This package performs simulations of binary spatial raster data using the Ising model (Ising (1925) <doi:10.1007/BF02980577>; Onsager (1944) <doi:10.1103/PhysRev.65.117>). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) <doi:10.1098/rsos.231005>).

Allows users to list data structures using path-based navigation. Provides intuitive methods for storing, accessing, and manipulating nested data through simple path strings. Key features include strict mode validation, path existence checking, recursive operations, and automatic parent-level creation. Designed for use cases requiring organized storage of complex nested data while maintaining simple access patterns. Particularly useful for configuration management, nested settings, and any application where data naturally forms a tree-like structure.

Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.

Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable. Test of independence and feature screening for classification problems can be implemented via the two dependence measures. For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>; Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>; Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.

We develop a novel matrix factorization tool named scINSIGHT to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, scINSIGHT simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) <doi:10.1186/s13059-022-02649-3>.

Database of genes which frequently sustain somatic mutations, but are unlikely to drive cancer.

An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model.

The steepness package computes steepness as a property of dominance hierarchies. Steepness is defined as the absolute slope of the straight line fitted to the normalized David's scores. The normalized David's scores can be obtained on the basis of dyadic dominance indices corrected for chance or by means of proportions of wins. Given an observed sociomatrix, it computes hierarchy's steepness and estimates statistical significance by means of a randomization test.

This program calculates bioclimatic indices and fluxes (radiation, evapotranspiration, soil moisture) for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios. Predictions are based on a minimum of required inputs: latitude, precipitation, air temperature, and cloudiness. Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>.

This package provides deep learning models for right-censored survival data using the torch backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.

This package provides a simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.

This package provides methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (â Equity Asset Valuation (4th Edition)â , 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).

This package provides a statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.

Datasets detailing the results, castaways, and events of each season of Survivor for the US, Australia, South Africa, New Zealand, and the UK. This includes details on the cast, voting history, immunity and reward challenges, jury votes, boot order, advantage details, and episode ratings. Use this for analysis of trends and statistics of the game.

Make interactive d3.js sequence sunburst diagrams in R with the convenience and infrastructure of an htmlwidget'.

Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche.

The President of the United States is constitutionally obligated to provide a report known as the State of the Union'. The report summarizes the current challenges facing the country and the president's upcoming legislative agenda. While historically the State of the Union was often a written document, in recent decades it has always taken the form of an oral address to a joint session of the United States Congress. This package provides the raw text from every such address with the intention of being used for meaningful examples of text analysis in R. The corpus is well suited to the task as it is historically important, includes material intended to be read and material intended to be spoken, and it falls in the public domain. As the corpus spans over two centuries it is also a good test of how well various methods hold up to the idiosyncrasies of historical texts. Associated data about each address, such as the year, president, party, and format, are also included.

The skew logistic distribution is a quantile-defined generalisation of the logistic distribution (van Staden and King 2015). Provides random numbers, quantiles, probabilities, densities and density quantiles for the distribution. It provides Quantile-Quantile plots and method of L-Moments estimation (including asymptotic standard errors) for the distribution.

Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.

An index is created using a mathematical model that transforms multi-dimensional variables into a single value. These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices. For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) <DOI:10.1080/01621459.1986.10478336>, Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) <DOI:10.3389/fams.2023.1274530>.

This package provides a coordinate descent algorithm for computing the solution paths of the sparse and coupled sparse asymmetric least squares, including the (adaptive) elastic net and Lasso penalized SALES and COSALES regressions.

Interface to the Sensor Tower API <https://app.sensortower.com/api/docs/app_analysis> for mobile app analytics and market intelligence. Provides functions to retrieve app metadata, publisher information, download and revenue estimates, active user metrics, category rankings, and market trends. The package includes data processing utilities to clean and aggregate metrics across platforms, automatic app name resolution, and tools for generating professional analytics dashboards. Supports both iOS and Android app ecosystems with unified data structures for cross-platform analysis.

This package provides functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and Chiou et al. (2018) <doi:10.1111/insr.12271>.

This package provides tests for segregation distortion in F1 polyploid populations under different assumptions of meiosis. These tests can account for double reduction, partial preferential pairing, and genotype uncertainty through the use of genotype likelihoods. Parallelization support is provided. Details of these methods are described in Gerard et al. (2025a) <doi:10.1007/s00122-025-04816-z> and Gerard et al. (2025b) <doi:10.1101/2025.06.23.661114>. Part of this material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation.