part 2, Feature Engineering

October 2, 2014
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Building a DGA Classifer: Part 2, Feature Engineering

This is part two of a three-part blog series on building a DGA classifier and it is split into the three phases of building a classifier: 1) Data preperation 2) Feature engineering and 3) Model selection. Back in part 1, we prepared the data and we are starting with a nice clean list of domains labeled as either legitamate (“legit”) or generated by...

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Working with NIfTI images in R

October 1, 2014
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Working with NIfTI images in R

The oro.nifti package is awesome for NeuRoimaging (couldn't help myself). It has functions to read/write images, introduces the S4 nifti class, and has useful plotting functions. There are some limitations and some gotchas that are important to discuss if you are working with these objects in R. Dataset Creation We'll read in some data (a

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Implementing an EM Algorithm for Probit Regressions

September 30, 2014
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Implementing an EM Algorithm for Probit Regressions

Users new to the Rcpp family of functionality are often impressed with the performance gains that can be realized, but struggle to see how to approach their own computational problems. Many of the most impressive performance gains are demonstrated with seemingly advanced statistical methods, advanced C++–related constructs, or both. Even when users are able to understand how various demonstrated features operate in isolation, examples...

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Building a DGA Classifier: Part 1, Data Preparation

September 30, 2014
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This will be a three-part blog series on building a DGA classifier and will be split into three logical phases of building a classifier: 1) Data preparation (this) 2) Feature engineering and 3) Model selection. And before I get too far into this, I want to give a huge thank you to Click Security for releasing a DGA classifier in python as part of...

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Structural Arb Analysis and Portfolio Management Functionality in R

September 30, 2014
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Structural Arb Analysis and Portfolio Management Functionality in R

I want to use this post to replicate an article I found on SeekingAlpha, along with demonstrating PerformanceAnalytics’s ability to … Continue reading →

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A bioinformatics walk-through: Accessing protein-protein interaction interfaces for all known protein structures with PDBe PISA

September 28, 2014
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A bioinformatics walk-through: Accessing protein-protein interaction interfaces for all known protein structures with PDBe PISA

If this summer’s posting became a little infrequent, part of the blame lies with computational research I’ve been working on, regarding the systems biology of chromosomal translocations and the ensuing chimeric proteins at the Medical Research Council Laboratory of Molecular Biology in Cambridge. A sizeable part of bioinformatics ‘dry lab’ work falls into what has been described in the Estimating Generalization Error with the PRESS statistic

As we’ve mentioned on previous occasions, one of the defining characteristics of data science is the emphasis on the availability of “large” data sets, which we define as “enough data that statistical efficiency is not a concern” (note that a “large” data set need not be “big data,” however you choose to define it). In Related posts:

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