This package provides a method for modeling genetic data as a combination of discrete layers, within each of which relatedness may decay continuously with geographic distance. This package contains code for running analyses (which are implemented in the modeling language rstan') and visualizing and interpreting output. See the paper for more details on the model and its utility.

This package provides functions to carry out the most important crystallographic calculations for crystal structures made of 1d Gaussian-shaped atoms, especially useful for methods development. Main reference: E. Smith, G. Evans, J. Foadi (2017) <doi:10.1088/1361-6404/aa8188>.

Graphically display the (causal) effect of a continuous variable on a time-to-event outcome using multiple different types of plots based on g-computation. Those functions include, among others, survival area plots, survival contour plots, survival quantile plots and 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. For details, see Robin Denz, Nina Timmesfeld (2023) <doi:10.1097/EDE.0000000000001630>.

Imports conversation transcripts into R, concatenates them into a single dataframe appending event identifiers, cleans and formats the text, then yokes user-specified psycholinguistic database values to each word. ConversationAlign then computes alignment indices between two interlocutors across each transcript for >40 possible semantic, lexical, and affective dimensions. In addition to alignment, ConversationAlign also produces a table of analytics (e.g., token count, type-token-ratio) in a summary table describing your particular text corpus.

This package provides a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

Uses the CMS application programming interface <https://dnav.cms.gov/api/healthdata> to provide users databases containing yearly Medicare reimbursement rates in the United States. Data can be acquired for the entire United States or only for specific localities. Currently, support is only provided for the Medicare Physician Fee Schedule, but support will be expanded for other CMS databases in future versions.

Augment clinical data with metadata to create output used in conventional publications and reports.

This package provides a spatially-aware cell clustering algorithm is provided with cluster significance assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment. More details can be referred to Peng Xie, et al. (2025) <doi:10.1016/j.cell.2025.05.035>.

This package provides methods and plotting functions for displaying categorical data on an interactive heatmap using plotly'. Provides functionality for strictly categorical heatmaps, heatmaps illustrating categorized continuous data and annotated heatmaps. Also, there are various options to interact with the x-axis to prevent overlapping axis labels, e.g. via simple sliders or range sliders. Besides the viewer pane, resulting plots can be saved as a standalone HTML file, embedded in R Markdown documents or in a Shiny app.

Includes functions to calculate scores and marks for track and field combined events competitions. The functions are based on the scoring tables for combined events set forth by the International Association of Athletics Federation (2001).

Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.

This package provides a set of fast tools for converting a textual corpus into a set of normalized tables. Users may make use of the udpipe back end with no external dependencies, or a Python back ends with spaCy <https://spacy.io>. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, and dependency parsing.

Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.

This package provides estimation procedures for copula-based stochastic frontier models for cross-sectional data. The package implements maximum likelihood estimation of stochastic frontier models allowing flexible dependence structures between inefficiency and noise terms through various copula families (e.g., Gaussian and Student-t). It enables estimation of technical efficiency scores, log-likelihood values, and information criteria (AIC and BIC). The implemented framework builds upon stochastic frontier analysis introduced by Aigner, Lovell and Schmidt (1977) <doi:10.1016/0304-4076(77)90052-5> and the copula theory described in Joe (2014, ISBN:9781466583221). Empirical applications of copula-based stochastic frontier models can be found in Wiboonpongse et al. (2015) <doi:10.1016/j.ijar.2015.06.001> and Maneejuk et al. (2017, ISBN:9783319562176).

Useful libraries for building a Java based GUI under R are provided.

The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided. See Martos, L.A.P., Garcà a-Vico, à .M., González, P. et al.(2023) <doi:10.1007/s13748-022-00294-2> "Clustering: an R library to facilitate the analysis and comparison of cluster algorithms.", Martos, L.A.P., Garcà a-Vico, à .M., González, P. et al. "A Multiclustering Evolutionary Hyperrectangle-Based Algorithm" <doi:10.1007/s44196-023-00341-3> and L.A.P., Garcà a-Vico, à .M., González, P. et al. "An Evolutionary Fuzzy System for Multiclustering in Data Streaming" <doi:10.1016/j.procs.2023.12.058>.

Maximum likelihood estimation in respondent driven samples.

This package provides a collection of utilities for the statistical analysis of multivariate circular data using distributions based on Multivariate Nonnegative Trigonometric Sums (MNNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more.

Allows Brownian motion, fractional Brownian motion, and integrated Ornstein-Uhlenbeck process components to be added to linear and non-linear mixed effects models using the structures and methods of the nlme package.

The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.

Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square statistic. Implemented are the tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998) <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>, Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010) <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996> included for comparison. The utility functions nested.to.contingency and paired.to.contingency convert data between various useful formats. Thyroids and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989) <doi:10.1016/0165-1781(89)90196-0> respectively.

This package provides functions and a workflow to easily and powerfully calculating specificity, sensitivity and ROC curves of biomarkers combinations. Allows to rank and select multi-markers signatures as well as to find the best performing sub-signatures, now also from single-cell RNA-seq datasets. The method used was first published as a Shiny app and described in Mazzara et al. (2017) <doi:10.1038/srep45477> and further described in Bombaci & Rossi (2019) <doi:10.1007/978-1-4939-9164-8_16>, and widely expanded as a package as presented in the bioRxiv pre print Ferrari et al. <doi:10.1101/2022.01.17.476603>.

Estimate and return the needed parameters for visualisations designed for OpenBudgets <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the OpenBudgets data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to JSON format and use them as input in a different graphical interface.

This package provides functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.