This package provides a function kitten() which creates cute little packages which pass R package checks. This sets it apart from package.skeleton() which it calls, and which leaves imperfect files behind. As this is not exactly helpful for beginners, kitten() offers an alternative. Unit test support can be added via the tinytest package (if present), and documentation-creation support can be added via roxygen2 (if present).

Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>, Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and Liao et al. (2015) <doi:10.1136/bmj.h1885>.

This package provides function for performing Bayesian survival regression using Horseshoe prior in the accelerated failure time model with log normal assumption in order to achieve high dimensional pan-cancer variable selection as developed in Maity et. al. (2019) <doi:10.1111/biom.13132>.

This package provides a small, dependency-free way to generate random names. Methods provided include the adjective-surname approach of Docker containers ('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go>'), and combinations of common English or Spanish words.

Power calculations are a critical component of any research study to determine the minimum sample size necessary to detect differences between multiple groups. Here we present an R package, PASSED', that performs power and sample size calculations for the test of two-sample means or ratios with data following beta, gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal, Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric, and negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>) distributions.

Analyzis and filtering of phylogenomics datasets. It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) <doi:10.1101/2021.09.08.459421>), a generalization of classical multidimensional scaling to multiple distance matrices.

Simulates judgments of frequency and duration based on the Probability Associator Time (PASS-T) model. PASS-T is a memory model based on a simple competitive artificial neural network. It can imitate human judgments of frequency and duration, which have been extensively studied in cognitive psychology (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010) <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model is an extension of the PASS model (Sedlmeier, 2002, ISBN:0198508638). The package provides an easy way to run simulations, which can then be compared with empirical data in human judgments of frequency and duration.

Given a SpatialPolygonsDataFrame and a set of populations for each polygon, compute a population density estimate based on Tobler's pycnophylactic interpolation algorithm. The result is a SpatialGridDataFrame. Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.

Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. phylopairs calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in Stan programs via the rstan package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) <doi:10.1111/j.1558-5646.1989.tb04233.x>, Fitzpatrick (2002) <doi:10.1111/j.0014-3820.2002.tb00860.x>, and Castillo (2007) <doi:10.1002/ece3.3093>.

This package provides functions and mined database from UniProt focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2025) <doi:10.1002/pmic.202400238>.

Classification based analysis of DNA sequences to taxonomic groupings. This package primarily implements Naive Bayesian Classifier from the Ribosomal Database Project. This approach has traditionally been used to classify 16S rRNA gene sequences to bacterial taxonomic outlines; however, it can be used for any type of gene sequence. The method was originally described by Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology 73(16):5261-7 <doi:10.1128/AEM.00062-07>. The package also provides functions to read in FASTA'-formatted sequence data.

Some functions at the intersection of dplyr and purrr that formerly lived in purrr'.

Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the Power Lindley distribution. Specificity, sensitivity, area under the curve and ROC curve are provided.

This package provides a novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.

Procrustes analyses to infer co-phylogenetic matching between pairs of phylogenetic trees.

This package implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

An alternative data structure and visual rendering for the profiling information generated by Rprof.

Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and best k treatments POTH can also be calculated to improve interpretation of treatment hierarchies.

This package provides tools for analyzing data generated from conjoint survey experiments, a method widely used in the social sciences for studying multidimensional preferences. The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) <https://gking.harvard.edu/conjointE>.

Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) <https://repec.unibz.it/bemps104.pdf>; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) <doi:10.1016/j.jeconom.2016.03.005>) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented.

This package provides a shiny application for visualizing high-dimensional data using non-linear dimensionality reduction (NLDR) techniques such as t-SNE and UMAP. It provides an interactive platform to explore high-dimensional datasets, diagnose the quality of the embeddings using the quollr package, and compare different NLDR methods.

This package implements the algorithm of Christensen (2024) <doi:10.1214/22-BA1353> for estimating marginal likelihoods via permutation counting.

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>.