Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
Estimates Shannon entropy, per gene and per genomic position,
    associated with non-synonymous mutation frequencies in viral populations,
    such as wastewater samples. The package uses codon translations for
    functional insights. Each amino acid can be treated as an individual state,
    resulting in a 20-state entropy computation, or grouped into one of six
    physicochemical classes, adding further functional context. Provides
    normalized values (0-1 scale) to facilitate the direct comparison of
    different genomic positions or total functional entropy across multiple
    metagenomes. Designed to analyze mutational data using tabular 'Single
    Nucleotide Variant' (SNV) frequency tables generated by variant callers
    (e.g., 'iVar' or 'LoFreq'), operating independently of consensus sequence
    estimation and multiple sequence alignment.
A tool to simulate salmon metapopulations and apply financial
    portfolio optimization concepts. The package accompanies the paper 
    Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
Implements metafrontier production function models for
    estimating technical efficiencies and technology gaps for firms
    operating under different technologies. Supports both stochastic
    frontier analysis (SFA) and data envelopment analysis (DEA) based
    metafrontiers. Includes the deterministic metafrontier of Battese,
    Rao, and O'Donnell (2004) <doi:10.1023/B:PROD.0000012454.06094.29>,
    the stochastic metafrontier of Huang, Huang, and Liu (2014)
    <doi:10.1007/s11123-014-0402-2>, and the metafrontier Malmquist
    productivity index of O'Donnell, Rao, and Battese (2008)
    <doi:10.1007/s00181-007-0119-4>. Additional features include
    panel SFA with time-varying inefficiency, bootstrap confidence
    intervals for technology gap ratios, latent class metafrontier
    estimation via the EM algorithm, Murphy-Topel corrected standard
    errors, and 'ggplot2' visualisation methods.
Fused lasso method to cluster and estimate regression coefficients
    of the same covariate across different data sets when a large number of
    independent data sets are combined. Package supports Gaussian, binomial,
    Poisson and Cox PH models.
Meta-analysis of generalized additive
    models and generalized additive mixed models. A typical use case is
    when data cannot be shared across locations, and an overall meta-analytic
    fit is sought. 'metagam' provides functionality for removing individual
    participant data from models computed using the 'mgcv' and 'gamm4' packages such
    that the model objects can be shared without exposing individual data.
    Furthermore, methods for meta-analysing these fits are provided. The implemented
    methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>,
    extending previous works by Schwartz and Zanobetti (2000)
    and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.
Provides functions to perform all steps of genome-wide
    association meta-analysis for studying Genotype x Environment
    interactions, from collecting the data to the manhattan plot.  The
    procedure accounts for the potential correlation between studies. In
    addition to the Fixed and Random models, one can investigate the
    relationship between QTL effects and some qualitative or quantitative
    covariate via the test of contrast and the meta-regression,
    respectively.  The methodology is available from: (De Walsche, A., et
    al. (2025) \doi{10.1371/journal.pgen.1011553}).
Functionalities for facilitating systematic reviews, data
    extractions, and meta-analyses. It includes a GUI (graphical user interface)
    to help screen the abstracts and titles of bibliographic data; tools to assign
    screening effort across multiple collaborators/reviewers and to assess inter-
    reviewer reliability; tools to help automate the download and retrieval of
    journal PDF articles from online databases; figure and image extractions 
    from PDFs; web scraping of citations; automated and manual data extraction 
    from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for
    Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools
    to fill gaps in incomplete or missing study parameters; generation of random
    effects sizes for Hedges' d, log response ratio, odds ratio, and correlation
    coefficients for Monte Carlo experiments; covariance equations for modelling
    dependencies among multiple effect sizes (e.g., effect sizes with a common
    control); and finally summaries that replicate analyses and outputs from 
    widely used but no longer updated meta-analysis software (i.e., metawin).
	Funding for this package was supported by National Science Foundation (NSF) 
	grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) 
	Facilitating systematic reviews, data extraction and meta-analysis with the 
	metagear package for R. Methods in Ecology and Evolution 7, 323-330 
	<doi:10.1111/2041-210X.12472>.
According to a phenomenon known as "the wisdom of the crowds," 
    combining point estimates from multiple judges often provides a 
    more accurate aggregate estimate than using a point estimate from
    a single judge. However, if the judges use shared information in 
    their estimates, the simple average will over-emphasize this common
    component at the expense of the judges’ private information. 
    Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds
    Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions"
    <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes
    a procedure for calculating a weighted average of the judges’ individual
    estimates such that resulting aggregate estimate appropriately combines
    the judges' collective information within a single estimation problem. 
    The authors use both simulation and data from six experimental studies
    to illustrate that the weighting procedure outperforms existing averaging-like
    methods, such as the equally weighted average, trimmed average, and median.
    This aggregate estimate -- know as "the knowledge-weighted estimate" --
    inputs a) judges' estimates of a continuous outcome (E) and 
    b) predictions of others' average estimate of this outcome (P).  
    In this R-package, the function knowledge_weighted_estimate(E,P) 
    implements the knowledge-weighted estimate. Its use is illustrated
    with a simple stylized example and on real-world experimental data.
Performs meaningful subgrouping in a
    meta-analysis. This is a two-step process; first, use the iterative
    grouping functions (e.g., mgbin(), mgcont() ) to partition studies
    into statistically homogeneous clusters based on their effect size
    data. Second, use the meaning() function to analyze these new
    subgroups and understand their composition based on study-level
    characteristics (e.g., country, setting). This approach helps to
    uncover hidden structures in meta-analytic data and provide a deeper
    interpretation of heterogeneity.
Performs multivariate meta-analysis for high-dimensional data
    to integrate and collectively analyse individual-level data from multiple
    studies, as well as to combine summary estimates. This approach accounts
    for correlation between outcomes, incorporates within- and between-study
    variability, handles missing values, and uses shrinkage estimation to
    accommodate high dimensionality. The 'MetaHD' R package provides access 
    to our multivariate meta-analysis approach, along with a comprehensive 
    suite of existing meta-analysis methods, including fixed-effects and 
    random-effects models, Fisher's method, Stouffer's method, the weighted 
    Z method, Lancaster's method, the weighted Fisher's method, and vote-counting 
    approach. Visualisation tools are provided for interpreting and comparing 
    results across methods, including Venn diagrams, UpSet plots, and ROC curves, 
    heatmaps of pooled effect sizes and correlations among outcomes. A detailed 
    vignette with example datasets and code for data preparation and analysis is 
    available at <https://alyshadelivera.github.io/MetaHD_vignette/>.
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>.
An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>),  whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).
Tools for privacy-preserving meta-analysis of function-valued
    quantities across heterogeneous studies. Implements the 'MetaHunt'
    pipeline, including the denoised functional Successive Projection
    Algorithm (d-fSPA) for basis hunting, constrained weight estimation,
    Dirichlet regression of weights on study-level covariates, target
    prediction, and split/cross conformal prediction intervals. Operates
    on aggregate-level function evaluations, so individual-level data
    from source studies are not required. Methodology described in
    Shi, Imai, and Zhang (2026) <doi:10.48550/arXiv.2604.23847>.
Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds.
Conduct network meta-analyses through a graphical user interface using 'bnma',
    'gemtc' and 'netmeta' with additional analysis provided by 'meta' and 'metafor'.
    Frequentist, Bayesian, meta-regression and baseline risk meta-regression analyses
    can all be conducted using a consistent data structure and terminology. Many options are
    provided for downloading publication-ready outputs and analyses can be reproduced
    outside of the application by downloading a 'quarto' file. The interface was generated
    using 'shinyscholar'. The initial version of the app was described by Owen et al. (2018)
    <doi:10.1002/jrsm.1373>, Bayesian ranking visualisations were described by Nevill et al.
    (2023) <doi:10.1016/j.jclinepi.2023.02.016> and metaregression was described by Morris
    et al. (2025) <doi:10.1016/j.jclinepi.2025.111839>.
An ensemble meta-prediction framework to integrate multiple regression 
    models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) 
    <arXiv:2010.09971>.
    A meta-analysis framework along with two weighted estimators as the ensemble 
    of empirical Bayes estimators, which combines the estimates from the different 
    external models. The proposed framework is flexible and robust in the ways 
    that (i) it is capable of incorporating external models that use a slightly 
    different set of covariates; (ii) it is able to identify the most relevant 
    external information and diminish the influence of information that is less 
    compatible with the internal data; and (iii) it nicely balances the bias-variance 
    trade-off while preserving the most efficiency gain. The proposed estimators 
    are more efficient than the naive analysis of the internal data and other 
    naive combinations of external estimators.
Tools to generate random landscape graphs, evaluate species
    occurrence in dynamic landscapes, simulate future landscape occupation and
    evaluate range expansion when new empty patches are available (e.g. as a
    result of climate change). References: Mestre, F., Canovas, F., Pita, R., 
    Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., 
    Risk, B., Mira, A., Beja, P., Pita, R. (2017) 
    <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja,
    P. (2020) <doi:10.1186/s12898-019-0273-5>.
The main function performs meta-analysis of genetic association study summary statistics that may be correlated due to cryptic relatedness or other confounders, generalizing inverse variance weighted methods.  The function that estimates the correlation structure is also provided standalone.  Another key innovation, the estimation of the correlation parameter from the median product of correlated standard normal variables, is provided, as well as a complete set of functions for their underlying distribution: density, cumulative, quantile, and random deviates.  Described in Tu and Ochoa (2025) <doi:10.1101/2025.05.10.653279>.
First- and higher-order likelihood inference in
        meta-analysis and meta-regression models.
A metadata structure for clinical data analysis
    and reporting based on Analysis Data Model (ADaM) datasets.
    The package simplifies clinical analysis and reporting tool development
    by defining standardized inputs, outputs, and workflow.
    The package can be used to create analysis and reporting planning grid,
    mock table, and validated analysis and reporting results based on
    consistent inputs.
Analyzes adverse events in clinical trials using the 'metalite'
    data structure. The package simplifies the workflow to create
    production-ready tables, listings, and figures discussed in the
    adverse events analysis chapters of
    "R for Clinical Study Reports and Submission"
    by Zhang et al. (2022) <https://r4csr.org/>.
Analyzes subject-level data in clinical trials using the 'metalite'
    data structure. The package simplifies the workflow to create
    production-ready tables, listings, and figures discussed in the
    subject-level analysis chapters of
    "R for Clinical Study Reports and Submission"
    by Zhang et al. (2022) <https://r4csr.org/>.
Create an interactive table of descriptive statistics in HTML. 
  This table is typically used for exploratory analysis in a clinical study (referred to as 'Table 1').
Tools for longitudinal meta-analysis where studies contribute
    effect sizes at multiple follow-up time points. Implements robust variance
    estimation (RVE) with Tipton small-sample corrections following Hedges,
    Tipton, and Johnson (2010) <doi:10.1002/jrsm.5> and Tipton (2015)
    <doi:10.1037/met0000011>, time-varying sensitivity analysis via the
    Impact Threshold for a Confounding Variable (ITCV) following Frank (2000)
    <doi:10.1177/0049124100029002003>, benchmark calibration of the ITCV
    threshold against observed study-level covariates, spline-based nonlinear
    time-trend modeling with a nonlinearity test, and leave-k-out fragility
    analysis across the follow-up trajectory. Designed for researchers
    synthesising evidence from studies with repeated outcome measurement in
    education, psychology, health, and the social sciences.
The 'MetAlyzer' S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app 'MetaboExtract' (<https://www.metaboextract.shiny.dkfz.de/MetaboExtract/>).
Combination of either p-values or modified effect sizes from different
    studies to find differentially expressed genes.
Implements several methods to meta-analyze studies that report the 
    sample median of the outcome. The methods described by 
    McGrath et al. (2019) <doi:10.1002/sim.8013>, 
    Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738>, and 
    McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be 
    applied to directly meta-analyze the median or difference of medians between 
    groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) 
    <doi:10.1177/0962280219889080>, Cai et al. (2021) 
    <doi:10.1177/09622802211047348>, and McGrath et al. (2023) 
    <doi:10.1177/09622802221139233>) are implemented to estimate 
    study-specific (difference of) means and their standard errors in order to 
    estimate the pooled (difference of) means. Methods for meta-analyzing median 
    survival times (McGrath et al. (2026) <doi:10.1002/sim.70533>) are 
    also implemented. See McGrath et al. (2024) <doi:10.1002/jrsm.1686> for a 
    detailed guide on using the package.
Creates data with identical statistics (metamers) using an iterative 
   algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012) <doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017) <doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001) <doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad et al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle et al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020) <doi:10.1038/s41598-020-72664-6>) transformation, centering and scaling (Van Den Berg et al. (2006) <doi:10.1186/1471-2164-7-142>) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) <doi:10.21105/joss.01686>) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'.
Performs stability analysis of multi-environment trial data
    using parametric and non-parametric methods. Parametric methods
    includes Additive Main Effects and Multiplicative Interaction (AMMI)
    analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence
    by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot
    analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric
    adaptability index by Mohammadi & Amri (2008)
    <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart
    & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>,
    genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's
    (2004) method, power law residuals (POLAR) statistics by Doring et al.
    (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of
    variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>,
    stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>,
    weighted average of absolute scores by Olivoto et al. (2019a)
    <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by
    Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>.
    Non-parametric methods includes superiority index by Lin & Binns
    (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
    stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third
    statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for
    computing biometrical analysis such as path analysis, canonical
    correlation, partial correlation, clustering analysis, and tools for
    inspecting, manipulating, summarizing and plotting typical
    multi-environment trial data are also provided.
Comprehensive network analysis package.
    Calculate correlation network fastly, accelerate lots of analysis by parallel computing.
    Support for multi-omics data, search sub-nets fluently.
    Handle bigger data, more than 10,000 nodes in each omics.
    Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize.
    Provide comprehensive topology indexes calculation, including ecological network stability.
A toolbox to handle and represent trophic networks in space or time across aggregation levels. This package contains a layout algorithm specifically designed for trophic networks, using dimension reduction on a diffusion graph kernel and trophic levels. Importantly, this package provides a layout method applicable for large trophic networks.
Given a CSV file with titles and abstracts, the package creates a
    document-term matrix that is lemmatized and stemmed and can directly be used to
    train machine learning methods for automatic title-abstract screening in the
    preparation of a meta analysis.
Provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, differential evolution Storn and Price (1997) <doi:10.1023/A:1008202821328>, grey wolf optimizer Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>, secretary bird optimization Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>, and Adam Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980>.
Novel method to unbiasedly include studies with Non-statistically Significant Unreported Effects (NSUEs) in a meta-analysis. First, the function calculates the interval where the unreported effects (e.g., t-values) should be according to the threshold of statistical significance used in each study. Afterward, the method uses maximum likelihood techniques to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance, and statistical significance bounds. Finally, it conducts a restricted-maximum likelihood random-effects meta-analysis separately for each set of imputations, and it performs estimations from these meta-analyses. Please read the reference in 'metansue' for details of the procedure.
The canonical way to perform meta-analysis involves using effect sizes.
   When they are not available this package provides a number of methods for
   meta-analysis of significance values including the methods of Edgington, Fisher,
   Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results;
   and routines for graphical display.
Simplifies Brazilian names phonetically using a custom 'metaphoneBR' algorithm that preserves ending vowels. 
  Useful for name matching processing preserving gender information carried generally by ending vowels in Portuguese.
  Mation (2025) <doi:10.6082/uchicago.15104>.
Designs plots in terms of core structure.  See 'example(metaplot)'.
 Primary arguments are (unquoted) column names; order and type (numeric or not)
 dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
 and any conditioning variables to metaplot() to generate density plots, boxplots, 
 mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() 
 to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' 
 are honored, producing fully annotated plots with minimal effort. Attribute 'guide' 
 is typically units, but may be encoded() to provide interpretations of categorical 
 values (see '?encode').  Utility unpack() transforms scalar column attributes to row 
 values and pack() does the reverse, supporting tool-neutral storage of metadata along 
 with primary data. The package supports customizable aesthetics such as such as reference 
 lines, unity lines, smooths, log transformation, and linear fits. The user may choose
 between trellis and ggplot output. Compact syntax and integrated metadata promote workflow 
 scalability.
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).
Provides an interface to 'MetaPost' (Hobby, 1998) 
    <http://www.tug.org/docs/metapost/mpman.pdf>.
    There are functions to generate an R description of a 'MetaPost'
    curve, functions to generate 'MetaPost' code from an R description,
    functions to process 'MetaPost' code, and functions to read
    solved 'MetaPost' paths back into R.
A simple and effective tool for computing and visualizing statistical power for meta-analysis,
    including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, 
    test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, 
    and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.
The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new method named weighted Fisher’s method we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method is useful for detecting partially associated features. Thus, the users can choose the function based on their purpose. Yoon et al. (2021) "Powerful p-value combination methods to detect incomplete association" <doi:10.1038/s41598-021-86465-y>.
Set of tools for descriptive analysis of metaproteomics data generated from high-throughput 
    mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, 
    expressed as spectral counts, and to manipulate them in groups of metaproteins. This information 
    can be represented using multiple visualization functions to portray the global metaproteome 
    landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, 
    taxonomic levels and/or functional annotation. The provided tools allow to implement flexible 
    analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.
Implements a novel density-based approach for estimating unknown parameters, distribution visualisations and meta-analyses of quantiles and ther functions. 
	A detailed vignettes with example datasets and code to prepare data and analyses is available at <https://bookdown.org/a2delivera/metaquant/>.
	The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, <doi:10.48550/arXiv.2411.10971>).
Build spatially and temporally explicit
    process-based species distribution models, that can include an arbitrary
    number of environmental factors, species and processes including metabolic
    constraints and species interactions. The focus of the package is simulating
    populations of one or multiple species in a grid-based landscape and studying
    the meta-population dynamics and emergent patterns that arise from the
    interaction of species under complex environmental conditions. It
    provides functions for common ecological processes such as
    negative exponential, kernel-based dispersal (see
    Nathan et al. (2012) <doi:10.1093/acprof:oso/9780199608898.003.0015>),
    calculation of the environmental suitability based on cardinal values (
    Yin et al. (1995) <doi:10.1016/0168-1923(95)02236-Q>, simplified by
    Yan and Hunt (1999) <doi:10.1006/anbo.1999.0955> see eq: 4), reproduction in
    form of an Ricker model (see Ricker (1954) <doi:10.1139/f54-039> and
    Cabral and Schurr (2010) <doi:10.1111/j.1466-8238.2009.00492.x>),
    as well as metabolic scaling based on the metabolic theory of ecology
    (see Brown et al. (2004) <doi:10.1890/03-9000> and
    Brown, Sibly and Kodric-Brown (2012)
    <doi:10.1002/9781119968535.ch>).
User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings.
 - Replicability-analysis for fixed-effects and random-effect meta analysis: 
 - r(u)-value;
 - lower bounds on the number of studies with replicated positive and\or negative effect;
 - Allows detecting inconsistency of signals;
 - forest plots with the summary of replicability analysis results;
 - Allows Replicability-analysis with or without the common-effect assumption.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) <doi:10.1109/WSC68292.2025.11338996>. 'MetaRVM' implements a stochastic 
    SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic
    stratification by user provided attributes. It supports complex
    epidemiological scenarios including asymptomatic and presymptomatic transmission,
    hospitalization dynamics, vaccination schedules, and time-varying contact patterns
    via mixing matrices.
Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. <doi:10.1016/j.jmp.2019.102297>.
A collection of functions for conducting meta-analysis using a
             structural equation modeling (SEM) approach via the 'OpenMx' and
             'lavaan' packages. It also implements various procedures to
             perform meta-analytic structural equation modeling on the
             correlation and covariance matrices, see Cheung (2015)
             <doi:10.3389/fpsyg.2014.01521>.
The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis':
 - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>;
 - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>;
 - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>;
 - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>;
 - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.
Functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by  Lee et al. (2013) <doi:10.1016/j.ajhg.2013.05.010>. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.
Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.
Pooling estimates reported in meta-analyses (literature-based, LB) and estimates based on individual participant data (IPD) is not straight-forward as the details of the LB nonlinear function estimate are not usually reported. This package pools the nonlinear IPD dose-response estimates based on a natural cubic spline from lm or glm with the pointwise LB estimates and their estimated variances. Details will be presented in Härkänen, Tapanainen, Sares-Jäske, Männistö, Kaartinen and Paalanen (2026) "Novel pooling method for nonlinear cohort analysis and meta-analysis estimates: Predicting health outcomes based on climate-friendly diets" Epidemiology <doi:10.1097/EDE.0000000000001932>.
Performs Bayesian meta-analysis, meta-regression and model-based meta-analysis 
             using 'Stan'. Includes binomial-normal hierarchical models and option to use 
             weakly informative priors for the heterogeneity parameter and the treatment effect 
             parameter which are described in Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.
If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. 
      The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 
      'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. 
      It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. 
      It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. 
      See for application: Nolte I.M. et al. (2017); <doi: 10.1038/ejhg.2017.50>.
Provides a step-based pipeline for reproducible survey data
    processing, building on the 'survey' package for complex sampling designs.
    Supports rotating panels with bootstrap replicate weights, and provides a
    recipe system for sharing and reproducing data transformation workflows
    across survey editions.
To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity.
Provides a hybrid modeling framework combining Support Vector Regression (SVR) with metaheuristic optimization algorithms, including the Archimedes Optimization Algorithm (AO) (Hashim et al. (2021) <doi:10.1007/s10489-020-01893-z>), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) <doi:10.1016/j.eswa.2021.115352>), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) <doi:10.1016/j.future.2019.02.028>), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>), Ant Lion Optimization (ALO) (Mirjalili (2015) <doi:10.1016/j.advengsoft.2015.01.010>), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) <doi:10.32604/cmes.2023.026019>). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) <doi:10.1016/j.procs.2020.12.003> and Liu et al. (2018) <doi:10.1155/2018/6076475>.
Fits and tests meta regression models and generates a
 number of useful test statistics: next to t- and z-tests, the likelihood ratio,
 bartlett corrected likelihood ratio and permutation tests are performed on
 the model coefficients.
Create meta tags for 'R Markdown' HTML documents and 'Shiny'
    apps for customized social media cards, for accessibility, and quality
    search engine indexing. 'metathis' currently supports HTML documents
    created with 'rmarkdown', 'shiny', 'xaringan', 'pagedown', 'bookdown',
    and 'flexdashboard'.
Uses the metadata information stored in 'metacore' objects to
    check and build metadata associated columns.
A comprehensive range of facilities to perform umbrella reviews with stratification of the evidence in R. The package accomplishes this aim by building on three core functions that: (i) automatically perform all required calculations in an umbrella review (including but not limited to meta-analyses), (ii) stratify evidence according to various classification criteria, and (iii) generate a visual representation of the results. Note that if you are not familiar with R, the core features of this package are available from a web browser (<https://www.metaumbrella.org/>).
Contains functions to estimate the proportion of effects stronger than a threshold
    of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval),
    to make effect size conversions (r_to_d, r_to_z, z_to_r, d_to_logRR), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from 
    existing meta-analyses for re-analysis (scrape_meta, parse_CI_string, ci_to_var).
Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).
A compilation of functions to create visually appealing and information-rich 
    plots of meta-analytic data using 'ggplot2'. Currently allows to create forest plots, 
    funnel plots, and many of their variants, such as rainforest plots, thick forest plots, 
    additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities 
    for visual inference with the funnel plot in the context of meta-analysis are provided.
A tool for implementing so called 'deft' approach
    (see Fisher, David J., et al. (2017) <DOI:10.1136/bmj.j573>) and model
    visualization.
Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains over 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using the '{colorblindcheck} package by Jakub Nowosad'<https://jakubnowosad.com/colorblindcheck/>.
Random Forest Spatial Interpolation (RFSI, Sekulić et al. (2020) <doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) <doi:10.1002/2013JD020803>, Sekulić et al. (2020) <doi:10.1007/s00704-019-03077-3>) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data.
Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in the atmosphere by combining conserved quantities from hydrodynamics and thermodynamics. The package 'meteoEVT' provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, <doi:10.1111/j.1600-0870.2007.00272.x>], for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter [Hewson, 1998, <doi:10.1017/S1350482798000553>], the F diagnostic [Parfitt et al., 2017, <doi:10.1002/2017GL073662>] and the DSI [Mack et al., 2022, <arXiv:2208.11438>].
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS <https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast>, MeteoGalicia <https://www.meteogalicia.gal/web/modelos/threddsIndex.action>, NAM <https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale>, and RAP <https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update>.
Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].
A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modeling, particularly crop and crop disease modeling.
Access to different Spanish meteorological stations data services and APIs (AEMET, SMC, MG, 
  Meteoclimatic...).
Fit and plot macroecological patterns predicted by the Maximum
    Entropy Theory of Ecology (METE).
In the omics data association studies, it is common to conduct the p-value corrections to control the false significance. Beyond the P-value corrections, E-value is recently studied to facilitate multiple testing correction based on V. Vovk and R. Wang (2021) <doi:10.1214/20-AOS2020>. This package provides E-value calculation for DNA methylation data and RNA-seq data. Currently, five data formats are supported: DNA methylation levels using DMR detection tools (BiSeq, DMRfinder, MethylKit, Metilene and other DNA methylation tools) and RNA-seq data. The relevant references are listed below: Katja Hebestreit and Hans-Ulrich Klein (2022) <doi:10.18129/B9.bioc.BiSeq>; Altuna Akalin et.al (2012) <doi:10.18129/B9.bioc.methylKit>.
Methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.
Identify and rank CpG DNA methylation conservation along the human 
    genome. Specifically it includes bootstrapping methods to provide ranking 
    which should adjust for the differences in length as without it short 
    regions tend to get higher conservation scores.
DNA methylation is an epigenetic modification involved in genomic stability,
      gene regulation, development and disease.
      DNA methylation occurs mainly through the addition of a methyl group to cytosines, 
      for example to cytosines in a CpG dinucleotide context (CpG stands for a cytosine followed by a guanine).
      Tissue-specific methylation patterns lead to genomic regions with different characteristic
      methylation levels.
      E.g. in vertebrates CpG islands (regions with high CpG content) that are associated to promoter regions of
      expressed genes tend to be unmethylated.
      'MethEvolSIM' is a model-based simulation software for the generation and modification
      of cytosine methylation patterns along a given tree, which can be a genealogy of
      cells within an organism, a coalescent tree of DNA sequences sampled from a population, or a species tree.
      The simulations are based on an extension of the model of
      Grosser & Metzler (2020) <doi:10.1186/s12859-020-3438-5> and allows for changes of
      the methylation states at single cytosine positions as well as simultaneous changes of methylation
      frequencies in genomic structures like CpG islands.
Implements FUSE (Functional Segmentation of DNA methylation data), a data-driven method 
    for identifying spatially coherent DNA methylation segments from 
    whole-genome bisulfite sequencing (WGBS) count data. The method performs 
    hierarchical clustering of CpG sites based on methylated and unmethylated 
    read counts across multiple samples and determines the optimal number of 
    segments using an information criterion (AIC or BIC). Resulting segments 
    represent regions with homogeneous methylation profiles across the input 
    cohort while allowing sample-specific methylation levels. The package 
    provides functions for clustering, model selection, tree cutting, 
    segment-level summarization, and visualization. Input can be supplied as 
    count matrices or extracted directly from 'BSseq' and 'methrix' objects.
Evaluate bias and precision in method comparison 
    studies. One provides measurements for each method and it takes care of 
    the estimates. Multiple plots to evaluate bias, precision and compare
    methods.
A graphical user interface to apply an advanced method optimization algorithm to various sampling and analysis instruments. This includes generating experimental designs, uploading and viewing data, and performing various analyses to determine the optimal method. Details of the techniques used in this package are published in Gamble, Granger, & Mannion (2024) <doi:10.1021/acs.analchem.3c05763>.
Methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME “.cg/.cm” files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity.
Meteorological Tools following the FAO56 irrigation paper of Allen et al. (1998) [1].
    Functions for calculating:
    reference evapotranspiration (ETref),
    extraterrestrial radiation (Ra),
    net radiation (Rn),
    saturation vapor pressure (satVP),
    global radiation (Rs),
    soil heat flux (G),
    daylight hours,
    and more.
    [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9).
Provides a number of functions to facilitate the handling and production of reports using time series data.
    The package was developed to be understandable for beginners, so some functions aim to transform processes that would be
    complex into functions with a few lines. The main advantage of using the 'metools' package is the ease of producing reports and
    working with time series using a few lines of code, so the code is clean and easy to understand/maintain. 
    Learn more about the 'metools' at <https://metoolsr.wordpress.com>.
Many useful functions and extensions for dealing with
    meteorological data in the tidy data framework. Extends 'ggplot2' for
    better plotting of scalar and vector fields and provides commonly used
    analysis methods in the atmospheric sciences.
A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.
Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) <doi:10.3150/23-BEJ1647>, Bolin, Kovacs, Kumar and Simas (2023) <doi:10.1090/mcom/3929> and Bolin, Simas and Wallin (2023) <doi:10.48550/arXiv.2304.03190> and <doi:10.48550/arXiv.2304.10372>.
Mine metrics on common places on the web through the power of their APIs (application programming interfaces). 
    It also helps make the data in a format that is easily used for a dashboard or other purposes.
    There is an associated dashboard template and tutorials that are underdevelopment that help you  
    fully utilize 'metricminer'.
An implementation of evaluation metrics in R that are commonly
             used in supervised machine learning. It implements metrics for
             regression, time series, binary classification, classification,
             and information retrieval problems. It has zero dependencies and
             a consistent, simple interface for all functions.
Provides weighted versions of several metrics and performance
    measures used in machine learning, including average unit deviances of
    the Bernoulli, Tweedie, Poisson, and Gamma distributions, see
    Jorgensen B. (1997, ISBN: 978-0412997112).  The package also contains
    a weighted version of generalized R-squared, see e.g. Cohen, J. et al.
    (2002, ISBN: 978-0805822236).  Furthermore, 'dplyr' chains are
    supported.
Calculate different metrics based on aquatic
    macroinvertebrate density data (individuals per square meter) to
    assess water quality (Prat N et al. 2009).
The Washington Metropolitan Area Transit Authority is a
    government agency operating light rail and passenger buses in the
    Washington D.C. area. With a free developer account, access their
    'Metro Transparent Data Sets API' <https://developer.wmata.com/> to
    return data frames of transit data for easy analysis.
Provides classes and calculation and plotting functions 
 for metrology applications, including measurement uncertainty estimation
 and inter-laboratory metrology comparison studies.
Learning and using the Metropolis algorithm for
    Bayesian fitting of a generalized linear model. The package vignette
    includes examples of hand-coding a logistic model using several
    variants of the Metropolis algorithm. The package also contains R
    functions for simulating posterior distributions of Bayesian
    generalized linear model parameters using guided, adaptive,
    guided-adaptive and random walk Metropolis algorithms. The random walk
    Metropolis algorithm was originally described in Metropolis et al
    (1953); <doi:10.1063/1.1699114>.
Provides passenger demand data for the 'São Paulo' metro system,
    covering 2012 to 2025. Datasets include monthly passenger entries and
    transported counts by line, average weekday station entries, daily station entries, and spatial geometries for metro and commuter train lines and stations.
Implementation of various statistical models for multivariate
    event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate
    cumulative incidence models <doi:10.1002/sim.6016>, and  bivariate random
    effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
    Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, 
    Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Provides a Shiny application to estimate the sample size required 
    for a metabolomic experiment to achieve a desired statistical power. 
    Estimation is possible with or without available data from a pilot study.
Provides an interface with the Meteo France Synop data API 
    (see <https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32> 
    for more information). 
    The Meteo France Synop data are made of meteorological data recorded 
    every three hours on 62 French meteorological stations.
Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes (Dombry, Engelke and Oesting, 2016, <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.
Compute the average of a sequence of random vectors
  in a moving expanding window using a fixed amount of memory.
Color palettes inspired by the works of 
  Mexican painters and muralists. The package includes functions that
  return vectors of colors and also functions to use color and fill
  scales in 'ggplot2' visualizations.
Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.
Provides functions to access data from public RESTful APIs including
    'World Bank API', and 'Nager.Date API', covering Mexico's economic 
    indicators, population statistics, literacy rates, and official public
    holidays. The package also includes curated datasets related to Mexico
    such as air quality monitoring stations, pollution zones, income surveys,
    postal abbreviations, election studies, forest productivity and
    demographic data by state. It supports research and analysis focused on
    Mexico by integrating reliable global APIs with structured national
    datasets drawn from open and academic sources. 
    For more information on the APIs, see: 
        'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, 
        and 'Nager.Date API' <https://date.nager.at/Api>.
A color palette generator inspired by Mexican politics, with 
    colors ranging from red on the left to gray in the middle and green on the 
    right. Palette options range from only a few colors to several colors, but with
    discrete and continuous options to offer greatest flexibility to the user. 
    This package allows for a range of applications, from mapping brief discrete scales 
    (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays 
    including dozens of shades graded from red to green.
The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.
Provide simple functions to (i) compute a class of 
  multi-functionality measures for a single ecosystem for given 
  function weights, (ii) decompose gamma multi-functionality 
  for pairs of ecosystems and K ecosystems (K can be greater than 2) 
  into a within-ecosystem component (alpha multi-functionality) 
  and an among-ecosystem component (beta multi-functionality). 
  In each case, the correlation between functions can be corrected for. 
  Based on biodiversity and ecosystem function data, this software 
  also facilitates graphics for assessing biodiversity-ecosystem 
  functioning relationships across scales.
Multivariate functional principal component analysis via fast covariance estimation for
             multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.
Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
Download meteorological data from the 'Meteo-France' public
    API 'Données climatologiques' <https://portail-api.meteofrance.fr> and 'SYNOP' 
    open data archives <https://meteo.data.gouv.fr/datasets/686f8595b351c06a3a790867>. It
    provides functions to authenticate, list stations, retrieve station
    metadata, request data, download files, and import data directly into
    'R'.
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.
Computing functional traits-based distances between pairs of 
    species for species gathered in assemblages allowing to build several
    functional spaces. The package allows to compute functional diversity
    indices assessing the distribution of species (and of their dominance) in a
    given functional space for each assemblage and the overlap between
    assemblages in a given functional space, see: Chao et al. (2018)
    <doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>,
    Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al.
    (2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009)
    <doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included.
    Visit the 'mFD' website for more information, documentation and examples.
Creates and manages a PostgreSQL database suitable for storing fisheries data
             and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model.
             See <https://mareframe.github.io/mfdb/> for more information.
Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), 
  MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale
  Multifractal Analysis (MMA). The MFDFA() function proposed in this package was 
  used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). 
  See references for more information. Interested users can find a parallel version of
  the MFDFA() function on GitHub.
Methods for controlling the median of the false discovery proportion (mFDP).
	Depending on the method, simultaneous or non-simultaneous inference is provided. 
	The methods take a vector of p-values or test statistics as input.
Implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026) <doi:10.1016/j.asoc.2026.114592> by aggregating predictions from multiple base learners using membership weights learned in the prediction space of validation set. The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) <doi:10.1016/j.asoc.2018.08.009>, Possibilistic FCM (PFCM) Tak (2021) <doi:10.1016/j.ins.2021.01.024>, Gustafson–Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via 'glmnet', random forests, gradient boosting with 'xgboost' and 'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test prediction matrices and evaluate performance using standard regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables flexible, interpretable ensemble regression where different base models contribute to different meta components according to learned memberships.
Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
The mFilter package implements several time series filters useful
        for smoothing and extracting trend and cyclical components of a
        time series. The routines are commonly used in economics and
        finance, however they should also be interest to other areas.
        Currently, Christiano-Fitzgerald, Baxter-King,
        Hodrick-Prescott, Butterworth, and trigonometric regression
        filters are included in the package.
A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.
Calculate the maximal fat oxidation, the exercise intensity that elicits the 
              maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. 
              Three variables can be obtained from the SIN model: dilatation, symmetry and translation. 
              Examples of these methods can be found in Montes de Oca et al (2021) <doi:10.1080/17461391.2020.1788650>
              and Chenevière et al. (2009) <doi:10.1249/MSS.0b013e31819e2f91>.
Multivariable Fractional Polynomial algorithm for model-building. Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references are Royston and Altman (1994) <doi:10.2307/2986270> and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). In addition, it can model a sigmoid relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) <doi:10.1177/1536867X1401400206>. This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling.
Calculate a multivariate functional principal component analysis
    for data observed on different dimensional domains. The estimation algorithm
    relies on univariate basis expansions for each element of the multivariate
    functional data  (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. 
    Multivariate and univariate functional data objects are
    represented by S4 classes for this type of data implemented in the package
    'funData'. For more details on the general concepts of both packages and a case 
    study, see Happ-Kurz (2020) <doi:10.18637/jss.v093.i05>.
Matrix-Based Flexible Project Planning. This package models, plans, and schedules flexible, such as agile, extreme, and hybrid project plans. The package contains project planning, scheduling, and risk assessment functions. Kosztyan (2022) <doi:10.1016/j.softx.2022.100973>.
Constructs and analyzes optimal row-column designs for
    mixed-level factorial experiments under square and rectangular field
    layouts. For square field layouts, the package implements direct
    common-factor constructions by first forming two component treatment
    arrays, one for each factor or super-factor, and then combining them
    through a symbolic cell-wise product following Gopinath, Parsad and
    Mandal (2018) <doi:10.1080/03610926.2017.1376091>. For rectangular
    field layouts, the package constructs designs by extracting a balanced
    principal block from a mixed-level block design, treating it as the
    principal column, taking the complete treatment set as the principal row,
    and generating the full row-column design by cyclic modular development.
    The package also includes repair utilities for improving disconnected or
    partially connected row-column designs through bounded treatment-swap
    searches while preserving the row-column layout structure. The package
    provides diagnostic tools for connectedness, orthogonal factorial
    structure, balance, estimability, and selected optimality criteria for
    row-column designs.
Native R implementation of many-facet ordered-response
    measurement models with arbitrary facet counts, rating-scale and
    partial-credit parameterizations, a bounded generalized partial-credit
    extension, and both marginal and joint maximum likelihood estimation.
    The package provides a fit / diagnose / report pipeline covering
    anchoring, linking, bias and differential-functioning screening, and
    publication-oriented reporting summaries, with reproducibility manifests
    for replay. See 'Andrich'
    (1978) <doi:10.1007/BF02293814>, 'Masters' (1982)
    <doi:10.1007/BF02296272>, and 'Muraki' (1992)
    <doi:10.1177/014662169201600206> for the underlying ordered-response
    models.
Analysis of multivariate functional spatial data, 
             including spectral multivariate functional principal 
             component analysis and related statistical procedures 
             (Si-Ahmed, Idris, et al. "Principal component analysis of multivariate spatial functional data." Big Data Research 39 (2025) 100504).
             (Kuenzer, T., Hörmann, S., & Kokoszka, P. (2021). "Principal component analysis of spatially indexed functions." Journal of the American Statistical Association, 116(535), 1444-1456.)
             (Happ, C., & Greven, S. (2018). "Multivariate functional principal component analysis for data observed on different (dimensional) domains." Journal of the American Statistical Association, 113(522), 649-659.)
Convenience functions for multivariate MCMC using univariate samplers including:
  slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>),
  adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>),
  adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and
  univariate Metropolis with Gaussian proposal.
Estimates probit, logit, Poisson, negative binomial, 
  and beta regression models, returning their marginal effects, odds ratios, 
  or incidence rate ratios as an output.
  Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
Procedures to simulate, estimate and diagnose MGARCH
    processes of BEKK and multivariate GJR (bivariate asymmetric GARCH
    model) specification.
Compute the multiple Grubbs-Beck low-outlier test on positively distributed
 data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow
 data processing discussed in Cohn et al. (2013) and
 England et al. (2017) <doi:10.3133/tm4B5>. Other utilities for working with peak streamflow
 are provided.
Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.
Generalized additive (mixed) models, some of their extensions and 
             other generalized ridge regression with multiple smoothing 
             parameter estimation by (Restricted) Marginal Likelihood, 
             Cross Validation and similar, or using iterated nested Laplace
	     approximation for fully Bayesian inference. See Wood (2025)
	     <doi:10.1146/annurev-statistics-112723-034249> for an overview.
	     Includes a gam() function, a wide variety of smoothers, 'JAGS'
	     support and distributions beyond the exponential family.
Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
Provides a model designed to be a reliable testbed where various gene 
    drive interventions for mosquito-borne diseases control. It is being developed to 
    accommodate the use of various mosquito-specific gene drive systems within a 
    population dynamics framework that allows migration of individuals between patches 
    in landscape. Previous work developing the population dynamics can be found in Deredec et al. 
    (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, 
    and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) 
    <doi:10.1038/s41598-017-02744-7>.
A simulation modeling framework which significantly extends capabilities from the
    'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets.
    For more information about 'MGDrivE', see our publication: Sánchez et al. (2019) <doi:10.1111/2041-210X.13318>
    Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations,
    epidemiological dynamics, time-varying parameters, and a continuous-time simulation
    framework with various sampling algorithms for both deterministic and stochastic interpretations.
    'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we
    suggest installing that package initially.
Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. 
 The naive analysis which is solely based on the observed data without adjustment may lead to bias.
 The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. 
 The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given
 validation set. This package is an implementation 
 of Chen (2013) <doi:10.1002/bimj.201200195>.
Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>. Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal, Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).
Provides readers for easy and consistent importing of
    Mouse Genome Informatics (MGI) report files:
    <https://www.informatics.jax.org/downloads/reports/index.html>. These data
    are provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ,
    Mouse Genome Informatics Group (2024) <doi:10.1093/genetics/iyae031>.
An aggressive dimensionality reduction and network estimation
    technique for a high-dimensional Gaussian graphical model (GGM). Please
    refer to: Efficient Dimensionality Reduction for High-Dimensional Network
    Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of
    The 31st International Conference on Machine Learning, 2014, p. 1953--1961.
Inference of Multiscale graphical models with neighborhood
    selection approach.  The method is based on solving a convex
    optimization problem combining a Lasso and fused-group Lasso
    penalties.  This allows to infer simultaneously a conditional
    independence graph and a clustering partition. The optimization is
    based on the Continuation with Nesterov smoothing in a
    Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
    <doi:10.1109/TMI.2018.2829802> implemented in python.
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.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.
Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.
Provides an interface to the Maxar Geospatial Platform (MGP) Application Programming Interface. <https://www.maxar.com/maxar-geospatial-platform>
    It facilitates imagery searches using the MGP Streaming Application Programming Interface via the Web Feature Service (WFS) method, and supports image downloads through Web Map Service (WMS) and Web Map Tile Service (WMTS)
    Open Geospatial Consortium (OGC) methods. 
    Additionally, it integrates with the Maxar Geospatial Platform Basemaps Application Programming Interface for accessing Maxar basemaps imagery and seamlines. 
    The package also offers seamless integration with the Maxar Geospatial Platform Discovery Application Programming Interface, allowing users to search, filter, and sort Maxar content, 
    while retrieving detailed metadata in formats like SpatioTemporal Asset Catalog (STAC) and GeoJSON.
This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image.
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.
Implements functional principal component analysis (FPCA) for
    univariate and multivariate sparse functional data. The package estimates
    eigenfunctions, eigenvalues, and error variance simultaneously via maximum
    likelihood estimation (MLE), using a spline basis representation of the
    eigenfunctions. Orthonormality of the estimated eigenfunctions is enforced
    through a modified Gram-Schmidt (MGS) orthogonalization procedure applied
    iteratively during estimation, avoiding direct optimization over the Stiefel
    manifold and improving numerical stability. The optimal number of basis
    functions and principal components is selected via an Akaike Information
    Criterion (AIC)-type criterion, supporting both a full grid-search strategy
    and a computationally efficient sequential selection approach. Principal
    component scores are estimated by conditional expectation, enabling
    reconstruction of individual trajectories over the entire domain from sparse
    observations. Pointwise confidence intervals for reconstructed trajectories 
    are also provided. Methods are described in Mbaka, Cao and Carey
    (2026) <doi:10.48550/arXiv.2603.18833> and Mbaka and Carey (2026)
    <doi:10.48550/arXiv.2603.19799>.
Designed to enable simultaneous substitution in strings in a safe fashion.
    Safe means it does not rely on placeholders (which can cause errors in same length matches).
Fits a geographically weighted regression model with different scales
	for each covariate. Uses the negative binomial distribution as default,
	but also accepts the normal, Poisson, or logistic distributions.
	Can fit the global versions of each regression and also the geographically
	weighted alternatives with only one scale, since they are all particular
	cases of the multiscale approach.
	Hanchen Yu (2024). "Exploring Multiscale Geographically Weighted Negative Binomial Regression", Annals of the American Association of Geographers <doi:10.1080/24694452.2023.2289986>.
	Fotheringham AS, Yang W, Kang W (2017). "Multiscale Geographically Weighted Regression (MGWR)", Annals of the American Association of Geographers <doi:10.1080/24694452.2017.1352480>.
	Da Silva AR, Rodrigues TCV (2014). "Geographically Weighted Negative Binomial Regression - incorporating overdispersion", Statistics and Computing <doi:10.1007/s11222-013-9401-9>.
Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to 
    Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196.
    <https://www.researchgate.net/profile/Hasbi-Yasin-2/publication/289689583_Mixed_geographically_weighted_regression_model_case_study_The_percentage_of_poor_households_in_Mojokerto_2008/links/58e46aa40f7e9bbe9c94d641/Mixed-geographically-weighted-regression-model-case-study-The-percentage-of-poor-households-in-Mojokerto-2008.pdf>.
Provides methods for Geographically Weighted Regression with spatial autocorrelation (Geniaux and Martinetti 2017) <doi:10.1016/j.regsciurbeco.2017.04.001>. Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) <doi:10.1007/s10109-025-00481-4>.
Nonparametric survival function estimates and semiparametric
    regression for the multivariate failure time data with right-censoring.
    For nonparametric survival function estimates, the Volterra, Dabrowska,
    and Prentice-Cai estimates for bivariate failure time data may be
    computed as well as the Dabrowska estimate for the trivariate failure
    time data. Bivariate marginal hazard rate regression can be fitted for
    the bivariate failure time data. Functions are also provided to compute
    (bootstrap) confidence intervals and plot the estimates of the bivariate
    survival function. For details, see "The Statistical Analysis of
    Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice,
    R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.
MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python
    tool to predict MHC class I and MHC class II epitopes.
    This package allows one to call MHCnuggets from R.
Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) <doi:10.1080/01621459.2021.2011298>.
Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories are demonstrated. Flexible and diverse applications of the function parameters make the data analyses powerful.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
An implementation of the multilevel (also known as mixed or random 
    effects) hidden Markov model using Bayesian estimation in R. The multilevel 
    hidden Markov model (HMM) is a generalization of the well-known hidden
    Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The 
    multilevel HMM is tailored to accommodate (intense) longitudinal data of 
    multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. 
    <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow 
    for heterogeneity in the model parameters (transition probability matrix and 
    conditional distribution), while estimating one overall HMM. The model can 
    be fitted on multivariate data with either a categorical, normal, or Poisson 
    distribution, and include individual level covariates (allowing for e.g., 
    group comparisons on model parameters). Parameters are estimated using 
    Bayesian estimation utilizing the forward-backward recursion within a hybrid 
    Metropolis within Gibbs sampler. Missing data (NA) in the dependent 
    variables is accommodated assuming MAR. The package also includes various 
    visualization options, a function to simulate data, and a function to obtain 
    the most likely hidden state sequence for each individual using the Viterbi 
    algorithm.
Provides density, distribution, quantile, and random generation
    functions for the Modified Half-Normal (MHN) distribution, along with
    moments, mode, and the Fox-Wright Psi function used as the normalizing
    constant. The MHN distribution arises as a conditional posterior in
    Bayesian MCMC and generalizes the half-normal, truncated normal, and
    square-root gamma distributions. Implements efficient sampling via
    the Sun, Kong & Pal (2023) <doi:10.1080/03610926.2021.1934700>
    algorithms and the Gao & Wang (2025)
    <doi:10.1080/03610918.2025.2524551> RTDR method.
Provides exact and approximate algorithms for the horseshoe prior
    in linear regression models, which were proposed by Johndrow et al. (2020)
    <https://www.jmlr.org/papers/v21/19-536.html>.
High-performance implementation of the Modified Hodrick-Prescott (HP) Filter 
    for decomposing macroeconomic time series into trend and cyclical components. Based on 
    the methodology of Choudhary, Hanif and Iqbal (2014) <doi:10.1080/00036846.2014.894631> 
    "On smoothing macroeconomic time series using the modified HP filter", which uses 
    generalized cross-validation (GCV) to automatically select the optimal smoothing 
    parameter lambda, following McDermott (1997) "An automatic method for choosing the 
    smoothing parameter in the HP filter" (as described in Coe and McDermott (1997) 
    <doi:10.2307/3867497>). Unlike the standard HP filter that uses fixed lambda values 
    (1600 for quarterly, 100 for annual data), this package estimates series-specific lambda 
    values that minimize the GCV criterion. Implements efficient C++ routines via 
    'RcppArmadillo' for fast computation, supports batch processing of multiple series, and 
    provides comprehensive visualization tools using 'ggplot2'. Particularly useful for 
    cross-country macroeconomic comparisons, business cycle analysis, and when the 
    appropriate smoothing parameter is uncertain.
Transforms, calculates, and presents results from the Mental Health Quality of Life Questionnaire (MHQoL), a measure of health-related quality of life for individuals with mental health conditions. Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) <doi:10.1007/s11136-021-02935-w>.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
A comprehensive tool for almost all existing multiple testing
    methods for discrete data. The package also provides some novel multiple testing
    procedures controlling FWER/FDR for discrete data. Given discrete p-values
    and their domains, the [method].p.adjust function returns adjusted p-values,
    which can be used to compare with the nominal significant level alpha and make
    decisions. For users' convenience, the functions also provide the output option 
    for printing decision rules.
A Comprehensive tool for almost all existing multiple testing
    methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.
Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
Estimation of models with dependent variable left-censored at zero.
             Null values may be caused by a selection process
	     Cragg (1971) <doi:10.2307/1909582>, insufficient resources
	     Tobin (1958) <doi:10.2307/1907382>, or infrequency of purchase
	     Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <doi:10.1371/journal.pcbi.1010420>. It is dedicated to dealing with multiple imputation for proteomics.
Tools for training, selecting, and evaluating maximum entropy
    (and standard logistic regression) distribution models. This package 
    provides tools for user-controlled transformation of explanatory variables, 
    selection of variables by nested model comparison, and flexible model 
    evaluation and projection. It follows principles based on the maximum-
    likelihood interpretation of maximum entropy modeling, and uses infinitely-
    weighted logistic regression for model fitting. The package is described in 
    Vollering et al. (2019; <doi:10.1002/ece3.5654>).
Implements methods to estimate conditional outcome means in 
    settings with missingness-not-at-random and incomplete auxiliary variables. 
    Specifically, this package implements the marginalization over incomplete
    auxiliaries (MIA) method. 
    The package supports continuous and binary outcomes, and 
    supports auxiliary variables that are normal, binary, and categorical.
Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data.
    Validate the results of an MIC experiment by comparing observed MIC values to
    a gold standard assay, in line with standards from the International Organization for
    Standardization (2021) <https://www.iso.org/standard/79377.html>.
'Mica' is a server application used to create data web portals for 
    large-scale epidemiological studies or multiple-study consortia. 'Mica' helps
    studies to provide scientifically robust data visibility and web presence 
    without significant information technology effort. 'Mica' provides a 
    structured description of consortia, studies, annotated and searchable data
    dictionaries, and data access request management. This 'Mica' client allows
    to perform data extraction for reporting purposes.
Modified functions of the package 'pcalg' and some additional functions
            to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based 
            causal discovery in incomplete and multiply imputed datasets.
            Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>;
            Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) <doi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.
Multiple imputation using Fully Conditional Specification (FCS)
    implemented by the MICE algorithm as described in Van Buuren and
    Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
    its own imputation model. Built-in imputation models are provided for
    continuous data (predictive mean matching, normal), binary data (logistic
    regression), unordered categorical data (polytomous logistic regression)
    and ordered categorical data (proportional odds). MICE can also impute
    continuous two-level data (normal model, pan, second-level variables).
    Passive imputation can be used to maintain consistency between variables.
    Various diagnostic plots are available to inspect the quality of the
    imputations.
Contains functions for multiple imputation which
    complements existing functionality in R.
    In particular, several imputation methods for the
    mice package (van Buuren & Groothuis-Oudshoorn, 2011,
    <doi:10.18637/jss.v045.i03>) are implemented.
    Main features of the miceadds package include
    plausible value imputation (Mislevy, 1991,
    <doi:10.1007/BF02294457>), multilevel imputation for
    variables at any level or with any number of hierarchical
    and non-hierarchical levels (Grund, Luedtke & Robitzsch,
    2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, 
    Ch.7, <doi:10.1201/9780429492259>), imputation using 
    partial least squares (PLS) for high dimensional 
    predictors (Robitzsch, Pham & Yanagida, 2016), 
    nested multiple imputation (Rubin, 2003, 
    <doi:10.1111/1467-9574.00217>), substantive model
    compatible imputation (Bartlett et al., 2015,
    <doi:10.1177/0962280214521348>), and features
    for the generation of synthetic datasets
    (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
    Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
Statistical Analyses and Pooling after Multiple Imputation. A large variety 
   of repeated statistical analysis can be performed and finally pooled. Statistical analysis 
   that are available are, among others, Levene's test, Odds and Risk Ratios, One sample 
   proportions, difference between proportions and linear and logistic regression models. 
   Functions can also be used in combination with the Pipe operator. 
   More and more statistical analyses and pooling functions will be added over time.
   Heymans (2007) <doi:10.1186/1471-2288-7-33>.
   Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	 Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	 Marshall (2009) <doi:10.1186/1471-2288-9-57>.
	 Sidi (2021) <doi:10.1080/00031305.2021.1898468>.
	 Lott (2018) <doi:10.1080/00031305.2018.1473796>.
	 Grund (2021) <doi:10.31234/osf.io/d459g>.
Various tools for microeconomic analysis and microeconomic modelling,
   e.g. estimating quadratic, Cobb-Douglas and Translog functions,
   calculating partial derivatives and elasticities of these functions,
   and calculating Hessian matrices, checking curvature
   and preparing restrictions for imposing monotonicity of Translog functions.
Functions and tools
   for analysing consumer demand
   with the Almost Ideal Demand System (AIDS)
   suggested by Deaton and Muellbauer (1980).
Tools for econometric analysis and economic modelling
   with the traditional two-input Constant Elasticity of Substitution (CES) function
   and with nested CES functions with three and four inputs.
   The econometric estimation can be done by the Kmenta approximation,
   or non-linear least-squares
   using various gradient-based or global optimisation algorithms.
   Some of these algorithms can constrain the parameters to certain ranges,
   e.g. economically meaningful values.
   Furthermore, the non-linear least-squares estimation
   can be combined with a grid-search for the rho-parameter(s).
   The estimation methods are described in Henningsen et al. (2021)
   <doi:10.4337/9781788976480.00030>.
Econometric analysis of multiple-input-multiple-output
  production technologies with ray-based input distance functions as suggested
  by Price and Henningsen (2023): "A Ray-Based Input Distance Function to Model
  Zero-Valued Output Quantities: Derivation and an Empirical Application",
  Journal of Productivity Analysis 60, p. 179-188,
  <doi:10.1007/s11123-023-00684-1>.
Tools for calculating Laspeyres, Paasche, and Fisher
  price and quantity indices.
Tools for econometric production analysis
   with the Symmetric Normalized Quadratic (SNQ) profit function,
   e.g. estimation, imposing convexity in prices,
   and calculating elasticities and shadow prices.
Provides a custom imputation method for the 'mice' package
    based on distributional random forests. The package implements the
    'mice.impute.DRF' method, which can be used within the standard
    'mice' workflow. Missing values are imputed by estimating
    conditional distributions with distributional random forests and
    sampling observed responses using forest weights.
Fast imputations under the object-oriented programming paradigm. 	
  Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
  The biggest improvement in time performance can be achieved for a calculation where a grouping variable is used.
  A single evaluation of a quantitative model for the multiple imputations is another major enhancement.
  A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.
Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021)<doi:10.1109/ACCESS.2021.3116526> and Tang et al. (2024)<doi:10.1109/TGRS.2024.3446950>). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results.
Multiple Imputation has been shown to 
  be a flexible method to impute missing values by 
  Van Buuren (2007) <doi:10.1177/0962280206074463>. 
  Expanding on this, random forests have been shown 
  to be an accurate model by Stekhoven and Buhlmann 
  <arXiv:1105.0828> to impute missing values in datasets. 
  They have the added benefits of returning out of bag 
  error and variable importance estimates, as well as 
  being simple to run in parallel.
Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. 
    His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuß am Frack an a Ma'nsgrëßt"),
    but he also wrote many more works, including Rodange, Tockert (1928) <https://www.autorenlexikon.lu/page/document/361/3614/1/FRE/index.html> 
    ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) <https://www.autorenlexikon.lu/page/document/361/3615/1/FRE/index.html>
    ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the
    plain text versions of his works available on 
    <https://data.public.lu/fr/datasets/the-works-in-luxembourguish-of-michel-rodange/>.
Implementation of a framework for cluster analysis with selection
    of the final number of clusters and an optional variable selection
    procedure. The package is designed to integrate the results of multiple
    imputed datasets while accounting for the uncertainty that the imputations
    introduce in the final results. In addition, the package can also be used
    for a cluster analysis of the complete cases of a single dataset. The
    package also includes specific methods to summarize and plot the results.
    The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.
A procedure for comparing multivariate samples associated with
    different groups. It uses principal component analysis to convert
    multivariate observations into a set of linearly uncorrelated statistical
    measures, which are then compared using a number of statistical methods. The
    procedure is independent of the distributional properties of samples and
    automatically selects features that best explain their differences, avoiding
    manual selection of specific points or summary statistics. It is appropriate
    for comparing samples of time series, images, spectrometric measures or
    similar multivariate observations. This package is described in Fachada et
    al. (2016) <doi:10.32614/RJ-2016-055>.
Fits Semiparametric Promotion Time Cure Models, taking into account (using a 
			 corrected score approach or the SIMEX algorithm) or not the measurement error
			 in the covariates, using a backfitting approach to maximize the likelihood.
A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
Provides infrastructure to accurately measure and compare
        the execution time of R expressions.
Provides functions to enhance the available 
            statistical analysis procedures in R by providing simple functions to 
            analysis and visualize the 16S rRNA data.Here we present a tutorial 
            with minimum working examples to demonstrate usage and dependencies.
Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) <doi:10.1128/aem.56.6.1875-1881.1990>), Baranyi model (Baranyi and Roberts (1994) <doi:10.1016/0168-1605%2894%2990157-0>), Rosso model (Rosso et al. (1993) <doi:10.1006/jtbi.1993.1099>) and linear model (Dantigny (2005) <doi:10.1016/j.ijfoodmicro.2004.10.013>).
Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent.
A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects. Includes the LinDA method for differential abundance analysis (Zhou et al. (2022)<doi:10.1186/s13059-022-02655-5>), the BMDD (Bimodal Dirichlet Distribution) method for accurate modeling and imputation of zero-inflated microbiome sequencing data (Zhou et al. (2025)<doi:10.1371/journal.pcbi.1013124>) and compositional sparse CCA methods for microbiome multi-omics data integration (Deng et al. (2024) <doi: 10.3389/fgene.2024.1489694>).
An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups.
 Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. 
 Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, Elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised partial least squares analysis by Inge S. Helland <https://www.jstor.org/stable/4616159>.
 Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation.
 During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
Provides tools specifically designed for analyzing longitudinal microbiome data. This tool integrates seven functional modules, providing a systematic framework for microbiome time-series analysis. For more details on inferences involving interspecies interactions see Fisher (2014) <doi:10.1371/journal.pone.0102451>. Details on this package are also described in an unpublished manuscript.
Stand-alone HTTP capable R-package repository,
    that fully supports R's install.packages() and available.packages().
    It also contains API endpoints for end-users to add/update packages.
    This package can supplement 'miniCRAN', which has functions for maintaining
    a local (partial) copy of 'CRAN'.
    Current version is bare-minimum without any access-control or much security.
Provides utilities for reading and processing microdata from Spanish official statistics with R.
A framework for analyzing broth microdilution assays in various
    96-well plate designs, visualizing results and providing descriptive and (simple)
    inferential statistics (i.e. summary statistics and sign test). The functions are 
    designed to add metadata to 8 x 12 tables of absorption values, creating a tidy
    data frame. Users can choose between clean-up procedures via function 
    parameters (which covers most cases) or user prompts (in cases with complex 
    experimental designs). Users can also choose between two validation methods,
    i.e. exclusion of absorbance values above a certain threshold or manual exclusion 
    of samples. A function for visual inspection of samples with their absorption values
    over time for certain group combinations helps with the decision. In addition,
    the package includes functions to subtract the background absorption (usually
    at time T0) and to calculate the growth performance compared to a baseline.
    Samples can be visually inspected with their absorption values displayed across
    time points for specific group combinations. Core functions of this package (i.e.
    background subtraction, sample validation and statistics) were inspired by the
    manual calculations that were applied in Tewes and Muller (2020)
    <doi:10.1038/s41598-020-67600-7>.
A series of data analysis approaches for microbiome data based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis.
Collect and normalize local microinverter energy and power production
    data through off-cloud API requests. Currently supports 'APSystems', 'Enphase',
    and 'Fronius' microinverters.
Most multilevel methodologies can only model macro-micro
    multilevel situations in an unbiased way, wherein group-level predictors
    (e.g., city temperature) are used to predict an individual-level
    outcome variable (e.g., citizen personality). In contrast,
    this R package enables researchers to model micro-macro situations, wherein
    individual-level (micro) predictors (and other group-level predictors) are
    used to predict a group-level (macro) outcome variable in an unbiased way.
This group of functions simplifies the creation of linked micromap plots. Please
  see <https://www.jstatsoft.org/v63/i02/> for additional details.
Provides a framework based on S3 dispatch for constructing models
  of mosquito-borne pathogen transmission which are constructed from submodels of various
  components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical
  expression for the distribution of bites on hosts means that different models
  (stochastic, deterministic, etc.) can be coherently incorporated and updated
  over a discrete time step.
Enables you to create accessible modal dialogs, with confidence and with minimal configuration.
Measures niche breadth and overlap of microbial taxa from large matrices.
			Niche breadth measurements include Levins' niche breadth (Bn) index, Hurlbert's Bn 
			and Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E.,
			Poole, R.W. (1981) <doi:10.2307/1936664>). Niche overlap measurements include 
			Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a Jaccard 
			similarity index of Feinsinger's PS values between taxa pairs, as Proportional Overlap.
Vitamin and mineral deficiencies continue to be a significant 
    public health problem. This is particularly critical in developing countries
    where deficiencies to vitamin A, iron, iodine, and other micronutrients
    lead to adverse health consequences. Cross-sectional surveys are helpful in
    answering questions related to the magnitude and distribution of
    deficiencies of selected vitamins and minerals. This package provides tools
    for calculating and determining select vitamin and mineral deficiencies
    based on World Health Organization (WHO) guidelines found at
    <https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system>.
A collection of functions for computations and visualizations of microbial pan-genomes.
The microplot function writes a set of R graphics files to be used as
  microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word',
  or 'Excel' files.  For 'LaTeX', we provide methods for the
  Hmisc::latex() generic function to construct 'latex' tabular
  environments which include the graphs.  These can be used directly
  with the operating system 'pdflatex' or 'latex' command, or by using
  one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an
  intermediary.  For 'MS Word', the msWord() function uses the
  'flextable' package to construct 'Word' tables which include the
  graphs.  There are several distinct approaches for constructing HTML
  files.  The simplest is to use the msWord() function with argument
  filetype="html".  Alternatively, use either 'Emacs org-mode' or the
  htmlTable::htmlTable() function to construct an 'HTML' file
  containing tables which include the graphs.  See the documentation
  for our as.htmlimg() function.  For 'Excel' use on 'Windows', the
  file examples/irisExcel.xls includes 'VBA' code which brings the
  individual panels into individual cells in the spreadsheet.
  Examples in the examples and demo subdirectories are shown with
  'lattice' graphics, 'ggplot2' graphics, and 'base' graphics.
  Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and
  'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input
  files and their 'pdf' output files.  Examples for 'HTML' include
  'org-mode' and 'Rmd' input files and their webarchive 'HTML' output
  files.  In addition, the as.orgtable() function can display a
  data.frame in an 'org-mode' document.  The examples for 'MS Word'
  (with either filetype="docx" or filetype="html") work with all
  operating systems.  The package does not require the installation of
  'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files.
Modelling interacting microbial populations - example applications
    include human gut microbiota, rumen microbiota and phytoplankton. Solves a
    system of ordinary differential equations to simulate microbial growth and
    resource uptake over time. This version contains network visualisation functions.
Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.
Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.
An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders.
A generalization of the 'Synth' package that is
    designed for data at a more granular level (e.g., micro-level).
    Provides functions to construct weights (including propensity
    score-type weights) and run analyses for synthetic control methods
    with micro- and meso-level data; see Robbins, Saunders, and Kilmer
    (2017) <doi:10.1080/01621459.2016.1213634> and Robbins and Davenport
    (2021) <doi:10.18637/jss.v097.i02>.
This toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model as described in Zinn (2014) <doi:10.34196/IJM.00105>.
Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) <doi: 10.1214/08-AOAS167>. The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative.
Functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series <doi:10.1201/9781003100263>. The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included.
Companion package of Carrion-i-Silvestre & Sansó (2026): 
  "Testing for Constant Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified 
  Iterative Cumulative Sum of Squares Algorithm, which is an extension of 
  the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the 
  unconditional variance of a time series controlling for the tail index of 
  the underlying distribution. The fourth order moment is estimated non-parametrically
  to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). 
  Critical values and p-values are generated using a Generalized Extreme Value distribution approach.
  References
  Carrion-i-Silvestre J.J & Sansó A (2026) <doi:10.1080/03610918.2026.2615207>.
  Inclan C & Tiao G.C (1994) <doi:10.1080/01621459.1994.10476824>,
  Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) <https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.
Implements order selection for Vector Autoregressive (VAR) models using the Mean Square Information Criterion (MIC). Unlike standard methods such as AIC and BIC, MIC is likelihood-free. This method consistently estimates VAR order and has robust performance under model misspecification. For more details, see Hellstern and Shojaie (2025) <doi:10.48550/arXiv.2511.19761>.
Contains functions for converting existing HTML/JavaScript
   source into equivalent 'shiny' functions. Bootstraps the process of making
   new 'shiny' functions by allowing us to turn HTML snippets directly into 
   R functions.
The rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging, which necessitates innovative, integrated, and efficient trial designs(Yuan, Y., et al. (2016) <doi:10.1002/sim.6971>). MIDAS-2 package enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We used a regression model to characterize the efficacy pattern in subgroups. Information borrowing was applied through Bayesian hierarchical model to improve trial efficiency considering the limited sample size in subgroups(Cunanan, K. M., et al. (2019) <doi:10.1177/1740774518812779>). MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion(Wathen, J. K., & Thall, P. F. (2017) <doi: 10.1177/1740774517692302>).
The 'MIDASim' package is a microbiome data simulator for generating realistic microbiome datasets by adapting a user-provided template. It supports the controlled introduction of experimental signals-such as shifts in taxon relative abundances, prevalence, and sample library sizes-to create distinct synthetic populations under diverse simulation scenarios. For more details, see He et al. (2024) <doi:10.1186/s40168-024-01822-z>.
The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.
Methods and tools for mixed frequency time series data analysis.
    Allows estimation, model selection and forecasting for MIDAS regressions.
Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's  MIDAS.
Provides tools and demonstrates methods for working with
    individual undergraduate student-level records (registrar's data) in
    'R'. Tools include filters for program codes, data sufficiency, and
    timely completion. Methods include gathering blocs of records,
    computing quantitative metrics such as graduation rate, and creating
    charts to visualize comparisons. 'midfieldr' interacts with practice
    data provided in 'midfielddata', an R data package available at
    <https://midfieldr.github.io/midfielddata/>.  'midfieldr' also
    interacts with the full MIDFIELD database for users who have access.
    As of the transfer of MIDFIELD to the American Society for Engineering
    Education in 2023, the development, expansion, and study of
    MIDFIELD has been supported by the National Science Foundation grants
    0337629, 0646441, 0729596, 0734062, 0835914, 0935157, 0935058,
    0969474, 1025171, 1129383, 1232740, 1329283, 1361058, 1545667,
    2142087, 2141903, and 2152441.
An implementation of a taxonomy of models of restricted diffusion
    in biological tissues parametrized by the tissue geometry (axis, diameter,
    density, etc.). This is primarily used in the context of diffusion magnetic
    resonance (MR) imaging to model the MR signal attenuation in the presence of
    diffusion gradients. The goal is to provide tools to simulate the MR signal
    attenuation predicted by these models under different experimental
    conditions. The package feeds a companion 'shiny' app available at
    <https://midi-pastrami.apps.math.cnrs.fr> that serves as a graphical
    interface to the models and tools provided by the package. Models currently
    available are the ones in Neuman (1974) <doi:10.1063/1.1680931>, Van
    Gelderen et al. (1994) <doi:10.1006/jmrb.1994.1038>, Stanisz et al. (1997)
    <doi:10.1002/mrm.1910370115>, Soderman & Jonsson (1995)
    <doi:10.1006/jmra.1995.0014> and Callaghan (1995)
    <doi:10.1006/jmra.1995.1055>.
Implementation of the mid-n algorithms presented in 
             Wellek S (2015) <DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact 
             sample size calculation for superiority trials with binary outcome.
Provides a 'parsnip' engine for the 'midr' package, enabling users to fit, tune, and evaluate Maximum Interpretation Decomposition (MID) models within the 'tidymodels' framework. Developed through research by the Moonlight Seminar 2025, a study group of actuaries from the Institute of Actuaries of Japan, to enhance practical applications of interpretable modeling. Detailed methodology is available in Asashiba et al. (2025) <doi:10.48550/arXiv.2506.08338>.
A guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. 'midoc' will advise which analysis approaches can be used, and how best to perform them. 'midoc' follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). <doi:10.1016/j.jclinepi.2021.01.008>.
The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) <doi:10.48550/arXiv.2506.08338>.
Apply tests of multiple comparisons based
    on studentized 'midrange' and 'range' distributions. 
    The tests are: Tukey Midrange ('TM' test),
    Student-Newman-Keuls Midrange ('SNKM' test), 
    Means Grouping Midrange ('MGM' test) and 
    Means Grouping Range ('MGR' test). The first two tests were published by 
    Batista and Ferreira (2020) <doi:10.1590/1413-7054202044008020>. The last two were published by Batista and Ferreira (2023) <doi:10.28951/bjb.v41i4.640>.
Provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) <doi:10.1007/s40617-025-01058-9>.
Evolutionary black box optimization algorithms building on the
  'bbotk' package. 'miesmuschel' offers both ready-to-use optimization
  algorithms, as well as their fundamental building blocks that can be used to
  manually construct specialized optimization loops. The Mixed Integer Evolution
  Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be
  implemented, as well as the multi-objective optimization algorithms NSGA-II
  by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.
Impute the covariance matrix of incomplete data so that factor 
  analysis can be performed. Imputations are made using multiple imputation 
  by Multivariate Imputation with Chained Equations (MICE) and combined with 
  Rubin's rules. Parametric Fieller confidence intervals and nonparametric
  bootstrap confidence intervals can be obtained for the variance explained by 
  different numbers of principal components. The method is described in 
  Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.
Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) <doi:10.48550/arXiv.2209.04757> for smoothing multivariate data on half-spaces.
Allows the user to estimate transition probabilities for
    migratory animals between any two phases of the annual cycle, using a
    variety of different data types. Also quantifies the strength of
    migratory connectivity (MC), a standardized metric to quantify the
    extent to which populations co-occur between two phases of the annual
    cycle. Includes functions to estimate MC and the more traditional
    metric of migratory connectivity strength (Mantel correlation)
    incorporating uncertainty from multiple sources of sampling error. For
    cross-species comparisons, methods are provided to estimate
    differences in migratory connectivity strength, incorporating
    uncertainty. See Cohen et al. (2018) <doi:10.1111/2041-210X.12916>,
    Cohen et al. (2019) <doi:10.1111/ecog.03974>, Roberts et al. (2023)
    <doi:10.1002/eap.2788>, and Hostetler et al. (2025)
    <doi:10.1111/2041-210X.14467> for details on some of these methods.
Implements methods for estimating generalized estimating equations (GEE) with advanced options for flexible modeling and handling missing data. This package provides tools to fit and analyze GEE models for longitudinal data, allowing users to address missingness using a variety of imputation techniques. It supports both univariate and multivariate modeling, visualization of missing data patterns, and facilitates the transformation of data for efficient statistical analysis. Designed for researchers working with complex datasets, it ensures robust estimation and inference in longitudinal and clustered data settings.
Provides tools for estimating, measuring, and analyzing migration data. Designed to assist researchers and analysts in working effectively with migration data.
Load, validate, and manipulate Clinical Data Interchange
    Standards Consortium ('CDISC') Analysis Data Model ('ADaM') dataset
    metadata stored as 'YAML' files. Metadata files are validated against
    a JSON schema. Provides functions to inspect and modify columns,
    parameters, and row-level operations within and across 'ADaM' domains.
    Designed for use with the 'mighty' framework.
A set of tools for testing networks.
   It includes functions for univariate and multivariate conditional uniform graph 
   and quadratic assignment procedure testing, and network regression.
   The package is a complement to 
   'Multimodal Political Networks' (2021, ISBN:9781108985000),
   and includes various datasets used in the book.
   Built on the 'manynet' package, all functions operate with matrices, 
   edge lists, and 'igraph', 'network', and 'tidygraph' objects,
   and on one-mode and two-mode (bipartite) networks.
Tools to help convert credit risk data at two timepoints
    into traditional credit state migration (aka, "transition") matrices.
    At a higher level, 'migrate' is intended to help an analyst understand
    how risk moved in their credit portfolio over a time interval.
    References to this methodology include:
    1. Schuermann, T. (2008) <doi:10.1002/9780470061596.risk0409>.
    2. Perederiy, V. (2017) <doi:10.48550/arXiv.1708.00062>.
Calculate various indices, like Crude Migration Rate,
    different Gini indices or the Coefficient of Variation among others, to
    show the (un)equality of migration.
Detection of migration events and segments of continuous residence 
    based on irregular time series of location data
    as published in Chi et al. (2020) <doi:10.1371/journal.pone.0239408>.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response.
             Reference for mean score method, inverse probability weighted method is
             Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.
Data-driven approach for Exploratory Factor Analysis (EFA)
    that uses Model Implied Instrumental Variables (MIIVs). The method
    starts with a one factor model and arrives at a suggested model with
    enhanced interpretability that allows cross-loadings and correlated
    errors.
Functions for estimating structural equation models using 
    instrumental variables.
An interface to build machine learning models for
    classification and regression problems. 'mikropml' implements the ML
    pipeline described by Topçuoğlu 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.
Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. 
  This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions.
  It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022.
  Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301–307 <doi:10.1111/2041-210X.14269>.
Weakly supervised (WS), multiple instance (MI) data lives in
    numerous interesting applications such as drug discovery, object
    detection, and tumor prediction on whole slide images. The 'mildsvm'
    package provides an easy way to learn from this data by training
    Support Vector Machine (SVM)-based classifiers. It also contains
    helpful functions for building and printing multiple instance data
    frames. The core methods from 'mildsvm' come from the following
    references: Kent and Yu (2024) <doi:10.1214/24-AOAS1876>; Xiao, Liu, and Hao
    (2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012)
    <https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>;
    Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews
    et al. (2003)
    <https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>.
    Many functions use the 'Gurobi' optimization back-end to improve the
    optimization problem speed; the 'gurobi' R package and associated
    software can be downloaded from <https://www.gurobi.com> after
    obtaining a license.
A variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted).
Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS).
  Two computationnally efficient methods are proposed for obtaining effect size estimates (beta) in 
  Mixed Logistic Regression in GWAS: the Approximate Maximum Likelihood Estimate (AMLE), and the Offset
  method. The wald test obtained with AMLE is identical to the score test. Data can be genotype matrices
  in plink format, or dosage (VCF files). The methods are described in details in 
  Milet et al (2020) <doi:10.1101/2020.01.17.910109>.
The multiple instance data set consists of many independent
    subjects (called bags) and each subject is composed of several components
    (called instances). The outcomes of such data set are binary or categorical responses,
    and, we can only observe the subject-level outcomes. For example, in manufacturing
    processes, a subject is labeled as "defective" if at least one of its own
    components is defective, and otherwise, is labeled as "non-defective". The
    'milr' package focuses on the predictive model for the multiple instance
    data set with binary outcomes and performs the maximum likelihood estimation
    with the Expectation-Maximization algorithm under the framework of logistic
    regression. Moreover, the LASSO penalty is attached to the likelihood function
    for simultaneous parameter estimation and variable selection.
Provides compact tools for missing-data analysis, including
    artificial amputation, chained single and multiple imputation, statistical
    and machine-learning-based imputation methods, diagnostic evaluation, and
    post-imputation pooling.
Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and Tubo, B.F. (2024) <doi:10.24412/1932-2321-2024-278-115-123>].
Guesses the MIME type from a filename extension using the data
    derived from /etc/mime.types in UNIX-type systems.
Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, 
  a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers
  a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes,
  particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing
  researchers to focus on their primary inquiries without being bogged down by wrangling complexities.
Our approach uses a  mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9).
The MIMS-unit algorithm is developed to compute Monitor Independent
    Movement Summary Unit, a measurement to summarize raw accelerometer data 
    while ensuring harmonized results across different devices. It also includes
    scripts to reproduce results in the related publication 
    (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).
Calculate dissolved gas concentrations from raw MIMS 
	(Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on 
	a formatted CSV file to return dissolved gas concentrations 
	(mg and microMole) of N2, O2, Ar based on 
	gas solubility at temperature, pressure, and salinity. See references 
	Benson and Krause (1984), Garcia and Gordon (1992), Stull (1947), 
	and Hamme and Emerson (2004) for more information. Easily save the 
	output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). 
	Supports dual-temperature standard calibration for dual-bath MIMS setups.
In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum  number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) <doi:10.1080/03610926.2016.1152490>. This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface.
Count data is prevalent and informative, with widespread
    application in many fields such as social psychology, personality, and
    public health. Classical statistical methods for the analysis of count
    outcomes are commonly variants of the log-linear model, including
    Poisson regression and Negative Binomial regression. However, a
    typical problem with count data modeling is inflation, in the sense
    that the counts are evidently accumulated on some integers. Such an
    inflation problem could distort the distribution of the observed
    counts, further bias estimation and increase error, making the classic
    methods infeasible. Traditional inflated value selection methods based
    on histogram inspection are easy to neglect true points and
    computationally expensive in addition. Therefore, we propose a
    multiple-inflated negative binomial model to handle count data
    modeling with multiple inflated values, achieving data-driven inflated
    value selection. The proposed approach provides simultaneous
    identification of important regression predictors on the target count
    response as well. More details about the proposed method are described in 
    Li, Y., Wu, M., Wu, M., & Ma, S. (2023) <arXiv:2309.15585>.
Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) <doi:10.1016/S0378-3758(98)00147-5>.
