When working with big data sets, RAM conservation is critically
important. However, it is not always enough to just monitor the
size of the objects created. So-called "copy-on-modify" behavior,
characteristic of R, means that some expressions or functions may
require an unexpectedly large amount of RAM overhead. For example,
replacing a single value in a matrix duplicates that matrix in the
back-end, making this task require twice as much RAM as that used
by the matrix itself. This package makes it easy to monitor the total
and peak RAM used so that developers can quickly identify and
eliminate RAM hungry code.

Package for Periodic Autoregression Analysis
S original by A. I. McLeod <aim@uwo.ca>.
R port by Mehmet Balcilar.
Package for estimating periodic autoregressive models.
Package also includes methods fo plotting periodic time
series data.
S original from StatLib, see COPYRIGHT.orig

Package for estimating periodic autoregressive models.
Package also includes methods for plotting periodic time
series data.

A. I. McLeod <aim@uwo.ca> and Mehmet Balcilar <mehmet@mbalcilar.net>.
Package for estimating periodic autoregressive models. Package also includes methods for plotting periodic time 	     series data.

A. I. McLeod <aim@uwo.ca> and Mehmet Balcilar
<mehmet@mbalcilar.net>.
Package for estimating periodic autoregressive models.
Datasets: monthly ozone and Fraser riverflow. Plots: periodic
versions of boxplot, auto/partial correlations, moving-average
expansion.

Maximum Likelihood Inference for the Pearson VII Distribution
with Shape Parameter 3/2
pearson7 supports maximum likelihood inference for the Pearson VII
distribution with shape parameter 3/2 and free location and scale
parameters. This distribution is relevant when estimating the velocity of
processive motor proteins with random detachment.

Supports maximum likelihood inference for the Pearson VII
distribution with shape parameter 3/2 and free location and scale
parameters. This distribution is relevant when estimating the velocity of
processive motor proteins with random detachment.