This package provides a set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is implemented, which returns inference on the true efficacy of the drug from the pre- and post-treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. Functions to perform power analyses for faecal egg counts (including FECRT) are also provided.

Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. <doi:10.1007/s11135-025-02351-z>.

Fit growth models to otoliths and/or tagging data, using the RTMB package and maximum likelihood. The otoliths (or similar measurements of age) provide direct observed coordinates of age and length. The tagging data provide information about the observed length at release and length at recapture at a later time, where the age at release is unknown and estimated as a vector of parameters. The growth models provided by this package can be fitted to otoliths only, tagging data only, or a combination of the two. Growth variability can be modelled as constant or increasing with length.

It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE) and Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG) at Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by Moraes, Ferreira and Machado (2021) <doi:10.1007/s40815-020-00936-4>.

Write beautiful yet customizable letters in R Markdown and directly obtain the finished PDF. Smooth generation of PDFs is realized by rmarkdown', the pandoc-letter template and the KOMA-Script letter class. KOMA-Script provides enhanced replacements for the standard LaTeX classes with emphasis on typography and versatility. KOMA-Script is particularly useful for international writers as it handles various paper formats well, provides layouts for many common window envelope types (e.g. German, US, French, Japanese) and lets you define your own layouts. The package comes with a default letter layout based on DIN 5008B'.

Analyses the stability and structural behaviour of export and import patterns across multiple countries using a Markov chain modelling framework. Constructs transition probability matrices to quantify changes in trade shares between successive periods, thereby capturing persistence, structural shifts, and inter-country interdependence in trade performance. By iteratively generating expected trade distributions over time, the approach facilitates assessment of stability, long-run equilibrium tendencies, and comparative dynamics in longitudinal trade data, providing a rigorous tool for empirical analysis of exportâ import behaviour. Methodological foundations follow standard Markov chain theory as described in Gagniuc (2017) <Doi:10.1002/9781119387596>.

This package provides a tidy workflow for landscape-scale analysis. multilandr offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the landscapemetrics package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about multilandr is provided in an introductory vignette included in this package and in the paper by Huais (2024) <doi:10.1007/s10980-024-01930-z>; see citation("multilandr") for details.

This package implements modern resampling and permutation methods for robust statistical inference without restrictive parametric assumptions. Provides bias-corrected and accelerated (BCa) bootstrap (Efron and Tibshirani (1993) <doi:10.1201/9780429246593>), wild bootstrap for heteroscedastic regression (Liu (1988) <doi:10.1214/aos/1176351062>, Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>), block bootstrap for time series (Politis and Romano (1994) <doi:10.1080/01621459.1994.10476870>), and permutation-based multiple testing correction (Westfall and Young (1993) <ISBN:0-471-55761-7>). Methods handle non-normal data, heteroscedasticity, time series correlation, and multiple comparisons.

This package provides a tool for visualizing numerical data (e.g., gene expression, protein abundance) on predefined anatomical maps of human/mouse organs and subcellular organelles. It supports customization of color schemes, filtering by organ systems (for organisms) or organelle types, and generation of optional bar charts for quantitative comparison. The package integrates coordinate data for organs and organelles to plot anatomical/subcellular contours, mapping data values to specific structures for intuitive visualization of biological data distribution.The underlying method was described in the preprint by Zhou et al. (2022) <doi:10.1101/2022.09.07.506938>.

This package provides adaptive direct sparse regression for high-dimensional multimodal data with heterogeneous missing patterns and measurement errors. AdapDISCOM extends the DISCOM framework with modality-specific adaptive weighting to handle varying data structures and error magnitudes across blocks. The method supports flexible block configurations (any K blocks) and includes robust variants for heavy-tailed distributions ('AdapDISCOM'-Huber) and fast implementations for large-scale applications (Fast-'AdapDISCOM'). Designed for realistic multimodal scenarios where different data sources exhibit distinct missing data patterns and contamination levels. Diakité et al. (2025) <doi:10.48550/arXiv.2508.00120>.

Helps visualizing what is summarized in Pearson's correlation coefficient. That is, it visualizes its main constituent, namely the distances of the single values to their respective mean. The visualization thereby shows what the etymology of the word correlation contains: In pairwise combination, bringing back (see package Vignette for more details). I hope that the correlatio package may benefit some people in understanding and critically evaluating what Pearson's correlation coefficient summarizes in a single number, i.e., to what degree and why Pearson's correlation coefficient may (or may not) be warranted as a measure of association.

Estimates interrater-reliability intraclass correlation coefficients (ICCs) within the generalizability-theory framework using modern variance-component estimation (linear mixed models) rather than the classical analysis-of-variance mean-squares approach. Provides the full ICC family (absolute agreement versus consistency, single versus average, fixed versus random raters, one-way versus two-way) with boundary-aware Monte-Carlo confidence intervals, support for imbalanced, incomplete, and multilevel (nested) designs, decision-study projection to other numbers of raters, and an interactive helper for choosing the correct coefficient. Multilevel methods follow ten Hove, Jorgensen and van der Ark (2022) <doi:10.1037/met0000391>.

