Provides a set of udev rules to allow using Mooltipass devices into a compatible udev Linux box. This package is intended to be added as a rule to the udev-service-type in your operating-system configuration.

Simply installing this package will not have any effect. It is meant to be passed to the udev service.

Provides a set of udev rules to allow using Mooltipass devices into a compatible udev Linux box. This package is intended to be added as a rule to the udev-service-type in your operating-system configuration.

Simply installing this package will not have any effect. It is meant to be passed to the udev service.

Spectral response data for broadband ultraviolet and visible radiation sensors. Angular response data for broadband ultraviolet and visible radiation sensors and diffusers used as entrance optics. Data obtained from multiple sources were used: author-supplied data from scientific research papers, sensor-manufacturer supplied data, and published sensor specifications. Part of the r4photobiology suite Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.

Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.

Crypt::RandPasswd provides three functions that can be used to generate random passwords, constructed from words, letters, or characters. This code is a Perl implementation of the Automated Password Generator standard, like the program described in "A Random Word Generator For Pronounceable Passwords". This code is a re-engineering of the program contained in Appendix A of FIPS Publication 181, "Standard for Automated Password Generator".

This package was factored out of Plots.jl to allow any other plotting package to use the recipe pipeline. In short, the extremely lightweight RecipesBase.jl package can be depended on by any package to define "recipes": plot specifications of user-defined types, as well as custom plot types. RecipePipeline.jl contains the machinery to translate these recipes to full specifications for a plot.

This package provides the alpha-adjustment correction from "Benjamini, Y., & Hochberg, Y. (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x> Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300". For researchers interested in using the exact mathematical formulas and procedures as used in the original paper.

This package creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests.

Nonparametric estimation and inference of a non-decreasing monotone hazard ratio from a right censored survival dataset. The estimator is based on a generalized Grenander typed estimator, and the inference procedure relies on direct plugin estimation of a first order derivative. More details please refer to the paper "Nonparametric inference under a monotone hazard ratio order" by Y. Wu and T. Westling (2023) <doi:10.1214/23-EJS2173>.

This package provides LaTeX macros to easily and concisely typeset vectors and matrices in a flexible way such as to follow the RIGID notation convention. The package enables the user to define custom commands that can then be used in any math-mode environment to efficiently and rigorously typeset the notational elements commonly used in robotics research (and many other fields) for position vectors, rotation matrices, pose matrices, etc.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to lowess for smoothing and strucchange for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection.

The strict_rfc3339 Python module provides strict, simple, lightweight RFC3339 procedures. It enables or aims to:

• Convert UNIX timestamps to and from RFC3339.

• Produce RFC3339 strings with a UTC offset (Z) or with the offset that the C time module reports is the local timezone offset.

• Be simple with minimal dependencies/libraries.

• Avoid timezones as much as possible.

• Be very strict and follow RFC3339.

Build and manipulate partially ordered sets (posets), to perform some data analysis on them and to implement multi-criteria decision making procedures. Several efficient ways for generating linear extensions are implemented, together with functions for building mutual ranking probabilities, incomparability, dominance and separation scores (Fattore, M., De Capitani, L., Avellone, A., Suardi, A. (2024). A fuzzy posetic toolbox for multi-criteria evaluation on ordinal data systems. ANNALS OF OPERATIONS RESEARCH <doi:10.1007/s10479-024-06352-3>).

Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.

Simple and transparent parsing of genotype/dosage data from an input Variant Call Format (VCF) file, matching of genotype coordinates to the component Single Nucleotide Polymorphisms (SNPs) of an existing polygenic score (PGS), and application of SNP weights to dosages for the calculation of a polygenic score for each individual in accordance with the additive weighted sum of dosages model. Methods are designed in reference to best practices described by Collister, Liu, and Clifton (2022) <doi:10.3389/fgene.2022.818574>.

Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility.

Distribution free heteroscedastic tests for functional data. The following tests are included in this package: test of no main treatment or contrast effect and no simple treatment effect given in Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>, no main time effect, and no interaction effect based on original observations given in Wang and Akritas (2010a) <doi:10.1080/10485250903171621> and tests based on ranks given in Wang and Akritas (2010b) <doi:10.1016/j.jmva.2010.03.012>.

TargetScan miRNA target predictions for mouse assembled using data from the TargetScan website. TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer and 7mer sites that match the seed region of each miRNA. Also identified are sites with mismatches in the seed region that are compensated by conserved 3 pairing. In mammals, predictions are ranked based on the predicted efficacy of targeting as calculated using the context scores of the sites.

TargetScan miRNA target predictions for human assembled using data from the TargetScan website. TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer and 7mer sites that match the seed region of each miRNA. Also identified are sites with mismatches in the seed region that are compensated by conserved 3 pairing. In mammals, predictions are ranked based on the predicted efficacy of targeting as calculated using the context scores of the sites.

Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>. F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. <doi:10.2139/ssrn.2297383>. T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. <arXiv:1311.4057>.

This is a pedagogical package, designed to help students understanding convergence of random variables. It provides a way to investigate interactively various modes of convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d. random variables. Visualisation of simulated sample paths is possible through interactive plots. The approach is illustrated by examples and exercises through the function investigate', as described in Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>. The user can study his/her own sequences of random variables.

This package provides SPSS'- and SAS'-like output for cross tabulations of two categorical variables (CROSSTABS) and for hierarchical loglinear analyses of two or more categorical variables (LOGLINEAR). The methods are described in Agresti (2013, ISBN:978-0-470-46363-5), Ajzen & Walker (2021, ISBN:9780429330308), Field (2018, ISBN:9781526440273), Norusis (2012, ISBN:978-0-321-74843-0), Nussbaum (2015, ISBN:978-1-84872-603-1), Stevens (2009, ISBN:978-0-8058-5903-4), Tabachnik & Fidell (2019, ISBN:9780134790541), and von Eye & Mun (2013, ISBN:978-1-118-14640-8).