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, directory structures, and other hierarchical structured documents into mind map widgets or 'Freemind' codes or 'Mermaid' mind map codes, and vice versa. 'Freemind' mind map ('.mm') files can be opened by or imported to common mind map software such as 'Freemind' (<https://freemind.sourceforge.io/wiki/index.php/Main_Page>). 'Mermaid' mind map codes (<https://mermaid.js.org/>) can be directly embedded in documents.
Citrus is a computational technique developed for the analysis
    of high dimensional cytometry data sets. This package extracts, statistically
    analyzes, and visualizes marker expression from 'citrus' data. This code was used to 
    generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. 
    “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile 
    Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus,
    please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download
    the 'citrus' package, please see <https://github.com/nolanlab/citrus>.
The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials.
Wrapper for 'minepy' implementation of Maximal
        Information-based Nonparametric Exploration statistics (MIC and
        MINE family). Detailed information of the ANSI C implementation of
	'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.
Play and record games of minesweeper using a graphics device that supports event handling.
    Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by
    Crow P. (1997) <https://minesweepergame.com/math/a-mathematical-introduction-to-the-game-of-minesweeper-1997.pdf>.
This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`.
In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum  number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017)<doi:10.1080/03610926.2016.1152490>.This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign().
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.
Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), <doi:10.1007/s12532-023-00238-4>).
Makes it possible to create an internally consistent
    repository consisting of selected packages from CRAN-like repositories.
    The user specifies a set of desired packages, and 'miniCRAN' recursively
    reads the dependency tree for these packages, then downloads only this
    subset. The user can then install packages from this repository directly,
    rather than from CRAN.  This is useful in production settings, e.g. server
    behind a firewall, or remote locations with slow (or zero) Internet access.
Create minimal, responsive, and style-agnostic HTML documents with
    the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'.
    Powerful features include table of contents floating as a sidebar,
    folding codes and results, and more.
Quick and simple Tcl/Tk Graphical User Interface 
  to call functions. Also comprises a very simple experimental 
  GUI framework.
Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" <doi:10.1111/biom.12079> and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" <doi:10.1146/annurev-statistics-033121-110134>.
Normally building a GODB is fairly complicated, involving downloading 
 multiple database files and using these to build e.g. a 'mySQL' database. 
 Accessing this database is also complicated, involving an intimate knowledge
 of the database in order to construct reliable queries.
 Here we have a more modest goal, generating GOGOA3, which is a stripped down
 version of the GODB that 
 was originally restricted to human genes as designated by the HUGO Gene Nomenclature
 Committee (HGNC) (see <https://geneontology.org/>). I have now added about two dozen
 additional species, namely all species represented on the Gene Ontology
 download page <https://current.geneontology.org/products/pages/downloads.html>.
 This covers most of the model organisms that are commonly used in bio-medical
 and basic research (assuming that anyone still has a grant to do such research).
 This can be built in a matter of seconds from 2 easily
 downloaded files (see <https://current.geneontology.org/products/pages/downloads.html>
 and <https://geneontology.org/docs/download-ontology/>), and it can be queried by e.g.
 w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3
 is a matrix representing the minimalist GODB and hgncList is a list of
 gene identifiers. This database will be used in my upcoming package 'GoMiner'
 which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)<doi:10.1186/gb-2003-4-4-r28>).
 Relevant .RData files are available from GitHub (<https://github.com/barryzee/GO/tree/main/databases>).
Generate central composite designs (CCD)with full as well 
    as fractional factorial points (half replicate) and Box Behnken 
    designs (BBD) with minimally changed run sequence.
Create tile grid maps, which are like choropleth maps except each
  region is represented with equal visual space.
The minimax family of distributions is a two-parameter
        family like the beta family, but computationally a lot more
        tractible.
A computationally efficient solution for generating optimal experimental designs in Accelerated Life Testing (ALT). Leveraging a Particle Swarm Optimization (PSO)-based hybrid algorithm, the package identifies optimal test plans that minimize estimation variance under specified failure models and stress profiles. For more detailed, see Lee et al. (2025), Optimal Robust Strategies for Accelerated Life Tests and Fatigue Testing of Polymer Composite Materials <doi:10.1214/25-AOAS2075>, and Hoang (2025), Model-Robust Minimax Design of Accelerated Life Tests via PSO-based Hybrid Algorithm, Master' Thesis, Unpublished.
Implements the algorithm of Remez (1962) for polynomial minimax
    approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for
    rational minimax approximation.
Shiny web application to run meta-analyses. 
    Essentially a graphical front-end to package 'meta' for R. 
    Can be useful as an educational tool, and for quickly 
    analyzing and sharing meta-analyses. 
    Provides output to quickly fill in GRADE (Grading of 
    Recommendations, Assessment, Development and Evaluations)
    Summary-of-Findings tables. 
    Importantly, it allows further processing of the results 
    inside R, in case more specific analyses are needed.
An R interface to the 'MinIO' Client. The 'MinIO' Client ('mc')
  provides a modern alternative to UNIX commands like 'ls', 'cat', 'cp',
  'mirror', 'diff', 'find' etc. It supports 'filesystems'  and Amazon "S3"
  compatible cloud storage service ("AWS" Signature v2 and v4).
  This package provides convenience functions for installing the 'MinIO'
  client and running any operations, as described in the official 
  documentation, <https://min.io/docs/minio/linux/reference/minio-mc.html?ref=docs-redirect>.
  This package provides a flexible and high-performance alternative to 'aws.s3'.
Density, distribution function, ... hazard function, cumulative
    hazard function, survival function for survival distributions with
    piece-wise constant hazards and multiple states and methods to plot and
    summarise those distributions. 
    A derivation of the used algorithms can be found in my masters thesis 
    <doi:10.25365/thesis.76098>.
PDF is a standard file format for laying out
    text and images in documents.  At its core, these documents are sequences of 
    objects defined in plain text.  This package allows for the creation
    of PDF documents at a very low level without any library or graphics
    device dependencies.
Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.
This package is deprecated. Please use 'redatamx' instead.
  Provides an API to work with 'Redatam' (see <https://redatam.org>) 
  databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 
  'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' 
  core and provides functions to open/close a database (redatam_open()/redatam_close()), 
  list entities and variables from the database (redatam_entities(), redatam_variables()) 
  and execute a 'SPC' program and gets the results as data frames 
  (redatam_query(), redatam_run()).
Provides UI widget and layout functions for writing Shiny apps
    that work well on small screens.
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>.
Tools for calculating I-Scores, a simple way to measure how successful minor political parties are at influencing the major parties in their environment. I-Scores are designed to be a more comprehensive measurement of minor party success than vote share and legislative seats won, the current standard measurements, which do not reflect the strategies that most minor parties employ. The procedure leverages the Manifesto Project's NLP model to identify the issue areas that sentences discuss, see Burst et al. (2024) <doi:10.25522/manifesto.manifestoberta.56topics.context.2024.1.1>, and the Wordfish algorithm to estimate the relative positions that platforms take on those issue areas, see Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x>.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Derivative-free optimization by quadratic approximation
        based on an interface to Fortran implementations by M. J. D.
        Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
Using this package, one can determine the minimum sample size required so that the absolute deviation of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
Using this package, one can determine the minimum sample size required so that the mean square error of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) <doi:10.1186/1471-2105-8-278> "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants."
Compute effect sizes and their sampling variances from factorial experimental designs. The package supports calculation of simple effects, overall effects, and interaction effects for use in factorial meta-analyses. See Gurevitch et al. (2000) <doi:10.1086/303337>, Morris et al. (2007) <doi:10.1890/06-0442>, Lajeunesse (2011) <doi:10.1890/11-0423.1> and Macartney et al. (2022) <doi:10.1016/j.neubiorev.2022.104554>.
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>.
Provides an extension to the 'lolog' package by introducing the minTriadicClosure() statistic to capture higher-order interactions among triplets of nodes. This function facilitates improved modelling of group formations and 'triadic' closure in networks. A smoothing parameter has been incorporated to avoid numerical errors.
Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom').
A streamlined toolkit specifically designed for genomic
    selection and quantitative genetics in animal breeding. It provides
    high-performance data manipulation backed by 'data.table', focusing on
    multi-breed and multi-trait nested grouping operations. Features
    include zero-copy data importing, automated cross-validation
    splitting, and robust tools to generate and batch-export formatted
    phenotypic files required by various breeding software (e.g.,
    'ASReml-R', 'HIBLUP', 'DMU'), heavily optimizing iterative variance
    component analysis and large-scale evaluation pipelines.
An implementation of the iterative proportional fitting (IPFP), 
    maximum likelihood, minimum chi-square and weighted least squares procedures
    for updating a N-dimensional array with respect to given target marginal 
    distributions (which, in turn can be multidimensional). The package also
    provides an application of the IPFP to simulate multivariate Bernoulli
    distributions.
Generic functions to produce area/bar/box/line plots of data following IAMC (Integrated Assessment Modeling Consortium) submission format.
Evaluates R expressions asynchronously and in parallel,
    locally or distributed across networks. An official parallel cluster
    type for R. Built on 'nanonext' and 'NNG', its non-polling,
    event-driven architecture scales from a laptop to thousands of
    processes across high-performance computing clusters and cloud
    platforms. Features FIFO scheduling with task cancellation and bounded
    queues, promises for reactive programming, 'OpenTelemetry' distributed
    tracing, and custom serialization for cross-language data types.
Estimates average treatment effects at the cutoff based on sharp 
    regression discontinuity designs (RDD) and multiple imputation regression 
    discontinuity designs (MIRDD). It provides diagnostic tools for RDD by 
    comparing results with those from MIRDD, as proposed in Takahashi (2023) 
    <doi:10.1080/03610918.2021.1960374>. The package includes datasets from
    Takahashi (2023) and Takahashi (2026) <doi:10.1016/j.softx.2026.102707>.
Fitting recurrent events survival models for
    left-censored data with multiple imputation of the number of previous episodes.
    See Hernández-Herrera G, Moriña D, Navarro A. (2020) <arXiv:2007.15031>.
Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.
Test for overall association between microbiome composition data 
  and phenotypes via phylogenetic kernels. The phenotype can be univariate 
  continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), 
  survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), 
  multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and 
  structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). 
  The package can also use robust regression (unpublished work) and 
  integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). 
  In each case, the microbiome community effect is modeled nonparametrically 
  through a kernel function, which can incorporate phylogenetic tree information.
A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 <doi:10.1186/s12859-015-0715-9>. The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 <doi:10.1016/j.softx.2020.100569>. Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information.
Machine learning method specifically designed for
    pre-miRNA prediction. It takes advantage of unlabeled sequences to improve
    the prediction rates even when there are just a few positive examples, when
    the negative examples are unreliable or are not good representatives of
    its class. Furthermore, the method can automatically search for negative
    examples if the user is unable to provide them. MiRNAss can find a good
    boundary to divide the pre-miRNAs from other groups of sequences; it
    automatically optimizes the threshold that defines the classes boundaries,
    and thus, it is robust to high class imbalance. Each step of the method is
    scalable and can handle large volumes of data.
Testing CRAN and Bioconductor mirror speed by recording download time of 'src/base/COPYING' (for CRAN) and 'packages/release/bioc/html/ggtree.html' (for Bioconductor).
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of discrete response data using
    unidimensional and multidimensional item analysis models under the Item
    Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>).
    Exploratory and confirmatory item factor analysis models
	are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
    bi-factor and two-tier models are available for modeling item testlets using
	dimension reduction EM algorithms, while multiple group analyses and
	mixed effects designs are included for detecting differential item, bundle,
	and test functioning, and for modeling item and person covariates.
	Finally, latent class models such as the DINA, DINO, multidimensional latent class,
	mixture IRT models, and zero-inflated response models are supported, as well
	as a wide family of probabilistic unfolding models.
Provides tools to generate HTML interfaces for adaptive
    and non-adaptive tests using the shiny
    package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). 
    Suitable for applying unidimensional and multidimensional
    computerized adaptive tests (CAT) using item response theory methodology and for
    creating simple questionnaires forms to collect response data directly in R.
    Additionally, optimal test designs (e.g., "shadow testing") are supported
    for tests that contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying test item banks.
Package for combined miRNA- and mRNA-testing.
Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.
Helpers for common data analysis tasks including missing-value
    summaries and filters, simple reporting and plotting utilities, 'Excel'
    import and export workflows, and reading geospatial formats (for example
    shapefiles in zip archives, file geodatabases, KMZ, and KML) via 'sf' and
    related packages. Also includes small project utilities such as creating
    directories, gitignore scaffolding, combined package loading, and optional
    'lintr' setup.
A collection of miscellaneous 3d plots, including
    isosurfaces.
Various functions for random number generation, density 
             estimation, classification, curve fitting, and spatial 
             data analysis.
Implementing various things including functions for LaTeX tables,
    the Kalman filter, QQ-plots with simulation-based confidence intervals, linear regression diagnostics, web scraping, development tools, relative risk and odds
    rati, GARCH(1,1) Forecasting.
Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) <doi:10.1007/s11222-016-9705-7>. Misclassification probabilities can either be specified as fixed or estimated. Models with time dependent misclassification may also be fitted.
Estimates models that extend the standard GLM to take
    misclassification into account. The models require side information from a secondary data set
    on the misclassification process, i.e. some sort of misclassification
    probabilities conditional on some common covariates.
    A detailed description of the algorithm can be found in
    Dlugosz, Mammen and Wilke (2015) <https://ftp.zew.de/pub/zew-docs/dp/dp15043.pdf>.
Some basic math calculators for finding angles for triangles and for finding the greatest common divisor of two numbers and so on.
Facilitate the description, transformation, exploration, and reproducibility of metabarcoding analyses. 'MiscMetabar' is mainly built on top of the 'phyloseq', 'dada2' and 'targets' 'R' packages. It helps to build reproducible and robust bioinformatics pipelines in 'R'. 'MiscMetabar' makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) <doi:10.21105/joss.06038>.
Miscellaneous small tools and utilities.
   Many of them facilitate the work with matrices,
   e.g. inserting rows or columns, creating symmetric matrices,
   or checking for semidefiniteness.
   Other tools facilitate the work with regression models,
   e.g. extracting the standard errors,
   obtaining the number of (estimated) parameters,
   or calculating R-squared values.
Penalized regression methods, such as lasso and elastic net, are used in
    many biomedical applications when simultaneous regression coefficient
    estimation and variable selection is desired. However, missing data
    complicates the implementation of these methods, particularly when
    missingness is handled using multiple imputation. Applying a variable
    selection algorithm on each imputed dataset will likely lead
    to different sets of selected predictors, making it difficult
    to ascertain a final active set without resorting to ad hoc
    combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet)
    and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes,
    developed by Du et al (2022) <doi:10.1080/10618600.2022.2035739>. They, 
    by construction, force selection of the same variables across multiply 
    imputed data. 'miselect' also provides cross validated variants of these 
    methods.
A toolkit for analysis of genomic data. The 'misha' package
    implements an efficient data structure for storing genomic data, and
    provides a set of functions for data extraction, manipulation and
    analysis. Some of the 2D genome algorithms were described in Yaffe and Tanay
    (2011) <doi:10.1038/ng.947>.
Performs multiple imputation of missing data using an ensemble
    super learner built with the tidymodels framework. For each incomplete
    column, a stacked ensemble of candidate learners is trained on a bootstrap
    sample of the observed data and used to generate imputations via predictive
    mean matching (continuous), probability draws (binary), or cumulative
    probability draws (categorical). Supports parallelism across imputed
    datasets via the future framework.
A comprehensive toolkit for missing person identification combining
    genetic and non-genetic evidence within a Bayesian framework. Computes
    likelihood ratios (LRs) for DNA profiles, biological sex, age, hair color,
    and birthdate evidence. Provides decision analysis tools including optimal
    LR thresholds, error rate calculations, and ROC curve visualization.
    Includes interactive Shiny applications for exploring evidence combinations.
    For methodological details see Marsico et al. (2023) <doi:10.1016/j.fsigen.2023.102891>
    and Marsico, Vigeland et al. (2021) <doi:10.1016/j.fsigen.2021.102519>.
Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.
Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to 
    Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) <arXiv:2205.14994>.
Provides a simple and trustworthy methodology for the analysis of
    misreported continuous time series using either a frequentist
    (bootstrap-based EM algorithm) or a Bayesian (MCMC via JAGS) approach.
    The frequentist method is described in Morina et al. (2021)
    <doi:10.1038/s41598-021-02620-5>. The Bayesian extension fits the same
    ARMA model with misreporting structure using a full posterior distribution,
    providing credible intervals and DIC for model comparison, as described in 
    Morina et al. (2024) <doi:10.1101/2024.02.26.24303373>.
Provides functions to calculate the minimum and maximum possible 
    values of Cronbach's alpha when item-level missing data are present. 
    Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used 
    measures of internal consistency in the social, behavioral, and medical sciences 
    (Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011 
    <doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume 
    complete data, and listwise deletion is often applied when missingness occurs, 
    which can lead to biased or overly optimistic reliability estimates (Enders, 2003 
    <doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies 
    including enumeration, Monte Carlo sampling, and optimization algorithms 
    (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares 
    Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under 
    arbitrary missing data patterns. The approach is motivated by Manski's partial 
    identification framework and pessimistic bounding ideas from optimization literature.
Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) <doi:10.1186/1471-2105-8-25>).
A four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) <https://drive.google.com/file/d/1a8sV3RJ8VofLWikTDTQ7W4XJ76cEj4Fg/view?usp=drive_link>.
Implements two methods: a nonparametric risk adjustment and a
    data imputation method that use general population mortality tables to allow a
    correct analysis of time to disease recurrence. Also includes a powerful set of
    object oriented survival data simulation functions.
Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) <arXiv:2107.05427>.
The function 'missForest' in this package is used to
    impute missing values particularly in the case of mixed-type
    data. It uses a random forest (via 'ranger' or 'randomForest') trained on the observed values of
    a data matrix to predict the missing values. It can be used to
    impute continuous and/or categorical data including complex
    interactions and non-linear relations. It yields an out-of-bag
    (OOB) imputation error estimate without the need of a test set
    or elaborate cross-validation. It can be run in parallel to 
    save computation time.
Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>)
    with adaptations for prediction settings. The function missForest() is used 
    to impute a (training) dataset with missing values and to learn imputation 
    models that can be later used for imputing new observations. 
    The function missForestPredict() is used to impute one or multiple new 
    observations (test set) using the models learned on the training data. For more details see 
    Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.
Many times, you will not find data for all dates. After first January, 2011 you may have next data on 20th January, 2011 and so on. Also available dates may have zero values. Try to gather all such kinds of data in different excel sheets of a single excel file. Every sheet will contain two columns (1st one is dates and second one is the data). After loading all the sheets into different elements of a list, using this you can fill the gaps for all the sheets and mark all the corresponding values as zeros. Here I am talking about daily data. Finally, it will combine all the filled results into one data frame (first column is date and other columns will be corresponding values of your sheets) and give one combined data frame. Number of columns in the data frame will be number of sheets plus one. Then imputation will be done. Daily to monthly and weekly conversion is also possible.  More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.
Contains a suite of functions for health economic evaluations with missing outcome data. 
  The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). 
  Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models.
  In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. 
  The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.
A system for Analysis of LSD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).
A system for Analysis of RBD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). 
    For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. <https://www.jstatsoft.org/v56/i06/> <doi:10.18637/jss.v056.i06>.
Supply functions for the creation and handling of missing
    data as well as tools to evaluate missing data methods. Nearly all
    possibilities of generating missing data discussed by Santos et al.
    (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
    implemented.  Functions are supplied to compare parameter estimates
    and imputed values to true values to evaluate missing data methods.
    Evaluations of these types are done, for example, by Cetin-Berber et
    al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
    <doi:10.1093/bioinformatics/bth499>.
Simultaneously estimates sparse regression coefficients and
    response network structure in multivariate models with missing data.
    Unlike traditional approaches requiring imputation, handles
    missingness natively through unbiased estimating equations (MCAR/MAR
    compatible). Employs dual L1 regularization with automated selection
    via cross-validation or information criteria. Includes parallel
    computation, warm starts, adaptive grids, publication-ready
    visualizations, and prediction methods.  Ideal for genomics,
    neuroimaging, and multi-trait studies with incomplete high-dimensional
    outcomes. See Zeng et al. (2025) <doi:10.48550/arXiv.2507.05990>.
A framework that boosts the imputation of 'missForest' by Stekhoven, D.J. and Bühlmann, P. (2012) <doi:10.1093/bioinformatics/btr597> by harnessing parallel processing and through the fast Gradient Boosted Decision Trees (GBDT) implementation 'LightGBM' by Ke, Guolin et al.(2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. 'misspi' has the following main advantages: 1. Allows embrassingly parallel imputation on large scale data. 2. Accepts a variety of machine learning models as methods with friendly user portal. 3. Supports multiple initializations methods. 4. Supports early stopping that prohibits unnecessary iterations.
A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).
Methods-first tooling for reproducing and extending the
    partial least squares regression studies on incomplete data described in
    Nengsih et al. (2019) <doi:10.1515/sagmb-2018-0059>. The package
    provides simulation helpers, missingness generators, imputation wrappers,
    component-selection utilities, real-data diagnostics, and reproducible
    study orchestration for Nonlinear Iterative Partial Least Squares (NIPALS)-Partial 
    Least Squares (PLS) workflows.
Classify missing data as missing completely at random (MCAR), missing
    at random (MAR), or missing not at random (MNAR). This step is required before
    handling missing data (e.g. mean imputation) so that bias is not introduced.
    See Little (1988) <doi:10.1080/01621459.1988.10478722> for the statistical
    rationale for the methods used.
Alternative implementation of the beautiful 'MissForest'
    algorithm used to impute mixed-type data sets by chaining random
    forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
    <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
    lightning fast random forest package 'ranger'. Between the iterative
    model fitting, we offer the option of using predictive mean matching.
    This firstly avoids imputation with values not already present in the
    original data (like a value 0.3334 in 0-1 coded variable).  Secondly,
    predictive mean matching tries to raise the variance in the resulting
    conditional distributions to a realistic level. This would allow,
    e.g., to do multiple imputation when repeating the call to
    missRanger(). Out-of-sample application is supported as well.
When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 
  due to missing information between node pairs), it is possible to account for the underlying process
  that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
  adjusts the popular stochastic block model from network data sampled under various missing data conditions, 
  as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.
A flexible computational framework for mixture distributions with the focus on the composite models.
Various reliability analysis methods for rare event inference (computing failure probability and quantile from model/function outputs).
Miscellaneous functions for (1) data handling (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with 'Blimp' and 'Mplus'.
Easily import the MI-SUVI data sets. The user can import data sets with full metrics, percentiles, Z-scores, or rankings.
    Data is available at both the County and Zip Code Tabulation Area (ZCTA) levels. This package also includes a function to import 
    shape files for easy mapping and a function to access the full technical documentation. All data is sourced from the Michigan Department of Health and Human Services.
Provides methods to estimate serial intervals and time-varying case
  reproduction numbers from infectious disease outbreak data. Serial intervals measure
  the time between symptom onset in linked transmission pairs, while case reproduction 
  numbers quantify how many secondary cases each infected individual generates 
  over time. These parameters are essential for understanding transmission 
  dynamics, evaluating control measures, and informing public health responses. 
  The package implements the maximum likelihood framework from Vink et al. (2014) 
  <doi:10.1093/aje/kwu209> for serial interval estimation and the retrospective 
  method from Wallinga & Lipsitch (2007) <doi:10.1098/rspb.2006.3754> for 
  reproduction number estimation. Originally developed for scabies transmission 
  analysis but applicable to other infectious diseases including influenza, 
  COVID-19, and emerging pathogens. Designed for epidemiologists, public health 
  researchers, and infectious disease modelers working with outbreak surveillance 
  data.
Provides tools for multiple imputation of missing data in multilevel
 modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo',
 and several functions for visualization, data management and the analysis 
 of multiply imputed data sets.
Allows the estimation and downstream statistical analysis of the mitochondrial DNA Heteroplasmy calculated from single-cell datasets <https://github.com/ScialdoneLab/MitoHEAR/tree/master>.
Tools to perform analyses and combine results from
        multiple-imputation datasets.
Extract, transform and load MITRE standards.
    This package gives you an approach to cybersecurity data sets.
    All data sets are build on runtime downloading raw data from MITRE public services.
    MITRE <https://www.mitre.org/> is a government-funded research organization 
    based in Bedford and McLean. Current version includes most used standards as
    data frames. It also provide a list of nodes and edges with all relationships.
Implements the Mittag-Leffler function, distribution,
  random variate generation, and estimation. Based on the Laplace-Inversion
  algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.
Estimation/multiple imputation programs for mixed categorical
    and continuous data.
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, García-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, García-Fiñana (2018, Stat. Meth. in Med. Res) <doi:10.1177/0962280216674496>.
Model time series using mixture autoregressive (MAR)
             models.  Implemented are frequentist (EM) and Bayesian
             methods for estimation, prediction and model
             evaluation. See Wong and Li (2002)
             <doi:10.1111/1467-9868.00222>, Boshnakov (2009)
             <doi:10.1016/j.spl.2009.04.009>), and the extensive
             references in the documentation.
Developed for the following tasks. 1- simulating realizations from the canonical, 
              restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for
              density function of the finite mixture models. 3- Monte Carlo approximation for the 
              observed Fisher information matrix, asymptotic standard error, and the corresponding  
              confidence intervals for parameters of the mixture models sing the method proposed by
              Basford et al. (1997) <https://espace.library.uq.edu.au/view/UQ:57525>.
Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects.
Deconvolution of thermal decay curves allows you to quantify proportions 
    of biomass components in plant litter. Thermal decay curves derived from 
    thermogravimetric analysis (TGA) are imported, modified, and then modelled in a 
    three- or four- part  mixture model using the Fraser-Suzuki function. The output 
    is estimates for weights of pseudo-components corresponding to hemicellulose, 
    cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, 
    H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. 
    (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and 
    Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.
Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.
Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet 
    (Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
    to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
    infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
    paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
    applicable to large datasets. It is described and tested in the accompanying paper by 
    Ahlmann-Eltze and Yau (2018) <doi:10.1109/DSAA.2018.00068>.
Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.
With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) <doi:10.1093/jrsssc/qlaf027>). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.
Provides a function to perform bias diagnostics on linear mixed models fitted with lmer() from the 'lme4' package. Implements permutation tests for assessing the bias of fixed effects, as described in Karl and Zimmerman (2021) <doi:10.1016/j.jspi.2020.06.004>. Karl and Zimmerman (2020) <doi:10.17632/tmynggddfm.1> provide R code for implementing the test using 'mvglmmRank' output. Development of this package was assisted by 'GPT o1-preview' for code structure and documentation.
Semi-parametric approach for sparse canonical correlation analysis 
    which can handle mixed data types: continuous, binary and truncated continuous.
    Bridge functions are provided to connect Kendall's tau to latent correlation
    under the Gaussian copula model. The methods are described in 
    Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and 
    Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.
Implementation of the co-clustering method for mixed type data proposed in M. Selosse, J. Jacques, C. Biernacki (2018) <https://hal.science/hal-01893457>. It consists in clustering simultaneously the rows (observations) and the columns (features) of a heterogeneous data set.
Generates blocked designs for mixed-level factorial experiments for a 
    given block size. Internally, it uses finite-field based, collapsed, and heuristic 
    methods to construct block structures that minimize confounding between block 
    effects and factorial effects. The package creates the full treatment combination 
    table, partitions runs into blocks, and computes detailed confounding diagnostics 
    for main effects and two-factor interactions. It also checks orthogonal factorial 
    structure (OFS) and computes efficiencies of factorial effects using the methods 
    of Nair and Rao (1948) <doi:10.1111/j.2517-6161.1948.tb00005.x>. When OFS is not satisfied but 
    the design has equal treatment replications and equal block sizes, a general method 
    based on the C-matrix and custom contrast vectors is used to compute efficiencies. 
    The output includes the generated design, finite-field metadata, confounding 
    summaries, OFS diagnostics, and efficiency results.
Provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectation–maximization (EM) algorithms. The package supports Wiener and Ornstein–Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM.
Functions for testing randomness for a univariate time series with arbitrary distribution  (discrete, continuous, mixture of both types) and for testing  independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in  Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022).
Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when 
  the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization 
  with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically 
  performing parameter selection to identify low-rank substructures in the coefficient matrix.
Fits mixed membership models with discrete multivariate data (with
    or without repeated measures) following the general framework of Erosheva et al
    (2004). This package uses a Variational EM approach by approximating the
    posterior distribution of latent memberships and selecting hyperparameters
    through a pseudo-MLE procedure. Currently supported data types are
    Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with 
    fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al 
    (2014) for other examples of mixed membership models.
The estimation of the parameters in mixed Poisson models.
Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) 
    and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). 
    Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets.
    For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).
Inference on stochastic differential models Ornstein-Uhlenbeck or
    Cox-Ingersoll-Ross, with one or two random effects in the drift function.
Implements seven estimators for average treatment effect (ATE)
    estimation in mixed-subjects designs (MSDs), where human subjects data is
    augmented with predictions from large language models (LLMs). Includes
    Difference-in-Means, GREG, PPI++, Doubly-Tuned, Difference-in-Predictions
    (DiP), DiP++, and D-T DiP estimators. Provides point estimates, variance
    estimation via delta-method or bootstrap, and optimal design selection for
    budget allocation between human observations and LLM predictions.
Integrates large language model generated item responses into
    psychometric calibration studies through a mixed-subjects design for
    unidimensional two-parameter and one-parameter logistic item response
    theory models. Human pilot responses are augmented with model-generated
    responses using a prediction-powered inference estimator (Angelopoulos,
    Bates, Fannjiang, Jordan and Zrnic (2023) <doi:10.1126/science.adi6000>;
    Angelopoulos, Duchi and Zrnic (2023) <doi:10.48550/arXiv.2311.01453>)
    adapted to marginal maximum-likelihood estimation, following the
    mixed-subjects design of Broska, Howes and van Loon (2025)
    <doi:10.1177/00491241251326865>. The estimator is anchored to the human
    responses and is asymptotically unbiased for the human item parameters at
    any tuning weight; the weight on the synthetic responses is chosen to
    minimize propagated ability-score risk, down-weighting uninformative or
    biased generated responses. Louis-corrected sandwich standard errors,
    ability scoring, cross-fitted tuning, and scale linking are also provided.
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.
Utilizing model-based clustering (unsupervised)
        for functional magnetic resonance imaging (fMRI) data.
        The developed methods (Chen and Maitra (2023) <doi:10.1002/hbm.26425>)
        include 2D and 3D clustering analyses
        (for p-values with voxel locations) and
        segmentation analyses (for p-values alone) for fMRI data where p-values
        indicate significant level of activation responding to stimulate
        of interesting. The analyses are mainly identifying active
        voxel/signal associated with normal brain behaviors.
        Analysis pipelines (R scripts) utilizing this package
        (see examples in 'inst/workflow/') is also implemented with high
        performance techniques.
Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, 
    alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following
    Coster (1993) <doi:10.1214/aos/1176349410>. Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) 
    <doi:10.3390/math9131455>. Regular fraction identification follows Guo, Simpson and Pignatiello (2007) 
    <doi:10.1080/00224065.2007.11917691>. Alias structure computation follows
    Rios-Lira et al.(2021) <doi:10.3390/math9233053>.
Multiple imputation using 'XGBoost', subsampling, and predictive mean 
    matching as described in Deng and Lumley (2024) 
    <doi:10.1080/10618600.2023.2252501>.  The package supports various types of 
    variables, offers flexible settings, and enables saving an imputation model to impute
    new data. Data processing and memory usage have been optimised to speed up 
    the imputation process.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of  multiple scaled generalized hyperbolic distributions, the 'cMSGHD'  is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.
Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>. Reference: <doi:10.1101/2024.08.25.608519>.
Kernel-based methods are powerful methods for integrating 
    heterogeneous types of data. mixKernel aims at providing methods to combine
    kernel for unsupervised exploratory analysis. Different solutions are 
    provided to compute a meta-kernel, in a consensus way or in a way that 
    best preserves the original topology of the data. mixKernel also integrates
    kernel PCA to visualize similarities between samples in a non linear space
    and from the multiple source point of view 
    <doi:10.1093/bioinformatics/btx682>. A method to select (as well as 
    funtions to display) important variables is also provided 
    <doi:10.1093/nargab/lqac014>.
Specification and estimation of multinomial logit
    models.  Large datasets and complex models are supported, with an
    intuitive syntax.  Multinomial Logit Models, Mixed models, random
    coefficients and Hybrid Choice are all supported.  For more
    information, see Molloy et al. (2021) <https://www.research-collection.ethz.ch/handle/20.500.11850/477416>.
Provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) <doi:10.1002/sim.9804>) and several tools for visualizing and interpreting the models' parameters.
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'.
Estimation and bootstrap utilities for single layer and multilayer 
    Mixed Graphical Models, including functions for centrality, bridge metrics,
    membership stability, and plotting (De Martino et al. (2026) <doi:10.48550/arXiv.2602.05716>).
Provides sampling and density functions for matrix
    variate normal, t, and inverted t distributions;  ML estimation for matrix
    variate normal and t distributions using the EM algorithm,
    including some restrictions on the parameters; and classification by linear and
    quadratic discriminant analysis for matrix variate normal and t
    distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>.
    Performs clustering with matrix variate normal and t mixture models.
A collection of functions to perform various meta-analytical models
  through a unified mixed-effects framework, including standard univariate
  fixed and random-effects meta-analysis and meta-regression, and non-standard
  extensions such as multivariate, multilevel, longitudinal, and dose-response
  models.
Mixed variable optimization for non-linear functions.
    Can optimize function whose inputs are a combination of continuous,
    ordered, and unordered variables.
Simple tools to perform mixture optimization based on the 'desirability' package by Max Kuhn.
    It also provides a plot routine using 'ggplot2' and 'patchwork'.
Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
A comprehensive, simulation-based toolkit for power and sample-size
    analysis for linear and generalized linear mixed-effects models (LMMs and
    GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families
    via 'lme4'; Wald and likelihood-ratio tests; multi-parameter sensitivity
    grids; power curves and minimum sample-size solvers; parallel evaluation with
    deterministic seeds; and full reproducibility (manifests, result bundling,
    and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate,
    singular-fit rate, effective N) and publication-ready summary tables.
    References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using
    lme4" <doi:10.18637/jss.v067.i01>; Green and MacLeod (2016) "SIMR: an R
    package for power analysis of generalized linear mixed models by simulation"
    <doi:10.1111/2041-210X.12504>.
An extensible expectation-maximization (EM) framework for finite
    mixtures of quantile regressions (clusterwise / mixture-of-experts quantile
    regression). A single EM substrate with an engine/extension contract carries
    a family of capabilities: the core free-weight mixture of Wu and Yao (2016)
    <doi:10.1016/j.csda.2014.04.014> -- a fast asymmetric-Laplace path and the
    nonparametric kernel-density EM with components constrained to have their
    tau-quantile equal to zero (Hall and Presnell 1999 device); expectile and
    M-quantile component-loss families (Newey and Powell 1987; Breckling and
    Chambers 1988); component-specific penalized variable selection (SCAD /
    adaptive-LASSO, the quantile analogue of Khalili and Chen 2007); and joint
    multi-quantile estimation with a shared latent classification and
    non-crossing component curves. Provides classification-aware standard errors
    (sparsity and stochastic-EM multiple imputation), multi-start estimation,
    component-count selection, and prediction. The companion package 'mixqrgate'
    adds location-varying gating.
Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
Provides supplemental functions for the 'mixRasch'
    package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a 
    plotting function to compare item parameters for multiple
    class models and a function that provides average theta values for each class in
    a mixture model.
It offers random-forest-based functions to impute clustered
    incomplete data. The package is tailored for but not limited to imputing
    multitissue expression data, in which a gene's expression is measured on the
    collected tissues of an individual but missing on the uncollected tissues.
The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).
Various functions are provided to estimate parametric mixture models
    (with Gaussian, t, Laplace, log-concave distributions, etc.) and
    non-parametric mixture models. The package performs hypothesis tests
    and addresses label switching issues in mixture models.
    The package also allows for parameter estimation in mixture of regressions,
    proportion-varying mixture of regressions, and robust mixture of regressions.
Creates and runs Bayesian mixing models to analyze
    biological tracer data (i.e. stable isotopes, fatty acids), which estimate the
    proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR'
    is not one model, but a framework that allows a user to create a mixing model
    based on their data structure and research questions, via options for fixed/
    random effects, source data types, priors, and error terms. 'MixSIAR' incorporates
    several years of advances since 'MixSIR' and 'SIAR'.
The utility of this package is in simulating mixtures of Gaussian
        distributions with different levels of overlap between mixture
        components.  Pairwise overlap, defined as a sum of two
        misclassification probabilities, measures the degree of
        interaction between components and can be readily employed to
        control the clustering complexity of datasets simulated from
        mixtures. These datasets can then be used for systematic
        performance investigation of clustering and finite mixture
        modeling algorithms. Among other capabilities of 'MixSim', there
        are computing the exact overlap for Gaussian mixtures,
        simulating Gaussian and non-Gaussian data, simulating outliers
        and noise variables, calculating various measures of agreement
        between two partitionings, and constructing parallel
        distribution plots for the graphical display of finite mixture
        models.
Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.
Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew 
    power exponential and power exponential distributions, respectively. A generalized expectation-maximization 
    framework is used for parameter estimation. See citation() for how to cite.
Provides an optimization method based on sequential
    quadratic programming (SQP) for maximum likelihood estimation of
    the mixture proportions in a finite mixture model where the
    component densities are known. The algorithm is expected to obtain
    solutions that are at least as accurate as the state-of-the-art
    MOSEK interior-point solver (called by function "KWDual" in the
    'REBayes' package), and they are expected to arrive at solutions
    more quickly when the number of samples is large and the number of
    mixture components is not too large. This implements the "mix-SQP"
    algorithm, with some improvements, described in Y. Kim,
    P. Carbonetto, M. Stephens & M. Anitescu (2020)
    <DOI:10.1080/10618600.2019.1689985>.
Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian
              stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating
              parameters of the symmetric stable distribution within the Bayesian framework.
Provides various functions for parameter estimation of one-dimensional
    stable distributions and their mixtures. It implements a diverse set of
    estimation methods, including quantile-based approaches, regression methods
    based on the empirical characteristic function (empirical, kernel, and
    recursive), and maximum likelihood estimation. For mixture models, it provides
    stochastic expectation–maximization (SEM) algorithms and Bayesian estimation
    methods using sampling and importance sampling to overcome the long burn-in
    period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes
    tools and statistical tests for analyzing whether a dataset follows a stable
    distribution. Some of the implemented methods are described in
    Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) <doi:10.1080/02664763.2024.2434627>.
Flexible time classes for time series analysis and forecasting with
    mixed temporal granularities. Supports linear and cyclical time 
    representations in discrete and continuous forms, with timezone support, 
    across multiple calendar systems including Gregorian and ISO week date 
    calendars. Time points are stored numerically relative to a chronon; an 
    atomic time granule defined by time units of a calendar. Calendrical 
    arithmetic enables conversion between time granules (e.g. days to months) 
    and calendar systems. Multi-unit arithmetic allows for temporal analysis
    with other granules of common calendars (e.g. fortnights are 2-week units).
    Time vectors of different granularities (e.g. monthly and quarterly) can be
    combined in a single vector, making 'mixtime' ideal for data that changes
    observation frequency over time or requires temporal reconciliation across 
    scales. The package is extensible, allowing users to define custom calendars 
    that build upon civil and astronomical time systems.
Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).
Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) 
 dose-response data. Predicting mixture toxicity based on reference 
 models such as 'concentration addition', 'independent action', and 'generalized 
 concentration addition'.
