Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration.

This package contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.

Rudimentary functions for sampling and calculating density from the matrix-variate variance-gamma distribution.

This package performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) <DOI:10419/161931>.

This package performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Functions that use isotonic regression in the first stage of binning process have an additional feature for correction of minimum percentage of observations and minimum target rate per bin. Additionally, monotonic trend can be identified based on raw data or, if known in advance, forced by functions argument. Missing values and other possible special values are treated separately from so-called complete cases.

This package provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.

The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm() and glm(). A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm(), glm() and associated summaries from the package stats'.

This package implements multi-factor curve analysis for grouped data in R', replicating and extending the functionality of the the Stata ado mfcurve (Krähmer, 2023) <https://ideas.repec.org/c/boc/bocode/s459224.html>. Related to the idea of specification curve analysis (Simonsohn, Simmons, and Nelson, 2020) <doi:10.1038/s41562-020-0912-z>. Includes data preprocessing, statistical testing, and visualization of results with confidence intervals.

Simulates Blockchain-Enabled Maritime Transportation Systems (BMTS) by integrating asynchronous, great-circle vessel kinematics with decentralised edge-computing consensus layers. The package provides a fully self-contained cyber-range running completely offline, permitting the risk-free injection of spatial and telemetry threat vectors (e.g., geofence breaches, coordinate modification, and node impersonation). It includes an interactive workspace engine built using shinydashboard and leaflet map layers for monitoring vessel states and ledger updates. Implemented consensus structures are modeled on a lightweight Proof-of-Authentication framework optimized for resource-constrained distributed nodes.

Generate multivariate discrete data with generalized Poisson, negative binomial and binomial marginal distributions using user-specified distribution parameters and a target correlation matrix. The method is described in Cheng and Demirtas (2026) <doi:10.48550/arXiv.2602.07707>.

Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).

Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in multimark combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, multimark also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.

Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) <http://www.jstor.org/stable/1392277> proposal.

This package provides tools to conduct Bayesian multivariate network meta-analysis providing - the single correlation coefficient model by Efthimiou et al. (2015) <doi:10.1093/biostatistics/kxu030>; - per-outcome consistency checks using the node-splitting method (Dias et al., 2010) <doi:10.1002/sim.3767>; - per-outcome treatment hierarchies using the surface under the cumulative ranking curve (SUCRA), the probability of best value, or median (or mean) ranks (Salanti et al., 2011) <doi:10.1016/j.jclinepi.2010.03.016>; - across-outcomes benefit-risk assessment using the ViÅ¡eKriterijumska Optimizacija I Kompromisno ReÅ¡enje (VIKOR) method (Opricovic & Tzeng, 2004) <doi:10.1016/S0377-2217(03)00020-1>; - convergence checks using trace plots, density plots, or the R-hat statistic; - forest plots of treatment estimates and consistency checks, scatter plots of per-outcome rankings, Hasse diagrams (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569> to visualize the partial order of the treatments across all outcomes.

This package provides functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.

This package provides a web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the mlr3 framework.

Complements the book "Using R for Modelling and Quantitative Methods in Fisheries" ISBN 9780367469894, published in 2021 by Chapman & Hall in their "Using R series". There are numerous functions and data-sets that are used in the book's many practical examples.

Sends supported R objects to the Mellio web app and creates polished, editable statistical tables in R'. The mellio_open interface handles common hypothesis tests, model objects, model comparisons, descriptive summaries, tabular data, plots, and image files. The melliotab interface formats data frames, model summaries, correlation matrices, and side-by-side comparison tables with APA-style numeric formatting, confidence intervals, table notes, and optional significance markers. Manual table helpers can copy or save melliotab output as HTML', LaTeX', or Markdown when file-based handoff is needed. Payloads include package-version metadata to support reproducible reporting and software citation.

This package provides functions, which make matrix creation conciser (such as the core package's function m() for rowwise matrix definition or runifm() for random value matrices). Allows to set multiple matrix values at once, by using list of formulae. Provides additional matrix operators and dedicated plotting function.

Analyzes production and dispersal of seeds dispersed from trees and recovered in seed traps. Motivated by long-term inventory plots where seed collections are used to infer seed production by each individual plant.

This package contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) <doi:10.1016/j.csda.2009.05.006> and Komárek and Komárková (2014, J. of Stat. Soft.) <doi:10.18637/jss.v059.i12>. It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) <doi:10.1214/12-AOAS580>, Hughes, Komárek, Bonnett, Czanner, Garcà a-Fiñana (2017, Stat. in Med.) <doi:10.1002/sim.7397>, Jaspers, Komárek, Aerts (2018, Biom. J.) <doi:10.1002/bimj.201600253> and Hughes, Komárek, Czanner, Garcà a-Fiñana (2018, Stat. Meth. in Med. Res) <doi:10.1177/0962280216674496>.

This package provides a modular framework for exact multi-objective spatial planning using mixed-integer programming. The package supports the definition of planning problems through planning units, features, management actions, action effects, spatial relations, targets, constraints, and objective functions. It enables the optimisation of spatial planning portfolios under considerations such as boundary structure, connectivity, and fragmentation. Supported multi-objective methods include weighted-sum aggregation, epsilon-constraint, and the augmented epsilon-constraint method. Problems can be solved with several commercial and open-source optimisation solvers. Optional solver backends include the gurobi R package, which is distributed with the Gurobi Optimizer installation <https://docs.gurobi.com/projects/optimizer/en/13.0/reference/r/setup.html>, and the rcbc R package, available from GitHub at <https://github.com/dirkschumacher/rcbc>. For background on multi-objective optimisation methods, see Halffmann et al. (2022) <doi:10.1002/mcda.1780>; for the augmented epsilon-constraint method, see Mavrotas (2009) <doi:10.1016/j.amc.2009.03.037>.

Calculates the Most Probable Number (MPN) to quantify the concentration (density) of microbes in serial dilutions of a laboratory sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>). Also calculates the Aerobic Plate Count (APC) for similar microbial enumeration experiments.

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>.