This package creates publication-quality forest plots and effect-size tables that display only the unadjusted and adjusted estimates for a user-specified exposure variable of interest, hiding confounder coefficients in accordance with causal inference principles. Supports logistic, linear, Poisson, and Cox proportional hazards models, with optional cumulative-adjustment visualisation. Built on ggplot2 and follows the tidyverse design philosophy.

Make acoustic cues to use with the R package ndl. The package implements functions used in the PLoS ONE paper "Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit." doi:10.1371/journal.pone.0174623

Calculates landscape metrics for categorical landscape patterns in a tidy workflow. landscapemetrics reimplements the most common metrics from FRAGSTATS (<https://www.fragstats.org/>) and new ones from the current literature on landscape metrics. This package supports terra SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.

This package implements nonparametric bootstrap tests for detecting monotonicity in regression functions from Hall, P. and Heckman, N. (2000) <doi:10.1214/aos/1016120363> Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with C++'. Tutorials and shiny application demo are available at <https://www.laylaparast.com/monotonicitytest> and <https://parastlab.shinyapps.io/MonotonicityTest>.

This package provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().

The package uses PStricks and pst-solides3d to draw three dimensional ribbons on a cylinder, torus, sphere, cone or paraboloid. The width of the ribbon, the number of turns, the colour of the outer and the inner surface of the ribbon may be set. In the case of circular and conical helices, one may also choose the number of ribbons.

Assorted files generated from droplet-based single-cell protocols, to be used for testing functions in DropletUtils. Primarily intended for storing files that directly come out of processing pipelines like 10X Genomics CellRanger software, prior to the formation of a SingleCellExperiment object. Unlike other packages, this is not designed to provide objects that are immediately ready for analysis.

Brings a set of tools to help and automatically realise the description of principal component analyses (from FactoMineR functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).

This package provides ggplot2'-based plotting functions for PanelMatch (Imai, Kim, Wang (2023) <doi:10.1111/ajps.12685>) results. Tidy-and-plot function pairs for three PanelMatch outputs: treatment effect estimates (PanelEstimate()), placebo test diagnostics (placebo_test()), and covariate balance checks (get_covariate_balance()). Supports multiple models, significance indicators, faceted balance grids, and full ggplot2 extensibility.

Set of tools to simplify application of atomic forecast verification metrics for (comparative) verification of ensemble forecasts to large data sets. The forecast metrics are imported from the SpecsVerification package, and additional forecast metrics are provided with this package. Alternatively, new user-defined forecast scores can be implemented using the example scores provided and applied using the functionality of this package.

This package provides an interactive shiny application to construct stage-structured life tables from tabular input data. The application includes input validation, demographic calculations, visualization tools, and export of tables and figures to support reproducible workflows in ecological and entomological studies. Methods for life table construction follow classical demographic approaches described in Martinez (2015) <doi:10.13140/RG.2.2.21333.65760>.

Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. <doi:10.1109/TVCG.2017.2744018>.

This package provides access to BAM files generated from RNA-seq data produced with different levels of gDNA contamination. It currently allows one to download a subset of the data published by Li et al., BMC Genomics, 23:554, 2022. This subset of data is formed by BAM files with about 100,000 alignments with three different levels of gDNA contamination.

Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.

Researchers doing a mixed-methods analysis (nested analysis as developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can use the package for the classification of cases and case selection using results of a linear regression. One can designate cases as typical, deviant, extreme and pathway case and use different case selection strategies for the choice of a case belonging to one of these types.

SpatialArtifacts provides a data-driven two-step workflow to identify, classify, and handle spatial artifacts in spatial transcriptomics data. The package combines median absolute deviation (MAD)-based outlier detection with morphological image processing (fill, outline, and star patterns) to detect edge and interior artifacts. It supports multiple platforms including 10x Genomics Visium (standard and HD), allowing for consistent quality control across different spatial resolutions.

Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.

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>.

The gem public_suffix is a domain name parser, written in Ruby, and based on the Public Suffix List. A public suffix is one under which Internet users can (or historically could) directly register names. Some examples of public suffixes are .com, .co.uk and pvt.k12.ma.us. The Public Suffix List is a list of all known public suffixes.

This package provides a number of useful hacks to solve common annoyances with the revtex4-1 package, and to define notation in common use within quantum information. In doing so, it imports and configures a number of commonly-available and used packages, and where reasonable, provides fallbacks. It also warns when users try to load packages which are known to be incompatible with revtex4-1.

This package provides tools for pricing and analysing financial derivatives under the classical lognormal diffusion model and geometric Brownian motion assumptions. The package provides analytical European option prices, Monte Carlo pricing with antithetic and control variates, confidence intervals, finite-difference Greeks, and path simulation for path-dependent payoffs. The simulation interfaces accept user-defined payoff functions, enabling transparent construction of custom contracts while reporting numerical uncertainty.

Responsive design aims to display text and other design elements well on variety of outputs, including electronic devices or various paper sizes. This package tries to achieve similar results with LaTeX. The main aim is to support the output of multiple editions of one document with readable font sizes regardless of the physical size of the produced PDF, from smartphones or e-ink readers to printed A4 pages.

This package provides a cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.