This package provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan. Model fitting is performed via the rstan package. Users can generate realistic interaction data where individual phenotypes influence and respond to those of their partners, with control over sampling design parameters such as the number of individuals, partners, and repeated dyads. The simulation framework allows flexible control over variation and correlation in mean trait values, social responsiveness, and social impact, making it suitable for research on interacting phenotypes and on direct and indirect genetic effects ('DGEs and IGEs'). The package also includes functions to fit and compare alternative models of social effects, including impactâ responsiveness, varianceâ partitioning, and trait-based models, and to summarise model performance in terms of bias and dispersion. For a more detailed description of the available models and impactâ responsiveness, see the accompanying article Wijnhorst et al. (2026) <doi:10.1093/jeb/voag013>.

Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.

Stochastic frontier analysis with advanced methods. In particular, it applies the approach proposed by Latruffe et al. (2017) <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical inefficiency effects when one input is endogenous.

This package provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.

R language bindings for SolveBio's API. SolveBio is a biomedical knowledge hub that enables life science organizations to collect and harmonize the complex, disparate "multi-omic" data essential for today's R&D and BI needs.

Supervised and unsupervised multivariate methods, supplemented by GUI and some visualizations, to perform various analyses in the field of computational stylistics, authorship attribution, etc. For further reference, see Eder et al. (2016), <https://journal.r-project.org/articles/RJ-2016-007/index.html>. You are also encouraged to visit the Computational Stylistics Group's website <https://computationalstylistics.github.io/>, where a reasonable amount of information about the package and related projects are provided.

Calculation of solar zenith and azimuth angles.

Set of functions that access information about deputies and votings in Polish diet from webpage <http://www.sejm.gov.pl>. The package was developed as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty of Mathematics and Information Science, Warsaw University of Technology.

Remove batch effects by projecting query batches into the reference batch space.

Manages and display stellar tracks and isochrones from Pisa low-mass database. Includes tools for isochrones construction and tracks interpolation.

Reference data sets of species sensitivities to compare the results of fitting species sensitivity distributions using software such as ssdtools and Burrlioz'. It consists of 17 primary data sets from four different Australian and Canadian organizations as well as five datasets from anonymous sources. It also includes a data set of the results of fitting various distributions using different software.

This package provides convenience functions to replace hyphen-minuses (ASCII 45) with proper minus signs (Unicode character 2212). The true minus matches the plus symbol in width, line thickness, and height above the baseline. It was designed for mathematics, looks better in presentation, and is understood properly by screen readers.

This package provides a consistent interface to use various methods to calculate the periodogram and estimate the period of a rhythmic time-course. Methods include Lomb-Scargle, fast Fourier transform, and three versions of the chi-square periodogram. See Tackenberg and Hughey (2021) <doi:10.1371/journal.pcbi.1008567>.

It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <doi:10.48550/arXiv.0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

Compute the position of the sun, and local solar time using Meeus formulae. Compute day and/or night length using different twilight definitions or arbitrary sun elevation angles. This package is part of the r4photobiology suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>. Algorithms from Meeus (1998, ISBN:0943396611).

This is a compilation of my preferred themes and related theme elements for ggplot2'. I believe these themes and theme elements are aesthetically pleasing, both for pedagogical instruction and for the presentation of applied statistical research to a wide audience. These themes imply routine use of easily obtained/free fonts, simple forms of which are included in this package.

Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).

This package provides a sparklyr <https://spark.posit.co/> extension that provides an R interface for XGBoost <https://github.com/dmlc/xgboost> on Apache Spark'. XGBoost is an optimized distributed gradient boosting library.

Density, distribution function, quantile function and random generation for the sum of independent non-identical binomial distribution with parameters \codesize and \codeprob.

Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.

This package implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.

Create sets of variables based on a mutual information approach. In this context, a set is a collection of distinct elements (e.g., variables) that can also be treated as a single entity. Mutual information, a concept from probability theory, quantifies the dependence between two variables by expressing how much information about one variable can be gained from observing the other. Furthermore, you can analyze, and visualize these sets in order to better understand the relationships among variables.