This package implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.

Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to Garcà a-Portugués and Meilán-Vila (2025) <doi:10.1080/01621459.2025.2521898> and Garcà a-Portugués and Meilán-Vila (2023) <doi:10.1007/978-3-031-32729-2_4>.

This package provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the (in)efficiency status and the probability of efficiency for decision-making units. The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>, Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>. The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication.

This package provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.

Bundles the datasets and functions used in the textbook by Philip Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis, Second Edition.

Wrangle and annotate different types of political texts. It also introduces Urgency Analysis, a new method for the analysis of urgency in political texts.

Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) <doi:10.1016/j.ecolmodel.2016.08.012>.

This package provides a collection of process capability index functions, such as C_p(), C_pk(), C_pm(), and others, along with metadata about each, like LaTeX equations and R expressions. Its primary purpose is to form a foundation for other quality control packages to build on top of, by providing basic resources and functions. The indices belong to the field of statistical quality control, and quantify the degree to which a manufacturing process is able to create items that adhere to a certain standard of quality. For details see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8).

This package provides a native R client library for querying the Prometheus time-series database, using the PromQL query language.

This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at <https://www.pathlit.io/docs/cli/>.

Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.

Evaluates the strength of a surrogate marker by estimating the proportion of treatment effect explained (PTE) and relative power(RP) for the optimally-transformed version of the surrogate. Details available in Wang et al (2022) <arXiv:2209.08414>.

This package provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon ggplot2 and other plotting packages, and is designed to be easy to use and to work seamlessly with ggplot2 objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the ggplot2 ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.

This package provides a collection of color palettes inspired by the enormous diversity of skin colors in Neotropical poison frog species. Suitable for use with ggplot2 and base R graphics.

Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the coda and lattice packages, and make it easy to adjust graphical details.

Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042>. For full functionality, the software ncbi-blast+ is needed, see <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html> for more information.

This package implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method, described in Guilmineau et al (2025) <doi:10.1186/s12859-025-06118-z> is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

Visualize the partitions of simple decision trees, involving one or two predictors, on the scale of the original data. Provides an intuitive alternative to traditional tree diagrams, by visualizing how a decision tree divides the predictor space in a simple 2D plot alongside the original data. The parttree package supports both classification and regression trees from rpart and partykit', as well as trees produced by popular frontend systems like tidymodels and mlr3'. Visualization methods are provided for both base R graphics and ggplot2'.

This package provides tools for calculating and viewing topological properties of phylogenetic trees.

This package provides tools for downloading, reading and analyzing the National Survey of Demographic and Health - PNDS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.

This package provides a friendly API for sequence iteration and set comprehension.

This package provides a user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The Phenotype can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.