Client for the Ocean Biodiversity Information System (<https://obis.org>).

This package provides a collection of programs for plotting SKEW-T,log p diagrams and wind profiles for data collected by radiosondes (the typical weather balloon-borne instrument). The format of this plot with companion lines to assess atmospheric stability are both standard in meteorology and difficult to create from basic graphics functions. Hence this package. One novel feature is being able add several profiles to the same plot for comparison. Use "help(ExampleSonde)" for an explanation of the variables needed and how they should be named in a data frame. See <https://github.com/dnychka/Radiosonde> for the package home page.

Administrative regions and other spatial objects of the Czech Republic.

This package provides a toolkit for making antigenic maps from immunological assay data, in order to quantify and visualize antigenic differences between different pathogen strains as described in Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World Health Organization influenza vaccine strain selection process. Additional functions allow for the diagnostic evaluation of antigenic maps and an interactive viewer is provided to explore antigenic relationships amongst several strains and incorporate the visualization of associated genetic information.

The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. <DOI: 10.1080/00273170802285693>. <DOI: 10.1037/a0025697>.

This package provides functions used in the R: Einführung durch angewandte Statistik (second edition).

Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>, Lele (2009) <doi:10.2193/2007-535>, and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.

Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).

The functions in this package compute robust estimators by minimizing a kernel-based distance known as MMD (Maximum Mean Discrepancy) between the sample and a statistical model. Recent works proved that these estimators enjoy a universal consistency property, and are extremely robust to outliers. Various optimization algorithms are implemented: stochastic gradient is available for most models, but the package also allows gradient descent in a few models for which an exact formula is available for the gradient. In terms of distribution fit, a large number of continuous and discrete distributions are available: Gaussian, exponential, uniform, gamma, Poisson, geometric, etc. In terms of regression, the models available are: linear, logistic, gamma, beta and Poisson. Alquier, P. and Gerber, M. (2024) <doi:10.1093/biomet/asad031> Cherief-Abdellatif, B.-E. and Alquier, P. (2022) <doi:10.3150/21-BEJ1338>.

This R package connects to SWI-Prolog, <https://www.swi-prolog.org/>, so that R can send deterministic and non-deterministic queries to prolog (consult, query/submit, once, findall).

An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point, for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted.".

Generates both total- and level-specific R-squared measures from Rights and Sterbaâ s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. Additionally generates graphical representations of these R-squared measures to allow visualizing and interpreting all measures in the framework together as an integrated set. This framework subsumes 10 previously-developed R-squared measures for multilevel models as special cases of 5 measures from the framework, and it also includes several newly-developed measures. Measures in the framework can be used to compute R-squared differences when comparing multilevel models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>). Bootstrapped confidence intervals can also be calculated. To use the confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.

This package provides a computational resource designed to accurately detect microbial nucleic acids while filtering out contaminants and false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. For more details, see Ghaddar (2023) <doi:10.1038/s43588-023-00507-1>. This implementation leverages the polars package for fast and systematic microbial signal recovery and denoising from host tissue genomic sequencing.

This companion package extends the package robmed (Alfons, Ates & Groenen, 2022b; <doi:10.18637/jss.v103.i13>) in various ways. Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>) to make the method more accessible to less proficient R users, as well as functions to export the results as a table in a Microsoft Word or Microsoft Powerpoint document, or as a LaTeX table. Furthermore, the package contains a shiny app to compare various bootstrap procedures for mediation analysis on simulated data.

The Radiant Basics menu includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The application extends the functionality in radiant.data'.

Visualizations to explain the results of a topological data analysis. The goal of topological data analysis is to identify persistent topological structures, such as loops (topological circles) and voids (topological spheres), in data sets. The output of an analysis using the TDA package is a Rips diagram (named after the mathematician Eliyahu Rips). The goal of RPointCloud is to fill in these holes in the data by providing tools to visualize the features that help explain the structures found in the Rips diagram. See McGee and colleagues (2024) <doi:10.1101/2024.05.16.593927>.

This package provides an interface to the Facebook API.

This package provides an easy way to report the results of regression analysis, including: 1. Proportional hazards regression from function coxph of package survival'; 2. Conditional logistic regression from function clogit of package survival'; 3. Ordered logistic regression from function polr of package MASS'; 4. Binary logistic regression from function glm of package stats'; 5. Linear regression from function lm of package stats'; 6. Risk regression model for survival analysis with competing risks from function FGR of package riskRegression'; 7. Multilevel model from function lme of package nlme'.

Reads in sample description and slide description files and annotates the expression values taken from GenePix results files (text file format used by many microarray scanner and software providers). After normalization data can be visualized as boxplot, heatmap or dotplot.

This package provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the RobustTuneC method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" <doi:10.1007/s00357-020-09368-z>.

R on FHIR is an easy to use wrapper around the HL7 FHIR REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official HL7 FHIR .NET API', also made by Firely.

This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.

Using this package, it is possible to call a BUGS model, summarize inferences and convergence in a table and graph, and save the simulations in arrays for easy access in R.

Simplify the process of extracting and processing Clinical Practice Research Datalink (CPRD) data in order to build datasets ready for statistical analysis. This process is difficult in R', as the raw data is very large and cannot be read into the R workspace. rcprd utilises RSQLite to create SQLite databases which are stored on the hard disk. These are then queried to extract the required information for a cohort of interest, and create datasets ready for statistical analysis. The processes follow closely that from the rEHR package, see Springate et al., (2017) <doi:10.1371/journal.pone.0171784>.