Statistical framework for comparing sets of trees using hypothesis testing methods. Designed for transmission trees, phylogenetic trees, and directed acyclic graphs (DAGs), the package implements chi-squared tests to compare edge frequencies between sets and PERMANOVA to analyse topological dissimilarities with customisable distance metrics, following Anderson (2001) <doi:10.1111/j.1442-9993.2001.01070.pp.x>.
A set of utility functions for analysing and modelling data from 
    continuous report short-term memory experiments using either the 2-component
    mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the 
    3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users 
    are also able to simulate from these models.
An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
Methods for fitting mixture distributions to univariate data using expectation maximization, HWHM and other methods. Supports Gaussian, Cauchy, Student's t, skew-normal and von Mises mixtures. For more details see Merkys (2018) <https://www.lvb.lt/permalink/370LABT_NETWORK/1m6ui06/alma9910036312108451>.
Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, <doi:10.18637/jss.v115.i03>). 
    Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and 
    Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting
    cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, 
    Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, 
    Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974.
Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.
Estimates Variable Length Markov Chains (VLMC) models and VLMC with
    covariates models from discrete sequences. Supports model selection via
    information criteria and simulation of new sequences from an estimated
    model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204>
    for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022)
    <doi:10.1111/jtsa.12615> for VLMC with covariates.
Optimization algorithms implemented in R, including
    conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the
    limited memory BFGS (L-BFGS) methods. Most internal parameters can be set 
    through the call interface. The solvers hold up quite well for 
    higher-dimensional problems.
A set of classes and methods to set up and run multi-species, trait
    based and community size spectrum ecological models, focused on the marine
    environment.
Multivariate joint models of longitudinal and time-to-event data based on functional principal components implemented with 'bamlss'. Implementation for Volkmann, Umlauf, Greven (2023) <arXiv:2311.06409>.
Provides multigroup Kitagawa-Blinder-Oaxaca ('mKBO') decompositions, that allow for more than two groups. Each group is compared to the sample average. For more details see Thaning and Nieuwenhuis (2025) <doi:10.31235/osf.io/6twvj_v1>.
Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), <doi:10.1016/B978-1-55860-247-2.50035-8>) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), <doi:10.1093/oxfordjournals.aje.a113284>; Hosmer et al (1997), <doi:10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O>). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), <doi:10.1093%2Facref%2F9780199976720.001.0001>).
Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.
Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), <doi:10.2307/1268758>), robust coefficient of variation (CV) (Arachchige et al. (2019), <doi:10.48550/arXiv.1907.01110>), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4).
Large-scale matrix-variate data have been widely observed nowadays in various research areas such as finance, signal processing and medical imaging. Modelling matrix-valued data by matrix-elliptical family not only provides a flexible way to handle heavy-tail property and tail dependencies, but also maintains the intrinsic row and column structure of random matrices. We proposed a new tool named matrix Kendall's tau which is efficient for analyzing random elliptical matrices. By applying this new type of Kendell’s tau to the matrix elliptical factor model, we propose a Matrix-type Robust Two-Step (MRTS) method to estimate the loading and factor spaces. See the details in He at al. (2022) <arXiv:2207.09633>. In this package, we provide the algorithms for calculating sample matrix Kendall's tau, the MRTS method and the Matrix Kendall's tau Eigenvalue-Ratio (MKER) method which is used for determining the number of factors.
Calculation routines based on the FOCUS Kinetics Report (2006,
  2014).  Includes a function for conveniently defining differential equation
  models, model solution based on eigenvalues if possible or using numerical
  solvers.  If a C compiler (on windows: 'Rtools') is installed, differential
  equation models are solved using automatically generated C functions.
  Non-constant errors can be taken into account using variance by variable or
  two-component error models <doi:10.3390/environments6120124>.  Hierarchical
  degradation models can be fitted using nonlinear mixed-effects model packages
  as a back end <doi:10.3390/environments8080071>.  Please
  note that no warranty is implied for correctness of results or fitness for a
  particular purpose.
Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), <doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), <doi:10.1093/biomet/86.4.948>) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), <doi:10.1016/S0140-6736(86)90837-8>; Shieh (2018), <doi:10.1186/s12874-018-0505-y>), mean difference plot (Boehning et al. (2008), <doi:10.1177/0962280207081867>), plot of test statistic for permutation and bootstrap tests as well as objects of class htest.
Package for fast computation of the maximum kernel likelihood estimator (mkle).
It's a Modern K-Means clustering algorithm which works for data of any number of dimensions, has no limit with the number of clusters expected, offers both methods with and without initial cluster centers, and can start with any initial cluster centers for the method with initial cluster centers.
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.
Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015), <doi:10.1093/nar/gkv007> ) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).
Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>), comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>), ANCOVA (Shieh (2020), <doi:10.1007/s11336-019-09692-3>), reference ranges (Jennen-Steinmetz and Wellek (2005), <doi:10.1002/sim.2177>), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), <doi:10.1148/radiology.143.1.7063747>).
Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs.
Implements the split-fit-evaluate-assess workflow from Hastie,
    Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0) "The Elements of
    Statistical Learning", Chapter 7. Provides three-way data splitting with
    automatic stratification, mandatory seeds for reproducibility, automatic
    data type handling, and 10 algorithms out of the box. Uses 'Rust'
    backend for cross-language deterministic splitting. Designed for tabular
    supervised learning with minimal ceremony. Polyglot parity with the 'Python'
    'mlw' package on 'PyPI'.
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>.
Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.
Provides 'R6' abstract classes for building machine learning models 
    with 'scikit-learn' like API. <https://scikit-learn.org/> is a popular module 
    for 'Python' programming language which design became de facto a standard 
    in industry for machine learning tasks.
Implements three bias-correction techniques from Battaglia et al. (2025 <doi:10.48550/arXiv.2402.15585>) to improve inference in regression models with covariates generated by AI or machine learning.
A collection of artificial and real-world machine learning
        benchmark problems, including, e.g., several
        data sets from the UCI repository.
Tools to help visualize Major League Baseball analysis in 'ggplot2' 
  and 'gt'. You provide team/player information and 'mlbplotR' will transform 
  that information into team colors, logos, or player headshots for graphics.
Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.
Provides an end-to-end workflow for estimating average
    treatment effects in clustered (multilevel) observational data.
    Core functionality includes cluster-aware propensity score estimation
    using fixed effects and Mundlak-style specifications, inverse
    probability weighting, within-cluster nearest-neighbor matching,
    covariate balance diagnostics at both individual and cluster-mean
    levels, outcome regression with cluster-robust standard errors,
    propensity score overlap visualization, and tipping-point sensitivity
    analysis for omitted cluster-level confounding.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.
Contains a collection of datasets for working with machine learning tasks.
    It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression.
    The aim of this package is to use data generated around health and other domains.
Exploratory data analysis and manipulation functions for multi-
    label data sets along with an interactive Shiny application to ease their use.
Large collection of multilabel datasets along with the functions
    needed to export them to several formats, to make partitions, and to obtain
    bibliographic information.
Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.
Difference scaling is a method for scaling perceived 
  supra-threshold differences.  The package contains functions that
  allow the user to design and run a difference scaling experiment, 
  to fit the resulting data by maximum likelihood and test the
  internal validity of the estimated scale.
Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 functions for univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" <ISBN:978-0-471-58495-7>, Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". <ISBN:978-0-471-71580-1> and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". <ISBN:978-0-471-95333-3>.
A unified interface is provided to various machine learning
  algorithms like linear or quadratic discriminant analysis, k-nearest
  neighbors, random forest, support vector machine, ... It allows to train,
  test, and apply cross-validation using similar functions and function
  arguments with a minimalist and clean, formula-based interface. Missing data
  are processed the same way as base and stats R functions for all algorithms,
  both in training and testing. Confusion matrices are also provided with a rich
  set of metrics calculated and a few specific plots.
Asymptotic efficient closed-form estimators (MLEces) are provided in this package for three multivariate distributions(gamma, Weibull and Dirichlet) whose maximum likelihood estimators (MLEs) are not in closed forms. Closed-form estimators are strong consistent, and have the similar asymptotic normal distribution like MLEs. But the calculation of MLEces are much faster than the corresponding MLEs. Further details and explanations of MLEces can be found in.
    Jang, et al. (2023) <doi:10.1111/stan.12299>.
    Kim, et al. (2023) <doi:10.1080/03610926.2023.2179880>.
We provide functions to compute the nonparametric 
  maximum likelihood estimator (MLE) for 
  the bivariate distribution of (X,Y), when 
  realizations of (X,Y) cannot be observed directly. 
  To be more precise, we consider the situation 
  where we observe a set of rectangles in R^2 that are known 
  to contain the unobservable realizations of (X,Y). We
  compute the MLE based on such a set of rectangles. 
  The methods can also be used for univariate censored data (see data set
  'cosmesis'), and for 
  censored data with competing risks (see data set 'menopause'). 
  We also provide functions to visualize the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
        univariate and multi-dimensional outputs with diagnostic plots 
        following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>.
        Contact the maintainer for a package version that includes 
        sensitivity analysis.
Values below the limit of detection (LOD) are a problem in several fields of science, and there are numerous approaches for replacing the missing data. We present a new mathematical solution for maximum likelihood estimation that allows us to estimate the true values of the mean and standard deviation for normal distributions and is significantly faster than previous implementations. The article with the details was submitted to JSS and can be currently seen on <https://www2.arnes.si/~tverbo/LOD/Verbovsek_Sega_2_Manuscript.pdf>.
Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (JCGS, 2006). The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.
Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.
Provides 'R6' objects to perform parallelized hyperparameter
    optimization and cross-validation. Hyperparameter optimization can be
    performed with Bayesian optimization (via 'rBayesianOptimization'
    <https://cran.r-project.org/package=rBayesianOptimization>) and grid
    search. The optimized hyperparameters can be validated using k-fold
    cross-validation. Alternatively, hyperparameter optimization and
    validation can be performed with nested cross-validation. While
    'mlexperiments' focuses on core wrappers for machine learning
    experiments, additional learner algorithms can be supplemented by
    inheriting from the provided learner base class.
Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.
Implements a high dimensional mediation analysis algorithm using Local False Discovery Rates. The methodology is described in Roy and Zhang (2024) <doi:10.48550/arXiv.2402.13933>.
The Iterative Proportional Fitting (IPF) algorithm operates on count data. 
    This package offers implementations for several algorithms that extend this to 
    nested structures: 'parent' and 'child' items for both of which constraints can be provided.
    The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>,
    Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>,
    and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods
    is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.
R interface to 'MLflow', open source platform for
    the complete machine learning life cycle, see <https://mlflow.org/>.
    This package supports installing 'MLflow', tracking experiments,
    creating and running projects, and saving and serving models.
Climate-sensitive, single-tree forest simulator based on
  data-driven machine learning. It simulates the main forest processes—
  radial growth, height growth, mortality, crown recession, regeneration,
  and harvesting—so users can assess stand development under climate and
  management scenarios. The height model is described by Skudnik and
  Jevšenak (2022) <doi:10.1016/j.foreco.2022.120017>, the basal-area
  increment model by Jevšenak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>,
  and an overview of the MLFS package, workflow, and applications is
  provided by Jevšenak, Arnič, Krajnc, and Skudnik (2023), Ecological
  Informatics <doi:10.1016/j.ecoinf.2023.102115>.
Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati".
It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).
Tools and functions to fit a multilevel index of dissimilarity.
Machine learning algorithms have been used for performing 
    single missing data imputation and most recently, multiple imputations. 
    However, this is the first attempt for using automated machine learning algorithms 
    for performing both 
    single and multiple imputation. Automated machine learning is a procedure for 
    fine-tuning the model automatic, performing a random search for a model that 
    results in less error, without overfitting the data. The main idea is 
    to allow the model to set its own parameters for imputing each variable separately 
    instead of setting fixed predefined parameters to impute all variables 
    of the dataset.
    Using automated machine learning, the package fine-tunes an Elastic 
    Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
    or Stacked Ensemble machine learning model (from one or a combination of other 
    supported algorithms) for imputing the missing 
    observations. This procedure has been implemented for the 
    first time by this package and is expected to outperform other packages for 
    imputing missing data that do not fine-tune their models. The multiple imputation 
    is implemented via bootstrapping without letting the duplicated observations to 
    harm the cross-validation procedure, which is the way imputed variables are evaluated.
    Most notably, the package implements automated procedure for handling imputing imbalanced 
    data (class rarity problem), which happens when a factor variable has a level that is far more 
    prevalent than the other(s). This is known to result in biased predictions, hence, biased 
    imputation of missing data. However, the autobalancing procedure ensures that instead of 
    focusing on maximizing accuracy (classification error) in imputing factor variables, 
    a fairer procedure and imputation method is practiced.
Enhances 'mlexperiments'
    <https://CRAN.R-project.org/package=mlexperiments> with additional
    machine learning ('ML') learners. The package provides R6-based
    learners for the following algorithms: 'glmnet'
    <https://CRAN.R-project.org/package=glmnet>, 'ranger'
    <https://CRAN.R-project.org/package=ranger>, 'xgboost'
    <https://CRAN.R-project.org/package=xgboost>, and 'lightgbm'
    <https://CRAN.R-project.org/package=lightgbm>. These can be used
    directly with the 'mlexperiments' R package.
Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.
An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008)
    <doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>,
    for R.  This package builds on the original 'Matlab' and 'C++' implementations
    by Mike Giles to provide a full MLMC driver and example level samplers.
    Multi-core parallel sampling of levels is provided built-in.
A collection of evaluation metrics, including loss, score and
    utility functions, that measure regression, classification and ranking performance.
A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015).
Implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputation methods are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using Rubin's rules (for proper imputation), or a modified version for maximum likelihood imputation. For maximum likelihood imputations a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935> is also available.
Maximum likelihood estimates (MLE) of the proportions
    of 5-mC and 5-hmC in the DNA using information from BS-conversion,
    TAB-conversion, and oxBS-conversion methods. One can use information from all three methods 
    or any combination of two of them. Estimates are based on Binomial model by
    Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and
    Kiihl et al. (2019) <doi:10.1515/sagmb-2018-0031>.
Provides a unified workflow for probing, plotting, and assessing
    the robustness of cross-level interaction effects in two-level mixed-effects
    models fitted with 'lme4' (Bates et al., 2015)
    <doi:10.18637/jss.v067.i01>. Implements simple slopes analysis following
    Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals
    following Johnson and Fay (1950) <doi:10.1007/BF02288864> and Bauer
    and Curran (2005) <doi:10.1207/s15327906mbr4003_5>, and grand- or
    group-mean centering as described in Enders and Tofighi (2007)
    <doi:10.1037/1082-989X.12.2.121>. Includes a slope variance decomposition
    that separates fixed-effect uncertainty from random-slope variance
    (tau11), a contour surface plot of predicted outcomes over the full
    predictor-by-moderator space, and robustness diagnostics comprising
    intraclass correlation coefficient shift analysis and
    leave-one-cluster-out (LOCO) stability checks. Designed for researchers
    in education, psychology, biostatistics, epidemiology, organizational
    science, and other fields where outcomes are clustered within
    higher-level units.
The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.
Integrating morphological modeling with machine learning to support
   structured decision-making (e.g., in management and consulting). The package
   enumerates a morphospace of feasible configurations and uses random forests
   to estimate class probabilities over that space, bridging deductive model
   exploration with empirical validation. It includes utilities for factorizing
   inputs, model training, morphospace construction, and an interactive 'shiny'
   app for scenario exploration.
A declarative language for specifying multilevel models,
    solving for population parameters based on specified variance-explained effect
    size measures, generating data, and conducting power analyses to determine
    sample size recommendations. The specification allows for any number of
    within-cluster effects, between-cluster effects, covariate effects
    at either level, and random coefficients. Moreover, the models do not
    assume orthogonal effects, and predictors can correlate at either level
    and accommodate models with multiple interaction effects.
Data and examples from a multilevel modelling software review
  as well as other well-known data sets from the multilevel modelling
  literature.
Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.
Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and 'McCaffrey' (2002) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ> is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X).
Maximum likelihood estimation of random utility discrete
             choice models. The software is described in Croissant (2020)
              <doi:10.18637/jss.v095.i11> and the underlying methods in
              Train (2009) <doi:10.1017/CBO9780511805271>.
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
A fast, flexible machine learning library, written in C++, that
             aims to provide fast, extensible implementations of cutting-edge
             machine learning algorithms.  See also Curtin et al. (2023)
             <doi:10.21105/joss.05026>.
Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.
An implementation of classifier chains (CC's) for multi-label
    prediction. Users can employ an external package (e.g. 'randomForest',
    'C50'), or supply their own. The package can train a single set of CC's or
    train an ensemble of CC's -- in parallel if running in a multi-core
    environment. New observations are classified using a Gibbs sampler since
    each unobserved label is conditioned on the others. The package includes
    methods for evaluating the predictions for accuracy and aggregating across
    iterations and models to produce binary or probabilistic classifications.
We implement a surrogate modeling algorithm to guide simulation-based
    sample size planning. The method is described in detail in our paper (Zimmer &
    Debelak (2023) <doi:10.1037/met0000611>). It supports multiple study design
    parameters and optimization with respect to a cost function. It can find
    optimal designs that correspond to a desired statistical power or that fulfill
    a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023)
    <doi:10.3758/s13428-023-02269-0>).
Quantification is a prominent machine learning task that has received an 
    increasing amount of attention in the last years. The objective is to predict the 
    class distribution of a data sample. This package is a collection of machine learning 
    algorithms for class distribution estimation. This package include algorithms from
    different paradigms of quantification. These methods are described in the paper: 
    A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set 
    size in quantification assessment. In Proceedings of the Twenty-Ninth International 
    Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020.
    <doi:10.24963/ijcai.2020/366>.
Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There
    is also an experimental extension for survival analysis, clustering
    and general, example-specific cost-sensitive learning. Generic
    resampling, including cross-validation, bootstrapping and subsampling.
    Hyperparameter tuning with modern optimization techniques, for single-
    and multi-objective problems.  Filter and wrapper methods for feature
    selection. Extension of basic learners with additional operations
    common in machine learning, also allowing for easy nested resampling.
    Most operations can be parallelized.
Efficient, object-oriented programming on the
    building blocks of machine learning. Provides 'R6' objects for tasks,
    learners, resamplings, and measures. The package is geared towards
    scalability and larger datasets by supporting parallelization and
    out-of-memory data-backends like databases. While 'mlr3' focuses on
    the core computational operations, add-on packages provide additional
    functionality.
Extends the 'mlr3' package with a connector to the package
    'batchtools'. This allows to run large-scale benchmark experiments on
    scheduled high-performance computing clusters.
Implements methods for post-hoc analysis and
    visualisation of benchmark experiments, for 'mlr3' and beyond.
Extends the 'mlr3' package with cluster analysis.
A small collection of interesting and educational machine
    learning data sets which are used as examples in the 'mlr3' book
    (<https://mlr3book.mlr-org.com>), the use case gallery
    (<https://mlr3gallery.mlr-org.com>), or in other examples. All data
    sets are properly preprocessed and ready to be analyzed by most
    machine learning algorithms.  Data sets are automatically added to the
    dictionary of tasks if 'mlr3' is loaded.
Extends the 'mlr3' package with a backend to transparently
    work with databases such as 'SQLite', 'DuckDB', 'MySQL', 'MariaDB', or
    'PostgreSQL'. The package provides three additional backends:
    'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to
    interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data
    bases and also on Apache Parquet files. 'DataBackendPolars' operates on
    'Polars' data frames.
Integrates fairness auditing and bias mitigation methods for
    the 'mlr3' ecosystem.  This includes fairness metrics, reporting
    tools, visualizations and bias mitigation techniques such as
    "Reweighing" described in 'Kamiran, Calders' (2012)
    <doi:10.1007/s10115-011-0463-8> and "Equalized Odds" described in
    'Hardt et al.' (2016)
    <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
    Integration with 'mlr3' allows for auditing of ML models as well as
    convenient joint tuning of machine learning algorithms and debiasing
    methods.
Extends the 'mlr3' ecosystem to functional analysis by adding
    support for irregular and regular functional data as defined in the
    'tf' package.  The package provides 'PipeOps' for preprocessing
    functional columns and for extracting scalar features, thereby
    allowing standard machine learning algorithms to be applied
    afterwards. Available operations include simple functional features
    such as the mean or maximum, smoothing, interpolation, flattening, and
    functional 'PCA'.
Extends 'mlr3' with filter methods for feature selection.
    Besides standalone filter methods built-in methods of any
    machine-learning algorithm are supported.  Partial scoring of
    multivariate filter methods is supported.
Feature selection package of the 'mlr3' ecosystem. It selects
    the optimal feature set for any 'mlr3' learner. The package works with
    several optimization algorithms e.g. Random Search, Recursive Feature
    Elimination, and Genetic Search. Moreover, it can automatically
    optimize learners and estimate the performance of optimized feature
    sets with nested resampling.
Successive Halving (Jamieson and Talwalkar (2016)
    <doi:10.48550/arXiv.1502.07943>) and Hyperband (Li et al. 2018
    <doi:10.48550/arXiv.1603.06560>) optimization algorithm for the mlr3
    ecosystem. The implementation in mlr3hyperband features improved
    scheduling and parallelizes the evaluation of configurations. The
    package includes tuners for hyperparameter optimization in mlr3tuning
    and optimizers for black-box optimization in bbotk.
Confidence interval and resampling methods for inference on
    the generalization error.
Recommended Learners for 'mlr3'. Extends 'mlr3' with
    interfaces to essential machine learning packages on CRAN.  This
    includes, but is not limited to: (penalized) linear and logistic
    regression, linear and quadratic discriminant analysis, k-nearest
    neighbors, naive Bayes, support vector machines, and gradient
    boosting.
A modern and flexible approach to Bayesian Optimization / Model
    Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox
    providing both ready-to-use optimization algorithms as well as their fundamental
    building blocks allowing for straightforward implementation of custom
    algorithms. Single- and multi-objective optimization is supported as well as
    mixed continuous, categorical and conditional search spaces. Moreover, using
    'mlr3mbo' for hyperparameter optimization of machine learning models within the
    'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use
    optimization algorithms include Efficient Global Optimization by Jones et al.
    (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006)
    <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008)
    <doi:10.1007/978-3-540-87700-4_78>.
Implements multiple performance measures for supervised
    learning.  Includes over 40 measures for regression and
    classification. Additionally, meta information about the performance
    measures can be queried, e.g. what the best and worst possible
    performances scores are.
Frequently used helper functions and assertions used in
    'mlr3' and its companion packages. Comes with helper functions for
    functional programming, for printing, to work with 'data.table', as
    well as some generally useful 'R6' classes. This package also
    supersedes the package 'BBmisc'.
Provides an interface to 'OpenML.org' to list and download
    machine learning data, tasks and experiments. The 'OpenML' objects can
    be automatically converted to 'mlr3' objects.  For a more
    sophisticated interface with more upload options, see
    the 'OpenML' package.
Dataflow programming toolkit that enriches 'mlr3' with a diverse
  set of pipelining operators ('PipeOps') that can be composed into graphs.
  Operations exist for data preprocessing, model fitting, and ensemble
  learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can
  therefore be resampled, benchmarked, and tuned.
A supervised learning algorithm inputs a train set,
 and outputs a prediction function, which can be used on a test set.
 If each data point belongs to a subset
 (such as geographic region, year, etc), then
 how do we know if subsets are similar enough so that
 we can get accurate predictions on one subset, 
 after training on Other subsets?
 And how do we know if training on All subsets would improve
 prediction accuracy, relative to training on the Same subset?
 SOAK, Same/Other/All K-fold cross-validation, <doi:10.1002/sam.70055>
 can be used to answer these questions, by fixing a test subset,
 training models on Same/Other/All subsets, and then
 comparing test error rates (Same versus Other and Same versus All).
 Also provides code for estimating how many train samples
 are required to get accurate predictions on a test set.
A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.
Extends the 'mlr3' ML framework with methods for spatial
    objects. Data storage and prediction are supported for packages
    'terra', 'raster' and 'stars'.
Extends the mlr3 machine learning framework with
    spatio-temporal resampling methods to account for the presence of
    spatiotemporal autocorrelation (STAC) in predictor variables. STAC may
    cause highly biased performance estimates in cross-validation if
    ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.
Concise and interpretable summaries for machine learning
    models and learners of the 'mlr3' ecosystem.  The package takes
    inspiration from the summary function for (generalized) linear models
    but extends it to non-parametric machine learning models, based on
    generalization performance, model complexity, feature importances and
    effects, and fairness metrics.
An implementation of the Super Learner prediction algorithm from
  van der Laan, Polley, and Hubbard (2007) <doi:10.2202/1544-6115.1309 
  using the 'mlr3' framework.
Deep Learning library that extends the mlr3 framework by building
  upon the 'torch' package. It allows to conveniently build, train,
  and evaluate deep learning models without having to worry about low level
  details. Custom architectures can be created using the graph language
  defined in 'mlr3pipelines'.
Hyperparameter optimization package of the 'mlr3' ecosystem.
    It features highly configurable search spaces via the 'paradox'
    package and finds optimal hyperparameter configurations for any 'mlr3'
    learner.  'mlr3tuning' works with several optimization algorithms e.g.
    Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo')
    and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically
    optimize learners and estimate the performance of optimized models
    with nested resampling.
Collection of search spaces for hyperparameter optimization in the
  'mlr3' ecosystem. It features ready-to-use search spaces for many popular
  machine learning algorithms. The search spaces are from scientific articles
  and work for a wide range of data sets.
The 'mlr3' package family is a set of packages for
    machine-learning purposes built in a modular fashion. This wrapper
    package is aimed to simplify the installation and loading of the core
    'mlr3' packages. Get more information about the 'mlr3' project at
    <https://mlr3book.mlr-org.com/>.
Visualization package of the 'mlr3' ecosystem. It features
    plots for mlr3 objects such as tasks, learners, predictions, benchmark
    results, tuning instances and filters via the 'autoplot()' generic of
    'ggplot2'.  The package draws plots with the 'viridis' color palette
    and applies the minimal theme. Visualizations include barplots,
    boxplots, histograms, ROC curves, and Precision-Recall curves.
Toolset that enriches 'mlr' with a diverse set of preprocessing
    operators. Composable Preprocessing Operators ("CPO"s) are first-class
    R objects that can be applied to data.frames and 'mlr' "Task"s to modify
    data, can be attached to 'mlr' "Learner"s to add preprocessing to machine
    learning algorithms, and can be composed to form preprocessing pipelines.
The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of
  incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility
  interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine
  learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization
  of general objective functions using the 'bbotk' black box optimization framework. The
  control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'.
Flexible and comprehensive R toolbox for model-based
    optimization ('MBO'), also known as Bayesian optimization. It
    implements the Efficient Global Optimization Algorithm and is designed
    for both single- and multi- objective optimization with mixed
    continuous, categorical and conditional parameters. The machine
    learning toolbox 'mlr' provide dozens of regression learners to model
    the performance of the target algorithm with respect to the parameter
    settings. It provides many different infill criteria to guide the
    search process. Additional features include multi-point batch
    proposal, parallel execution as well as visualization and
    sophisticated logging mechanisms, which is especially useful for
    teaching and understanding of algorithm behavior. 'mlrMBO' is
    implemented in a modular fashion, such that single components can be
    easily replaced or adapted by the user for specific use cases.
The stepwise regression with assumptions checking and the possible Box-Cox transformation.
Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024)<doi:10.3150/23-BEJ1680> Zhou Zhou and Wei Biao Wu(2010)<doi:10.1111/j.1467-9868.2010.00743.x> Jianqing Fan and Wenyang Zhang<doi:10.1214/aos/1017939139> Lujia Bai and Weichi Wu(2024)<doi:10.1093/biomet/asae013> Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999)<doi:10.1007/978-1-4612-1554-7> Weichi Wu and Zhou Zhou(2018)<doi:10.1214/17-AOS1582>.
Provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from 'glmnet', 'lightgbm', 'ranger', 'e1071', and 'caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility.
Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).
Generate a stream of pseudo-random numbers generated using the MLS 
    Junk Generator algorithm. Functions exist to generate single pseudo-random 
    numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.
Provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). 'Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model' <doi:10.5281/zenodo.16529986> and <doi:10.5281/zenodo.16529016>. It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. 'mlspatial' enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, 'XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions.
Fits latent Dirichlet allocation (LDA), supervised topic models,
    and multilevel supervised topic models for text data with multiple
    outcome variables. Core estimation routines are implemented in C++
    using the 'Rcpp' ecosystem. 
    For topic models, see Blei et al. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>.
    For supervised topic models, see Blei and McAuliffe (2007) <https://papers.nips.cc/paper_files/paper/2007/hash/d56b9fc4b0f1be8871f5e1c40c0067e7-Abstract.html>.
Functions to support compatibility between 'Maelstrom' R packages 
     and 'Opal' environment. 'Opal' is the 'OBiBa' core database application for 
     biobanks. It is used to build data repositories that integrates data 
     collected from multiple sources. 'Opal Maelstrom' is a specific 
     implementation of this software. This 'Opal' client is specifically 
     designed to interact with 'Opal Maelstrom' distributions to perform 
     operations on the R server side. The user must have adequate credentials.
     Please see <https://opaldoc.obiba.org/> for complete documentation.
Enhances 'mlexperiments'
    <https://CRAN.R-project.org/package=mlexperiments> with additional
    machine learning ('ML') learners for survival analysis. The package
    provides R6-based survival learners for the following algorithms:
    'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger'
    <https://CRAN.R-project.org/package=ranger>, 'xgboost'
    <https://CRAN.R-project.org/package=xgboost>, and 'rpart'
    <https://CRAN.R-project.org/package=rpart>. These can be used directly
    with the 'mlexperiments' R package.
Likelihood-based estimation of conditional transformation
  models via the most likely transformation approach described in
  Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020)
  <DOI:10.18637/jss.v092.i01>.  Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>)
  and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models
  are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and
  more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.
Additional documentation, a package vignette and 
  regression tests for package mlt.
A fast, robust and easy-to-use calculation 
    of multi-class classification evaluation metrics based on confusion matrix.
A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM).
  The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference.
  Path models and formulas for user-defined models can be easily created with functions using 'knitr'. 
  Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.
Bayesian multilevel unanchored meta-regression (ML-UMR) for
    indirect treatment comparisons using individual patient data (IPD) and
    aggregate data (AgD). Implements shared prognostic factor assumption
    (SPFA) and relaxed SPFA models for binary, continuous, and count
    outcomes via 'Stan'. Also
    provides simulated treatment comparison (STC) via parametric
    G-computation and naive unadjusted benchmarks. ML-UMR is an
    adaptation of the ML-NMR methodology (Phillippo et al. 2020,
    <doi:10.1111/rssa.12579>) implemented in the 'multinma' package
    (GPL-3) to the unanchored two-trial case; the public API
    deliberately mirrors multinma's so users can move between ML-NMR
    and ML-UMR with the same workflow.
Estimates the multi-level vector autoregression model on time-series data.
             Three network structures are obtained: temporal networks, contemporaneous
             networks and between-subjects networks.
A minimal library specifically designed to make the estimation of Machine Learning
             (ML) techniques as easy and accessible as possible, particularly within the framework of
             the Knowledge Discovery in Databases (KDD) process in data mining. The package provides
             essential tools to structure and execute each stage of a predictive or classification
             modeling workflow, aligning closely with the fundamental steps of the KDD methodology,
             from data selection and preparation, through model building and tuning, to the
             interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow
             is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and
             sensitivity_analysis(). It also includes global and pairwise interaction analysis based on
             Friedman’s H-statistic to support a more detailed interpretation of complex feature
             relationships.These steps correspond, respectively, to data preparation and transformation,
             model construction, hyperparameter optimization, and sensitivity analysis. The user can
             access comprehensive model evaluation results including fit assessment metrics, plots,
             predictions, and performance diagnostics for ML models implemented through 'Neural
             Networks', 'Random Forest', 'XGBoost' (Extreme Gradient Boosting), and 'Support Vector
             Machines' (SVM) algorithms. By streamlining these phases, 'MLwrap' aims to simplify the
             implementation of ML techniques, allowing analysts and data scientists to focus on
             extracting actionable insights and meaningful patterns from large datasets, in line with
             the objectives of the KDD process.
Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.
Various utilities for the Multiplicative Multinomial distribution.
Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. (2022) <doi:10.1371/journal.pcbi.1009659>).
Used for general multiple mediation analysis. 
	The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.
Used for general multiple mediation analysis with big data sets.
Contains the data sets for the first and second editions of the textbook 
    "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. 
    The first edition of the book was published by Oxford University Press in 2018 with 
    ISBN-13: 978-019882472. The second edition is expected to be published in January 
    2027.
A formula-driven framework for maximizing target functions
    via the minorization-maximization (MM) algorithm. The package
    represents the target as a symbolic expression tree, infers its
    curvature via disciplined-convex-programming rules, and constructs
    a separable surrogate at each iterate using only Jensen's
    inequality and the supporting hyperplane. The driver maximizes the
    surrogate via block-coordinate Newton with line search, falling
    back to a multivariate step on any non-separable residue. A
    formula interface accepts standard R expressions (including
    `sum()` reductions and `X %*% theta` design-matrix products) so
    statistical models such as Poisson regression can be written in
    one line.
Provides tools for performing mathematical morphology operations,
    such as erosion and dilation, on data of arbitrary dimensionality. Can also
    be used for finding connected components, resampling, filtering, smoothing
    and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile.
Uses memory-mapping to enable the random access of elements of
  a text file of characters separated by characters as if it were a simple
  R(cpp) matrix.
Mobile-monitoring or "sensors on a mobile platform", is an increasingly 
    popular approach to measure high-resolution pollution data at the street level. 
    Coupled with location data, spatial visualisation of air-quality parameters 
    helps detect localized areas of high air-pollution, also called hotspots. 
    In this approach, portable sensors are mounted on a vehicle and driven on 
    predetermined routes to collect high frequency data (1 Hz). 
    'mmaqshiny' is for analysing, visualising and spatial mapping of 
    high-resolution air-quality data collected by specific devices installed on 
    a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate  
    matter with size less than 2.5 microns), Black carbon mass concentrations 
    (BC), ultra-fine particle number concentrations, carbon dioxide along with 
    GPS coordinates and relative humidity (RH) data collected by popular 
    portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, 
    LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It 
    incorporates device specific cleaning and correction algorithms. RH correction 
    is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) 
    <doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear 
    regression coefficients for correcting the PM2.5 data (if required). BC data
    will be cleaned for the vibration generated noise, by adopting the statistical 
    procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>, 
    followed by a loading correction as suggested by Ban-Weiss et al., (2009)  
    <doi:10.1021/es8021039>. For the number concentration data, provision is 
    given for dilution correction factor (if a diluter is used with CPC3007; 
    default value is 1). The package joins the raw, cleaned and corrected data 
    from the above said instruments and outputs as a downloadable csv file.
Supports Bayesian models with full and partial (hence
    arbitrary) dependencies between random variables. Discrete and continuous
    variables are supported, and conditional joint probabilities and probability
    densities are estimated using Kernel Density Estimation (KDE). The full
    general form, which implements an extension to Bayes' theorem, as well as
    the simple form, which is just a Bayesian network, both support regression
    through segmentation and KDE and estimation of probability or relative
    likelihood of discrete or continuous target random variables. This package
    also provides true statistical distance measures based on Bayesian models.
    Furthermore, these measures can be facilitated on neighborhood searches,
    and to estimate the similarity and distance between data points.
    Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053>
    and by Scutari (2010) <doi:10.18637/jss.v035.i03>.
Computes robust and bias-corrected sandwich variance estimators for multi-state Cox models with clustered time-to-event data. The methodology extends the marginal Cox model bias-correction framework of Wang et al. (2023) <doi:10.1002/bimj.202200113> to the multi-state setting.
Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
Early insights in probability theory were largely influenced by
    questions about gambling and games of chance, as noted by Blitzstein and
    Hwang (2019, ISBN:978-1138369917). In modern times, playing cards continue
    to serve as an effective teaching tool for probability, statistics, and even
    'R' programming, as demonstrated by Grolemund (2014, ISBN:978-1449359010).
    The 'mmcards' package offers a collection of utility functions designed to
    aid in the creation, manipulation, and utilization of playing card decks in
    multiple formats. These include a standard 52-card deck, as well as
    alternative decks such as decks defined by custom anonymous functions and
    custom interleaved decks. Optimized for the development of educational
    'shiny' applications, the package is particularly useful for teaching
    statistics and probability through card-based games. Functions include
    shuffle_deck(), which creates either a shuffled standard deck or a shuffled
    custom alternative deck; deal_card(), which takes a deck and returns a list
    object containing both the dealt card and the updated deck; and i_deck(),
    which adds image paths to card objects, further enriching the package's
    utility in the development of interactive 'shiny' application card games.
An implementation of modified maximum contrast methods (Sato et al. (2009) <doi:10.1038/tpj.2008.17>;
  Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997)
  <doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte
  Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.
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>.
Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) <doi:10.1534/genetics.109.105486>.
Matching longitudinal methodology models with complex sampling design. It fits fixed
    and random effects models and covariance structured models so far. It also
    provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." <https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.
The aim of the package is two-fold: (i) To implement 
    the MMD method for attribution of individuals to sources using 
    the Hamming distance between multilocus genotypes. (ii) To select 
    informative genetic markers based on information theory concepts 
    (entropy, mutual information and redundancy). The package implements the 
    functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., 
    Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to 
    efficiently attribute individuals to source populations. 
    Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>.
    See more details and examples in the README file.
A method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.
Provides functions for the robust estimation of 
	parametric families of copulas using minimization of 
	the Maximum Mean Discrepancy, following the article
	Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022)
	<doi:10.1080/01621459.2021.2024836>.
Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one
    random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing
    independent time effect; Model 3, introducing correlated time effect.
    mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.
Fit multivariate mixture of normal distribution using
        covariance structure.
Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. 
  See Meyer (1985) <doi:10.2307/2530651> and Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.
Calculate morphine milligram equivalents (MME) for opioid dose comparison using standardized methods. Can directly call the 'NIH HEAL MME Online Calculator' <https://research-mme.wakehealth.edu/api> API or replicate API calculations on the user's local machine from the comfort of 'R'. Creation of the 'NIH HEAL MME Online Calculator' and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) <doi:10.1097/j.pain.0000000000003529>.
Multiple 2 by 2 tables often arise in meta-analysis which combines statistical evidence from multiple studies. Two risks within the same study are possibly correlated because they share some common factors such as environment and population structure. This package implements a set of novel Bayesian approaches for multivariate meta analysis when the risks within the same study are independent or correlated. The exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 by 2 table or multiple 2 by 2 tables is provided. Luo, Chen, Su, Chu, (2014) <doi:10.18637/jss.v056.i11>, Chen, Luo, (2011) <doi:10.1002/sim.4248>, Chen, Chu, Luo, Nie, Chen, (2015) <doi:10.1177/0962280211430889>, Chen, Luo, Chu, Su, Nie, (2014) <doi:10.1080/03610926.2012.700379>, Chen, Luo, Chu, Wei, (2013) <doi:10.1080/19466315.2013.791483>.
We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented  covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of  factors. More details can be referred to Liu et al. (2025) <doi:10.48550/arXiv.2507.09889>.
Researchers often have expectations about the relations between means
    of different groups or standardized regression coefficients; using informative
    hypothesis testing to incorporate these expectations into the analysis through
    order constraints increases statistical power
    Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>. Another valuable
    tool, the Bayes factor, can evaluate evidence for multiple hypotheses without
    concerns about multiple testing, and can be used in Bayesian updating
    Hoijtink, Mulder, van Lissa & Gu (2019) <doi:10.1037/met0000201>. The 'bain'
    R package enables informative hypothesis testing using the Bayes factor. The
    'mmibain' package provides 'shiny' web applications based on 'bain'. The
    RepliCrisis() function launches a 'shiny' card game to simulate the evaluation
    of replication studies while the mmibain() function launches a 'shiny'
    application to fit Bayesian informative hypotheses evaluation models from
    'bain'.
