Collect, Assemble and Model Air Pollution, Weather and Health Data

Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.

API Wrapper Around 'Postcodes.io'

Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around < https://postcodes.io>.

UK National River Flow Archive Data from R

Utility functions to retrieve data from the UK National River Flow Archive (< https://nrfa.ceh.ac.uk/>, terms and conditions: < https://nrfa.ceh.ac.uk/costs-terms-and-conditions>). The package contains R wrappers to the UK NRFA data temporary-API. There are functions to retrieve stations falling in a bounding box, to generate a map and extracting time series and general information. The package is fully described in Vitolo et al (2016) "rnrfa: An R package to Retrieve, Filter and Visualize Data from the UK National River Flow Archive" < https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.

Accurate Timing Functions

Provides infrastructure to accurately measure and compare the execution time of R expressions.

'testthat' Unit Test Enhancements

Enhance package 'testthat' by allowing tests to be attached to the function/object they test. This allows to keep functional and unit test code together.

Conversion of R Regression Output to LaTeX or HTML Tables

Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft . (If the Zelig package, which this package enhances, cannot be found on CRAN, you can find it at < https://github.com/IQSS/Zelig>. If the mnlogit package, which this package enhances, cannot be found on CRAN, you can find an old version in the CRAN Archive at < https://cran.r-project.org/src/contrib/Archive/mnlogit/>.)

Circular Genomic Permutation using Genome Wide Association p-Values

Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure(Cabrera et al (2012) ). All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user.

Unimodal Penalized Spline Regression using B-Splines

Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or self-defined penalties on the B-spline coefficients.

Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)

Comfortable ways to work with hyperspectral data sets. I.e. spatially or time-resolved spectra, or spectra with any other kind of information associated with each of the spectra. The spectra can be data as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS, etc. More generally, any data that is recorded over a discretized variable, e.g. absorbance = f(wavelength), stored as a vector of absorbance values for discrete wavelengths is suitable.

Moving Sum Based Procedures for Changes in the Mean

Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) and the multiscale algorithmic extension from Cho and Kirch (2022) , as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) . See also Meier, Kirch and Cho (2021) which accompanies the R package.