Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 24 packages in 0.01 seconds

kehra — by Claudia Vitolo, 3 years ago

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.

rnrfa — by Claudia Vitolo, 2 months ago

UK National River Flow Archive Data from R

Utility functions to retrieve data from the UK National River Flow Archive (< http://nrfa.ceh.ac.uk/>). 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.

hddtools — by Claudia Vitolo, 5 months ago

Hydrological Data Discovery Tools

Facilitates discovery and handling of hydrological data, access to catalogues and databases.

rdefra — by Claudia Vitolo, 6 months ago

Interact with the UK AIR Pollution Database from DEFRA

Get data from DEFRA's UK-AIR website < https://uk-air.defra.gov.uk/>. It basically scrapes the HTML content.

microbenchmark — by Joshua M. Ulrich, 3 months ago

Accurate Timing Functions

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

hyperSpec — by Claudia Beleites, 7 months ago

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.

IsingFit — by Claudia van Borkulo, 2 years ago

Fitting Ising Models Using the ELasso Method

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

edesign — by Claudia Gebhardt, 3 years ago

Maximum Entropy Sampling

An implementation of maximum entropy sampling for spatial data is provided. An exact branch-and-bound algorithm as well as greedy and dual greedy heuristics are included.

VineCopula — by Thomas Nagler, 4 months ago

Statistical Inference of Vine Copulas

Provides tools for the statistical analysis of vine copula models. The package includes tools for parameter estimation, model selection, simulation, goodness-of-fit tests, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided.

uniReg — by Claudia Koellmann, 3 years ago

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.