It can be used to create/encode molecular "license-plates" from sequences and to also decode the "license-plates" back to sequences. While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) <doi:10.1093/bioinformatics/btw194>. It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) <doi:10.1038/srep41184>.

An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.

Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.

This package implements multi-study learning algorithms such as merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. Embedded within the caret framework, this package allows for a wide range of single-study learners (e.g., neural networks, lasso, random forests). The package offers over 20 default similarity measures and allows for specification of custom similarity measures for covariate-profile similarity weighting and an accept/reject step. This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019) <doi:10.1101/856385>.

Reconstruct phylogenetic trees from discrete data. Inapplicable character states are handled using the algorithm of Brazeau, Guillerme and Smith (2019) <doi:10.1093/sysbio/syy083> with the "Morphy" library, under equal or implied step weights. Contains a "shiny" user interface for interactive tree search and exploration of results, including character visualization, rogue taxon detection, tree space mapping, and cluster consensus trees (Smith 2022a, b) <doi:10.1093/sysbio/syab099>, <doi:10.1093/sysbio/syab100>. Profile Parsimony (Faith and Trueman, 2001) <doi:10.1080/10635150118627>, Successive Approximations (Farris, 1969) <doi:10.2307/2412182> and custom optimality criteria are implemented.

CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes.

Utilities for working with hourly air quality monitoring data with a focus on small particulates (PM2.5). A compact data model is structured as a list with two dataframes. A meta dataframe contains spatial and measuring device metadata associated with deployments at known locations. A data dataframe contains a datetime column followed by columns of measurements associated with each "device-deployment". Algorithms to calculate NowCast and the associated Air Quality Index (AQI) are defined at the US Environmental Projection Agency AirNow program: <https://document.airnow.gov/technical-assistance-document-for-the-reporting-of-daily-air-quailty.pdf>.

This package provides a modular toolkit for feature extraction and density-based clustering of time-series data. It provides classical statistical, discrete wavelet, Hilbert-based phase, and circular statistical features. The Hilbert-based phase representation can support the analysis of periodic patterns, phase relationships, and circular behavior in time-series data. The package supports DBSCAN and OPTICS clustering, cluster evaluation, visualization, data preparation, and comparison of multiple feature extraction and clustering combinations. Methods are described in Karakaya and Purutcuoglu (2026) <doi:10.15672/hujms.1821412> and Karakaya et al. (2026) <doi:10.1007/978-3-032-17020-0_27>.

Dual Wavelet based Nonlinear Autoregressive Distributed Lag model has been developed for noisy time series analysis. This package is designed to capture both short-run and long-run relationships in time series data, while incorporating wavelet transformations. The methodology combines the NARDL model with wavelet decomposition to better capture the nonlinear dynamics of the series and exogenous variables. The package is useful for analyzing economic and financial time series data that exhibit both long-term trends and short-term fluctuations. This package has been developed using algorithm of Jammazi et al. <doi:10.1016/j.intfin.2014.11.011>.

This package provides a literature-derived database of immune cell markers formatted as Gene Matrix Transposed (GMT) files. Users can search immune cell signatures, retrieve marker lists, export GMT files, create custom marker sets, and score gene-by-cell expression matrices with dependency-free rank-based or mean-expression methods. Cell subpopulations are distinguished by their source PMIDs. For the core curation of the lung cell atlas, see Travaglini et al. (2020) <doi:10.1038/s41586-020-2922-4>. For the pan-cancer B cell signatures, see Fitzsimons et al. (2024) <doi:10.1016/j.ccell.2024.09.011>.

This package contains kernel smoothing tools designed for use by historical dialectologists and philologists for exploring spatial and temporal patterns in noisy historical language data, such as that obtained from historical texts. The main way in which these might differ from other implementations of kernel smoothing is that they assume that the function (linguistic variable) being explored has the form of the relative frequency of a series of discrete possibilities (linguistic variants). This package also offers a way of exploring distributions in 2-dimensional space and in time with separate kernels, and tools for identifying appropriate bandwidths for these.

The developed function is designed to facilitate the seamless conversion of KML (Keyhole Markup Language) files to Shapefiles while preserving attribute values. It provides a straightforward interface for users to effortlessly import KML data, extract relevant attributes, and export them into the widely compatible Shapefile format. The package ensures accurate representation of spatial data while maintaining the integrity of associated attribute information. For details see, Flores, G. (2021). <DOI:10.1007/978-3-030-63665-4_15>. Whether for spatial analysis, visualization, or data interoperability, it simplifies the conversion process and empowers users to seamlessly work with geospatial datasets.