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Analysis of Heavy Tailed Distributions
An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.
Interface to 'typeform' Results
An R interface to the 'typeform' < https://typeform.com> application program interface. Also provides functions for downloading your results.
Crowd Sourced System Benchmarks
Benchmark your CPU and compare against other CPUs. Also provides functions for obtaining system specifications, such as RAM, CPU type, and R version.
Data Set for the 'benchmarkme' Package
Crowd sourced benchmarks from running the 'benchmarkme' package.
'Drat' R Archive Template
Creation and use of R Repositories via helper functions to insert packages into a repository, and to add repository information to the current R session. Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template.
Fit the Gambin Model to Species Abundance Distributions
Fits unimodal and multimodal gambin distributions to species-abundance distributions
from ecological data, as in in Matthews et al. (2014)
Automatic Fixed Rank Kriging
Automatic fixed rank kriging for (irregularly located)
spatial data using a class of basis functions with multi-resolution features
and ordered in terms of their resolutions. The model parameters are estimated
by maximum likelihood (ML) and the number of basis functions is determined
by Akaike's information criterion (AIC). For spatial data with either one
realization or independent replicates, the ML estimates and AIC are efficiently
computed using their closed-form expressions when no missing value occurs. Details
regarding the basis function construction, parameter estimation, and AIC calculation
can be found in Tzeng and Huang (2018)