Solves the Multi-Criteria Minimum Spanning Tree (mc-MST) problem on complete weighted graphs by combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with optional Pareto local search operators. Chromosomes are represented as Prufer sequences so that every random individual decodes to a valid spanning tree (Cayley's theorem), avoiding repair operators. Four solver variants are provided: pure NSGA-II ("base"), Path Relinking ("PR"), Pareto Local Search ("PLS"), and Tabu Search ("TS"). The package supports 2 and 3 objective formulations and provides convenience functions to plot Pareto fronts and best-compromise spanning trees. This package is the reference implementation of the method described in Parraga-Alava, Inostroza-Ponta and Dorn (2017) <doi:10.1109/CEC.2017.7969432>.

An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes are able to be calculated for either null model, and subsequently classified into one of four different interaction classifications (e.g., antagonistic or synergistic interactions). Analyses can be conducted on data for single experiments through to large meta-analytical datasets. Minimal input (or statistical knowledge) is required, with any output easily understood. Summary figures are also able to be easily generated.

Fit and plot macroecological patterns predicted by the Maximum Entropy Theory of Ecology (METE).

Regularly spaced grids containing continuous data are transformed to contour polygons. A grid can be defined by a data.frame (x, y, value), an sf object or a raster from terra'.

This package provides a model-agnostic framework for reconstructing and analysing spatially explicit ecological networks from species distributions and ecological inference models. Supports arbitrary ecological groups and includes stochastic block and maximum-entropy inference backends, simulation helpers, network summaries, and spatial mapping utilities.

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.

Includes functions for conducting univariate and multivariate meta-analysis. This includes the estimation of the asymptotic variance-covariance matrix of effect sizes. For more details see Becker (1992) <doi:10.2307/1165128>, Cooper, Hedges, and Valentine (2019) <doi:10.7758/9781610448864>, and Schmid, Stijnen, and White (2020) <doi:10.1201/9781315119403>.

Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <arXiv:2009.07662>.

An interface to build machine learning models for classification and regression problems. mikropml implements the ML pipeline described by TopçuoÄ lu et al. (2020) <doi:10.1128/mBio.00434-20> with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website <https://www.schlosslab.org/mikropml/> for more information, documentation, and examples.

The time series forecasting framework for use with the tidymodels ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the forecast and prophet packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

The Multivariate Asymptotic Non-parametric Test of Association (MANTA) enables non-parametric, asymptotic P-value computation for multivariate linear models. MANTA relies on the asymptotic null distribution of the PERMANOVA test statistic. P-values are computed using a highly accurate approximation of the corresponding cumulative distribution function. Garrido-Martà n et al. (2022) <doi:10.1101/2022.06.06.493041>.

Metadynamics is a state of the art biomolecular simulation technique. Plumed Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in Plumed can be analyzed by metadynminer'. The package metadynminer reads 1D and 2D metadynamics hills files from Plumed package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037> to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy surfaces, minima and transition paths can be plotted to produce publication quality images.

Tests of comparison of two or more survival curves. Allows for comparison of more than two survival curves whether the proportional hazards hypothesis is verified or not.

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>.

This package provides functions for estimating structural equation models using instrumental variables.

Simulation results detailed in Esarey and Menger (2019) <doi:10.1017/psrm.2017.42> demonstrate that cluster adjusted t statistics (CATs) are an effective method for correcting standard errors in scenarios with a small number of clusters. The mmiCATs package offers a suite of tools for working with CATs. The mmiCATs() function initiates a shiny web application, facilitating the analysis of data utilizing CATs, as implemented in the cluster.im.glm() function from the clusterSEs package. Additionally, the pwr_func_lmer() function is designed to simplify the process of conducting simulations to compare mixed effects models with CATs models. For educational purposes, the CloseCATs() function launches a shiny application card game, aimed at enhancing users understanding of the conditions under which CATs should be preferred over random intercept models.

This package implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) <https://www.proquest.com/docview/3218878972>.

Helps calculate statistical values commonly used in meta-analysis. It provides several methods to compute different forms of standardized mean differences, as well as other values such as standard errors and standard deviations. The methods used in this package are described in the following references: Altman D G, Bland J M. (2011) <doi:10.1136/bmj.d2090> Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) <doi:10.1002/9780470743386.ch4> Chinn S. (2000) <doi:10.1002/1097-0258(20001130)19:22%3C3127::aid-sim784%3E3.0.co;2-m> Cochrane Handbook (2011) <https://www.cochrane.org/authors/handbooks-and-manuals/handbook/archive/v5.1.0> Cooper, H., Hedges, L. V., & Valentine, J. C. (2009) <https://psycnet.apa.org/record/2009-05060-000> Cohen, J. (1977) <https://psycnet.apa.org/record/1987-98267-000> Ellis, P.D. (2009) <https://www.psychometrica.de/effect_size.html> Goulet-Pelletier, J.-C., & Cousineau, D. (2018) <doi:10.20982/tqmp.14.4.p242> Hedges, L. V. (1981) <doi:10.2307/1164588> Hedges L. V., Olkin I. (1985) <doi:10.1016/C2009-0-03396-0> Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et al. (2023) <doi:10.1136/bmj-2022-073141> Mayer M (2023) <https://search.r-project.org/CRAN/refmans/confintr/html/ci_proportion.html> Stackoverflow (2014) <https://stats.stackexchange.com/questions/82720/confidence-interval-around-binomial-estimate-of-0-or-1> Stackoverflow (2018) <https://stats.stackexchange.com/q/338043>.

Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.

Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, T Haldar, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1093/bioinformatics/btaa1083>.

This package provides tools for creating agents with persistent state using R6 classes <https://cran.r-project.org/package=R6> and the ellmer package <https://cran.r-project.org/package=ellmer>. Tracks prompts, messages, and agent metadata for reproducible, multi-turn large language model sessions.

Computes model-based effect sizes for fixed-effect coefficients in multilevel (hierarchical) models. The coefficient effect sizes are standardized mean differences from zero (d) and unique variance-explained measures (squared semi-partial correlations, sr2). The package also reports variance components and level-specific and total R-squared values. It supports 2-level and 3-level linear and binary logistic models fitted with lme4 (Bates et al., 2015) <doi:10.18637/jss.v067.i01>, and 2-level Gaussian and Bernoulli models fitted with brms (Bürkner, 2017) <doi:10.18637/jss.v080.i01>. Sanders, Konold, and Cheng (in press), "Model-based effect sizes for multilevel linear regression coefficients," Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, describe the 2-level linear-model methods.

Automatically estimate 14 effect size measures from a well-formatted dataset, including Cohen's d, Hedges g, mean difference, odds ratio, risk ratio, incidence rate ratio, risk difference, number needed to treat, Pearson correlation, Fisher's z, Cronbach's alpha, intraclass correlation coefficient, and single-group proportion. Provides a two-tier quality-flag diagnostic system for input validation and post-computation plausibility checks, missing-data guidance that tells users which columns would unlock additional estimators, post-hoc correction for attenuation due to measurement error, and standalone psychometric utilities (standard error of measurement, smallest detectable change, change-score reliability). Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.

Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023) <doi:10.1007/978-3-031-29168-5_18>.