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.
Implements a computational framework to predict microbial community-based 
     metabolic profiles with 'O2PLS' model. It provides procedures of model 
     training and prediction. Paired microbiome and metabolome data are needed 
     for modeling, and the trained model can be applied to predict metabolites 
     of analogous environments using new microbial feature abundances.
Sharing statistical methods or simulation frameworks through 'shiny'
    applications often requires workflows for handling data. To help save and
    display simulation results, the postgresUI() and postgresServer() functions
    in 'mmints' help with persistent data storage using a 'PostgreSQL' database.
    The 'mmints' package also offers data upload functionality through the
    csvUploadUI() and csvUploadServer() functions which allow users to upload
    data, view variables and their types, and edit variable types before fitting
    statistical models within the 'shiny' application. These tools aim to
    enhance efficiency and user interaction in 'shiny' based statistical and
    simulation applications.
Offering enhanced statistical power compared to traditional
    hypothesis testing methods, informative hypothesis testing allows researchers
    to explicitly model their expectations regarding the relationships among
    parameters. An important software tool for this framework is 'restriktor'.
    The 'mmirestriktor' package provides 'shiny' web applications to implement
    some of the basic functionality of 'restriktor'. The mmirestriktor() function
    launches a 'shiny' application for fitting and analyzing models with
    constraints. The FbarCards() function launches a card game application which
    can help build intuition about informative hypothesis testing. The
    iht_interpreter() helps interpret informative hypothesis testing results based
    on guidelines in Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>.
A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space.    
    Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition.
    Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix.
    These models have significant differences in terms of the analytical approach.
    Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes).
    Research project No. 1.1.1.2/VIAA/1/16/075.
Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression
	coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
Multi-omic (or any multi-view) spectral clustering methods often assume the same number of clusters across all datasets. We supply methods for multi-omic spectral clustering when the number of distinct clusters differs among the omics profiles (views).
Provides functions for measuring
    population divergence from genotypic data.
Perform multivariate modeling of evolved traits, with special attention to
 understanding the interplay of the multi-factorial determinants of their origins
 in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>).
 This software primarily concentrates on phylogenetic regression analysis, enabling
 implementation of tree transformation averaging and visualization functionality.
 Functions additionally support information theoretic approaches
 (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>;
 Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>)
 such as  model averaging and selection of phylogenetic models.
 Accessory functions are also implemented for coef standardization (Cade 2015), 
 selection uncertainty, and variable importance (Burnham & Anderson 2000).
 There are other numerous functions for visualizing confounded variables,
 plotting phylogenetic trees, as well as reporting and exporting modeling results.
 Lastly, as challenges to ecology are inherently multifarious, and therefore often
 multi-dataset, this package features several functions to support the identification,
 interpolation, merging, and updating of missing data and outdated nomenclature.
To determine the number of quantitative assays needed for a sample 
    of data using pooled testing methods, which include mini-pooling (MP), MP 
    with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number 
    of assays needed, the package also provides a tool to conduct Monte Carlo (MC) 
    to simulate different orders in which the sample would be collected to form pools. 
    Using MC avoids the dependence of the estimated number of assays on any specific 
    ordering of the samples to form pools.
A generalization of principal component analysis for integrative
  analysis. The method finds principal components that describe single matrices
  or that are common to several matrices. The solutions are sparse. Rank of
  solutions is automatically selected using cross validation. The method is
  described in Kallus et al. (2019) <doi:10.48550/arXiv.1911.04927>.
Simple helpers for matrix multiplication on data.frames. These allow for more
    concise code during low level mathematical operations, and help ensure code is more
    easily read, understood, and serviced.
Researchers doing a mixed-methods analysis (nested analysis as
    developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can
    use the package for the classification of cases and case selection using
    results of a linear regression. One can designate cases 
    as typical, deviant, extreme and pathway case and use different case 
    selection strategies for the choice of a case belonging to one of
    these types.
Mixed models for repeated measures (MMRM) are a popular
    choice for analyzing longitudinal continuous outcomes in randomized
    clinical trials and beyond; see Cnaan, Laird and Slasor (1997)
    <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>
    for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso
    (2008) <doi:10.1177/009286150804200402> for a review. This package
    implements MMRM based on the marginal linear model without random
    effects using Template Model Builder ('TMB') which enables fast and
    robust model fitting. Users can specify a variety of covariance
    matrices, weight observations, fit models with restricted or standard
    maximum likelihood inference, perform hypothesis testing with
    Satterthwaite or Kenward-Roger adjustment, and extract least square
    means estimates by using 'emmeans'.
Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985).
Provides access to teaching materials for various statistics courses, including R and Python programs, 
    Shiny apps, data, and PDF/HTML documents. These materials are stored on the Internet as a ZIP file 
    (e.g., in a GitHub repository) and can be downloaded and displayed or run locally. The content of the ZIP file 
    is temporarily or permanently stored. By default, the package uses the GitHub repository 
    'sigbertklinke/mmstat4.data.' Additionally, the package includes 'association_measures.R' 
    from the archived package 'ryouready' by Mark Heckman and some auxiliary functions.
Implements the moment-matching approximation for differences
    of non-standardized t-distributed random variables in both univariate and 
    multivariate settings. The package provides density, distribution function,
    quantile function, and random generation for the approximated distributions
    of t-differences. The methodology establishes the univariate approximated
    distributions through the systematic matching of the first, second, and
    fourth moments, and extends it to multivariate cases, considering both
    scenarios of independent components and the more general multivariate
    t-distributions with arbitrary dependence structures. Methods build
    on the classical moment-matching approximation method (e.g., Casella and
    Berger (2024) <doi:10.1201/9781003456285>).
An implementation of multiple maps t-distributed stochastic
    neighbor embedding (t-SNE). Multiple maps t-SNE is a method for
    projecting high-dimensional data into several low-dimensional maps such that
    non-metric space properties are better preserved than they would be by a single
    map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE.
    When projecting onto more than one map, multiple maps t-SNE estimates a set
    of latent weights that allow each point to contribute to one or more maps
    depending on similarity relationships in the original data. This
    implementation is a port of the original 'Matlab' library by Laurens van der
    Maaten. 
    See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>.
    This material is based upon work supported by the United States Air Force 
    and Defense Advanced Research Project Agency (DARPA) under Contract No. 
    FA8750-17-C-0020.
    Any opinions, findings and conclusions or recommendations expressed in this
    material are those of the author(s) and do not necessarily reflect the views 
    of the United States Air Force and Defense Advanced Research Projects Agency.
    Distribution Statement A: Approved for Public Release; Distribution Unlimited.
The first day of any MMWR week is Sunday.
    MMWR week numbering is sequential beginning with 1
    and incrementing with each week to a maximum of 52
    or 53. MMWR week #1 of an MMWR year is the first week
    of the year that has at least four days in the calendar
    year. This package provides functionality to convert
    Dates to MMWR day, week, and year and the reverse.
Density computation, random matrix generation and maximum likelihood estimation of the matrix normal distribution. References: Pocuca N., Gallaugher M. P., Clark K. M. & McNicholas P. D. (2019). Assessing and Visualizing Matrix Variate Normality. <doi:10.48550/arXiv.1910.02859> and the relevant wikipedia page.
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>.
Diagnostic tools as residual analysis, global, 
    local and total-local influence for the multivariate model 
    from the random intercept Poisson generalized log gamma model 
    are available in this package. Including also, the estimation 
    process by maximum likelihood method, for details see 
    Fabio, L. C; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2023) 
    <doi:10.1080/03610926.2021.1939380> and Fábio, L. C.; Villegas, C.; 
    Mamun, A. S. M. A. and Carrasco, J. M. F. (2025) <doi:10.28951/bjb.v43i1.728>.
Interactions between different biological entities are crucial for the function of biological systems. 
    In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted.
    The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. 
    However, such variations often occur locally and do not concern the whole network. 
    To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). 
    MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation.
    Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.
A toolbox for modeling manifest and latent group differences and moderation effects in various statistical network models.
Read, process, and analyse data from muscle near-infrared
    spectroscopy (mNIRS) devices. Import raw data from .csv or .xls(x)
    files and return time-series data and metadata. Includes standardised
    methods for cleaning, filtering, and pre-processing mNIRS data for
    subsequent analysis. Also includes a custom plot theme and colour
    palette. Intended for mNIRS researchers and practitioners in exercise
    physiology, sports science, and clinical rehabilitation with minimal
    coding experience required.
Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions.  Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra.  Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.
Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014,
    <doi:10.1080/00273171.2014.889594>). 
    Regularization methods are implemented for assessing non-invariant items. 
    Currently, the package includes dichotomous items and unidimensional
    item response models. Extensions will be included in future package
    versions.
An interactive presentation on  the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/MultinomPresentation>.
Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.
A data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis.
Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution and Gaussian copula (with various marginal distributions) using methods described in A. Genz (2004) <doi:10.1023/B:STCO.0000035304.20635.31>, A. Genz, F. Bretz (2009) <doi:10.1007/978-3-642-01689-9>, H. I. Gassmann (2003) <doi:10.1198/1061860032283> and E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>.
Functions are provided for computing the density and the
  distribution function of multi-dimensional normal and "t" random variables,
  possibly truncated (on one side or two sides),  and for generating random 
  vectors sampled from these distributions, except sampling from the truncated
  "t". Moments of arbitrary order of a multivariate truncated normal are
  computed, and converted to cumulants up to order 4. 
  Probabilities are computed via non-Monte Carlo methods; different routines 
  are used in the case d=1, d=2, d=3, d>3, if d denotes the dimensionality.
Hypothesis testing of the parameters of multivariate normal distributions, including the testing of a single mean vector, two mean vectors, multiple mean vectors, a single covariance matrix, multiple covariance matrices, a mean and a covariance matrix simultaneously, and the testing of independence of multivariate normal random vectors. Huixuan, Gao (2005, ISBN:9787301078587), "Applied Multivariate Statistical Analysis".
Fits the Bayesian multinomial probit model via Markov chain
 Monte Carlo.  The multinomial probit model is often used to analyze 
 the discrete choices made by individuals recorded in survey data. 
 Examples where the multinomial probit model may be useful include the 
 analysis of product choice by consumers in market research and the 
 analysis of candidate or party choice by voters in electoral studies.  
 The MNP package can also fit the model with different choice sets for 
 each individual, and complete or partial individual choice orderings 
 of the available alternatives from the choice set. The estimation is
 based on the efficient marginal data augmentation algorithm that is 
 developed by Imai and van Dyk (2005). "A Bayesian Analysis of the 
 Multinomial Probit Model Using the Data Augmentation." Journal of 
 Econometrics, Vol. 124, No. 2 (February), pp. 311-334. 
 <doi:10.1016/j.jeconom.2004.02.002>  Detailed examples are given in 
 Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial 
 Probit Model."  Journal of Statistical Software, Vol. 14, No. 3 (May), 
 pp. 1-32. <doi:10.18637/jss.v014.i03>.
Computes dielectric response and optical cross-sections of metallic nanoparticles using Drude dielectric model and Rayleigh approximation.
An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) <arXiv:2004.07332> titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns).
Generate the monotonic binning and
    perform the woe (weight of evidence) transformation for the logistic regression
    used in the consumer credit scorecard development. The woe transformation is a piecewise
    transformation that is linear to the log odds. For a numeric variable, all of its monotonic 
    functional transformations will converge to the same woe transformation.
Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.
Simulation, analysis and sampling of spatial
    biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017)
    <doi:10.1111/2041-210x.12986>.
    In the simulation tools user define the numbers of
    species and individuals, the species abundance distribution and species
    aggregation. Functions for analysis include species rarefaction 
    and accumulation curves, species-area relationships and the distance
    decay of similarity.
Implements the Multi-Objective Clustering Algorithm Guided
    by a-Priori Biological Knowledge ('MOC-GaPBK') proposed by
    Parraga-Alava and others (2018) <doi:10.1186/s13040-018-0178-4>.
    The algorithm performs gene clustering using 'NSGA-II' as the
    underlying multi-objective evolutionary engine, together with
    Path-Relinking and Pareto Local Search as intensification and
    diversification strategies. Two versions of the Xie-Beni validity
    index are used as objective functions, one per distance matrix,
    so that prior biological knowledge can be incorporated through
    the second matrix.
Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.
The two main functionalities of this package are creating
    mock objects (functions) and selectively intercepting calls to a given
    function that originate in some other function. It can be used with
    any testing framework available for R. Mock objects can be injected
    with either this package's own stub() function or a similar
    with_mocked_binding() facility present in the 'testthat' package.
Provides a means to mock a package function, i.e.,
    temporarily substitute it for testing. Designed as a drop-in
    replacement for the now deprecated 'testthat::with_mock()' and
    'testthat::local_mock()'.
With the deprecation of mocking capabilities shipped with
    'testthat' as of 'edition 3' it is left to third-party packages to replace
    this functionality, which in some test-scenarios is essential in order to
    run unit tests in limited environments (such as no Internet connection).
    Mocking in this setting means temporarily substituting a function with a
    stub that acts in some sense like the original function (for example by
    serving a HTTP response that has been cached as a file). The only exported
    function 'with_mock()' is modeled after the eponymous 'testthat' function
    with the intention of providing a drop-in replacement.
Creates modules inline or from a file. Modules can contain any R object and be nested. Each module have their own scope and package "search path" that does not interfere with one another or the user's working environment.
Package for processing downloaded MODIS Surface reflectance
    Product HDF files. Specifically, MOD09 surface reflectance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" <doi:10.3758/s13428-018-1088-6> .
A series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) <doi:10.3389/fmed.2023.1118824>.
Implements parametric modal regression for continuous positive
    distributions of the exponential family under right censoring.
    Provides functions to link the conditional mode to a linear predictor
    using reparameterizations for Gamma, Beta, Weibull, and Inverse Gaussian
    families. Includes maximum likelihood estimation via numerical
    optimization, asymptotic inference based on the observed Fisher
    information matrix, and model diagnostics using randomized quantile
    residuals.
Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
Implements parametric modal Autoregressive Integrated Moving Average (ARIMA) models utilizing the Skewed Distribution (SKD) family. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The conditional mode is parameterized and optimized via maximum likelihood using analytical gradients. Includes comprehensive residual diagnostics, robustness options (heavy tails, asymmetry), robust parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix. Methods are described in Galarza, C.E., Lachos, V.H., Cabral, C.R.B., & Castro, L.M. (2017) <doi:10.1002/sta4.140>.
Provides estimators of the mode of univariate
    data or univariate distributions.
Given independent and identically distributed observations X(1), ..., X(n) from a density f,
 provides five methods to perform a multiscale analysis about f as well as the necessary critical
 values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
 for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
 any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
 I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
 correction term Gamma in either method offers another two approaches which are more powerful on small scales and
 less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
 compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
 equivalent to the first and was introduced in Rufibach and Walther (2010).
Model-based trees for subgroup analyses in clinical trials and
  model-based forests for the estimation and prediction of personalised
  treatment effects (personalised models). Currently partitioning of linear
  models, lm(), generalised linear models, glm(), and Weibull models,
  survreg(), is supported.  Advanced plotting functionality is supported for
  the trees and a test for parameter heterogeneity is provided for the
  personalised models. For details on model-based trees for subgroup analyses
  see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for
  details on model-based forests for estimation of individual treatment effects
  see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.
Implements a general interface for model-based estimations
    for a wide variety of models, used in the computation of
    marginal means, contrast analysis and predictions. For a list of supported models,
    see 'insight::supported_models()'.
Fits the neighboring models of a fitted
  structural equation model and assesses the model
  uncertainty of the fitted model based on BIC posterior
  probabilities (BPP), using the method presented in
  Wu, Cheung, and Leung (2020)
  <doi:10.1080/00273171.2019.1574546>. See
  Pesigan, Cheung, Wu, Chang, and Leung (2026)
  <doi:10.3758/s13428-025-02921-x> for an introduction to
  the package.
This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.
Provides two important functions for producing Gain chart and Lift chart for any classification model.
Data sets used for demonstrating or testing model-related
    packages are contained in this package.
More data sets used for demonstrating or testing model-related 
    packages are contained in this package. The data sets are downloaded and
    cached, allowing for more and bigger data sets.
Uses 'dplyr' and 'tidyeval' to fit statistical models inside
    the database. It currently supports KMeans and linear regression
    models.
Provides a unified framework for diagnosing common issues in statistical models including linear models, generalized linear models (logistic and Poisson regression), and survival models. Implements tests for multicollinearity, heteroscedasticity, autocorrelation, normality, influential observations, overdispersion, zero-inflation, and proportional hazards assumptions. Includes visualization methods for graphical diagnostics. Methods are based on established approaches including Fox and Monette (1992) <doi:10.1080/01621459.1992.10475190>, Breusch and Pagan (1979) <doi:10.2307/1911963>, and Dean and Lawless (1989) <doi:10.1080/01621459.1989.10478792>.
Generates 'DiagrammeR' model diagrams for hierarchical linear mixed effects models. Details can be found in Linse (2026) <doi:10.6339/26-JDS1222>.
Website generator with HTML summaries for predictive models.
    This package uses 'DALEX' explainers to describe global model behavior. 
    We can see how well models behave (tabs: Model Performance, Auditor),
    how much each variable contributes to predictions (tabs: Variable Response) 
    and which variables are the most important for a given model (tabs: Variable Importance).
    We can also compare Concept Drift for pairs of models (tabs: Drifter).
    Additionally, data available on the website can be easily recreated in current R session.
    Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 
    at Warsaw University of Technology, Faculty of Mathematics and Information Science.
An developer focused, low dependency package in 'tidymodels' that
    provides functions to register how models are to be used. Functions to
    register models are complimented with accessor functions to retrieve
    registered model information to aid in model fitting and error handling.
Defines classes and methods to learn models and use them
  to predict binary outcomes.  These are generic tools, but we also
  include specific examples for many common classifiers.
Local linear estimation of psychometric functions. Provides functions for nonparametric estimation of a psychometric function and for estimation of a derived threshold and slope, and their standard deviations and confidence intervals.For more details see Zychaluk and Foster (2009) <doi:10.3758/APP.71.6.1414> and Foster and Zychaluk (2007) <doi:10.1109/MSP.2007.4286564>.
Calculate and visualise the financial impact of using a
    classification model, such as a churn model, to target customers. Provides
    cost, revenue, profit and return-on-investment curves as a function of the
    share of customers targeted, cumulative gains and lift, marginal profit per
    bin, and confusion-matrix based payoff across probability thresholds. Also
    includes 'ggplot2' 'autoplot()' methods and an interactive 'shiny'
    application for exploring the results.
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.
Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
The model.matrix() function in R is  convenient for transforming training dataset for modeling. 
    But it does not save any parameter used in transformation, so it is hard to apply the same transformation 
    to test dataset or new dataset. This package is created to solve the problem.
Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.
Functions for modelling that help you seamlessly integrate
    modelling into a pipeline of data manipulation and visualisation.
A comprehensive package for comparing multiple Structural
    Equation Models (SEM). Supports both nested and non-nested
    model comparisons, chi-square difference tests, and extraction
    of multiple fit indices including AIC (Akaike Information Criterion),
    BIC (Bayesian Information Criterion), CFI (Comparative Fit Index),
    TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation),
    and SRMR (Standardized Root Mean Square Residual). Built on top of the
    'lavaan' package for seamless SEM model comparison workflows. The Vuong test
    (Vuong, 1989) for non-nested models is used as the statistical test.
Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) <doi:10.5281/zenodo.17119597> (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.
Comprehensive analytical tools are provided to characterize infectious disease superspreading from contact tracing surveillance data. The underlying theoretical frameworks of this toolkit include branching process with transmission heterogeneity (Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>), case cluster size distribution (Nishiura et al. (2012) <doi:10.1016/j.jtbi.2011.10.039>, Blumberg et al. (2014) <doi:10.1371/journal.ppat.1004452>, and Kucharski and Althaus (2015) <doi:10.2807/1560-7917.ES2015.20.25.21167>), and decomposition of reproduction number (Zhao et al. (2022) <doi:10.1371/journal.pcbi.1010281>).
Automate the explanatory analysis of machine learning predictive 
    models. Generate advanced interactive model explanations in the form of 
    a serverless HTML site with only one line of code. This tool is 
    model-agnostic, therefore compatible with most of the black-box predictive
    models and frameworks. The main function computes various (instance and 
    model-level) explanations and produces a customisable dashboard, which 
    consists of multiple panels for plots with their short descriptions. It is 
    possible to easily save the dashboard and share it with others. modelStudio 
    facilitates the process of Interactive Explanatory Model Analysis introduced 
    in Baniecki et al. (2023) <doi:10.1007/s10618-023-00924-w>.
Create beautiful and customizable tables to summarize several
    statistical models side-by-side. Draw coefficient plots, multi-level
    cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and
    correlation matrices. This package supports dozens of statistical models, and
    it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel,
    RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr'
    dynamic documents. Details can be found in Arel-Bundock (2022)
    <doi:10.18637/jss.v103.i01>.
Provides a number of testthat tests that can be
    used to verify that tidy(), glance() and augment() methods meet
    consistent specifications. This allows methods for the same generic to
    be spread across multiple packages, since all of those packages can
    make the same guarantees to users about returned objects.
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/>.).
A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, 
    weighted averaging, and stacking. These techniques are popular methods 
    to improve forecast accuracy and stability.
A 'modeltime' extension that implements forecast resampling tools
    that assess time-based model performance and stability for a single time series, 
    panel data, and cross-sectional time series analysis.
A collection of tools to deal with statistical models. 
  The functionality is experimental and the user interface is likely to
  change in the future. The documentation is rather terse, but packages `coin'
  and `party' have some working examples. However, if you find the
  implemented ideas interesting we would be very interested in a discussion
  of this proposal. Contributions are more than welcome!
Provides a lightweight framework for model selection
    and hyperparameter tuning in R. The package offers intuitive tools for
    grid search, cross-validation, and combined grid search with cross-validation
    that work seamlessly with virtually any modeling package. Designed for
    flexibility and ease of use, it standardizes tuning workflows while
    remaining fully compatible with a wide range of model interfaces and
    estimation functions.
Determines single or multiple modes (most frequent values).
    Checks if missing values make this impossible, and returns 'NA'
    in this case. Dependency-free source code. See Franzese and Iuliano (2019)
    <doi:10.1016/B978-0-12-809633-8.20354-3>.
Causal moderated mediation analysis using the methods proposed by Qin and Wang (2023) <doi:10.3758/s13428-023-02095-4>. Causal moderated mediation analysis is crucial for investigating how, for whom, and where a treatment is effective by assessing the heterogeneity of mediation mechanism across individuals and contexts. This package enables researchers to estimate and test the conditional and moderated mediation effects, assess their sensitivity to unmeasured pre-treatment confounding, and visualize the results. The package is built based on the quasi-Bayesian Monte Carlo method, because it has relatively better performance at small sample sizes, and its running speed is the fastest. The package is applicable to a treatment of any scale, a binary or continuous mediator, a binary or continuous outcome, and one or more moderators of any scale.
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.
Provides a single unified interface for computing a wide variety of
    classical and modern correlation and association measures. Continuous methods
    include classical correlations (Pearson, Spearman, Kendall), modern dependence
    measures (distance correlation, maximal information coefficient, Hilbert-Schmidt
    independence criterion, Chatterjee's xi, Hoeffding's D, mutual information),
    robust correlations (biweight midcorrelation, percentage bend, Winsorized),
    ordinal correlations (polychoric, tetrachoric), partial and semi-partial
    correlations, and nonparametric measures (ball correlation, Bergsma-Dassios
    tau*). Categorical association measures (Cramer's V, phi coefficient,
    Goodman-Kruskal gamma, Somers' D, contingency coefficient, Tschuprow's T)
    are available via moderncor_cat().
Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.
Provides functions that fit two modern education-based value-added models.  
                 One of these models is the quantile value-added model.  This model 
                 permits estimating a school's value-added based on specific quantiles of 
                 the post-test distribution.  Estimating value-added based on quantiles 
                 of the post-test distribution provides a more complete picture of an 
                 education institution's contribution to learning for students of all 
                 abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017)
                 <doi:10.1111/rssa.12195> for more details.  The second model is a temporally 
                 dependent value-added model. This model takes into account the temporal 
                 dependence that may exist in school performance  between two cohorts in one 
                 of two ways.  The first is by modeling school  random effects with a 
                 non-stationary AR(1) process.  The second is by  modeling school effects 
                 based on previous cohort's post-test performance. In addition to more 
                 efficiently estimating value-added, this model permits making statements 
                 about the persistence of a schools effectiveness. The standard  value-added 
                 model is also an option.
User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.
Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.
Compute important quantities when we consider stochastic systems that are observed continuously. 
             Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. 
             The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.
Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.
Generation of mock data from a real dataset using rank normal inverse transformation.
Algorithms for multivariate outlier detection when missing values
    occur. Algorithms are based on Mahalanobis distance or data depth.
    Imputation is based on the multivariate normal model or uses nearest
    neighbour donors. The algorithms take sample designs, in particular
    weighting, into account. The methods are described in Bill and Hulliger
    (2016) <doi:10.17713/ajs.v45i1.86>.
Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by  Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size.
    Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>.
    Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. 
    sen, P. K. (1968).<doi:10.2307/2285891>.
    Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>.
    Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>.
    Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>.
    Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>.
    Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>.
    Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.
Programmatic interface to several NASA Earth Observation 'OPeNDAP' servers (Open-source Project for a Network Data Access Protocol) (<https://www.opendap.org/>). Allows for easy downloads of MODIS subsets, as well as other Earth Observation datacubes, in a time-saving and efficient way : by sampling it at the very downloading phase (spatially, temporally and dimensionally).
Programmatic interface to the Oak Ridge National Laboratories
    'MODIS Land Products Subsets' web services 
    (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy
    downloads of 'MODIS' time series directly to your R workspace or
    your computer.
Provides a robust implementation of information-theoretic moderation analysis
    using multi-model inference based on Akaike's Information Criterion (AIC) and its small-sample corrected form (Corrected AIC).
    The package enables researchers to compare competing model specifications
    and helps distinguish true interaction effects from nonlinear relationships
    that may produce spurious moderation. The methods build on Daryanto (2019)
    <doi:10.1016/j.jbusres.2019.06.012>.
Calculate predicted levels and marginal effects,
    using the delta method to calculate standard errors. This is an R-based
    version of the 'margins' command from Stata.
The algorithms implemented here are used to detect the community structure of a network. 
            These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) <https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.
'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent.
Contains basic tools for performing 
  multiple-output quantile regression and computing
  regression quantile contours by means of directional
  regression quantiles. In the location case, one can thus
  obtain halfspace depth contours in two to six dimensions.
  Hallin, M., Paindaveine, D. and Šiman, M. (2010)
  Multivariate quantiles and multiple-output regression quantiles:
  from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669
  For more references about the method, see Help pages.
Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.
Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453> to 
    estimate 50 percent effective dose (ED50) in the study of drug sensitivity. The method has 
    its own advantages for that it can provide robust ED50 estimation. This package contains 
    the modified function of Choi's turning point method.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
Provides modules as an organizational unit for source code. Modules
    enforce to be more rigorous when defining dependencies and have
    a local search path. They can be used as a sub unit within packages
    or in scripts.
Modular implementation of Multiobjective Evolutionary Algorithms 
              based on Decomposition (MOEA/D) [Zhang and Li (2007), 
              <DOI:10.1109/TEVC.2007.892759>] for quick assembling and 
              testing of new algorithmic components, as well as easy 
              replication of published MOEA/D proposals. The full framework is
              documented in a paper published in the Journal of Statistical 
              Software [<doi:10.18637/jss.v092.i06>].
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
Methods for maximum likelihood and Bayesian estimation for the Wishart mixture model and the mixture-of-experts Wishart (MoE-Wishart) model. The package provides four inference algorithms for these models, each implemented using the expectation–maximization (EM) algorithm for maximum likelihood estimation and a fully Bayesian approach via Gibbs-within-Metropolis–Hastings sampling.
Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Xiao, Joseph, and Ray (2022) <doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT design can be viewed as an improvement to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991) <doi:10.1080/00401706.1991.10484804>. The improvement is achieved by exploiting the connection between Morris screening designs and Monte Carlo-based Sobol' designs, and optimizing the design using a space-filling criterion. This work is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.
Density, distribution function, quantile function, 
    and random generation function based on Salem, H. M. (2019)<doi:10.5539/mas.v13n2p54>.
    In addition, a numerical method for maximum likelihood estimation is provided.
Support JSON flattening in a long data frame way, where the nesting keys will be stored in the absolute path. It also
   provides an easy way to summarize the basic description of a JSON list. The idea of 'mojson' is to transform a JSON object in an 
   absolute serialization way, which means the early key-value pairs will appear in the heading rows of the resultant data frame. 
   'mojson' also provides an alternative way of comparing two different JSON lists, returning the left/inner/right-join style results.
Contains functions for performing Mokken
   scale analysis on test and questionnaire data.
   It includes an automated item selection algorithm, and various checks of model assumptions.
Surface topography calculations of Dirichlet's normal energy,
    relief index, surface slope, and orientation patch count for teeth using scans of
    enamel caps.
    Importantly, for the relief index and orientation patch count calculations to
    work, the scanned tooth files must be oriented with the occlusal plane parallel
    to the x and y axes, and perpendicular to the z axis. The files should also be
    simplified, and smoothed in some other software prior to uploading into R.
A set of functions to manage data shared on a 
  'MOLGENIS Armadillo' server.
Discover 'OpenID Connect' endpoints and authenticate
    using device flow. Used by 'MOLGENIS' packages.
Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.
A collection of functions to do some statistical inferences. On estimation, it has the function to get the method of moments estimates,
            the sampling interval. In terms of testing it has function of doing most powerful test.
Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and  the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc.
Functions to calculate: moments, Pearson's kurtosis,
        Geary's kurtosis and skewness; tests related to them
        (Anscombe-Glynn, D'Agostino, Bonett-Seier).
Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
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>.
It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.
A HTML widget rendering the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the built-in features of the 'Monaco' editor, the widget allows to prettify multiple languages, to view the 'HTML' rendering of 'Markdown' code, and to view and resize 'SVG' images.
Compose generic monadic function pipelines with %>>% and %>-%
    based on implementing the 'S7' generics fmap() and bind(). Methods are
    provided for the built-in list type and the maybe class from the
    'maybe' package. The concepts are modelled directly after the Monad
    typeclass in Haskell, but adapted for idiomatic use in R.
Implements the method to analyse weighted mobility networks or distribution networks as outlined in: 
    Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>. 
    The purpose of the model is to analyse the structure of mobility, 
    incorporating exogenous predictors pertaining to individuals and locations 
    known from classical mobility analyses, as well as modelling emergent mobility 
    patterns akin to structural patterns known from the statistical analysis of social networks.
An API wrapper for the 'Monash University Probabilistic Footy 
    Tipping Competition' <https://probabilistic-footy.monash.edu/~footy/index.shtml>. 
    Allows users to submit tips directly to the competition from R.
Keep track of dates in terms of fractional calendar months 
  per Damien Laker "Time Calculations for Annualizing Returns: the Need for Standardization", 
  The Journal of Performance Measurement, 2008.
  Model dates as of close of business.
  Perform date arithmetic in units of "months" and "years".
  Allow "infinite" dates to model "ultimate" time.
The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample. 
  As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). 
  We named the package after the famous painter as the graphical output recalls Mondrian’s paintings.
High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'.
    Includes support for aggregation, indexing, map-reduce, streaming, encryption,
    enterprise authentication, and GridFS. The online user manual provides an overview 
    of the available methods in the package: <https://jeroen.github.io/mongolite/>.
A 'tidyverse'-friendly client for the National Statistics Office of Mongolia
    'PXWeb' API <https://data.1212.mn/> with helpers to discover tables, variables, and fetch
    statistical data. Also includes utilities to retrieve Mongolia administrative boundaries
    (ADM0-ADM2) as 'sf' objects from open sources for mapping and spatial analysis.
Translate R code into MongoDB aggregation pipelines.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
These guidelines are meant to provide a pragmatic, yet rigorous, help to drug developers and decision makers, since they are shaped by three fundamental ingredients: the clinically determined margin of detriment on OS that is unacceptably high (delta null); the benefit on OS that is plausible given the mechanism of action of the novel intervention (delta alt); and the quantity of information (i.e. survival events) it is feasible to accrue given the clinical and drug development setting. The proposed guidelines facilitate transparent discussions between stakeholders focusing on the risks of erroneous decisions and what might be an acceptable trade-off between power and the false positive error rate.
Train and make predictions from a multi-layer perceptron neural
        network with optional partial monotonicity constraints.
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 is an add-on package to the 'monobin' package that simplifies its use. It provides shiny-based user interface (UI) 
	     that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning 
	     of numeric risk factors when building credit rating models. The additional functions implemented in 
	     'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. 
	     The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, 
	     while special case and outlier imputation functions are written to be used with shiny UI.
Generate a monochrome palette from a starting colour 
    for a specified number of colours. The package can also be used to display 
    colour palettes in the plot window, with or without hex codes and colour labels.
Implementation of the Monothetic Clustering
    algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on
    continuous data sets. A lot of extensions are included in the package,
    including applying Monothetic clustering on data sets with circular
    variables, visualizations with the results, and permutation and
    cross-validation based tests to support the decision on the number of
    clusters.
Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available.
'Monolix' is a tool for running mixed effects model using
    'saem'. This tool allows you to convert 'Monolix' models to 'rxode2'
    (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the
    form compatible with 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    If available, the 'rxode2' model will
    read in the 'Monolix' data and compare the simulation for the
    population model individual model and residual
    model to immediately show how well the translation is
    performing. This saves the model development time for people who are
    creating an 'rxode2' model manually.  Additionally, this package reads
    in all the information to allow simulation with uncertainty (that is the
    number of observations, the number of subjects, and the covariance
    matrix) with a 'rxode2' model.  This is complementary to the
    'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can
    convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not
    required, you can  get/install the 'lixoftConnectors' package in the 'Monolix'
    installation, as described at the following url   <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When
  'lixoftConnectors' is available, 'Monolix' can be used to load its model library
  instead manually setting up text files (which only works with old versions of
  'Monolix').
Estimation of multivariate normal (MVN) and student-t data of 
 arbitrary dimension where the pattern of missing data is monotone.
 See Pantaleo and Gramacy (2010) <doi:10.48550/arXiv.0907.2135>.
 Through the use of parsimonious/shrinkage regressions 
 (plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
 the package can handle a nearly arbitrary amount of missing data. 
 The current version supports maximum likelihood inference and 
 a full Bayesian approach employing scale-mixtures for Gibbs sampling.
 Monotone data augmentation extends this Bayesian approach to arbitrary 
 missingness patterns.  A fully functional standalone interface to the 
 Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown),
 Horseshoe (from Carvalho, Polson, & Scott), and ridge regression 
 with model selection via Reversible Jump, and student-t errors 
 (from Geweke) is also provided.
Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. 
 "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) <doi:10.7717/peerj-cs.56>.
Functions for fitting monotone polynomials to data.
             Detailed discussion of the methodologies used can be
             found in Murray, Mueller and Turlach (2013)
             <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and
             Turlach (2016) <doi:10.1080/00949655.2016.1139582>.
An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>. The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas <DOI:10.1214/22-BA1310>.
The monotone package contains a fast up-and-down-blocks implementation for the pool-adjacent-violators algorithm 
  for simple linear ordered monotone regression, including two spin-off functions
  for unimodal and bivariate monotone regression (see <doi:10.18637/jss.v102.c01>).
Nonparametric estimation and inference of a non-decreasing monotone hazard
    ratio from a right censored survival dataset.  The
    estimator is based on a generalized Grenander typed estimator, and the
    inference procedure relies on direct plugin estimation of a first order derivative.  More
    details please refer to the paper "Nonparametric inference under a monotone
    hazard ratio order" by Y. Wu and T. Westling (2023) <doi:10.1214/23-EJS2173>.
Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) <doi:10.1016/j.jfineco.2010.06.006> and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.
Implements nonparametric bootstrap tests for detecting monotonicity
  in regression functions from Hall, P. and Heckman, N. (2000) <doi:10.1214/aos/1016120363>
  Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports 
  efficient computation with 'C++'. Tutorials and shiny application demo are 
  available at <https://www.laylaparast.com/monotonicitytest> and <https://parastlab.shinyapps.io/MonotonicityTest>.
Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.
Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.
Provides tools to conduct Monte Carlo simulations under different conditions (e.g., varying sample size, data normality) for structural equation models (SEMs).
  Data can be simulated based on user-defined factor loadings and correlations, with optional non-normality added via Fleishman's power method (1978) <doi:10.1007/BF02293811>.
  Once generated, models can be estimated using 'lavaan'. This package facilitates testing model performance across multiple simulation scenarios.
  When data generation is completed (or when generated data sets are given) model tests can also be run.
  Please cite as "Orçan, F. (2021). MonteCarloSEM An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713."
Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, and  Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.
Provides functionality to generate compound optimal designs for targeting the multiple experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. Designs can be found using point or coordinate exchange algorithms combining estimation, inference and lack-of-fit criteria that account for model inadequacy. Details and examples are given by Koutra et al. (2024) <doi:10.48550/arXiv.2412.17158>.
The main objective of this package is to support the
    definition of 'Moodle' elements taking advantage of the power that R
    offers. In this first version, it allows the definition of quizzes
    to be included in the question bank.
Enables the creation of 'Moodle' quiz questions using literate
  programming with R Markdown. This makes it easy to quickly create a quiz that
  can be randomly replicated with new datasets, questions, and options for 
  answers.
Routines to generate fully randomized 'moodle' quizzes. It also contains 15 examples and a 'shiny' app.
A collection of functions to connect to a 'Moodle' database, cache relevant tables locally and generate learning analytics. 
    'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit <https://moodle.org>.
Several analysis-related functions for the book entitled "R
    statistics and graph for medical articles" (written in Korean), version 1,
    by Keon-Woong Moon with Korean demographic data with several plot
    functions.
Functions and examples based on the m-out-of-n bootstrap suggested by
  Politis, D.N. and Romano, J.P. (1994) <doi:10.1214/aos/1176325770>.
  Additionally there are functions to estimate the scaling factor tau
  and the subsampling size m. For a detailed description and a full list
  of references, see Dalitz, C. and Lögler, F. (2025)
  <doi:10.32614/RJ-2025-031>.
Tools for predicting moonlight intensity on the ground based on
    the position of the moon, atmospheric conditions, and other factors.
    Provides functions to calculate moonlight intensity and related statistics
    for ecological and behavioral research, offering more accurate estimates
    than simple moon phase calculations. The underlying model is described in
    Smielak (2023) <doi:10.1007/s00265-022-03287-2>.
Predicts ground-level illuminance from moonlight, sunlight, and twilight for specified locations and time periods. The package is intended for field studies in ecology and behavior where natural light levels are used as predictor variables. See Poon et al. (2024) <doi:10.1111/2041-210X.14299>. Calculations use astronomical quantities from 'suncalc' and published illuminance models, including Austin et al. (1976) <doi:10.2307/2402251> and Seidelmann (1992) <ISBN:0935702687>.
Visualization of multi-dimensional data arising in multi-objective optimization, including plots of the empirical attainment function (EAF), M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>, and symmetric Vorob'ev expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.
Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>.  It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.
A set of tools to perform multiple versions of the Mobility
    Oriented-Parity metric. This multivariate analysis helps to characterize 
    levels of dissimilarity between a set of conditions of reference and another
    set of conditions of interest. If predictive models are transferred to 
    conditions different from those over which models were calibrated (trained),
    this metric helps to identify transfer conditions that differ 
    substantially from those of calibration. These tools are implemented 
    following principles proposed in Owens et al. (2013) 
    <doi:10.1016/j.ecolmodel.2013.04.011>, and expanded to obtain more detailed 
    results that aid in interpretation as in Cobos et al. (2024)
    <doi:10.21425/fob.17.132916>.
