Handle data from evolve and resequence experiments. Measured allele frequencies (e.g., from variants called from high-throughput sequencing data) are compared using an update of the PsiSeq algorithm (Earley, Eric and Corbin Jones (2011) <doi:10.1534/genetics.111.129445>). Functions for saving and loading important files are also included, as well as functions for basic data visualization.

Performant interactive scatterplot for ~ 1 million points. Zoom, pan, and pick points. Includes tooltips, labels, a grid overlay, legend, and coupled interactions across multiple plots.

This package provides a collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages.

Translating messages in R packages is managed using the po top-level directory and the gettext program. This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks.

Price comparisons within or between countries provide an overall measure of the relative difference in prices, often denoted as price levels. This package provides index number methods for such price comparisons (e.g., The World Bank, 2011, <doi:10.1596/978-0-8213-9728-2>). Moreover, it contains functions for sampling and characterizing price data.

Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.

This package provides a toolbox to facilitate the calculation of political system indicators for researchers. This package offers a variety of basic indicators related to electoral systems, party systems, elections, and parliamentary studies, as well as others. Main references are: Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>; Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>; Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>; Rae (1968) <doi:10.1177/001041406800100305>; HirschmaÅ (1945) <ISBN:0-520-04082-1>; Kesselman (1966) <doi:10.2307/1953769>; Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>; Rice (1925) <doi:10.2307/2142407>; Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>; SANTOS (2002) <ISBN:85-225-0395-8>.

An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.

Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read "Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection" <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.

This package provides functions to perform Bayesian inference on absorption time data for Phase-type distributions. The methods of Bladt et al (2003) <doi:10.1080/03461230110106435> and Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf> are provided.

Run simulations to assess the impact of various designs features and the underlying biological behaviour on the outcome of a Patient Derived Xenograft (PDX) population study. This project can either be deployed to a server as a shiny app or installed locally as a package and run the app using the command populationPDXdesignApp()'.

Manipulation and analysis of phylogenetically simulated data sets and phylogenetically based analyses using GLS.

Reviews other packages during code review by looking at their dependencies, code style, code complexity, and how internally defined functions interact with one another.

This package provides methods for reducing the number of features within a data set. See Bauer JO (2021) <doi:10.1145/3475827.3475832> and Bauer JO, Drabant B (2021) <doi:10.1016/j.jmva.2021.104754> for more information on principal loading analysis.

This package provides a comprehensive and easy to use R implementation of confirmatory phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.

Preregistrations, or more generally, registrations, enable explicit timestamped and (often but not necessarily publicly) frozen documentation of plans and expectations as well as decisions and justifications. In research, preregistrations are commonly used to clearly document plans and facilitate justifications of deviations from those plans, as well as decreasing the effects of publication bias by enabling identification of research that was conducted but not published. Like reporting guidelines, (pre)registration forms often have specific structures that facilitate systematic reporting of important items. The preregr package facilitates specifying (pre)registrations in R and exporting them to a human-readable format (using R Markdown partials or exporting to an HTML file) as well as human-readable embedded data (using JSON'), as well as importing such exported (pre)registration specifications from such embedded JSON'.

Conduct power analyses and inference of marginal effects. Uses plug-in estimation and influence functions to perform robust inference, optionally leveraging historical data to increase precision with prognostic covariate adjustment. The methods are described in Højbjerre-Frandsen et al. (2025) <doi:10.48550/arXiv.2503.22284>.

Bayesian variable selection for regression models of under-reported count data as well as for (overdispersed) Poisson, negative binomal and binomial logit regression models using spike and slab priors.

Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>). Likelihood approximation based on adaptive Gauss Hermite quadrature rule.

Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.

Allows the user to perform ANOVA tests (in a strict sense: continuous and normally-distributed Y variable and 1 or more factorial/categorical X variable(s)), with the possibility to specify the type of sum of squares (1, 2 or 3), the types of variables (Fixed or Random) and their relationships (crossed or nested) with the sole function of the package (FullyParamANOVA()). The resulting outputs are the same as in SAS software. A dataset (Butterfly) to test the function is also joined.

This package provides a collection of tools for approximating the PDQ functions (respectively, the cumulative distribution, density, and quantile) of probability distributions via classical expansions involving moments and cumulants.

This package provides functionality for Bayesian analysis of replication studies using power prior approaches (Pawel et al., 2023) <doi:10.1007/s11749-023-00888-5>.

This package infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.