Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances. Data is licensed under the Open Database License.

This package provides methods (<doi:10.7717/peerj.11534>) are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include shift.dot.ggplot()', shift.pie.ggplot()', shift.moon.ggplot()', shift.waffle.ggplot() and shift.surf.ggplot() that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the gganimate package. Function amova.rda() shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis.

Estimates and plots effect estimates from models with all possible combinations of a list of variables. It can be used for assessing treatment effects in clinical trials or risk factors in bio-medical and epidemiological research. Like Stata command confall (Wang Z (2007) <doi:10.1177/1536867X0700700203> ), allestimates calculates and stores all effect estimates, and plots them against p values or Akaike information criterion (AIC) values. It currently has functions for linear regression: all_lm(), logistic and Poisson regression: all_glm(), and Cox proportional hazards regression: all_cox().

R codes for the (adaptive) Sum of Powered Score ('SPU and aSPU') tests, inverse variance weighted Sum of Powered score ('SPUw and aSPUw') tests and gene-based and some pathway based association tests (Pathway based Sum of Powered Score tests ('SPUpath'), adaptive SPUpath ('aSPUpath') test, GEEaSPU test for multiple traits - single SNP (single nucleotide polymorphism) association in generalized estimation equations, MTaSPUs test for multiple traits - single SNP association with Genome Wide Association Studies ('GWAS') summary statistics, Gene-based Association Test that uses an extended Simes procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version of GATES test for pathway-based association testing ('GATES-Simes'). ). The tests can be used with genetic and other data sets with covariates. The response variable is binary or quantitative. Summary; (1) Single trait-'SNP set association with individual-level data ('aSPU', aSPUw', aSPUr'), (2) Single trait-'SNP set association with summary statistics ('aSPUs'), (3) Single trait-pathway association with individual-level data ('aSPUpath'), (4) Single trait-pathway association with summary statistics ('aSPUsPath'), (5) Multiple traits-single SNP association with individual-level data ('GEEaSPU'), (6) Multiple traits- single SNP association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').

Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.

This package provides tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands.

An R wrapper for agena.ai <https://www.agena.ai> which provides users capabilities to work with agena.ai using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to agena.ai cloud or local API for calculations. Note: running calculations requires a valid agena.ai API license (past the initial trial period of the local API).

The Langmuir and Freundlich adsorption isotherms are pivotal in characterizing adsorption processes, essential across various scientific disciplines. Proper interpretation of adsorption isotherms involves robust fitting of data to the models, accurate estimation of parameters, and efficiency evaluation of the models, both in linear and non-linear forms. For researchers and practitioners in the fields of chemistry, environmental science, soil science, and engineering, a comprehensive package that satisfies all these requirements would be ideal for accurate and efficient analysis of adsorption data, precise model selection and validation for rigorous scientific inquiry and real-world applications. Details can be found in Langmuir (1918) <doi:10.1021/ja02242a004> and Giles (1973) <doi:10.1111/j.1478-4408.1973.tb03158.x>.

Reads Arena <https://www.arenasimulation.com/> CSV output files and generates nice tables and plots. The package contains a Shiny App that can be used to interactively visualize Arena's results.

NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.

Wraps the Ace editor in a HTML widget. The Ace editor has support for many languages. It can be opened in the viewer pane of RStudio', and this provides a second source editor.

This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictionsâ generated by idiosyncratic tastes and local ties â and trade frictions â generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) <https://econpapers.repec.org/RePEc:boc:bocode:s459382> "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper.

This package provides basic functionalities to calculate the position of satellites given a known state vector. The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the asteRiskData package, available in a drat repository. To install this data package, run install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.org/NORAD/documentation/spacetrk.pdf>. David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>. Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

Create awesome Bootstrap 4 dashboards powered by Argon'.

The Aquo Standard is the Dutch Standard for the exchange of data in water management. With *aquodom* (short for aquo domaintables) it is easy to exploit the API (<https://www.aquo.nl/index.php/Hoofdpagina>) to download domaintables of the Aquo Standard and use them in R.

Simple animated versions of basic R plots, using the animation package. Includes animated versions of plot, barplot, persp, contour, filled.contour, hist, curve, points, lines, text, symbols, segments, and arrows.

Uses Auth0 API (see <https://auth0.com> for more information) to use a simple authentication system. It provides tools to log in and out a shiny application using social networks or a list of e-mails.

Many complex plots are actually composite plots, such as oncoplot', funkyheatmap', upsetplot', etc. We can produce subplots using ggplot2 and combine them to create composite plots using aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.

This package provides functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) <doi:10.1109/TSMC.1973.4309314>.

For a binary classification the adjusted sensitivity and specificity are measured for a given fixed threshold. If the threshold for either sensitivity or specificity is not given, the crossing point between the sensitivity and specificity curves are returned. For bootstrap procedures, mean and CI bootstrap values of sensitivity, specificity, crossing point between specificity and specificity as well as AUC and AUCPR can be evaluated.

Utility functions to download and process data produced by the ALARM Project, including 2020 redistricting files Kenny and McCartan (2021) <https://alarm-redist.org/posts/2021-08-10-census-2020/> and the 50-State Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.7910/DVN/SLCD3E>. The package extends the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.1038/s41597-022-01808-2> to also include states with only a single district.

Toolkit for the analysis of multiple gene data (Jombart et al. 2017) <doi:10.1111/1755-0998.12567>. apex implements the new S4 classes multidna', multiphyDat and associated methods to handle aligned DNA sequences from multiple genes.

Estimate aquatic species life history using robust techniques. This package supports users undertaking two types of analysis: 1) Growth from length-at-age data, and 2) maturity analyses for length and/or age data. Maturity analyses are performed using generalised linear model approaches incorporating either a binomial or quasibinomial distribution. Growth modelling is performed using the multimodel approach presented by Smart et al. (2016) "Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future" <doi:10.1111/faf.12154>.