Provides real & simulated datasets containing time-series traffic observations
    and additional information pertaining to Loop 1 "Mopac" located in Austin, Texas.
With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.
Various kinds of plots (observations, variables, correlations, weights, regression coefficients and Variable Importance in the Projection) and aids to interpretation (coefficients, Q2, correlations, redundancies) for partial least squares regressions computed with the 'pls' package, following Tenenhaus (1998, ISBN:2-7108-0735-1).
A standalone package combining several stop-word lists for 65 languages with a median of 329 stop words for language and over 1,000 entries for English, Breton, Latin, Slovenian, and Ancient Greek! The user automatically gets access to all the unique stop words contained in: the 'StopwordISO' repository; the 'Natural Language Toolkit' for 'python'; the 'Snowball' stop-word list; the R package 'quanteda'; the 'marimo' repository; the 'Perseus' project; and A. Berra's list of stop words for Ancient Greek and Latin.
Share R objects across processes on the same machine via a
    single copy in 'POSIX' shared memory (Linux, macOS) or a 'Win32' file
    mapping (Windows). Every process reads from the same physical pages
    through the R Alternative Representation ('ALTREP') framework, giving
    lazy, zero-copy access. Shared objects serialize compactly as their
    shared memory name rather than their full contents.
Automatically segments a 3D array of voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been accepted for publication in the journal Landscape Ecology. Detailed references will be updated here once those are known.
Tokenize text into morphemes. The morphemepiece algorithm uses a 
  lookup table to determine the morpheme breakdown of words, and falls back on a 
  modified wordpiece tokenization algorithm for words not found in the lookup 
  table.
Provides data about morphemes, the smallest units of meaning in a 
    language.
Mixture of logistic regressions parameters (H)estimation with
    (U)spectral methods. The main methods take d-dimensional inputs and a vector
    of binary outputs, and return parameters according to the GLMs mixture model
    (General Linear Model). For more details see chapter 3 in the PhD thesis of
		Mor-Absa Loum: <https://theses.fr/s156435>, available here
		<https://theses.hal.science/tel-01877796/document>.
A toolset for Geometric Morphometrics and mesh processing. This
    includes (among other stuff) mesh deformations based on reference points,
    permutation tests, detection of outliers, processing of sliding
    semi-landmarks and semi-automated surface landmark placement.
Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section.
Mitteroecker & Gunz (2009) <doi:10.1007/s11692-009-9055-x> describe
    how geometric morphometric methods allow researchers to quantify the size
    and shape of physical biological structures. We provide tools to extend geometric
    morphometric principles to the study of non-physical structures, hormone profiles, 
    as outlined in Ehrlich et al (2021) <doi:10.1002/ajpa.24514>. Easily transform 
    daily measures into multivariate landmark-based data. Includes custom functions 
    to apply multivariate methods for data exploration as well as hypothesis testing. 
    Also includes 'shiny' web app to streamline data exploration. Developed to study 
    menstrual cycle hormones but functions have been generalized and should be 
    applicable to any biomarker over any time period.
Computes the optimal number of regions (or subdivisions) and 
    their position in serial structures without a priori assumptions and to visualize 
    the results. After reducing data dimensionality with the built-in function for
    data ordination, regions are fitted as segmented linear regressions
    along the serial structure. Every region boundary
    position and increasing number of regions are iteratively fitted and
    the best model (number of regions and boundary positions) is selected
    with an information criterion. This package expands on the previous
    'regions' package (Jones et al., Science 2018) with improved
    computation and more fitting and plotting options.
Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022.
Tools to simulate morphological traits along phylogenetic trees
 with branch lengths representing evolutionary distance or time.
 Includes functions for visualizing evolutionary processes along trees
 and within morphological character matrices.
Agricultural data for 1888-2021 from the Morrow Plots at the 
    University of Illinois. The world's second oldest ongoing agricultural 
    experiment, the Morrow Plots measure the impact of crop rotation and 
    fertility treatments on corn yields. The data includes planting information
    and annual yield measures for corn grown continuously and in rotation with
    other crops, in treated and untreated soil.
Advanced methods for a valuable quantitative environmental risk 
   assessment using Bayesian inference of survival and reproduction Data. Among
   others, it facilitates Bayesian inference of the general unified
   threshold model of survival (GUTS). See our companion paper 
   Baudrot and Charles (2021) <doi:10.21105/joss.03200>,
   as well as complementary details in Baudrot et al. (2018)
   <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al.
   (2017) <doi:10.1021/acs.est.6b05326>.
Advanced methods for a valuable quantitative environmental
    risk assessment using Bayesian inference of several type of
    toxicological data. 'binary' (e.g., survival, mobility), 'count'
    (e.g., reproduction) and 'continuous' (e.g., growth as length,
    weight).  Estimation procedures can be used without a deep knowledge
    of their underlying probabilistic model or inference methods. Rather,
    they were designed to behave as well as possible without requiring a
    user to provide values for some obscure parameters. That said, models
    can also be used as a first step to tailor new models for more
    specific situations.
Advanced methods for a valuable quantitative environmental risk 
   assessment using Bayesian inference of survival Data with toxicokinetics
   toxicodynamics (TKTD) models. Among others, it facilitates Bayesian inference of 
   the general unified threshold model of survival (GUTS). See models description
   in Jager et al. (2011) <doi:10.1021/es103092a> and implementation
   using Bayesian inference in Baudrot and Charles (2019)
   <doi:10.1038/s41598-019-47698-0>.
A toolkit for identifying potential mortalities and expelled tags in aquatic acoustic telemetry arrays. 
    Designed for arrays with non-overlapping receivers.
A collection of functions for the analysis of archaeological mortality
    data (on the topic see e.g. Chamberlain 2006 
    <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). 
    It takes demographic data in different formats and displays the result in a standard life table
    as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, 
    life expectancy, percentage of population). It also checks for possible biases in the age 
    structure and applies corrections to life tables.
Life expectancy is highly correlated over time among countries and 
  between males and females. These associations can be used to improve forecasts. 
  Here we have implemented a method for forecasting female life expectancy based on 
  analysis of the gap between female life expectancy in a country compared with
  the record level of female life expectancy in the world. Second, to forecast 
  male life expectancy, the gap between male life expectancy and female life 
  expectancy in a country is analysed. We named this method the Double-Gap model.
  For a detailed description of the method see Pascariu et al. (2018). 
  <doi:10.1016/j.insmatheco.2017.09.011>.
Fit the most popular human mortality 'laws', and construct 
  full and abridge life tables given various input indices. A mortality
  law is a parametric function that describes the dying-out process of 
  individuals in a population during a significant portion of their 
  life spans. For a comprehensive review of the most important mortality 
  laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. 
  Practical functions for downloading data from various human mortality 
  databases are provided as well.
Classes to implement, analyze and plot cohort life tables
    for actuarial calculations. Birth-year dependent cohort mortality
    tables using a yearly trend to extrapolate from a base year are implemented,
    as well as period life table, cohort life tables using an age shift, and
    merged life tables. Additionally, several data sets from various countries 
    are included to provide widely-used tables out of the box.
Helper functions to standardizes common workflows in the USGS Data
    Science Community of Practice to produce more robust, reproducible
    pipelines. It contains helper functions to standardize (1) the organization
    of project repositories and (2) the creation ofpipelines from the 'targets'
    R Package (Landau et al. (2026) <doi:10.5281/zenodo.18555866>), using the DS
    CoP best practices. We draw upon community developed best practices as well
    as certain USGS-specific requirements. See 
    Shrycock et al. (2023) <doi:10.3133/tm7B2> for examples of these USGS
    requirements.
Age-specific mortality rates are estimated and projected using 
    the Kannisto, Lee-Carter and related methods as described in 
    Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.
The Society of Actuaries (SOA) provides an extensive online 
    database called 'Mortality and Other Rate Tables' ('MORT') at 
    <https://mort.soa.org/>. This database contains mortality, lapse, and 
    valuation tables that cover a variety of product types and nations. Users of
    the database can download any tables in 'Excel', 'CSV', or 'XML' formats. 
    This package provides convenience functions that read 'XML' formats from the 
    database and return R objects.
Provides a comprehensive set of tools for working with order statistics,
    including functions for simulating order statistics, censored samples (Type I and Type II),
    and record values from various continuous distributions. Additionally, it offers functions 
    to compute moments (mean, variance, skewness, kurtosis) of order statistics for several 
    continuous distributions. These tools assist researchers and statisticians in understanding 
    and analyzing the properties of order statistics and related data. The methods and algorithms 
    implemented in this package are based on several published works, including 
    Ahsanullah et al (2013, ISBN:9789491216831), 
    Arnold and Balakrishnan (2012, ISBN:1461236444), 
    Harter and Balakrishnan (1996, ISBN:9780849394522), 
    Balakrishnan and Sandhu (1995) <doi:10.1080/00031305.1995.10476150>,
    Genç (2012) <doi:10.1007/s00362-010-0320-y>, 
    Makouei et al (2021) <doi:10.1016/j.cam.2021.113386> and
    Nagaraja (2013) <doi:10.1016/j.spl.2013.06.028>.
Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Software to support the introductory *MOSAIC Calculus* 
    textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>),
    one of many data- and modeling-oriented educational resources developed by 
    Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and
    numerical differentiation and integration, as well as support for 
    applied linear algebra (for data science), and differential equations/dynamics.
    Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc.
    The software is suitable for general use, but intended mainly for teaching calculus.
Common utilities used in other MOSAIC-family packages are 
    collected here.
Data sets from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Provides a framework for multipurpose optimal resource allocation in survey sampling,
    extending the classical optimal allocation principles introduced by Tschuprow (1923) and
    Neyman (1934) to multidomain and multivariate allocation problems. The primary method
    mosalloc() allows for the consideration of precision and cost constraints at the subpopulation
    level while minimizing either a vector of sampling errors or survey costs across a broad range
    of optimal sample allocation problems. The approach supports both single- and multistage
    designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user-
    friendly interface. Sensitivity analysis is supported through the problem's dual variables, which
    are naturally obtained via the internal use of the Embedded Conic Solver from the 'ECOSolveR'
    package. See Willems (2025, <doi:10.25353/ubtr-9200-484c-5c89>) for a detailed description
    of the theory behind 'MOSAlloc'.
Stability based methods for model order selection in clustering problems
 (Valentini, G (2007), <doi:10.1093/bioinformatics/btl600>).
 Using multiple perturbations of the data the stability of clustering solutions is assessed. Different
 perturbations may be used: resampling techniques, random projections and noise injection. Stability measures
 for the estimate of clustering solutions and statistical tests to assess their significance are provided.
Provides tools for analyzing Marshall-Olkin shock models semi-independent time.
    It includes interactive 'shiny' applications for exploring copula-based dependence 
    structures, along with functions for modeling and visualization. The methods are 
    based on Mijanovic and Popovic (2024, submitted) "An R package for Marshall-Olkin 
    shock models with semi-independent times."
Provides functions similar to the 'SAS' macros previously provided
              to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826>
              and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers
              which outline practical benefits and challenges of factorial
              and fractional factorial experiments for scientists interested
              in developing biological and/or behavioral interventions, especially
              in the context of the multiphase optimization strategy
              (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
              currently contains three functions. First, RelativeCosts1() draws a graph
              of the relative cost of complete and reduced factorial designs versus
              other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
              which contains a list of random numbers that can be used to conveniently
              assign participants to conditions in an experiment with
              many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
              size, or required sample size of a factorial or fractional factorial
              experiment, for main effects or interactions, given several possible choices
              of effect size metric, and allowing pretests and clustering.
Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.
Learning, manipulation and  evaluation of mixtures of  truncated basis  functions 
  (MoTBFs),  which include mixtures of  polynomials (MOPs) and  mixtures of truncated 
  exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
  networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The  package provides  functionality for learning  univariate, multivariate and
  conditional  densities, with the  possibility of incorporating prior  knowledge. Structural
  learning of hybrid Bayesian  networks is also provided. A set of useful tools is provided,
  including  plotting, printing  and likelihood  evaluation. This  package  makes use of  S3 
  objects, with two new classes called 'motbf' and 'jointmotbf'.
Measure of the Effect ('MOTE') is an effect size calculator, including a 
    wide variety of effect sizes in the mean differences family (all versions of d) and 
    the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central 
    confidence intervals for each effect size, relevant test statistics, and output 
    for reporting in APA Style (American Psychological Association, 2010, 
    <ISBN:1433805618>) with 'LaTeX'. In research, an over-reliance on p-values 
    may conceal the fact that a study is under-powered (Halsey, Curran-Everett, 
    Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>). A test may be statistically 
    significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 
    <doi:10.1177/0959354312436870>). Although the American Psychological Association 
    has long advocated for the inclusion of effect sizes (Wilkinson & American 
    Psychological Association Task Force on Statistical Inference, 1999 
    <doi:10.1037/0003-066X.54.8.594>), the vast majority of peer-reviewed, 
    published academic studies stop short of reporting effect sizes and confidence 
    intervals (Cumming, 2013, <doi:10.1177/0956797613504966>). 'MOTE' simplifies 
    the use and interpretation of effect sizes and confidence intervals.
Provides helper functions, metadata utilities, and workflows for 
    administering and managing databases on the 'Motherduck' cloud platform. 
    Some features require a 'Motherduck' account (<https://motherduck.com/>).
Describes spatial patterns of categorical raster data for 
    any defined regular and irregular areas. 
    Patterns are described quantitatively using built-in signatures 
    based on co-occurrence matrices but also allows for 
    any user-defined functions. 
    It enables spatial analysis such as search, change detection,
    and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).
Tools for spectral clustering of weighted directed networks using motif
    adjacency matrices. Methods perform well on large and sparse networks, and
    random sampling methods for generating weighted directed networks are also
    provided. Based on methodology detailed in Underwood, Elliott and Cucuringu
    (2020) <arXiv:2004.01293>.
Tools for motif analysis in multi-level networks.
    Multi-level networks combine multiple networks in one, e.g.
    social-ecological networks. Motifs are small configurations of nodes
    and edges (subgraphs) occurring in networks. 'motifr' can visualize
    multi-level networks, count multi-level network motifs and compare
    motif occurrences to baseline models. It also identifies contributions
    of existing or potential edges to motifs to find critical or missing
    edges. The package is in many parts an R wrapper for the excellent
    'SESMotifAnalyser' 'Python' package written by Tim Seppelt.
Functions for fitting models of trait evolution
        on phylogenies for continuous traits. The majority of functions are
        described in Thomas and Freckleton (2012)
        <doi:10.1111/j.2041-210X.2011.00132.x> and allow tests of variation 
        in the rates of trait evolution.
The temporal relationship between motor neurons can offer 
    explanations for neural strategies. We combined functions to reduce neuron 
    action potential discharge data and analyze it for short-term, time-domain 
    synchronization. Even more so, motoRneuron combines most available methods 
    for the determining cross correlation histogram peaks and most available 
    indices for calculating synchronization into simple functions. See 
    Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> 
    for a more thorough introduction.
Functions to estimate start and duration of moult from moult 
             data, based on models developed in Underhill 
             and Zucchini (1988, 1990).
Lattice functions for drawing folded empirical cumulative
    distribution plots, or mountain plots. A mountain plot is similar
    to an empirical CDF plot, except that the curve increases from
    0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
    the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.
Helping psychologists and other behavioural scientists
    to analyze mouse movement (and other 2-D trajectory) data. Bundles
    together several functions that compute spatial measures (e.g., maximum
    absolute deviation, area under the curve, sample entropy) or provide a
    shorthand for procedures that are frequently used (e.g., time 
    normalization, linear interpolation, extracting initiation and movement 
    times). For more information on these dependent measures, see Wirth et al. 
    (2020) <doi:10.3758/s13428-020-01409-0>.
Mouse-tracking, the analysis of mouse movements in computerized
    experiments, is a method that is becoming increasingly popular in the
    cognitive sciences. The mousetrap package offers functions for importing,
    preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
    An introduction into mouse-tracking analyses using mousetrap can be found
    in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2023)
    <doi:10.31234/osf.io/v685r> (preprint: 
    <https://osf.io/preprints/psyarxiv/v685r>).
Provides methods for detecting multivariate outliers in numeric datasets. The package implements classical Mahalanobis distance, robust Minimum Covariance Determinant (MCD), and Principal Component Analysis (PCA)-based approaches for outlier detection. The methodology is informed by Aggarwal (2017) <doi:10.1007/978-3-319-47578-3> and Grentzelos, Caroni and Barranco-Chamorro (2020) <doi:10.1002/cmm4.1129>. Visualization functions are included to aid interpretation of detected outliers. Mahalanobis distance calculations are accelerated using 'C++' through 'Rcpp'.
Contains functions to access movement data stored in 'movebank.org'
    as well as tools to visualize and statistically analyze animal movement data,
    among others functions to calculate dynamic Brownian Bridge Movement Models.
    Move helps addressing movement ecology questions.
Tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from 'movebank' <https://www.movebank.org/cms/movebank-main> and files is facilitated.
Provides the facility to calculate non-isotropic accumulated cost surfaces, least-cost paths, least-cost corridors, least-cost networks, ranked alternative paths, cost allocation and cost boundaries, using a number of human-movement-related cost functions that can be selected by the user. The package is built around a compute-once design: a single cost surface object is created first and then reused by every analysis function, avoiding redundant computation. Visualisation is fully decoupled from computation and is provided through 'ggplot2' methods that can be invoked, customised, and re-invoked at any time without re-running any analysis. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) <doi:10.1016/j.softx.2019.100331>.
Toolbox and 'shiny' application to help researchers design
    movement ecology studies, focusing on two key objectives: estimating
    home range areas, and estimating fine-scale movement behavior,
    specifically speed and distance traveled.  It provides interactive
    simulations and methodological guidance to support study planning and
    decision-making.  The application is described in Silva et al. (2023)
    <doi:10.1111/2041-210X.14153>.
Create animated biplots that enables dynamic visualisation of temporal or sequential changes in multivariate data by animating a single biplot across the levels of a time variable. It builds on objects from the 'biplotEZ' package, Lubbe S, le Roux N, Nienkemper-Swanepoel J, Ganey R, Buys R, Adams Z, Manefeldt P (2024) <doi:10.32614/CRAN.package.biplotEZ>, allowing users to create animated biplots that reveal how both samples and variables evolve over time.
Offers an easy and automated way to scale up individual-level space use analysis to 
    that of groups. Contains functions from the 'move' package to calculate either a dynamic Brownian 
    bridge movement model or dynamic Bivariate Gaussian bridges from movement data for individual animals, as well as functions to 
    visualize and quantify space use for individuals aggregated in groups. Originally written with 
    passive acoustic telemetry in mind, this package also provides functionality to account for
    unbalanced acoustic receiver array designs, and satellite tag data.
Provides tools for animal movement modelling using hidden Markov
    models. These include processing of tracking data, fitting hidden Markov models
    to movement data, visualization of data and fitted model, decoding of the state
    process, etc. <doi:10.1111/2041-210X.12578>.
Analysis and visualisation of synchrony, interaction, and joint
    movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and 
    Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>, while example analyses 
    can be found in Clayton, Jakubowski, and Eerola (2019) 
    <doi:10.1177/1029864919844809>. Additionally, wavelet analysis 
    techniques have been applied to examine movement-related 
    musical interactions, as shown in Eerola et al. (2018)
    <doi:10.1098/rsos.171520>.
Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.
Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature:
  - making the classification subsets flexible to cover those scenarios where both extremes of the
  marker are associated with a higher risk of being positive, considering two thresholds 
  (gROC() function);
  - transforming the marker by a proper function trying to improve the classification performance 
  (hROC() function);
  - when dealing with multivariate markers, considering a proper transformation to univariate space 
  trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function).
  The classification regions behind each point of the ROC curve are displayed in both static 
  graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or 
  videos (movieROC() function).
Fit and simulate mixtures of von Mises-Fisher distributions.
Multidimensional projection techniques are used to create two
    dimensional representations of multidimensional data sets.
An 'R' implementation of the 'python' program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) <doi:10.1021/acs.analchem.2c04632>). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow.
Functions and datasets used in the book: Fernandez-Casal, R., 
    Costa, J. and Oviedo-de la Fuente, M. (2024) "Metodos predictivos de 
    aprendizaje estadistico" <https://rubenfcasal.github.io/aprendizaje_estadistico/>.
Implements Main Path Analysis (MPA) as introduced by Hummon and
    Doreian (1989) <doi:10.1016/0378-8733(89)90017-8>. Given a directed acyclic
    graph (DAG) representing a citation or precedence network, the package
    computes traversal weights (SPC, SPLC, SPNP) for each edge and extracts
    the global, local, and key-route main paths. Also provides tools for DAG
    validation, node role classification (source/terminal/user), per-component
    path extraction for disconnected networks, and scale-free network testing.
    Accepts 'igraph' objects or edge-list data frames as input. Includes
    readers for 'Pajek' (.net) and 'Gephi' export (.gexf, .graphml) files.
Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>,
      Wang et al. (2015) <doi:10.1002/bimj.201400143>,
      Wang et al. (2016) <doi:10.1177/0962280214530608>,
      Wang (2021) <doi:10.1007/s11749-021-00770-2>,
      Wang (2024) <doi:10.1111/anzs.12409>.
Provides tools for importing and cleaning Experience Sampling
    Method (ESM) data collected via the 'm-Path' platform. The goal is
    to provide with a few utility functions to be able to read and perform
    some common operations in ESM data collected through the 'm-Path'
    platform (<https://m-path.io/landing/>). Functions include raw data
    handling, format standardization, and basic data checks, as well as to
    calculate the response rate in data from ESM studies.
Overcomes one of the major challenges in mobile (passive)
    sensing, namely being able to pre-process the raw data that comes from
    a mobile sensing app, specifically 'm-Path Sense' <https://m-path.io>.
    The main task of 'mpathsenser' is therefore to read 'm-Path Sense'
    JSON files into a database and provide several convenience functions
    to aid in data processing.
Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.
It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
Provides new data-structure support for multi-precision computing for R users. The package supports 16-bit, 32-bit, and 64-bit operations. To the best of our knowledge, 'MPCR' differs from the currently available packages in the following: 'MPCR' introduces a new data structure that supports three different precisions (16-bit, 32-bit, and 64-bit), allowing for optimized memory allocation based on the desired precision. This feature offers significant advantages in memory optimization. 'MPCR' extends support to all basic linear algebra methods across different precisions. Optional GPU acceleration via CUDA is available for 32-bit and 64-bit operations when CUDA Toolkit is detected during installation, while 16-bit operations are GPU-only and limited to matrix-matrix multiplication. 'MPCR' maintains a consistent interface with normal R functions, allowing for seamless code integration and a user-friendly experience.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
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>.
Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold.  The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator.  Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.
Estimate Multidimensional Poverty Index (MPI) measures from household
    survey microdata using the Alkire-Foster dual-cutoff counting method (Alkire
    and Foster, 2011). Load indicator
    specifications from CSV, Excel, JSON, or plain-text files; compute the
    headcount ratio (H), intensity (A), and MPI = H x A across any subgroup;
    and export results to formatted Excel reports. Supports complex survey
    designs — stratification, clustering, and probability weights — and
    optionally appends design-based standard errors and confidence intervals.
Estimate Multidimensional Poverty Indices disaggregated by population subgroups based on the Alkire and Foster method (2011) <doi:10.1016/j.jpubeco.2010.11.006>. This includes the calculation of standard errors and confidence intervals. Other partial indices such as incidence, intensity and indicator-specific measures as well as intertemporal changes analysis can also be estimated. The standard errors and confidence intervals are calculated considering the complex survey design.
Model stability and variable inclusion plots [Mueller and Welsh
    (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
    (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
    (2008, <doi:10.1214/07-AOS517>); Jiang et al. 
    (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
Leverages the R language to automate latent variable model estimation
	and interpretation using 'Mplus', a powerful latent variable modeling program
	developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package
    provides routines for creating related groups of models, running batches of
    models, and extracting and tabulating model parameters and fit statistics.
Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in 'Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). 
  The package builds upon the capabilities of the 'MplusAutomation' package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. 
  “MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.” Structural Equation Modeling, 25(4), 621–638. <doi:10.1080/10705511.2017.1402334>
  The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). 
  “An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.” Advances in Life Course Research, 43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.
Offers automation tools to parallelize 'Mplus' operations when using 'R' for data generation. It facilitates streamlined integration between 'Mplus' and 'R', allowing users to run and manage multiple 'Mplus' models simultaneously and efficiently in 'R'.
Uses recursive partitioning to create homogeneous subgroups based on structural equation models 
             fit in 'Mplus', a stand-alone program developed by Muthen and Muthen.
Exploratory graphical analysis of multivariate data,
    specifically gene expression data with different projection
    methods: principal component analysis, correspondence analysis,
    spectral map analysis.
Aggregates matrix population models (MPMs) in both the lambda (stable 
    growth rate) and R0 (net reproductive rate) frameworks, including standard and 
    elasticity-consistent aggregators. Standard aggregation in the lambda framework 
    maintains consistent lambda and stable stage distribution, while standard aggregation 
    in the R0 framework maintains consistent R0 and cohort stable stage distribution. 
    Elasticity-consistent aggregators maintain these same consistencies with respect
    to the chosen framework and additionally preserve consistent reproductive values in 
    the lambda framework and cohort reproductive values in the R0 framework. Aggregation 
    can take the form of general-to-general MPM (mpm_aggregate) or Leslie-to-Leslie
    MPM (leslie_aggregate).
Functions to compute and plot multivariate (partial) Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
    for comparisons between all types of variables including continuous vs
    continuous, continuous vs discrete and discrete vs discrete. Uses a
    nonparametric bias correction giving Bias Corrected Mutual Information
    (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
    large genomic datasets.
Allows users to simulate matrix population models with
    particular characteristics based on aspects of life history such as
    mortality trajectories and fertility trajectories. Also allows the
    exploration of sampling error due to small sample size.
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.
Symbolic computing with multivariate polynomials in R.
A flexible framework for power analysis using Monte
    Carlo simulation for settings in which considerations of the correlations
    between predictors are important. Users can set up a data generative model
    that preserves dependence structures among predictors given existing data
    (continuous, binary, or ordinal). Users can also generate power curves to
    assess the trade-offs between sample size, effect size, and power of a design.
    This package includes several statistical models common in environmental
    mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.
Analysis of experimental multi-parent populations to detect regions
             of the genome (called quantitative trait loci, QTLs) influencing
             phenotypic traits measured in unique and multiple environments.
             The population must be composed of crosses between a set of at
             least three parents (e.g. factorial design, 'diallel', or nested
             association mapping). The functions cover data processing,
             QTL detection, and results visualization. The implemented methodology
             is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017)
             <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti
             and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>,
             and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi: 10.1093/genetics/iyae003>.
Fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards. See Burke & MacKenzie (2016) <doi:10.1111/biom.12625> and Burke et al (2020) <doi:10.1111/rssc.12398>.
Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.
Computational tools to represent phylogenetic signals using adapted
  eigenvector maps.
Provides a curated multi-country collection of monetary
    policy shock and stance series from the empirical macroeconomics
    literature, bundled as tidy data frames with provenance metadata.
    Version 0.1.0 includes thirteen series covering the United States,
    United Kingdom, and Australia: for the US, the policy news shock of
    Nakamura and Steinsson (2018) <doi:10.1093/qje/qjy004>, the
    orthogonalised surprise of Bauer and Swanson (2023)
    <doi:10.1257/aer.20201220>, the target and path factors of the
    Swanson (2021) <doi:10.1016/j.jmoneco.2020.09.003> extension of
    Gurkaynak, Sack, and Swanson (2005), the pure monetary policy and
    central bank information shocks of Jarocinski and Karadi (2020)
    <doi:10.1257/mac.20180090>, the informationally-robust shock of
    Miranda-Agrippino and Ricco (2021) <doi:10.1257/mac.20180124>, and
    the shadow federal funds rate of Wu and Xia (2016)
    <doi:10.1111/jmcb.12300>; for the UK, the UK Monetary Policy
    Event-Study Database of Braun, Miranda-Agrippino, and Saha (2025)
    <doi:10.1016/j.jmoneco.2024.103645>, the high-frequency surprise of
    Cesa-Bianchi, Thwaites, and Vicondoa (2020)
    <doi:10.1016/j.euroecorev.2020.103375>, and the narrative shock of
    Cloyne and Hurtgen (2016) <doi:10.1257/mac.20150093>; for Australia,
    the three-component RBA surprise of Hambur and Haque (2023)
    <doi:10.1111/1475-4932.12786> and the credit-spread-augmented RBA
    narrative shock of Beckers (2020). Helpers support date alignment,
    frequency conversion, and shock cumulation. All data is bundled;
    no runtime network access is required.
A low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>,
  which applies a mass-preserving spline to soil attributes. Splining soil data 
  is a safe way to make continuous down-profile estimates of attributes measured
  over discrete, often discontinuous depth intervals.
Supplementary materials and datasets for the book "Modern Psychometrics With R" (Mair, 2018, Springer useR! series).
Fitting and testing multinomial processing tree (MPT) models, a
  class of nonlinear models for categorical data.  The parameters are the
  link probabilities of a tree-like graph and represent the latent cognitive
  processing steps executed to arrive at observable response categories
  (Batchelder & Riefer, 1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009
  <doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988
  <doi:10.1037/0033-295x.95.3.318>).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis (3rd ed), by Montgomery, Peck
             and Vining.
             Some additional data sets and functions are also included.
Useful functions to analyze proteomic workflows including
    number of identifications, data completeness, missed cleavages,
    quantitative and retention time precision etc. Various software
    outputs are supported such as 'ProteomeDiscoverer', 'Spectronaut',
    'DIA-NN' and 'MaxQuant'.
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.
Implements bivariate and Multivariate Quantile-on-Quantile
    Granger causality tests building on the Quantile-on-Quantile
    regression framework of Sim and Zhou (2015)
    <doi:10.1016/j.jbankfin.2015.01.013> and the quantile Granger
    causality test of Troster (2018)
    <doi:10.1080/07474938.2016.1172400>. The bivariate test estimates
    the local-linear slope in the quantile regression of y_t on lagged
    x_t with lagged y_t as control, using Gaussian kernel weights, and
    tests it against zero by paired bootstrap. The multivariate
    (conditional) test additionally conditions on a set of moderators Z
    and optional x times Z interaction terms, in the spirit of Sinha,
    Ghosh, Hussain, Nguyen and Das (2023)
    <doi:10.1016/j.eneco.2023.107021>. A Sup-Wald summary across the
    quantile grid is also provided. Heatmaps and 3D surfaces default to
    the 'MATLAB' 'Parula' colour map.
Implements Multivariate Quantile-on-Quantile Regression
    (m-QQR) of Sinha, Ghosh, Hussain, Nguyen and Das (2023)
    <doi:10.1016/j.eneco.2023.107021>, extending the bivariate
    Quantile-on-Quantile regression of Sim and Zhou (2015)
    <doi:10.1016/j.jbankfin.2015.01.013> to include exogenous moderators
    and controls with optional interaction terms. For each pair of
    quantile levels (theta of the response and tau of the regressor) the
    package fits a locally-weighted quantile regression of y on the
    principal regressor x, a lagged dependent variable, moderators Z and
    the x*Z interaction terms, using Gaussian kernel weights on the
    empirical cumulative distribution function (CDF) distance. Bootstrap
    standard errors and Koenker-Machado pseudo R-squared are reported.
    Visualisations include 'MATLAB'-style 'Parula' and 'Jet' 3D
    surfaces, heatmaps and contour plots through 'plotly'.
Parametric modeling of M-quantile regression coefficient functions.
Provides functions for actuarial risk modeling, including survival models,
    life annuities, multiple-decrement models, and mortality improvement projections.
    The package is designed to align with standard actuarial notation and supports
    teaching, exam preparation, and reproducible actuarial analysis. The methods are
    based on standard actuarial references including Camilli, Duncan and London (2014,
    ISBN:9781625423474) "Models for Quantifying Risk" and Dickson, Hardy and Waters
    (2020, ISBN:9781108478083) "Actuarial Mathematics for Life Contingent Risks".
Provides an implementation of methods for multivariate 
    multiple regression with adaptive shrinkage priors as
    described in F. Morgante et al (2023) <doi:10.1371/journal.pgen.1010539>.
The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.
Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, 'MR.RGM' extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. 'MR.RGM' facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, 'MR.RGM' provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).
Implementations of an estimator for the multivariate regression association measure (MRAM) proposed in Shih and Chen (2026) <doi:10.1016/j.csda.2025.108288> and its associated variable selection algorithm. The MRAM quantifies the predictability of a random vector Y from a random vector X given a random vector Z. It takes the maximum value 1 if and only if Y is almost surely a measurable function of X and Z, and the minimum value of 0 if Y is conditionally independent of X given Z. The MRAM generalizes the Kendall's tau copula correlation ratio proposed in Shih and Emura (2021) <doi:10.1016/j.jmva.2020.104708> by employing the spatial sign function. The estimator is based on the nearest neighbor method, and the associated variable selection algorithm is adapted from the feature ordering by conditional independence (FOCI) algorithm of Azadkia and Chatterjee (2021) <doi:10.1214/21-AOS2073>. For further details, see the paper Shih and Chen (2026) <doi:10.1016/j.csda.2025.108288>.
Flexible, mechanistic, and spatially explicit simulator of
    metacommunities. It extends our previous package - 'rangr' (see
    <https://github.com/ropensci/rangr>), which implemented a mechanistic
    virtual species simulator integrating population dynamics and
    dispersal. The 'mrangr' package adds the ability to simulate multiple
    species interacting through an asymmetric matrix of pairwise
    relationships, allowing users to model all types of biotic
    interactions — competitive, facilitative, or neutral — within
    spatially explicit virtual environments. This work was supported by 
    the National Science Centre, Poland, grant no. 2018/29/B/NZ8/00066 and 
    the Poznań Supercomputing and Networking Centre (grant no. pl0090-01).
You can use the set of wrappers for analytical schemata 
    to reduce the effort in writing machine-readable data. The set of all-in-one
    wrappers will cover widely used functions from data analysis packages.
Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.
Bayesian estimation of inverse variance weighted (IVW),
    Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden
    et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for
    Mendelian randomization analyses.
A collection of functions for processing and analyzing metabolite data. 
    The namesake function mrbin() converts 1D 
    or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and
    performs basic processing steps in a reproducible way. Negative values, a
    common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used
    parameters are stored in a readable text file and can be restored from that
    file to enable exact reproduction of the data at a later time. The function fia() ranks features according
    to their impact on classifier models, especially artificial neural network models.
A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
    mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
    To infer which of the captured features are credible, Bayesian analysis is used.
    The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
    (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
    (3) Posterior credibility analysis of the differences of smooths created.
    The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury, Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.
Compute bootstrap confidence intervals for the adjusted Schnabel and Schumacher-Eschmeyer multi-visit mark-recapture estimators based on Dettloff (2023) <doi:10.1016/j.fishres.2023.106756>.
Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>.  This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix.
Detect outlying observations in functional data sets based on the minimum regularized covariance trace (MRCT) estimator. Includes implementation of Oguamalam et al. (2023) <arXiv:2307.13509>.
Provides functions for analyzing the association between
    one single response categorical variable (SRCV) and one multiple response
    categorical variable (MRCV), or between two or three MRCVs.  A modified Pearson
    chi-square statistic can be used to test for marginal independence for the one or
    two MRCV case, or a more general loglinear modeling approach can be used to examine
    various other structures of association for the two or three MRCV case.  Bootstrap-
    and asymptotic-based standardized residuals and model-predicted odds ratios are
    available, in addition to other descriptive information. Statisical methods implemented 
    are described in Bilder et al. (2000) <doi:10.1080/03610910008813665>,
    Bilder and Loughin (2004) <doi:10.1111/j.0006-341X.2004.00147.x>,
    Bilder and Loughin (2007) <doi:10.1080/03610920600974419>,
    and Koziol and Bilder (2014) <https://journal.r-project.org/articles/RJ-2014-014/>.
Global testing for regression discontinuity designs with more than one running variable. The function cef_disc_test() is used for testing whether there exist non-zero treatment effects along the boundary of the treated region. The function density_disc_test() is used for testing whether there exist discontinuities in the joint density of the running variables along the boundary of the treated region. The methodology follows Samiahulin (2026), "Global Testing for Regression Discontinuity Designs with Multiple Running Variables" <doi:10.48550/arXiv.2602.03819>.
Animal abundance estimation via conventional, multiple covariate
    and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
    fitting is performed via maximum likelihood. Also included are diagnostics
    and plotting for fitted detection functions. Abundance estimation is via a
    Horvitz-Thompson-like estimator.
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.
Explore and retrieve marine geospatial data from the Marine Regions Gazetteer <https://marineregions.org/gazetteer.php?p=webservices> and the Marine Regions Data Products <https://marineregions.org/webservices.php>.
Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. <doi:10.3390/pr9101697>.
Model fitting, sampling and visualization
    for the (Hidden) Markov Random Field model with pairwise interactions and 
    general interaction structure from 
    Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>,
    which has many popular models used in 2-dimensional lattices
    as particular cases, like the Ising Model and Potts Model.
    A complete manuscript describing the package is available in
    Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.
Performs the MRFA approach proposed by Sung et al. (2020) <doi:10.1080/01621459.2019.1595630> to fit
    and predict nonlinear regression problems, particularly for large-scale and
    high-dimensional problems. The application includes deterministic or stochastic
    computer experiments, spatial datasets, and so on.
Approximate node interaction parameters of Markov Random Fields 
    graphical networks. Models can incorporate additional covariates, allowing users to estimate
    how interactions between nodes in the graph are predicted to change across
    covariate gradients. The general methods implemented in this package are described 
    in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Tools to compute depth measures and implementations of related 
             tasks such as outlier detection, data exploration and 
            classification of multivariate, regression and functional data.
Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).
The need for anonymization of individual survey responses often leads to many suppressed grid cells in a regular grid. Here we provide functionality for creating multi-resolution gridded data, respecting the confidentiality rules, such as a minimum number of units and dominance by one or more units for each  grid cell. The functions also include the possibility for contextual suppression of data. For more details see Skoien et al. (2025) <doi:10.48550/arXiv.2410.17601>.
Researchers often need to calculate body-size growth rates for individuals
    that do not have associated age data. These growth rates are based on
    mark-recapture data where an individual was captured and measured at time 1
    then recaptured and measured at time 2. The sizes at each time and amount
    of time between captures can be used to calculate growth rates. 'MRgrowth'
    follows the approach in Edmonds et al. (2021) <doi:10.1371/journal.pone.0259978>
    and provides functions to calculate growth using three formulas, the
    Faben's reformulation of the von Bertalanffy formula, the Gompertz 
    formula, and a logistic formula.
Simulation from an 'mrgsolve' 
    <https://cran.r-project.org/package=mrgsolve> model using a parallel backend.  
    Input data sets are split (chunked) and simulated in parallel using 
    mclapply() or future_lapply() 
    <https://cran.r-project.org/package=future.apply>.
Perform sensitivity analysis on ordinary differential equation 
    based models, including ad-hoc graphical analyses based on structured 
    sequences of parameters as well as local sensitivity analysis. Functions 
    are provided for creating inputs, simulating scenarios and plotting outputs.
Fast simulation from ordinary differential equation
    (ODE) based models typically employed in quantitative pharmacology and
    systems biology.
Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) 
    self-exciting process, with given immigrant hazard rate functions and 
    offspring density function. Calculate the likelihood of a MRHawkes process 
    with given hazard rate functions and offspring density function for an 
    (increasing) sequence of event times. Calculate the Rosenblatt residuals of the 
    event times. Predict future event times based on observed event times up to a 
    given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.
