This package makes it possible on most UNIX platforms to contact your own DNS implementation in your test environment. It requires socket_wrapper to be able to contact the server. Alternatively, the wrapper is able to fake DNS queries and return valid responses to your application. It provides the following features:

• Redirects name queries to the nameservers specified in your resolv.conf.

• Can fake DNS queries using a simple formatted DNS hosts file.

Programming neuroscience specific Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>). This package extends the admiral package.

These dataset contains daily quality air measurements in Spain over a period of 18 years (from 2001 to 2018). The measurements refer to several pollutants. These data are openly published by the Government of Spain. The datasets were originally spread over a number of files and formats. Here, the same information is contained in simple dataframe for convenience of researches, journalists or general public. See the Spanish Government website <http://www.miteco.gob.es/> for more information.

Binding to the C++ implementation of the flexible polyline encoding by HERE <https://github.com/heremaps/flexible-polyline>. The flexible polyline encoding is a lossy compressed representation of a list of coordinate pairs or coordinate triples. The encoding is achieved by: (1) Reducing the decimal digits of each value; (2) encoding only the offset from the previous point; (3) using variable length for each coordinate delta; and (4) using 64 URL-safe characters to display the result.

This package provides the posterior estimates of the regression coefficients when horseshoe prior is specified. The regression models considered here are logistic model for binary response and log normal accelerated failure time model for right censored survival response. The linear model analysis is also available for completeness. All models provide deviance information criterion and widely applicable information criterion. See <doi:10.1111/rssc.12377> Maity et. al. (2019) <doi:10.1111/biom.13132> Maity et. al. (2020).

The Hybrid design is a combination of model-assisted design (e.g., the modified Toxicity Probability Interval design) with dose-toxicity model-based design for phase I dose-finding studies. The hybrid design controls the overdosing toxicity well and leads to a recommended dose closer to the true maximum tolerated dose (MTD) due to its ability to calibrate for an intermediate dose. More details can be found in Liao et al. 2022 <doi:10.1002/ijc.34203>.

This package provides a set of functions to make tracking the hidden movements of the Jack player easier. By tracking every possible path Jack might have traveled from the point of the initial murder including special movement such as through alleyways and via carriages, the police can more accurately narrow the field of their search. Additionally, by tracking all possible hideouts from round to round, rounds 3 and 4 should have a vastly reduced field of search.

This package supports the computation of an F-test for the association between expression values and clinical entities. In many cases a two way layout with gene and a dichotomous group as factors will be considered. However, adjustment for other covariates and the analysis of arbitrary clinical variables, interactions, gene co-expression, time series data and so on is also possible. The test is carried out by comparison of corresponding linear models via the extra sum of squares principle.

We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster.

Example software for the analysis of data from designed experiments, especially agricultural crop experiments. The basics of the analysis of designed experiments are discussed using real examples from agricultural field trials. A range of statistical methods using a range of R statistical packages are exemplified . The experimental data is made available as separate data sets for each example and the R analysis code is made available as example code. The example code can be readily extended, as required.

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) <doi:10.1016/S0168-1923(97)00027-0>), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website <https://www.longpaddock.qld.gov.au/>. This data is freely available under the Creative Commons 4.0 licence.

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>, Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

This package provides a comprehensive set of datasets and tools for causal inference research. The package includes data from clinical trials, cancer studies, epidemiological surveys, environmental exposures, and health-related observational studies. Designed to facilitate causal analysis, risk assessment, and advanced statistical modeling, it leverages datasets from packages such as causalOT', survival', causalPAF', evident', melt', and sanon'. The package is inspired by the foundational work of Pearl (2009) <doi:10.1017/CBO9780511803161> on causal inference frameworks.

Integrates fairness auditing and bias mitigation methods for the mlr3 ecosystem. This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as "Reweighing" described in Kamiran, Calders (2012) <doi:10.1007/s10115-011-0463-8> and "Equalized Odds" described in Hardt et al. (2016) <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>. Integration with mlr3 allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods.

Estimate and return the needed parameters for visualisations designed for OpenBudgets <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the OpenBudgets data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to JSON format and use them as input in a different graphical interface.

Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models.

This package implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.

This package provides a suite of functions for conducting and interpreting analysis of statistical interaction in regression models that was formerly part of the jtools package. Functionality includes visualization of two- and three-way interactions among continuous and/or categorical variables as well as calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These capabilities are implemented for generalized linear models in addition to the standard linear regression context.

The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.

Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>, while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) <doi:10.1177/1029864919844809>. Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) <doi:10.1098/rsos.171520>.

This package provides functions for computing fit indices for evaluating the path component of latent variable structural equation models. Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) <doi:10.1177/1094428117736137>. Also included are fit indices described by Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.

This package provides methods for generating .dat files for use with the AMPL software using spatial data, particularly rasters. It includes support for various spatial data formats and different problem types. By automating the process of generating AMPL datasets, this package can help streamline optimization workflows and make it easier to solve complex optimization problems. The methods implemented in this package are described in detail in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).

The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs).