We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.

This Python module provides line editing functions similar to the default Emacs-style ones of GNU Readline. Unlike the Python standard library's readline package, this one allows access to those capabilities in settings outside of a standard command-line interface. It is especially well-suited to interfacing with Urwid, due to a shared syntax for describing key inputs.

Currently, all stateless Readline commands are implemented. Yanking and history are not supported.

With Serverspec, you can write RSpec tests for checking your servers are configured correctly.

Serverspec tests your servers’ actual state by executing command locally, via SSH, via WinRM, via Docker API and so on. So you don’t need to install any agent softwares on your servers and can use any configuration management tools, Puppet, Ansible, CFEngine, Itamae and so on.

But the true aim of Serverspec is to help refactoring infrastructure code.

Catalogues of resolution IV regular fractional factorial designs in 128 runs are provided for up to 33 2-level factors. The catalogues are complete, excluding resolution IV designs without 5-letter words, because these do not add value for a search for unblocked clear designs. The previous package version 1.0 with complete catalogues up to 24 runs (24 runs and a namespace added later) can be downloaded from the authors website.

Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS <https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast>, MeteoGalicia <https://www.meteogalicia.gal/web/modelos/threddsIndex.action>, NAM <https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale>, and RAP <https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update>.

The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

Uses a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. Details are described in Parast et al (2024) <doi:10.1093/biomtc/ujad035> and Hughes A et al (2025) <doi:10.1002/sim.70241>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogaterank> and a Shiny App implementing the package can be found at <https://parastlab.shinyapps.io/SurrogateRankApp/>.

planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies.

This package implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.

Loads and displays images, selectively masks specified background colors, bins pixels by color using either data-dependent or automatically generated color bins, quantitatively measures color similarity among images using one of several distance metrics for comparing pixel color clusters, and clusters images by object color similarity. Uses CIELAB, RGB, or HSV color spaces. Originally written for use with organism coloration (reef fish color diversity, butterfly mimicry, etc), but easily applicable for any image set.

The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines.

This package provides functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022).

Check concordance of a vector of mutation impacts with standard dictionaries such as Sequence Ontology (SO) <http://www.sequenceontology.org/>, Mutation Annotation Format (MAF) <https://docs.gdc.cancer.gov/Encyclopedia/pages/Mutation_Annotation_Format_TCGAv2/> or Prediction and Annotation of Variant Effects (PAVE) <https://github.com/hartwigmedical/hmftools/tree/master/pave>. It enables conversion between SO/PAVE and MAF terms and selection of the most severe consequence where multiple ampersand (&) delimited impacts are given.

Split Knockoff is a data adaptive variable selection framework for controlling the (directional) false discovery rate (FDR) in structural sparsity, where variable selection on linear transformation of parameters is of concern. This proposed scheme relaxes the linear subspace constraint to its neighborhood, often known as variable splitting in optimization. Simulation experiments can be reproduced following the Vignette. Split Knockoffs is first defined in Cao et al. (2021) <doi:10.48550/arXiv.2103.16159>.

Calculates federal and state income taxes in the United States. It acts as a wrapper to the NBER's TAXSIM 35 (<http://taxsim.nber.org/taxsim35/>) tax simulator. TAXSIM 35 conducts the calculations, while usincometaxes prepares the data for TAXSIM 35, sends the data to TAXSIM 35's server or communicates with the Web Assembly file, retrieves the data, and places it into a data frame. All without the user worrying about this process.

This package implements the chain binomial model for analysis of infectious disease data. Contains functions for calculating probabilities of the final size of infectious disease outbreaks using the method from D. Ludwig (1975) <doi:10.1016/0025-5564(75)90119-4> and for outbreaks that are not concluded, from Lindstrøm et al. (2024) <doi:10.48550/arXiv.2403.03948>. The package also contains methods for estimation and regression analysis of secondary attack rates.

This package implements Data Envelopment Analysis (DEA) with a hyperbolic orientation using a non-linear programming solver. It enables flexible estimations with weight restrictions, non-discretionary variables, and a generalized distance function. Additionally, it allows for the calculation of slacks and super-efficiency scores. The methods are detailed in à ttl et al. (2023), <doi:10.1016/j.dajour.2023.100343>. Furthermore, the package provides a non-linear profitability estimation built upon the DEA framework.

The Programme for International Student Assessment (PISA) is a global study conducted by the Organization for Economic Cooperation and Development (OECD) in member and non-member countries to assess educational systems by assessing 15-year-old school students academic performance in mathematics, science, and reading. This datasets contains information on their scores and other socioeconomic characteristics, information about their school and its infrastructure, as well as the countries that are taking part in the program.

This package provides a collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012) <doi:10.1093/bioinformatics/btr690>.

Table 1 is the classical way to describe the patients in a clinical study. The amount of splits in the data in such a table is limited. Table1Heatmap draws a heatmap of all crosstables that can be generated with the data. Users can choose between showing the actual crosstables or direction of effect of associations, and highlight associations by number of patients or p-values. v1.2 - fixed "missing "no visible global function definition for ..".

RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes.

Import gaze data from edf files generated by the SR Research <https://www.sr-research.com/> EyeLink eye tracker. Gaze data, both recorded events and samples, is imported per trial. The package allows to extract events of interest, such as saccades, blinks, etc. as well as recorded variables and custom events (areas of interest, triggers) into separate tables. The package requires EDF API library that can be obtained at <https://www.sr-research.com/support/>.

This package provides functions and classes for spatial resampling to use with the rsample package, such as spatial cross-validation (Brenning, 2012) <doi:10.1109/IGARSS.2012.6352393>. The scope of rsample and spatialsample is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by spatialsample do not contain much overhead in memory.

This package provides functionality of a statistical testing implementation whether a dataset comes from a symmetric distribution when the center of symmetry is unknown, including Wilcoxon test and sign test procedure. In addition, sample size determination for both tests is provided. The Wilcoxon test procedure is described in Vexler et al. (2023) <https://www.sciencedirect.com/science/article/abs/pii/S0167947323000579>, and the sign test is outlined in Gastwirth (1971) <https://www.jstor.org/stable/2284233>.