It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices.  The indices are proposed in Cugmas and Ferligoj (2018) <http://ibmi.mf.uni-lj.si/mz/2018/no-1/Cugmas2018.pdf>.
Builds and interprets multi-response machine learning models using 'tidymodels' syntax. Users can supply a tidy model, and 'mrIML' automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) <doi:10.1111/1755-0998.13495> and Fountain-Jones et al. (2024) <doi:10.22541/au.172676147.77148600/v1>.
Converts NIfTI format T1/FL neuroimages into structured,
    high-dimensional 2D data frames with a focus on region of interest
    (ROI) based processing. The package incorporates the partition
    algorithm, which offers a flexible framework for agglomerative
    partitioning based on the Direct-Measure-Reduce approach. This
    method ensures that each reduced variable maintains a user-specified
    minimum level of information while remaining interpretable, as each
    maps uniquely to one variable in the reduced dataset. The
    partition framework is described in Millstein et al. (2020)
    <doi:10.1093/bioinformatics/btz661>. The package allows
    customization in variable selection, measurement of information
    loss, and data reduction methods for neuroimaging analysis and
    machine learning workflows.
Implements various methods for tissue classification in magnetic
        resonance (MR) images of the brain, including normal mixture models
        and hidden Markov normal mixture models, as outlined in Feng &
        Tierney (2011) <doi:10.18637/jss.v044.i07>. These methods allow a
        structural MR image to be classified into gray matter, white matter
        and cerebrospinal fluid tissue types.
Estimation and comparison of the performances of diagnostic tests
    in multi-reader multi-case studies where true case statuses (or ground
    truths) are known and one or more readers provide test ratings for multiple
    cases.  Reader performance metrics are provided for area under and expected
    utility of ROC curves, likelihood ratio of positive or negative tests, and
    sensitivity and specificity.  ROC curves can be estimated empirically or
    with binormal or binormal likelihood-ratio models.  Statistical comparisons
    of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and
    the unified framework of Hillis (2005) <doi:10.1002/sim.2024>.  The ANOVA
    can be conducted with data from a full factorial, nested, or partially
    paired study design; with random or fixed readers or cases; and covariances
    estimated with the DeLong method, jackknifing, or an unbiased method.  Smith
    and Hillis (2020) <doi:10.1117/12.2549075>.
Implements methods for comparing sensitivities and specificities in balanced (or fully crossed) multi-reader multi-case (MRMC) studies with binary diagnostic test results. It implements conditional logistic regression and provides score tests equivalent to Cochran's Q test (which corresponds to McNemar's test when comparing two modalities only). The methodology is based on Lee et al. (2026) <doi:10.1002/sim.70471>.
Sample size estimations for MRMC studies based on the Obuchowski-Rockette (OR) methodology is implemented. The function can calculate sample sizes where the 
             endpoint of interest in the study is either ROC AUC (Area-Under-the-Receiver-Operating-Characteristics-Curve) or sensitivity. The package can also return sample sizes for studies
             expected to have clustering effect (e.g.- multiple pulmonary nodules per patient). All calculations assume that the study design is fully crossed (paired-reader, paired-case) where
             each reader reads/interprets each case and that there are two interventions/imaging-modalities/techniques in the study. In addition to MRMC, it can also be used to estimate sample sizes 
             for standalone studies where sensitivity or AUC are the primary endpoints.
             The methods implemented are based on the methods described in Zhou et.al. (2011) <doi:10.1002/9780470906514> and Obuchowski (2000) <doi:10.1097/EDE.0b013e3181a663cc>.
A causal mediation approach under the counterfactual framework 
    to test the significance of total, direct and indirect effects. In this approach, 
    a group of methylated sites from a predefined region are utilized as the mediator, 
    and the functional transformation is used to reduce the possible high dimension 
    in the region-based methylated sites and account for their location information.
Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, <doi:10.1093/bib/bbw145>), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, <doi:10.1016/j.molp.2022.02.012>).
Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.
Computes mutual information matrices from continuous, categorical 
  and survival variables, as well as feature selection with minimum redundancy, 
  maximum relevance (mRMR) and a new ensemble mRMR technique. Published in
  De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.
Computes multiple correlation coefficient when the data matrix is given and tests its significance.
A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC 
            (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also 
            contains functions to simulate data under a certain topology, to visualize a graph in different 
            ways, and to compare graphs and quantify the differences. 
            See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,
            Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>,
            Kvamme and Badsha, et al. (2025) <doi:10.1093/genetics/iyaf064>.
An easy-to-use IMAP client that provides tools for message searching,
    selective fetching of message attributes, mailbox management, attachment extraction, 
    and several other IMAP features, paving the way for e-mail data analysis in R.
Provides multiply robust estimators of principal survival causal effects among always-takers, compliers, and never-takers in studies with treatment noncompliance. The methods are based on Cheng et al. (2026) <doi:10.1214/25-AOAS2117>.
To calculate the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.
We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q).   We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from  independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals  in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) <doi:10.1145/3424670>.
Frequentist and Bayesian linear regression for large data sets. Useful 
    when the data does not fit into memory (for both frequentist and Bayesian regression), 
    to make running time manageable (mainly for Bayesian regression), and to reduce 
    the total running time because of reduced or less severe memory-spillover into 
    the virtual memory. This is an implementation of Merge & Reduce for linear regression
    as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020).
    'Streaming statistical models via Merge & Reduce'. International Journal of
    Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.
Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>.
Provides estimation and leave-one-cluster-out jackknife standard errors
    for four longitudinal cluster-randomized trial estimands: horizontal individual
    average treatment effect (h-iATE), horizontal cluster average treatment effect
    (h-cATE), vertical individual average treatment effect (v-iATE), and vertical
    cluster-period average treatment effect (v-cATE), using unadjusted and augmented
    (model-robust standardization) estimators. The working model may be fit using
    linear mixed models for continuous outcomes or generalized estimating equations
    and generalized linear mixed models for binary outcomes. Period inclusion for
    aggregation is determined automatically: only periods with both treated and
    control clusters are included in the construction of the marginal means and
    treatment effect contrasts. See Fang et al. (2025) <doi:10.48550/arXiv.2507.17190>.
Provides methods to analyze micro-randomized trials (MRTs) with binary treatment options.  Supports four types of analyses: (1) proximal causal excursion effects, including weighted and centered least squares (WCLS) for continuous proximal outcomes by Boruvka et al. (2018) <doi:10.1080/01621459.2017.1305274> and the estimator for marginal excursion effect (EMEE) for binary proximal outcomes by Qian et al. (2021) <doi:10.1093/biomet/asaa070>; (2) distal causal excursion effects (DCEE) for continuous distal outcomes using a two-stage estimator by Qian (2025) <doi:10.1093/biomtc/ujaf134>; (3) mediated causal excursion effects (MCEE) for continuous distal outcomes, estimating natural direct and indirect excursion effects in the presence of time-varying mediators by Qian (2025) <doi:10.48550/arXiv.2506.20027>; and (4) standardized proximal effect size estimation for continuous proximal outcomes, generalizing the approach in Luers et al. (2019) <doi:10.1007/s11121-017-0862-5> to allow adjustment for baseline and time-varying covariates for improved efficiency.
Provide a sample size calculator for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.
Provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on Cohn et al. (2023) <doi:10.1002/sim.9748>. Also provides a power calculator when the sample size is input by the user.
Constructs multi-resolution thin-plate spline basis
    functions on the sphere for use in spatial regression and large-scale
    spatial prediction problems. Implements the basis system described in
    Huang, Huang, and Ing (2025) "Multi-Resolution Spatial Methods on the
    Sphere: Efficient Prediction for Global Data", Environmetrics,
    <doi:10.1002/env.70092>. Heavy computations are written in 'C++' via
    'Rcpp' with optional 'OpenMP' parallelism.
Encodes several methods for performing Mendelian randomization analyses with summarized data. Similar to the 'MendelianRandomization' package, but with fewer bells and whistles, and less frequent updates. As described in Yavorska (2017) <doi:10.1093/ije/dyx034> and Broadbent (2020) <doi:10.12688/wellcomeopenres.16374.2>.
Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.
Implements area level of multivariate small area estimation using Hierarchical Bayesian method under Normal and T distribution. The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Multi-step adaptive elastic-net (MSAENet) algorithm for
    feature selection in high-dimensional regressions proposed in
    Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
    with support for multi-step adaptive MCP-net (MSAMNet) and
    multi-step adaptive SCAD-net (MSASNet) methods.
Estimates the sample size needed to detect microbial contamination 
 in a lot with a user-specified detection probability and user-specified analytical sensitivity.
 Various patterns of microbial contamination are accounted for: homogeneous (Poisson), 
 heterogeneous (Poisson-Gamma) or localized(Zero-inflated Poisson).
  Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004> 
  "Impact of microbial distributions on food safety".
  Leroy Simon (1963) <doi:10.1017/S0515036100001975>
  "Casualty Actuarial Society - The Negative Binomial and Poisson Distributions Compared".
Visualizes multiple sequence alignments dynamically within the
    Shiny web application framework.
Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element
 isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks,
 examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI)
 ion sources.
Provides sample data sets that are used in statistics and data science courses at the Münster School of Business. The datasets refer to different business topics but also other domains, e.g. sports, traffic, etc.
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.
Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters, see Heidenreich, Luebeck, Moolgavkar (1997) <doi:10.1111/j.1539-6924.1997.tb00878.x>. Numerical solutions are provided for its extensions, see also Little, Vineis, Li (2008) <doi:10.1016/j.jtbi.2008.05.027>.
Create native charts for 'Microsoft PowerPoint', 'Microsoft
    Excel' and 'Microsoft Word' documents. The resulting charts can then
    be edited and annotated in the host application. It provides functions
    to create charts and to modify their content and formatting. The
    chart's underlying data is automatically saved within the 'Word',
    'Excel' or 'PowerPoint' file. It extends the 'officer' package, which
    does not provide native 'Microsoft' chart production.
Functions to classify mass spectra in known categories and to determine discriminant mass-to-charge values (m/z). Includes easy-to-use preprocessing pipelines for Matrix Assisted Laser Desorption Ionisation - Time Of Flight Mass Spectrometry (MALDI-TOF) mass spectra, methods to select discriminant m/z from labelled libraries, and tools to predict categories (species, phenotypes, etc.) from selected features. Also provides utilities to build design matrices from peak intensities and labels. While this package was developed with the aim of identifying very similar species or phenotypes of bacteria from  MALDI-TOF MS, the functions of this package can also be used to classify other categories associated to mass spectra; or from mass spectra obtained with other mass spectrometry techniques. Parallelized processing and optional C++-accelerated functions are available (notably to deal with large datasets) from version 0.5.0.  If you use this package in your research, please cite the associated publication (<doi:10.1016/j.eswa.2025.128796>). For a comprehensive guide, additional applications, and detailed examples, see <https://github.com/agodmer/MSclassifR_examples>.
Model based clustering using 
    the multivariate multiple Scaled t (MST) and multivariate multiple 
    scaled contaminated normal (MSCN) distributions. The MST is an 
    extension of the multivariate Student-t distribution to include 
    flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a  heavy-tailed
    generalization of the multivariate normal (MN) distribution to
    model elliptical contoured scatters in the presence of mild outliers
    (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.
Three generalizations of the synthetic control method (which has 
    already an implementation in package 'Synth') are implemented: first, 
    'MSCMT' allows for using multiple outcome variables, second, time series 
    can be supplied as economic predictors, and third, a well-defined 
    cross-validation approach can be used.
    Much effort has been taken to make the implementation as stable as possible 
    (including edge cases) without losing computational efficiency.
    A detailed description of the main algorithms is given in 
    Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
Find common entities detected in both positive and negative
    ionization mode, delete this entity in the less sensible mode and combine both
    matrices.
Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.
Methods for analyzing and using quartets displayed on a collection 
 of gene trees, primarily to make inferences about the species tree or network 
 under the multi-species coalescent model. These include quartet hypothesis tests
 for the model, as developed by Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, 
 simplex plots of quartet concordance factors as presented by Allman et al. (2020) 
 <doi:10.1101/2020.02.13.948083>, species tree inference methods based on quartet 
 distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and Yourdkhani and 
 Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, the NANUQ algorithm for inference 
 of level-1 species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>, 
 the TINNIK algorithm for inference of the tree of blobs of an arbitrary 
 network of Allman et al.(2022) <doi:10.1007/s00285-022-01838-9>,
 NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in
 Rhodes et al.(2024) <doi:10.1186/s13015-025-00274-w>, and the ECToBlob algorithm for inference of a 
 network with no anomalous quartets of Allman et al. (2026) (forthcoming).
 Software announcement by Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.
Statistical tests for validating multispecies coalescent gene tree simulators, using pairwise distances and rooted triple counts. See Allman ES, Baños HD, Rhodes JA 2023. Testing multispecies coalescent simulators using summary statistics, IEEE/ACM Trans Comput Biol Bioinformat, 20(2):1613–1618. <doi:10.1109/TCBB.2022.3177956>.
R Client for the Microsoft Cognitive Services Text Analytics
    REST API, including Sentiment Analysis, Topic Detection, Language Detection,
    and Key Phrase Extraction. An account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
R Client for the Microsoft Cognitive Services 
  'Text-to-Speech' REST API, including voice synthesis. A valid account 
  must be registered at the Microsoft Cognitive Services website 
  <https://azure.microsoft.com/en-us/products/ai-services/> in order to 
  obtain a (free) API key. Without an API key, this package will not 
  work properly.
R Client for the Microsoft Cognitive Services Web Language Model
    REST API, including Break Into Words, Calculate Conditional
    Probability, Calculate Joint Probability, Generate Next Words, and List
    Available Models. A valid account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
Implements the method of successive dichotomizations by
    Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, 
    which estimates item measures, person measures and ordered rating
    category thresholds given ordinal rating scale data.
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) <doi:10.5705/ss.202016.0117>.
Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components.  The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
Characterization of a mid-summer drought (MSD) with precipitation
    based statistics. The MSD is a phenomenon of decreased rainfall during a 
    typical rainy season. It is a feature of rainfall in much of Central America
    and is also found in other locations, typically those with a Mediterranean 
    climate. Details on the metrics are in Maurer et al. (2022) 
    <doi:10.5194/hess-26-1425-2022>.
Computing metabolite set enrichment analysis (MSEA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), single sample enrichment analysis (SSEA) (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>) and over-representation analysis (ORA) that accounts for undetected metabolites (Yamamoto, H. (2024) <doi:10.51094/jxiv.954>).
Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy similarity, and entropy similarity for mass spectrometry data. The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" <doi:10.1038/s41592-021-01331-z>.
Development, simulation testing, and implementation of management procedures for fisheries 
    (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).
A companion to the Chinese book ``Modern Statistical Graphics''.
Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2019) <doi:10.18637/jss.v091.i04>.
Implements the three parallel forecast combinations of Markov Switching GARCH and extreme learning machine model along with the selection of appropriate model for volatility forecasting. For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>.
Computes the degrees of freedom of the lasso,
        elastic net, generalized elastic net and adaptive lasso based
        on the generalized path seeking algorithm.  The optimal model
        can be selected by model selection criteria including Mallows'
        Cp, bias-corrected AIC (AICc), generalized cross validation
        (GCV) and BIC.
Provides new functions info(), warn() and error(), similar to message(),
    warning() and stop() respectively. However, the new functions can have a 'level'
    associated with them, so that when executed the global level option determines whether
    they are shown or not. This allows debug modes, outputting more information. The can also
    output all messages to a log file.
The Molecular Signatures Database ('MSigDB') is one of the most widely 
    used and comprehensive databases of gene sets for performing gene set 
    enrichment analysis <doi:10.1016/j.cels.2015.12.004>. The 'msig' package provides 
    you with powerful, easy-to-use and flexible query functions for the 'MsigDB' 
    database.
        There are 2 query modes in the 'msig' package: online query and local query. 
    Both queries contain 2 steps: gene set name and gene.
        The online search is divided into 2 modes: registered search and 
    non-registered browse. For registered search, email that you registered should 
    be provided.
        Local queries can be made from local database, which can be updated by msig_update() function.
Provides the 'Molecular Signatures Database' (MSigDB) gene
    sets typically used with the 'Gene Set Enrichment Analysis' (GSEA)
    software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>,
    Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>, Castanza et al.
    2023 <doi:10.1038/s41592-023-02014-7>) as an R data frame. The package
    includes the human genes as listed in MSigDB as well as the
    corresponding symbols and IDs for frequently studied model organisms
    such as mouse, rat, pig, fly, and yeast.
Plotting functions for mutational signatures and mutational
    spectra, including single base substitutions (SBS), doublet base
    substitutions (DBS), and small insertions and deletions (indels).
    Generates plots similar to those used previously in 
    Alexandrov et al. (2020)<doi:10.1038/s41586-020-1943-3> and
    Rozen et al. (2026)<doi:10.5281/zenodo.18451842>.
Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. 
    However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. 
    By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields.
    The segmentation(), lambda_estimator() functions are the main functions of this package.
Utility functions for mutational signature analysis
             as described in Alexandrov, L. B. (2020)
             <doi:10.1038/s41586-020-1943-3>.
             This package provides two groups of functions. One is
             for dealing with mutational signature "exposures"
             (i.e. the counts of mutations in a sample that are
             due to each mutational signature). The other group of
             functions is for matching or comparing sets of 
             mutational signatures. 'mSigTools' stands for 
             mutational Signature analysis Tools.
Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.
Incorporates a Bayesian monotonic single-index mixed-effect model with a multivariate skew-t likelihood, specifically designed to handle survey weights adjustments. Features include a simulation program and an associated Gibbs sampler for model estimation. The single-index function is constrained to be monotonic increasing, utilizing a customized Gaussian process prior for precise estimation. The model assumes random effects follow a canonical skew-t distribution, while residuals are represented by a multivariate Student-t distribution. Offers robust Bayesian adjustments to integrate survey weight information effectively.
Performs a multiscale analysis of a nonparametric
  regression or nonparametric regressions with time series errors. In case
  of one regression, with the help of this package it is possible to detect
  the regions where the trend function is increasing or decreasing.
  In case of multiple regressions, the test identifies regions where
  the trend functions are different from each other. See
  Khismatullina and Vogt (2020) <doi:10.1111/rssb.12347>,
  Khismatullina and Vogt (2022) <doi:10.48550/arXiv.2209.10841> and
  Khismatullina and Vogt (2023) <doi:10.1016/j.jeconom.2021.04.010>
  for more details on theory and applications.
An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
Functions for fitting continuous-time Markov and hidden
    Markov multi-state models to longitudinal data.  Designed for
    processes observed at arbitrary times in continuous time (panel data)
    but some other observation schemes are supported. Both Markov
    transition rates and the hidden Markov output process can be modelled
    in terms of covariates, which may be constant or piecewise-constant
    in time.
Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.
A 'Shiny' application for converting 'Excel'-based
    Life Cycle Inventory (LCI) data into 'SimaPro' CSV
    (Comma-Separated Values) format for use in Life Cycle
    Assessment (LCA) modeling. Developed by the Mine Sustainability
    Modeling Group (MSMG) at Missouri University of Science and
    Technology under NSF (National Science Foundation) funding
    (Award No. 2219086). See Pizzol (2022)
    <https://github.com/massimopizzol/Simapro-CSV-converter>
    for the original 'Python' implementation that inspired this tool.
Model evaluation based on a modified version of the recursive feature elimination algorithm. This package is designed to determine the optimal model(s) by leveraging all available features.
A fast and general method for restructuring classical longitudinal
    observational data into augmented transition data suitable for multi-state
    modeling with the 'msm' package. Works with any longitudinal data where
    subjects accumulate repeated observations with start and end times and an
    optional terminal outcome. Methods are described in Grossetti, Ieva and
    Paganoni (2018) <doi:10.1007/s10729-017-9400-z>.
Provides fundamental functions for descriptive statistics,
    including MODE(), estimate_mode(), center_stats(), position_stats(), pct(),
    spread_stats(), kurt(), skew(), and shape_stats(), which assist in
    summarizing the center, spread, and shape of numeric data. For more details,
    see McCurdy (2025), "Introduction to Data Science with R" 
    <https://jonmccurdy.github.io/Introduction-to-Data-Science/>.
Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). 
             The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".
Implements a semi-parametric estimation framework combined with a boosting algorithm to marginally estimate the conditional cumulative distribution function of survival times given informative covariates. It then utilizes the graphical lasso method to reconstruct network structures among multivariate time-to-event variables, accommodating both multivariate outcomes measured within a single dataset and survival times integrated from heterogeneous (multi-source) datasets..
Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This also serves as a companion package for the 
    STAT 571: Multivariate Analysis course offered by the Department of Statistics
    at the University of Illinois at Urbana-Champaign ('UIUC').
Enable operationalized evaluation of disease outcomes in
    multiple sclerosis. ‘MSoutcomes’ requires longitudinally recorded
    clinical data structured in long format. The package is based on the
    research developed at Clinical Outcomes Research unit (CORe),
    University of Melbourne and Neuroimmunology Centre, Royal Melbourne
    Hospital. Kalincik et al. (2015) <doi:10.1093/brain/awv258>.
    Lorscheider et al.  (2016) <doi:10.1093/brain/aww173>. Sharmin et al.
    (2022) <doi:10.1111/ene.15406>.  Dzau et al. (2023)
    <doi:10.1136/jnnp-2023-331748>.
Implements an algorithm for computing multiple sparse principal 
    components of a dataset. The method is based on Cory-Wright and Pauphilet 
    "Sparse PCA with Multiple Principal Components" (2026) 
    <doi:10.1287/opre.2023.0598>. The algorithm uses an iterative deflation 
    heuristic with a truncated power method applied at each iteration to compute 
    sparse principal components with controlled sparsity.
Analyse disability course in multiple sclerosis (MS) from
    longitudinal data. The package provides a flexible framework for
    identifying disability events under user-specified criteria, allowing
    adaptation to different study designs and endpoints. Tools are included
    to facilitate transparent and reproducible reporting of the settings
    used in the analysis. For an introduction to the package and illustrative
    applications, see Montobbio et al. (2024) <doi:10.1177/13524585241243157>.
Given the maximum available sample size (N) for an experiment, and the target levels of Type I 
             and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT 
             the package can also obtain its operating characteristics and implement the test for a given 
             sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. 
             The proposed test has shown evidence of reducing the average sample size required to perform 
             statistical hypothesis tests at specified levels of significance and power. Currently, the package
             implements one-sample proportion tests, one and two-sample z tests, and one and two-sample 
             t tests. A brief user guidance for this package is provided below. One can also refer to the 
             supplemental information for the same.
Perform library searches against electron ionization mass spectral
    databases using either the API provided by 'MS Search' software
    (<https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nistlibs>) or
    custom implementations of the Identity and Similarity algorithms.
A collection of helper functions for analyzing Second Primary Cancer data, 
    including functions to reshape data, to calculate patient states and analyze cancer incidence.
Tools that extend the functionality of the 'RODBC' package to work
  with Microsoft SQL Server databases. Makes it easier to browse the database
  and examine individual tables and views.
Constructs trees for multivariate survival data using marginal and frailty models.
    Grows, prunes, and selects the best-sized tree.
Provides a suite of mixed-integer linear programming (MILP) model builders and solvers—including 'Gurobi', 'HiGHS', 'Symphony', 'GNU Linear Programming Kit (GLPK)', and 'lpSolve'—for automated test assembly (ATA) in multistage testing (MST). Offers filtering of decision variables through item–module eligibility and the application of explicit bounds to simplify the MILP model and accelerate the optimization process. Supports bottom up, top down, and hybrid assembly strategies; enemy-item and enemy-stimulus exclusions; stimulus all in/all out or partial selection; anchor item/stimulus specification; and item exposure control. Accommodates both single-objective and multi-objective optimization ('weighted sum', 'maximin', 'capped maximin', 'minimax', and 'goal programming'). Enables simultaneous assembly of multiple panels with item and stimulus content balancing and exposure control. Provides analytical evaluation of assembled MST performance within seconds. Includes tools for diagnosing infeasible optimization models by systematically identifying sources of infeasibility and reformulating models with slack variables to restore feasibility.Methods implemented in this package build on established work in optimal test assembly (van der Linden, 2005 <doi:10.1007/0-387-29054-0>), item-set constrained test assembly (van der Linden, 2000 <doi:10.1177/01466210022031697>), hybrid assembly (Xiong, 2018 <doi:10.1177/0146621618762739>), recursion-based analytic methods (Lim et al., 2021 <doi:10.1111/jedm.12276>), and classification evaluation (Rudner, 2000 <doi:10.7275/an9m-2035>; Rudner, 2005 <doi:10.7275/56a5-6b14>).
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.
This is a tool for epidemiologist, medical data analyst, 
    medical or public health professionals. It contains three domains of functions:
    1) data management, 2) statistical analysis and 3) calculating 
    epidemiological measures.
Implements a minimum-spanning-tree-based heuristic for k-means clustering using a union-find disjoint set and the algorithm in Kruskal (1956) <doi:10.1090/S0002-9939-1956-0078686-7>.
A collection of statistical tests for the detection of differential
    item functioning (DIF) in multistage tests. Methods entail logistic regression,
    an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests.
    The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform 
    DIF effects are available depending on which method is used.
A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.
Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2025_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20251014.pdf> that can be used to identify the number of regimes in Markov switching models.
Software to aid in modeling and analyzing mass-spectrometry-based
    proteome melting data. Quantitative data is imported and normalized and
    thermal behavior is modeled at the protein level. Methods exist for
    normalization, modeling, visualization, and export of results. For a
    general introduction to MS-based thermal profiling, see Savitski et al.
    (2014) <doi:10.1126/science.1255784>.
Implements the MST-kNN clustering algorithm proposed by
    Inostroza-Ponta (2008) <https://trove.nla.gov.au/work/28729389>.
    The algorithm determines the number of clusters automatically by
    recursively intersecting the Minimum Spanning Tree (MST) and the
    k-Nearest Neighbor (kNN) proximity graphs constructed from a pairwise
    distance matrix.  The value of k is selected via a connectivity
    criterion (the smallest k such that the kNN graph is connected,
    bounded by floor(log(n))).  The package requires only a distance
    matrix as input and returns cluster assignments, an 'igraph' network,
    and partition metadata.
Distance between multivariate t distributions, as presented by N. Bouhlel and D. Rousseau (2023) <doi:10.1109/LSP.2023.3324594>.
Estimators for multivariate symmetrical uncertainty based
    on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>,
    total correlation, information gain and symmetrical uncertainty of
    categorical variables.
Defines colour palettes and themes for Michigan State
  University (MSU) publications and presentations. Palettes and
  themes are supported in both base R and 'ggplot2' graphics, and are intended
  to provide consistency between those creating documents and presentations.
Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).
Functions for metabolomics data analysis: data preprocessing, 
  orthogonal signal correction, PCA analysis, PCA-DA analysis, 
	PLS-DA analysis, classification, feature selection, correlation 
	analysis, data visualisation and re-sampling strategies.
Implements survival analyses across multiple abundance thresholds,
    repeatedly partitioning samples into groups and evaluating survival
    differences to assess taxonomic associations with outcomes.
Build multiscalar territorial analysis based on various contexts.
Dealing with neutrosophic data in single valued form using score,
    accuracy and certainty functions to calculate ranks of Single Valued 
    Neutrosophic Set (SVNS), also to calculate the  Mann-Whitney test,
    and making a post-hoc test after rejecting the null hypothesis using the Neutrosophic
    Statistics Kruskal-Wallis test. For more information see
    Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022)
    <https://digitalrepository.unm.edu/nss_journal/vol51/iss1/60/>.
Provides helper functions to access datasets from the
    Metropolitan Transportation Authority (MTA) portion of the New York
    State Open Data platform <https://data.ny.gov/>. Returns results as
    tidy tibbles with support for optional filtering, sorting, and row
    limits through the Socrata API.
Perform multi-trait rare-variant association tests using the summary statistics and adjust for possible sample overlap. Package is based on "Multi-Trait Analysis of Rare-Variant Association Summary Statistics using MTAR" by Luo, L., Shen, J., Zhang, H., Chhibber, A. Mehrotra, D.V., Tang, Z., 2019 (submitted).
Estimation, inference and forecasting using the Bayesian approach for multivariate threshold autoregressive (TAR) models in which the distribution used to describe the noise process belongs to the class of Gaussian variance mixtures.
The purpose of this package is to share a collection of functions the author wrote during weekends for managing
    kitchen and garden tasks, e.g. making plant growth charts or Thanksgiving kitchen schedule charts, etc. 
    Functions might include but not limited to:
    (1) aiding summarizing time related data; 
    (2) generating axis transformation from data; and
    (3) aiding Markdown (with html output) and Shiny file editing.
Implements Mander & Thompson's (2010) 
    <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs
    optimal under the alternative hypothesis for phase II [cancer] trials.  Also
    provides an implementation of Simon's (1989) 
    <doi:10.1016/0197-2456(89)90015-9> original methodology and 
    allows exploration of the operating characteristics of sub-optimal designs.
Several robust estimators for linear regression and variable selection are provided. 
              Included are Maximum tangent likelihood estimator by Qin, et al., (2017), arXiv preprint <doi:10.48550/arXiv.1708.05439>, 
              least absolute deviance estimator and Huber regression. The penalized version of each of these 
              estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to 
              produce consistent estimates for both fixed and high-dimensional settings.
Functions for detecting multicollinearity. This test gives statistical support to two of the most famous methods for detecting multicollinearity in applied work: Klein’s rule and Variance Inflation Factor (VIF). See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2015) <doi:10.33333/rp.vol51n2.05>.
Auto-downloads Parquet data from the 'MTGJSON' CDN and exposes the
    full Magic: The Gathering dataset through R6-based query interfaces backed
    by 'DuckDB'.
Calculate Sample Size and Power for
    Association Studies Involving Mitochondrial DNA Haplogroups.
    Based on formulae by Samuels et al. AJHG, 2006. 78(4):713-720. <DOI:10.1086/502682>.
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>.
An implementation of Multi-Task Logistic Regression (MTLR) for R. 
  This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves
  by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.
Simultaneous multiple outcomes prediction based on revised stacking algorithms, which enables the integration of information from predictions of individual models. An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019)  Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" <doi:10.1093/bioinformatics/btz531>.
Estimation of treatment hierarchies in network meta-analysis using a novel frequentist approach based on treatment choice criteria (TCC) and probabilistic ranking models, as described by Evrenoglou et al. (2024) <DOI:10.48550/arXiv.2406.10612>. The TCC are defined using a rule based on the smallest worthwhile difference (SWD). Using the defined TCC, the NMA estimates (i.e., treatment effects and standard errors) are first transformed into treatment preferences, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). These treatment preferences are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatments ability to outperform all the other competing treatments in the network. Here the terms ability to outperform indicates the propensity of each treatment to yield clinically important and beneficial effects when compared to all the other treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list.
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
Implementation of Multidimensional Top Scoring
    method for creativity assessment proposed in
    Boris Forthmann, Maciej Karwowski, Roger E. Beaty (2023) <doi:10.1037/aca0000571>.
This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
Access the 'Red List of Montane Tree Species of the Tropical Andes' Tejedor Garavito et al.(2014, ISBN:978-1-905164-60-8). This package allows users to search for globally threatened tree species within the andean montane forests, including cloud forests and seasonal (wet) forests above 1500 m a.s.l.
Mahalanobis-Taguchi (MT) system is a collection of multivariate
    analysis methods developed for the field of quality engineering. MT system
    consists of two families depending on their purpose. One is a family of
    Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see
    Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and
    Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a
    family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y.
    (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods
    for the family of MT methods and one basic method for the family of T
    methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi
    Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the
    MT method and the two-sided Taguchi (T1) method is for the family of T
    methods. In addition, the Ta and Tb methods, which are the improved versions
    of the T1 method, are included.
Counting process structure is fundamental to model time varying covariates.
    This package restructures dataframes in the counting process format for one or more variables.
    F. W. Dekker, et al. (2008) <doi:10.1038/ki.2008.328>.
Generates mid upper arm circumference (MUAC) and body mass index (BMI) 
            for age z-scores and percentiles based on LMS method for children and 
            adolescents up to 19 years that can be used to assess nutritional and health status and
            define risk of adverse health events.
Nonparametric approach to estimate the location of block boundaries (change-points) of 
    non-overlapping blocks in a random symmetric matrix which consists of random variables whose 
    distribution changes from block to block.
    BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) <doi:10.1016/j.jmva.2017.12.005>.
A data package containing public domain information on requests made by the
             'MuckRock' (https://www.muckrock.com/) project under the United States
             Freedom of Information Act.
Nonparametric unfolding item response theory (IRT) model for dichotomous data (see W.H. Van Schuur (1984). Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method.
Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.
Multi-Fidelity emulator for data from computer simulations of the
    same underlying system but at different input locations and fidelity level,
    where both the input locations and fidelity level can be continuous. Active
    Learning can be performed with an implementation of the Integrated Mean Square 
    Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, <doi:10.48550/arXiv.2503.23158>).
We develop Multi-source Graph Synthesis (MUGS), an algorithm designed to create 
    embeddings for pediatric Electronic Health Record (EHR) codes by leveraging graphical information from three distinct sources:
    (1) pediatric EHR data, (2) EHR data from the general patient population, and 
    (3) existing hierarchical medical ontology knowledge shared across different patient populations. 
    See Li et al. (2024) <doi:10.1038/s41746-024-01320-4> for details.
Produces a smooth estimate of the hazard
  function for censored data.
Provides access to 'MUI X Data Grid', a fast and extensible React
  data table and React data grid, with filtering, sorting, pagination,
  and more. Bundles the MIT-licensed community edition of the
  '@mui/x-data-grid' JavaScript library (the commercial 'Pro' and
  'Premium' tiers are not included).
Wraps the 'Material UI' React components <https://mui.com/> for use in R, 'shiny'
  applications and 'quarto' documents, including inputs, layouts, navigation, and
  surfaces. All inputs come with R usage examples.
A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.
Give access to 'MUI X Tree View' components, which lets users navigate hierarchical 
  lists of data with nested levels that can be expanded and collapsed.
Background - Traditional gene set enrichment analyses are 
    typically limited to a few ontologies and do not account for the 
    interdependence of gene sets or terms, resulting in overcorrected p-values. 
    To address these challenges, we introduce mulea, an R package offering 
    comprehensive overrepresentation and functional enrichment analysis. 
    Results - mulea employs a progressive empirical false discovery rate 
    (eFDR) method, specifically designed for interconnected biological data, 
    to accurately identify significant terms within diverse ontologies. mulea 
    expands beyond traditional tools by incorporating a wide range of 
    ontologies, encompassing Gene Ontology, pathways, regulatory elements, 
    genomic locations, and protein domains. This flexibility enables 
    researchers to tailor enrichment analysis to their specific questions, 
    such as identifying enriched transcriptional regulators in gene expression 
    data or overrepresented protein domains in protein sets. To facilitate 
    seamless analysis, mulea provides gene sets (in standardised GMT format) 
    for 27 model organisms, covering 22 ontology types from 16 databases and 
    various identifiers resulting in almost 900 files. Additionally, the 
    muleaData ExperimentData Bioconductor package simplifies access to these 
    pre-defined ontologies. Finally, mulea's architecture allows for easy 
    integration of user-defined ontologies, or GMT files from external 
    sources (e.g., MSigDB or Enrichr), expanding its applicability across 
    diverse research areas. Conclusions - mulea is distributed as a CRAN R 
    package. It offers researchers a powerful and flexible toolkit for 
    functional enrichment analysis, addressing limitations of traditional 
    tools with its progressive eFDR and by supporting a variety of ontologies. 
    Overall, mulea fosters the exploration of diverse biological questions 
    across various model organisms.
This is a companion to the book Cook, D. and Laa, U. (2023) <https://dicook.github.io/mulgar_book/>
  "Interactively exploring high-dimensional data and models in R".  
  by Cook and Laa. It contains useful functions for processing data in preparation for 
  visualising with a tour. There are also several sample data sets.
Allows the user to create graphs with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized.
    Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>.
    More about multilayered graphs and their usage can be found in our review paper:
    Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.
A set of functions for some multivariate analyses utilizing a
             structural equation modeling (SEM) approach through the 'OpenMx' package.
             These analyses include canonical correlation analysis (CANCORR),
             redundancy analysis (RDA), and multivariate principal component regression (MPCR).
             It implements procedures discussed in Gu and Cheung (2023) <doi:10.1111/bmsp.12301>,
             Gu, Yung, and Cheung (2019) <doi:10.1080/00273171.2018.1512847>, and
             Gu et al. (2023) <doi:10.1080/00273171.2022.2141675>.
Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, 'mulset' can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.
Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.
A comprehensive and computationally fast framework to analyze high dimensional data associated with an experimental design based on Multiple ANOVAs (MultANOVA). It includes testing the overall significance of terms in the model, post-hoc analyses of significant terms and variable selection. Details may be found in Mahieu, B., & Cariou, V. (2025). MultANOVA Followed by Post Hoc Analyses for Designed High‐Dimensional Data: A Comprehensive Framework That Outperforms ASCA, rMANOVA, and VASCA. Journal of Chemometrics, 39(7). <doi:10.1002/cem.70039>.
Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.
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>.
Computes a finite-sample tail bound for the log-likelihood ratio test
    (LRT) statistic under multinomial sampling. The resulting bound is used to compute
    finite-sample conservative p-values and critical values when the standard 
    chi-squared asymptotics can be unreliable. The package also supports multiple 
    independent multinomial trials.
Simultaneous tests and confidence intervals
  for general linear hypotheses in parametric models, including 
  linear, generalized linear, linear mixed effects, and survival models.
  The package includes demos reproducing analyzes presented
  in the book "Multiple Comparisons Using R" (Bretz, Hothorn, 
  Westfall, 2010, CRC Press).
Convert a logical vector or a vector of p-values or a correlation,
    difference, or distance matrix into a display identifying the pairs for
    which the differences were not significantly different. Designed for use
    in conjunction with the output of functions like TukeyHSD, dist (stats),
    simint, simtest, csimint, csimtest (multcomp), friedmanmc, kruskalmc (pgirmess).
Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.
Implements contamination bias diagnostics and alternative
    estimators for regressions with multiple treatments. The implementation is
    based on Goldsmith-Pinkham, Hull, and Kolesár (2024)
    <doi:10.48550/arXiv.2106.05024>.
Equivalence tests and related confidence intervals for the
 comparison of two treatments, simultaneously for one or many normally
 distributed, primary response variables (endpoints). The step-up
 procedure of Quan et al. (2001) is both applied for differences and
 extended to ratios of means. A related single-step procedure is also
 available.
Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints.
    The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative
    hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate
    permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated 
    that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal
    significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved
    by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple
    testing procedures.
GEE solver for correlated nominal or ordinal multinomial responses
    using a local odds ratios parameterization.
Provides a multi action button for usage in 'shiny'
    applications.
The base apply function and its variants, as well as the related
    functions in the 'plyr' package, typically apply user-defined functions to a
    single argument (or a list of vectorized arguments in the case of mapply). The
    'multiApply' package extends this paradigm with its only function, Apply, which
    efficiently applies functions taking one or a list of multiple unidimensional
    or multidimensional arrays (or combinations thereof) as input. The input
    arrays can have different numbers of dimensions as well as different dimension
    lengths, and the applied function can return one or a list of unidimensional or
    multidimensional arrays as output. This saves development time by preventing the
    R user from writing often error-prone and memory-inefficient loops dealing with
    multiple complex arrays. Also, a remarkable feature of Apply is the transparent
    use of multi-core through its parameter 'ncores'. In contrast to the base apply
    function, this package suggests the use of 'target dimensions' as opposite
    to the 'margins' for specifying the dimensions relevant to the function to be
    applied.
