Fitting heavy tailed distributions: the poweRlaw package

Over the last few years, the power law distribution has been used as the data generating mechanism in many disparate fields. However, at times the techniques used to fit the power law distribution have been inappropriate. This paper describes the poweRlaw R package, which makes fitting power laws and other heavy-tailed distributions straightforward. This package contains R functions for fitting, comparing and visualising heavy tailed distributions. Overall, it provides a principled approach to power law fitting.Comment: The code for this paper can be found at https://github.com/csgillespie/poweRla

Severe acute respiratory syndrome (SARS): breathtaking progress

Reports of a new severe respiratory disease, now defined as severe acute respiratory syndrome (SARS), began to emerge from Guangdong, in southern China, in late 2002. The condition came to international attention through an explosive outbreak in Hong Kong in March 2003. Cases appeared throughout South-East Asia and in Toronto, the spread of SARS being accelerated by international air travel. A global emergency was declared by the World Health Organization, bringing together an international team of epidemiologists, public health physicians and microbiologists to study and contain the disease. This response has enabled the nature of the infectious agent to be identified, its mode of transmission to be established and diagnostic tests to be created rapidly.</p

Scalable Inference for Markov Processes with Intractable Likelihoods

Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo (MCMC) techniques can lead to exact inference in such models but in practice can suffer performance issues including long burn-in periods and poor mixing. On the other hand approximate Bayesian computation techniques can allow rapid exploration of a large parameter space but yield only approximate posterior distributions. Here we consider the combined use of approximate Bayesian computation (ABC) and MCMC techniques for improved computational efficiency while retaining exact inference on parallel hardware

The evolution of tree nursery offerings in Los Angeles County over the last 110 years

Interest in urban vegetation has increased dramatically. Urban trees are an important aspect of the urban environment but there is little known about the potential sources of those trees, change in tree species diversity over time and the factors leading to the contemporary floristic composition in cities. We investigate tree nursery offerings in Los Angeles County over the past 110 years through the use of here-to-fore unexplored nursery catalogs to determine the diversity of trees that have been commercially available over time. Tree species information was collected spanning a 110-year study period and analyzed the data for four time periods (1900-1929, 1930-1959, 1960-1989, and 1990-2011). We found the number of genera and tree species offered significantly increased in the past 20 years (1990-2011). The numbers of non-native trees, angiosperms, and deciduous species all significantly increased with but no changes were observed in the numbers of native, evergreen, or gymnosperm species offered over this time period. The largest numbers of palm species were offered in 1900-1929. Overall there were 562 unique species offered belonging to 201 different genera in the 120-year study period, 48 species were California native trees and 514 of these were non-native species indicating that perhaps Los Angeles has one of the most diverse number of tree species offered for sale by the nursery industry. © 2013 Elsevier B.V