Category Archives: R statistical programming language

Reanalysis of business visits from deployments of a mobile phone app

This reports a reanalysis of data from the deployment of a mobile phone app, as reported in: M. Yauck, L.-P. Rivest, G. Rothman, “Capture-recapture methods for data on the activation of applications on mobile phones“, Journal of the American Statistical … Continue reading

Posted in Bayesian computational methods, biology, capture-mark-recapture, capture-recapture, Christian Robert, count data regression, cumulants, diffusion, diffusion processes, Ecological Society of America, ecology, epidemiology, experimental science, field research, Gibbs Sampling, Internet measurement, Jean-Michel Marin, linear regression, mark-recapture, mathematics, maximum likelihood, Monte Carlo Statistical Methods, multilist methods, multivariate statistics, non-mechanistic modeling, non-parametric statistics, numerics, open source scientific software, Pierre-Simon Laplace, population biology, population dynamics, quantitative biology, quantitative ecology, R, R statistical programming language, sampling, sampling algorithms, segmented package in R, statistical ecology, statistical models, statistical regression, statistical series, statistics, stepwise approximation, stochastic algorithms, surveys, V. M. R. Muggeo | Leave a comment

On odds of storms, and extreme precipitation

People talk about “thousand year storms”. Rather than being a storm having a recurrence time of once in a thousand years, these are storms which have a 0.001 chance per year of occurring. Storms aren’t the only weather events of … Continue reading

Posted in American Meteorological Association, American Statistical Association, AMETSOC, catastrophe modeling, climate disruption, climate economics, climate education, ecopragmatism, evidence, extreme events, extreme value distribution, flooding, floods, games of chance, global warming, global weirding, insurance, meteorological models, meteorology, R, R statistical programming language, real estate values, risk, Risky Business, riverine flooding, science, Significance | Leave a comment

Macros in R

via Macros in R See also: The gtools package of R which enables these. There’s a description and motivation beginninng on page 11 of an (old: 2001) R News issue. They have been around a long time, but I haven’t … Continue reading

Quote | Posted on by | Leave a comment

Procrustes tangent distance is better than SNCD

I’ve written two posts here on using a Symmetrized Normalized Compression Divergence or SNCD for comparing time series. One introduced the SNCD and described its relationship to compression distance, and the other applied the SNCD to clustering days at a … Continue reading

Posted in data science, dependent data, descriptive statistics, divergence measures, hydrology, Ian Dryden, information theoretic statistics, J.T.Kent, Kanti Mardia, non-parametric statistics, normalized compression divergence, quantitative ecology, R statistical programming language, spatial statistics, statistical series, time series | Leave a comment

On bag bans and sampling plans

Plastic bag bans are all the rage. It’s not the purpose of this post to take a position on the matter. Before you do, however, I’d recommend checking out this: and especially this: (Note: My lovely wife, Claire, presents this … Continue reading

Posted in bag bans, citizen data, citizen science, Commonwealth of Massachusetts, Ecology Action, evidence, Google, Google Earth, Google Maps, goverance, lifestyle changes, microplastics, municipal solid waste, oceans, open data, planning, plastics, politics, pollution, public health, quantitative ecology, R, R statistical programming language, reasonableness, recycling, rhetorical statistics, sampling, sampling networks, statistics, surveys, sustainability | 2 Comments

Sampling: Rejection, Reservoir, and Slice

An article by Suilou Huang for catatrophe modeler AIR-WorldWide of Boston about rejection sampling in CAT modeling got me thinking about pulling together some notes about sampling algorithms of various kinds. There are, of course, books written about this subject, … Continue reading

Posted in accept-reject methods, American Statistical Association, Bayesian computational methods, catastrophe modeling, data science, diffusion processes, empirical likelihood, Gibbs Sampling, insurance, Markov Chain Monte Carlo, mathematics, Mathematics and Climate Research Network, maths, Monte Carlo Statistical Methods, multivariate statistics, numerical algorithms, numerical analysis, numerical software, numerics, percolation theory, Python 3 programming language, R statistical programming language, Radford Neal, sampling, slice sampling, spatial statistics, statistics, stochastic algorithms, stochastic search | Leave a comment