Open source software has many virtues. Being free is not the least of which. However, open source comes with “ABSOLUTELY NO WARRANTY” and with no power comes no responsibility (I wonder..). Since no one is paying, by definition it is your sole responsibility to make sure the code does what it is supposed to be doing. Thus, looking “under the hood” of a function written by someone else is can be of service. There are more reasons to examine the actual underlying code.

# Category: Code

R-code

## Live volatility monitor

In April this year, Rstudio notified early users of shiny that Glimmer and Spark servers which host interactive-applications would be decommissioned. Basically, the company is moving forward to generate revenues from this great interactive application service. For us aspirants who use the service strictly as a hobby, that means, in a word: *pay*.

Basic subscription now costs around 40$ per month. Keeping your applications free of charge is possible BUT, as long as it is not used for more than 25 hours per month. So if your site generate some traffic, most users would simply not be able to access the app. Apart from that, you are subject to some built-in Rstudio’s logo which can’t be removed without having a paid subscription. That is a shame, but a company’s gotta eat right? I am using Rstudio’s services from their very beginning, and the company definitely deserve to eat! only I wish there would be another step between the monthly 0$ option which provides too slim capabilities, and the monthly 40$ option which is, in my admittedly biased opinion, too pricey for a ‘sometimes’ hobby.

## Adding text to R plot

Diversity is a real strength. By now it is common knowledge. I often see institutions openly encourage multinational environment and multidisciplinary professionals, with specific “on-the-job” training to tailor for own needs. No one knows a lot about a lot, so bringing different together enhance independent thinking and knowledge available to the organization. Clarity of communication then becomes even more important, and making sure your figures are quickly understandable goes a long way.

## Yield curve forecasting

One of my Ph.D papers was published recently. It deals with yield curve forecasting.

Here is the code for applying the Nelson-Siegel model to any yield curve.

## Eplot (1)

Package Eplot on cran.

## R vs MATLAB (Round 3)

At least for me, R by faR. MATLAB has its own way of doing things, which to be honest can probably be defended from many angles. Here are few examples for not so subtle differences between R and MATLAB:

## R vs MATLAB – round 2

R takes it. I prefer coding in R over MATLAB. I feel R understands that I do not like to type too much. A few examples:

## R and Dropbox

When you woRk, you probably have a set of useful functions/packages you constantly use. For example, I often use the excellent quantmod package, and the nice multi.sapply function. You want your tools loaded when R session fires.

## Multivariate summary function in R

Some time ago, I wrote a Better summary function in R . Here is its multivariate extension:

## Intraday volatility measures

In the last few decades there has been tremendous progress in the realm of volatility estimation. A major step is the additional use of intraday price path. It has been shown that estimates which consider intraday information are more accurate. Which is to say they converge faster to the real unobserved value of the true volatility.

## Better summary function in R

The summary function in R returns:

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summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 9.14 10.70 11.10 11.30 12.10 13.60 |

For the univariate case I wrote what I consider to be a better summary function which returns:

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usum(x) # For univariate Summary Summary Statistics: min med mean max sd skew kurt 1 9.14 11.13 11.35 13.65 1.057 0.3028 -0.6389 ------ NA's?: No NA's in the series ------ Head Tail 13.65 13.55 13.08 13.13 ------ Length Class 207 numeric |