Efficient finite difference method for valuing European and American multi-asset options.
Package for estimating, analyzing, and forecasting multi-country macro-finance affine term structure models (ATSMs). All setups build on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions by Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (2024, JFEC) <doi:10.1093/jjfinec/nbae008> are also available. The package also provides tools for bias correction as in Bauer Rudebusch and Wu (2012, JBES) <doi:10.1080/07350015.2012.693855>, bootstrap analysis, and several graphical/numerical outputs.
Computationally efficient functions to provide direct likelihood-based
    inference for partially-observed multivariate birth-death processes.  Such processes
    range from a simple Yule model to the complex susceptible-infectious-removed model
    in disease dynamics.  Efficient likelihood evaluation facilitates maximum likelihood
    estimation and Bayesian inference.
Quantify exposure-outcome causal effects
    with adjustment for multiple biases. The functions can
    simultaneously adjust for any combination of uncontrolled confounding,
    exposure/outcome misclassification, and selection bias.
    The underlying method generalizes the combination of inverse
    probability of selection weighting with predictive value weighting.
    Simultaneous multi-bias analysis can be used to enhance the validity
    and transparency of real-world evidence obtained from observational,
    longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.
Meta-analyses can be compromised by studies' internal biases (e.g.,
  confounding in nonrandomized studies) as well as by publication bias. This
  package conducts sensitivity analyses for the joint effects of these biases
  (per Mathur (2022) <doi:10.31219/osf.io/u7vcb>). These sensitivity analyses 
  address two questions: (1) For a given severity of internal bias across
  studies and of publication bias, how much could the results change?; and
  (2) For a given severity of publication bias, how severe would internal bias
  have to be, hypothetically, to attenuate the results to the null or by a given
  amount?
Provides a GUI with which users can construct and interact
        with Multibiplot Analysis.
Functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) 
   "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". 
   This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting 
   functions, several real data sets and six vignettes covering a range different applications.
Flexible implementation of a structural change point detection algorithm for multivariate time series.
    It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system.
    Bai, Lumsdaine, and Stock (1998) <doi:10.1111/1467-937X.00051>.
Evaluate hypotheses concerning the distribution of multinomial
  proportions using bridge sampling. The bridge sampling routine is able to
  compute Bayes factors for hypotheses that entail inequality constraints,
  equality constraints, free parameters, and mixtures of all three. These
  hypotheses are tested against the encompassing hypothesis, that all parameters
  vary freely or against the null hypothesis that all category proportions are equal.
  For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.
Implements a generalization of the Cochran-Armitage trend test to
    multinomial data. In addition to an overall test, multiple testing adjusted
    p-values for trend in individual outcomes and power calculation is
    available.
Provides a basic interface for accessing annotation data from
   the Multi-CAST collection, a database of spoken natural language texts
   edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
   diverse set of languages and has been annotated across multiple levels.
   Annotation data is downloaded on request from the servers of the
   University of Bamberg. See the Multi-CAST website
   <https://multicast.aspra.uni-bamberg.de/> for more information and a list
   of related publications.
Implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, 
    with a focus on multivariate repeated measures and functional data. The package provides two approaches: 
    (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture 
    nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth 
    functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance 
    operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. 
    The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional 
    representations for exploratory analysis and visualization of dependence structures across multiple feature sets. 
    The implementation follows the framework developed in Górecki, Krzyśko, Gnettner and Kokoszka (2025) 
    <doi:10.48550/arXiv.2510.04457>.
Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) <doi:10.1348/000711005X64817>, determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values.
A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret.  For complex problems RF performs better.  Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...).  Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model.  We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) <doi:10.1093/bioinformatics/btab088>.
Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029).
A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.
We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.
The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from <http://tamivox.org/dave/multinomial/index.html> and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here <https://jeromekelleher.net/category/combinatorics.html>. The C++ code and Python code are distributed without conditions.
Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Ilmarinen et al. (2023) <doi:10.1177/08902070221088155>. Deconstructing difference score correlations (e.g., gender-equality paradox), see Ilmarinen & Lönnqvist (2024) <doi:10.1037/pspp0000508>.
    Includes also tools that help in understanding difference score reliability, conditional intra-class correlations, tail-dependency, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.
Performs multi-omic differential network 
      analysis by revealing differential interactions between molecular entities
      (genes, proteins, transcription factors, or other biomolecules) across the
      omic datasets provided. 
      For each omic dataset, a differential network is constructed where
      links represent statistically significant differential interactions between 
      entities. These networks are then integrated into a comprehensive visualization
      using distinct colors to distinguish interactions from different omic layers.
      This unified display allows interactive exploration of cross-omic
      patterns, such as differential interactions present at both transcript and
      protein levels. For each link, users can access differential statistical 
      significance metrics (p values or adjusted p values, calculated via robust or 
      traditional linear regression with interaction term) and differential regression
      plots.  
      The methods implemented in this package are described in Sciacca et al. (2023)
      <doi:10.1093/bioinformatics/btad192>.
Deploy file changes across multiple 'GitHub'
    repositories using the 'GitHub' 'Web API' <https://docs.github.com/en/rest>.
    Allows synchronizing common files, Continuous Integration ('CI') workflows,
    or configurations across many repositories with a single command.
Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.
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>.
Multi-criteria design of experiments algorithm that
    simultaneously optimizes up to six different criteria ('I', 'Id', 'D', 'Ds', 'A'
    and 'As'). The algorithm finds the optimal Pareto front and, if
    requested, selects a possible symmetrical design on it. The
    symmetrical design is selected based on two techniques: minimum
    distance with the Utopia point or the 'TOPSIS' approach.
Partition a data frame across multiple worker processes to
    provide simple multicore parallelism.
An implementation for multivariate functional additive mixed
    models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse 
    functional regression models and multivariate functional principal component
    analysis. This package contains the function to run a multiFAMM and some
    convenience functions useful when working with large models. An additional 
    package on GitHub contains more convenience functions to reproduce the 
    analyses of the corresponding paper 
    (<https://github.com/alexvolkmann/multifammPaper>).
The provided package implements multiple contrast tests for functional data (Munko et al., 2023, <arXiv:2306.15259>). These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.
A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos (2022) <doi:10.1016/j.brat.2022.104072>. Since November 2025, this package contains code from the 'ez' R package (Copyright (c) 2016-11-01, Michael A. Lawrence <mike.lwrnc@gmail.com>), originally distributed under the 'GPL' (equal and above 2) license.
Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022.
Methods for the analysis of how ecological drivers affect the
    multifunctionality of an ecosystem based on methods of Byrnes et al. 
    2016 <doi:10.1111/2041-210X.12143> and Byrnes et al. 
    2022 <doi:10.1101/2022.03.17.484802>. Most standard
    methods in the literature are implemented (see vignettes) in a tidy
    format.
Read a table of fixed width formatted data of different types into
    a data.frame for each type.
Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2022), <arXiv:2208.14721>.
Functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options.
Methods and models for analysing multigraphs as introduced by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit tests.
Grey model is commonly used in time series forecasting when statistical assumptions are violated with a limited number of data points. The minimum number of data points
             required to fit a grey model is four observations. This package fits Grey model of First order and One Variable, i.e., GM (1,1) for multivariate time series data and returns
             the parameters of the model, model evaluation criteria and h-step ahead forecast values for each of the time series variables. For method details see, Akay, D. and Atak, M. (2007) <DOI:10.1016/j.energy.2006.11.014>,
             Hsu, L. and Wang, C. (2007).<DOI:10.1016/j.techfore.2006.02.005>.
Model infectious disease dynamics in populations 
    with multiple subgroups having different vaccination rates, transmission
    characteristics, and contact patterns. Calculate final and intermediate
    outbreak sizes, form age-structured contact models with automatic fetching
    of U.S. census data, and explore vaccination scenarios with an interactive
    'shiny' dashboard for a model with two subgroups, as described in Nguyen et
    al. (2024) <doi:10.1016/j.jval.2024.03.039> and Duong et al. (2026)
    <doi:10.1093/ofid/ofaf695.217>.
Two method new of multigroup and simulation of data.
  The first technique called multigroup PCA (mgPCA) this multivariate exploration approach
  that has the idea of considering the structure of groups and / or different types
  of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality
  Reduction (MDR) it is another multivariate exploration method that is based on
  projections. In addition, a method called Single Dimension Exploration (SDE) was
  incorporated for to analyze the exploration of the data. It could help us in a better
  way to observe the behavior of the multigroup data with certain variables of interest.
It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The 'MultiGroupSequential' package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the graphical procedure proposed by Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>. Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013).
Wrapper around the Unix join facility which is more efficient than the built-in R routine merge().
 The package enables the joining of multiple files on disk at once. 
 The files can be compressed and various filters can be deployed before joining.
 Compiles only under Unix.
Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d data. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. For longitudinal data, we also develop the GEE estimator to incorporate the within-subject correlations.  A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, ``Wei Zhong, Chuang Wan and Wenyang Zhang (2022). Estimation and inference for multikink quantile regression, JBES'' and ``Chuang Wan, Wei Zhong, Wenyang Zhang and Changliang Zou (2022). Multi-kink quantile regression for longitudinal data with application to progesterone data analysis, Biometrics".
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.
Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations.
Implement Bayesian multilevel modelling for compositional data. 
             Compute multilevel compositional data and 
             perform log-ratio transforms at between and within-person levels, 
             fit Bayesian multilevel models for compositional predictors and outcomes, 
             and run post-hoc analyses such as isotemporal substitution models.
             References: 
             Le, Stanford, Dumuid, and Wiley (2025) <doi:10.1037/met0000750>,
             Le, Dumuid, Stanford, and Wiley (2025) <doi:10.1080/00273171.2025.2565598>.
The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for
 multilevel mediation, the option of a moderating variable for either the a, b,
 or both paths, and covariates. Currently the 1-1-1 model is supported
 and several options of random effects; the initial code for bootstrapping was
 evaluated in simulations by Falk, Vogel, Hammami, and Miočević (2024) <doi:10.3758/s13428-023-02079-4>.
 Support for Bayesian estimation using 'brms' comprises ongoing work. Currently
 only continuous mediators and outcomes are supported. Factors for any
 predictors must be numerically represented.
Bindings for hierarchical regression models for use with the
    'parsnip' package. Models include longitudinal generalized linear
    models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and
    mixed-effect models (Pinheiro and Bates)
    <doi:10.1007/978-1-4419-0318-1_1>.
Implements a regularized Bayesian estimator that optimizes the estimation
 of between-group coefficients for multilevel latent variable models by minimizing
 mean squared error (MSE) and balancing variance and bias. The package provides more reliable
 estimates in scenarios with limited data, offering a robust solution for accurate
 parameter estimation in two-level latent variable models. It is designed for
 researchers in psychology, education, and related fields who face challenges in
 estimating between-group effects under small sample sizes and low intraclass
 correlation coefficients. The package includes comprehensive S3 methods for result
 objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(),
 dim(), length(), names(), and update() for enhanced usability and integration
 with standard R workflows. Dashuk et al. (2025a) <doi:10.1017/psy.2025.10045>
 derived the optimal regularized Bayesian estimator;
 Dashuk et al. (2025b) <doi:10.1007/s41237-025-00264-7> extended it to 
 the multivariate case; and Luedtke et al. (2008) <doi:10.1037/a0012869>
 formalized the two-level latent variable framework.
Conducts and visualizes propensity score analysis for
    multilevel, or clustered data. Bryer & Pruzek (2011)
    <doi:10.1080/00273171.2011.636693>.
Effect sizes, diagnostics and performance metrics for
  multilevel and mixed effects models.
  Includes marginal and conditional 'R2' estimates for linear mixed effects models
  based on Johnson (2014) <doi:10.1111/2041-210X.12225>.
Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. For more technical details, see Lyrvall et al. (2025) <doi:10.1080/00273171.2025.2473935>.
Provides install functions of other languages 
             such as 'java', 'python'.
Implementation of the methodology of Aleshin-Guendel & Sadinle (2022) <doi:10.1080/01621459.2021.2013242>. It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates.
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>.
Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.
Estimates key quantities in causal mediation analysis - including 
    average causal mediation effects (indirect effects), average direct 
    effects, total effects, and proportions mediated - in the presence of 
    multiple uncausally related mediators. Methods are described by 
    Jerolon et al., (2021) <doi:10.1515/ijb-2019-0088> and extended to 
    accommodate survival outcomes as described by Domingo-Relloso et al., 
    (2024) <doi:10.1101/2024.02.16.24302923>.
A set of functions which use the Expectation Maximisation (EM) 
    algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) 
    <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from 
    incomplete data via the EM algorithm, Journal of the Royal Statistical 
    Society, 39(1), 1--22) to take a finite mixture model approach to 
    clustering. The package is designed to cluster multivariate data that have 
    categorical and continuous variables and that possibly contain missing 
    values. The method is described in Hunt, L. and Jorgensen, M. (1999) 
    <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 
    41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) 
    <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data 
    with missing information, Computational Statistics & Data Analysis, 41(3-4), 
    429--440.
Fast Bayesian probability estimation for multimodal categorical 
    data using speed-optimized Markov chain Monte Carlo (MCMC) implementation 
    (Metropolis-Hastings-within-partial-Gibbs).
    The package provides efficient algorithms for detecting subpopulations, estimating
    mixture components, and assigning observations to subgroups with probability estimates.
    The methods are described in Dioszegi, G. et al. (2026) "Automatic Bayesian Mixture 
    Modeling for Multimodal Categorical Data via Integrated Mode Detection and 
    Metropolis-Hastings-within-Gibbs Sampling" (submitted to Journal of Statistical Software).
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
Provides methods for quantifying the information gain contributed by individual 
	modalities in multimodal regression models. Information gain is measured using Expected 
	Relative Entropy (ERE) or pseudo-R² metrics, with corresponding p-values and confidence 
	intervals. Currently supports linear and logistic regression models with plans for 
	extension to additional Generalized Linear Models and Cox proportional hazard model.
Process 'OpenPose' human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA 'NewsScape' archive.
Identifying comorbidities, frailty, and multimorbidity in claims 
    and administrative data is often a duplicative process.
    The functions contained in this package are meant to first prepare the data to a format
    acceptable by all other packages, then provide a uniform and simple approach to
    generate comorbidity and multimorbidity metrics based on these claims data. The package
    is ever evolving to include new metrics, and is always looking for new measures to include.
    The citations used in this package include the following publications: 
    Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>,
    Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>,
    Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>,
    Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>,
    Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>,
    Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>,
    Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>,
    Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>,
    Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.
Model fitting and simulation for Gaussian and logistic inner 
    product MultiNeSS models for multiplex networks. The package implements 
    a convex fitting algorithm with fully adaptive parameter tuning, 
    including options for edge cross-validation. For more details see 
    MacDonald et al. (2020).
Functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.
Analyze multilevel networks as described in Lazega et al (2008)
    <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders 
    (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an 
    extension to 'igraph'.
Network meta-analysis and network meta-regression models for 
    aggregate data, individual patient data, and mixtures of both individual 
    and aggregate data using multilevel network meta-regression as described by
    Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a
    Bayesian framework using 'Stan'.
Simulating data and fitting multi-species N-mixture models using 'nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) <doi:10.1007/s10651-022-00542-7>. Royle, J. A. (2004) <doi:10.1111/j.0006-341X.2004.00142.x>.
Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) <DOI:10.1007/s11634-023-00547-5>. These models are estimated under  a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>. In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>.
Implements Gibbs sampling and Bayes factors for multinomial models with
    linear inequality constraints on the vector of probability parameters. As
    special cases, the model class includes models that predict a linear order 
    of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models 
    assuming that the parameter vector p must be inside the convex hull of a 
    finite number of predicted patterns (i.e., vertices). A formal definition of 
    inequality-constrained multinomial models and the implemented computational
    methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). 
    Multinomial models with linear inequality constraints: Overview and improvements 
    of computational methods for Bayesian inference. Journal of Mathematical 
    Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>.
    Inequality-constrained multinomial models have applications in the area of 
    judgment and decision making to fit and test random utility models  
    (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of 
    preferences. Psychological Review, 118, 42–56, <doi:10.1037/a0021150>) or to 
    perform outcome-based strategy classification to select the decision strategy 
    that provides the best account for a vector of observed choice frequencies 
    (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information 
    processing to decisions: Formalizing and comparing probabilistic choice models. 
    Cognitive Psychology, 96, 26–40. <doi:10.1016/j.cogpsych.2017.05.003>).
Provides implementations of functions that can be used to
    test multivariate integration routines. The package covers six
    different integration domains (unit hypercube, unit ball, unit sphere,
    standard simplex, non-negative real numbers and R^n). For each domain
    several functions with different properties (smooth,
    non-differentiable, ...) are available. The functions are available in
    all dimensions n >= 1. For each function the exact value of the
    integral is known and implemented to allow testing the accuracy of
    multivariate integration routines. Details on the available test
    functions can be found at on the development website.
Compendium of the most representative algorithms in print---vector-valued dynamic programming, linear programming, policy iteration, the weighting factor approach---for solving 
	     multi-objective Markov decision processes, with or without reward discount, over a finite or infinite horizon. Mifrani, A. (2024) <doi:10.1007/s10479-024-06439-x>; Mifrani, A. & Noll, D. <doi:10.48550/arXiv.2502.13697>; Wakuta, K. (1995) <doi:10.1016/0304-4149(94)00064-Z>.
Matching algorithm based on network-flow structure. 
  Users are able to modify the emphasis on three different 
  optimization goals: two different distance measures and 
  the number of treated units left unmatched. The method is proposed by Pimentel
  and Kelz (2019) <doi:10.1080/01621459.2020.1720693>.
  The 'rrelaxiv' package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.
Spatio-temporal multivariate occupancy models can handle multiple species in occupancy models.  This method for fitting such models is described in Hepler and Erhardt (2021) "A spatiotemporal model for multivariate occupancy data".
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.
Tools to create a layout for figures made of multiple panels, and
    to fill the panels with base, 'lattice', 'ggplot2' and 'ComplexHeatmap'
    plots, grobs, as well as content from all image formats supported by
    'ImageMagick' (accessed through 'magick').
Performs genetic association tests between SNPs
        (one-at-a-time) and multiple phenotypes (separately or in joint
        model).
The iterative procedure estimates structural changes in the success probability of Bernoulli variables. It estimates the number and location of the breakpoints as well as the success probability of the different sequences between the breakpoints. In addition, it provides a graphical illustration of the result.
Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.
Build CPMs (cumulative probability models, also known as cumulative link models) to account for detection limits (both single and multiple detection limits) in response variables. Conditional quantiles and conditional CDFs can be calculated based on fitted models. The package implements methods described in Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E. (2022). "Addressing Detection Limits with Semiparametric Cumulative Probability Models". <arXiv:2207.02815>.
Provides tools to perform interrupted-time series through a generalised least squares (GLS) framework on linear outcomes. Allows for multiple interventions and a control with ARMA (autoregressive and moving-average) correction. For more details see Lopez Bernal, Cummins, and Gasparrini (2017) <doi:10.1093/ije/dyw098>.
Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints 
  Stoer NC and Samuelsen SO (2016) <doi:10.32614/rj-2016-030>.
Fits generalized linear models, Cox proportional-hazards
    models, log-rank tests, generalized estimating equations, mixed models
    with repeated measures, Kaplan-Meier curves, quantile differences, and
    hierarchical net-benefit (win-difference) and log win-ratio statistics
    jointly across multiple endpoints, and returns the full asymptotic
    covariance matrix linking them. Implements PATED (Prognostic Assisted
    Treatment Effect Detection), a randomized-trial method that exploits
    balanced prognostic covariates to tighten standard errors and increase
    statistical power without introducing bias.
Tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.
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.
Algebraic procedures for analyses of multiple social networks are delivered with this 
	    package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. 'multiplex' makes 
	    possible, among other things, to create and manipulate multiplex, multimode, and 
	    multilevel network data with different formats. Effective ways are available to treat 
	    multiple networks with routines that combine algebraic systems like the partially ordered 
	    semigroup with decomposition procedures or semiring structures with the relational 
	    bundles occurring in different types of multivariate networks. 'multiplex' provides also 
	    an algebraic approach for affiliation networks through Galois derivations between families 
	    of the pairs of subsets in the two domains of the network with visualization options.
Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.
Various utilities to manipulate multivariate polynomials.  The
    package is almost completely superceded by the 'spray' and 'mvp' packages,
    which are much more efficient.
Implements likelihood-based estimation and diagnostics for multi-type recurrent
    event data with dynamic risk that depends on prior events and accommodates terminating events.
    Methods are described in Ghosh, Chan, Younes and Davis (2023) "A Dynamic Risk Model for Multitype Recurrent Events"
    <doi:10.1093/aje/kwac213>.
Provides a multiple-response chi-square framework for the analysis of contingency tables arising from multiple-response questionnaires, such as check-all-that-apply tasks, where response options are crossed with a known grouping factor. The framework accommodates within-block (e.g., within-subject) designs, as commonly encountered in sensory evaluation. It comprises a multiple-response chi-square test of homogeneity with an associated dimensionality test, a multiple-response Correspondence Analysis (CA), and per-cell multiple-response hypergeometric tests. These methods extend their classical counterparts by grounding inference in a null model that properly accounts for the multiple-response nature of the data, treating evaluations, rather than individual citations, as the experimental units, yielding more statistically valid conclusions than standard contingency table analyses. Details may be found in Mahieu, Schlich, Visalli, and Cardot (2021). <doi:10.1016/j.foodqual.2021.104256>.
We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) <doi:10.48550/arXiv.2506.18478>.
Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap. See Keyel and Wiegand
    (2016) <doi:10.1111/2041-210X.12558> for more details.
Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.
A flexible general-purpose toolbox for implementing Rescorla-Wagner models 
    in multi-armed bandit tasks. 
  As the successor and functional extension of the 'binaryRL' package, 
    'multiRL' modularizes the Markov Decision Process (MDP) into six core 
    components. This framework enables users to construct custom models via 
    intuitive if-else syntax and define latent learning rules for agents. 
  For parameter estimation, it provides both likelihood-based 
    inference (MLE and MAP) and simulation-based inference (ABC and 
    RNN), with full support for parallel processing across subjects. 
  The workflow is highly standardized, featuring four main functions 
    that strictly follow the four-step protocol (and ten rules) 
    proposed by Wilson & Collins (2019) <doi:10.7554/eLife.49547>. 
  Beyond the three built-in models (TD, RSTD, and Utility), users 
    can easily derive new variants by declaring which variables are 
    treated as free parameters.
Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.
Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.
Implementation of a next-generation, multi-stock age-structured fisheries assessment model. 
  'multiSA' is intended for use in mixed fisheries where stock composition can not be readily identified in 
  fishery data alone, e.g., from catch and age/length composition. Models can be fitted to genetic data, 
  e.g., stock composition of catches and close-kin pairs, with seasonal stock availability and movement.
Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Ilich et al. (2023) <doi:10.1111/tgis.13067>).
A tool for optimizing scales of effect when modeling ecological processes in space. Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2026) <doi:10.1007/s10980-025-02267-x>.
Provides tools for multiscale systematic conservation planning
    using the H3 hierarchical hexagonal grid system (Uber Technologies (2024)
    <https://h3geo.org>) and the 'prioritizr' package (Hanson et al. (2025)
    <doi:10.1111/cobi.14376>). Supports the definition and solution of
    conservation problems across nested H3 resolutions with resolution-specific
    features, costs, and management attributes, including cross-scale
    connectivity penalties derived from parent-child relationships. Also
    includes utilities to evaluate solutions using multiscale-aware diagnostics
    and to post-process optimization outputs into alternative area-targeted
    conservation scenarios.
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>.
Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.)
Functions to perform sensitivity analysis on a model with multivariate output.
The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and applying the model in the 'test set' (Solanes et al., Neuroimage 2023, 265:119800). This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes.
Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879>, Qin, Hong, Deutsch, and Bein (2019) <doi:10.1111/rssa.12446>, and Qin, Deutsch, and Hong (2021) <doi:10.1002/pam.22268>. It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias.
Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.
Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
Provides a unified framework for fitting, predicting, and
    interpreting nonlinear relationships in single-level, multilevel, and
    longitudinal regression models. Flexible functional forms are supported
    using natural cubic splines ('splines'), B-splines ('splines'), and GAM
    smooths ('mgcv'). Supports two-way and nested clustering via 'lme4',
    automatic knot selection by AIC or BIC, multilevel R-squared
    decomposition (Nakagawa-Schielzeth marginal and conditional R-squared
    with level-specific variance partitioning), a postestimation suite
    returning first and second derivatives with confidence bands, turning
    points and inflection regions, and a model comparison workflow
    contrasting linear, polynomial, and spline fits by AIC, BIC, and
    likelihood-ratio tests. Cluster heterogeneity in nonlinear effects is
    supported via random-slope spline terms.
Algorithms to build set partitions and commutator matrices and their use in the 
    construction of multivariate d-Hermite polynomials;  estimation and derivation of theoretical  vector moments and vector
    cumulants  of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to
    estimation and derivation of multivariate measures of skewness and kurtosis;  estimation and derivation of asymptotic 
    covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis.
    The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".
Estimate the causal effect of sustained treatment strategies on overall survival in  clinical trials with possible treatment crossover and switch to subsequent therapy. Simulate faithful longitudinal clinical trials data with survival endpoints and multi-way treatment switches allowing for time-dependent prognostic factors. For more on methodological background, please see: Keogh and colleagues (2021) <doi:10.1002/bimj.202000040> and Suarez and colleagues (2008) <doi:10.1016/j.jclinepi.2007.11.007>.
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates. This package is an implementation of the method described in D.J. Thomson (1982) "Spectrum estimation and harmonic analysis" <doi:10.1109/PROC.1982.12433>.
Run the same analysis over a range of arbitrary data processing 
    decisions. 'multitool' provides an interface for creating alternative 
    analysis pipelines and turning them into a grid of all possible pipelines. 
    Using this grid as a blueprint, you can model your data across all possible 
    pipelines and summarize the results.
Implements analytical methods for multidimensional plant traits, including Competitors-Stress tolerators-Ruderals strategy analysis using leaf traits, Leaf-Height-Seed strategy analysis, Niche Periodicity Table analysis, and Trait Network analysis. Provides functions for data analysis, visualization, and network metrics calculation. Methods are based on He et al. (2026) <doi:10.1002/ecog.08026>.
Provides utility functions for multivariate analysis (factor analysis, discriminant analysis, and others). The package is primary written for the course Multivariate analysis and for the course Computer intensive methods at the masters program of Applied Statistics at University of Ljubljana.
Simulate, estimate, and forecast vector autoregressive (VAR)
    models for multiple-subject data using structured penalization. Decomposes
    dynamics into shared (common) and subject-specific (unique) components via
    adaptive LASSO with FISTA optimization. Supports cross-validation and
    extended BIC model selection and subgroup detection, and time-varying
    parameters.
Distance multivariance is a measure of dependence which can be used to detect 
    and quantify dependence of arbitrarily many random vectors. The necessary functions are
    implemented in this packages and examples are given. It includes: distance multivariance, 
    distance multicorrelation, dependence structure detection, tests of independence and
    copula versions of distance multivariance based on the Monte Carlo empirical transform.
    Detailed references are given in the package description, as starting point for the 
    theoretic background we refer to:
    B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using 
    the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), 
    <doi:10.1515/stat-2020-0001>.
Package with multivariate analysis methodologies for experiment evaluation.
	 The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA,
	 principal components, canonical variables, etc. (Pacote com metodologias de analise
	 multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade,
	 construi de dendogramas, obtem a MANOVA,  componentes principais, variaveis canonicas, etc.)
Models and predicts multiple output features in single random forest considering the 
    linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.
With foundations on the work by Goutali and Chebana (2024) <doi:10.1016/j.envsoft.2024.106090>, this package contains various 
  univariate and multivariate trend tests. The main functions regard the Multivariate 
  Dependence Trend and Multivariate Overall Trend tests as proposed by Goutali 
  and Chebana (2024), as well as a plotting function that proves useful as a summary 
  and complement of the tests.
  Although many packages and methods carry univariate tests, the Mann-Kendall 
  and Spearman's rho test implementations are included in the package with an 
  adapted version to hydrological formulation (e.g. as in Rao and Hamed 1998 <doi:10.1016/S0022-1694(97)00125-X> or Chebana 2022 <doi:10.1016/C2021-0-01317-1>).
  For better understanding of the example use of the functions, three datasets 
  are included. These are synthetic data and shouldn't be used beyond that purpose.
Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.
A multivariate generalization of the emulator package.
Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016)
    <doi:10.1177/1745691616658637> to show the robustness of statistical inference. 
    'Multiverse analysis' is a philosophy of statistical reporting where paper authors 
    report the outcomes of many different statistical analyses in order to show how 
    fragile or robust their findings are. The 'multiverse' package 
    (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) 
    <doi:10.31219/osf.io/yfbwm> allows users to concisely and flexibly implement 'multiverse-style' 
    analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.
Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).
Computation of an estimation of the long-memory parameters and
     the long-run covariance matrix using a multivariate model
	 (Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
	 implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
	 approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>; Achard and Gannaz (2024) <doi:10.1111/jtsa.12719>). Real and complex wavelets are implemented.
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>].  Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.
Exports two functions implementing
    multi-way clustering using the method suggested by Cameron, Gelbach, &
    Miller (2011) and cluster (or block)
    bootstrapping for estimating variance-covariance matrices. Normal one and
    two-way clustering matches the results of other common statistical
    packages.  Missing values are handled transparently and rudimentary
    parallelization support is provided.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics.  Supported by grant NSF DMS 1712839.  See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.
In the case of multivariate ordinal responses, parameter estimates can be severely biased if personal response styles are ignored. This packages provides methods to account for personal response styles and to explain the effects of covariates on the response style, as proposed by Schauberger and Tutz 2021 <doi:10.1177/1471082X20978034>. The method is implemented both for the multivariate cumulative model and the multivariate adjacent categories model.
Dichotomous responses having two categories can be analyzed
    with stats::glm() or lme4::glmer() using the family=binomial option.
    Unfortunately, polytomous responses with three or more unordered
    categories cannot be analyzed similarly because there is no analogous
    family=multinomial option. For between-subjects data,
    nnet::multinom() can address this need, but it cannot handle random
    factors and therefore cannot handle repeated measures. To address this
    gap, we transform nominal response data into counts for each categorical 
    alternative. These counts are then analyzed using (mixed) Poisson regression
    as per Baker (1994) <doi:10.2307/2348134>. Omnibus analyses of variance can be 
    run along with post hoc pairwise comparisons. For users wishing to analyze nominal 
    responses from surveys or experiments, the functions in this package essentially 
    act as though stats::glm() or lme4::glmer() provide a family=multinomial option.
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.
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).
Multivariate generalized Gaussian distribution,
              Multivariate Cauchy distribution,
              Multivariate t distribution.
              Distance between two distributions (see N. Bouhlel and A. Dziri (2019): <doi:10.1109/LSP.2019.2915000>,
              N. Bouhlel and D. Rousseau (2022): <doi:10.3390/e24060838>,
              N. Bouhlel and D. Rousseau (2023): <doi:10.1109/LSP.2023.3324594>).
              Manipulation of these multivariate probability distributions.
              This package replaces 'mggd', 'mcauchyd' and 'mstudentd'.
Implementation of commonly used p-value-based and
        parametric multiple testing procedures (computation of adjusted
        p-values and simultaneous confidence intervals) and parallel
        gatekeeping procedures based on the methodology presented in
        the book "Multiple Testing Problems in Pharmaceutical
        Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
        Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.
Computation of the multivariate marine recovery index, including functions for data visualization and ecological diagnostics of marine ecosystems. The computational details are described in the original publication. Reference: Chauvel, N., Grall, J., Thiébaut, E., Houbin, C., Pezy, J.-P., 2026. A general-purpose multivariate marine recovery index (MUMARINEX) for quantifying the influence of human activities on benthic habitat ecological status. Ecological Indicators 188, 115002.
Tools for model selection and model averaging with support for a 
             wide range of statistical models. Automated model selection through
             subsets of the maximum model, with optional constraints for model 
             inclusion. Averaging of model parameters and predictions based on 
             model weights derived from information criteria (AICc and alike) 
             or custom model weighting schemes.
Tools that researchers can use to analyze untargeted metabolomics data generated using tandem mass spectroscopy from microbial communities. The overall approach taken to analyze metabolomics data parallels that used to analyze microbial communities using 16S rRNA gene sequencing data. Thus, we have a number of methods a user is able to use to generate data. Firstly, users can import Mass Spectrometry 1(MS1) data and filter it. Users are then able to match Mass Spectrometry 2(MS2) data to the filtered (or unfiltered) MS1 data. With the matched data users are able to cluster it, annotate it, predict de novo chemical formulas and calculate alpha and beta diversity. For chemical formula predictions, this was the method used; "Towards de novo identification of metabolites by analyzing tandem mass spectra" (Sebastian Böcker, Florian Rasche (2008) <doi:10.1093/bioinformatics/btn270>). The similarity/dissimilarity calculations we used to cluster our data together was: "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" (Li, Y., Kind, T., Folz, J. et al. (2021) <doi:10.1038/s41592-021-01331-z>) and "Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking" (Wang, M., Carver, J., Phelan, V. et al. (2021) <doi:10.1038/nbt.3597>).
Renders rich inline text (bold, italic, code, links, images)
    in grid graphics and 'ggplot2', from markdown or 'flextable' chunks.
    Provides grobs, theme elements, and geometry layers for styled text rendering.
    Only works with graphics devices that support 'systemfonts', such as
    those provided by 'ragg', 'svglite', or 'ggiraph'. The 'cairo_pdf'
    device is also supported when fonts are installed at the system level.
Provides Spanish-language educational tools for descriptive
    statistics, probability calculations, frequency tables, and graphics.
    The package is designed for introductory statistics workflows and returns
    printable summaries, tables, and optional 'ggplot2' visualizations. The
    graphical tools build on the grammar of graphics described by Wickham
    (2016, ISBN:978-3-319-24277-4).
Multidimensional unfolding using Schoenemann's algorithm for metric
   and Procrustes rotation of unfolding results.
Provides easy access to, and manipulation of, the Munsell 
    colours. Provides a mapping between Munsell's 
    original notation (e.g. "5R 5/10") and hexadecimal strings suitable 
    for use directly in R graphics. Also provides utilities 
    to explore slices through the Munsell colour tree, to transform 
    Munsell colours and display colour palettes.
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB.  Includes ISCC-NBS color block lookup.  Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Implementation of custom 'tidymodels' metrics for multi-class
    prediction models with a single negative class. Currently are implemented
    macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020)
    "Imbalance accuracy metric for model selection in multi-class imbalance
    classification problems” <doi:10.1016/j.knosys.2020.106490> and a
    generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng
    J.X and Chen P.Y. (2013) “Weighted Youden index and its
    two-independent-sample comparison based on weighted sensitivity and
    specificity” <doi:10.3760/cma.j.issn.0366-6999.20123102>.
A graphical user interface tool to estimate ploidy from DNA cells stained with fluorescent dyes and analyzed by flow cytometry, following the methodology of Gómez-Muñoz and Fischer (2024) <doi:10.1101/2024.01.24.577056>. Features include multiple file uploading and configuration, peak fluorescence intensity detection, histogram visualizations, peak error curation, ploidy and genome size calculations, and easy results export.
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
Data and code for the paper by Ehm, Gneiting, Jordan and
    Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet
    Representations, and Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).
Calculates k-best solutions and costs for an assignment problem following the method outlined in Murty (1968) <doi:10.1287/opre.16.3.682>.
Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.
Provides a framework to factorise electromyography (EMG) data.
    Tools are provided for raw data pre-processing, non negative matrix factorisation,
    classification of factorised data and plotting of obtained outcomes.
    In particular, reading from ASCII files is supported, along with wide-used
    filtering approaches to process EMG data. All steps include one or more sensible
    defaults that aim at simplifying the workflow. Yet, all functions are largely
    tunable at need. Example data sets are included.
Implements the Power / Trend / Seasonal (PTS) model, a unified
    state-space framework based on the Multiple Source of Error (MSOE) model. It brings
    the trend, seasonal and irregular component models of Harvey (1989)
    <doi:10.1017/CBO9781107049994>, Durbin and Koopman (2012)
    <doi:10.1093/acprof:oso/9780199641178.001.0001>, Proietti (2000)
    <doi:10.1016/S0169-2070(00)00037-6>, Sbrana and Silvestrini (2023)
    <doi:10.1016/j.ijforecast.2022.03.003> and others together under a single
    estimation, selection and forecasting interface, with an optional Box-Cox power
    transformation. Models are estimated by maximum likelihood through the
    Kalman filter and smoother, with automatic component selection by
    information criteria.
An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package.
Analysis of musical scales (& modes, grooves, etc.) in the vein of
    Sherrill 2025 <doi:10.1215/00222909-11595194>.
    The initials MCT in the package title refer to the article's title: "Modal
    Color Theory." Offers support for conventional musical pitch class set
    theory as developed by Forte (1973, ISBN: 9780300016109) and David Lewin
    (1987, ISBN: 9780300034936), as well as for the continuous geometries of
    Callender, Quinn, & Tymoczko (2008) <doi:10.1126/science.1153021>.
    Identifies structural properties of scales and calculates derived values
    (sign vector, color number, brightness ratio, etc.). Creates plots such as
    "brightness graphs" which visualize these properties.
A collection of functions for converting and visualization the free induction decay of mono dimensional nuclear magnetic resonance (NMR) spectra into an audio file. It facilitates the conversion of Bruker datasets in files WAV. The sound of NMR signals could provide an alternative to the current representation of the individual metabolic fingerprint and supply equally significant information. The package includes also NMR spectra of the urine samples provided by four healthy donors. Based on Cacciatore S, Saccenti E, Piccioli M. Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments. OMICS. 2015;19(3):147-56. <doi:10.1089/omi.2014.0131>.
A set of tools to facilitate data sonification and handle the 'musicXML' format <https://usermanuals.musicxml.com/MusicXML/Content/XS-MusicXML.htm>.
    Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. 
    Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration.
    A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the 'musicXML' score, 
    which can then be further processed using specialized music software (e.g. 'MuseScore', 'GuitarPro', etc.).
    Examples can be found in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).
Check concordance of a vector of mutation impacts with standard dictionaries such as Sequence Ontology (SO) <http://www.sequenceontology.org/>, Mutation Annotation Format (MAF) <https://docs.gdc.cancer.gov/Encyclopedia/pages/Mutation_Annotation_Format_TCGAv2/> or Prediction and Annotation of Variant Effects (PAVE) <https://github.com/hartwigmedical/hmftools/tree/master/pave>. 
  It enables conversion between SO/PAVE and MAF terms and selection of the most severe consequence where multiple ampersand (&) delimited impacts are given.
Designed to ease the application and comparison of multiple
    hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are 
    standardized and usable by the accompanying 'mutossGUI'.
Provides a graphical user interface for the MuToss Project.
Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The framework includes modules that support raw data import and processing, mutational signature extraction, and results interpretation and visualization. The framework accepts widely used file formats storing information about DNA variants, such as Variant Call Format files. The framework performs Non-Negative Matrix Factorization to extract mutational signatures explaining the observed set of DNA mutations. Bootstrapping is performed as part of the analysis. The framework supports parallelization and is optimized for use on multi-core systems. The software was described by Fantini D et al (2020) <doi:10.1038/s41598-020-75062-0> and is based on a custom R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <doi:10.1016/j.celrep.2012.12.008>.
Measure quality of your tests.
    'muttest' introduces small changes (mutations) to your code
    and runs your tests to check if they catch the changes.
    If they do, your tests are good.
    If not, your assertions are not specific enough.
    'muttest' gives you percent score of how often your tests catch the changes.
The Mutual Information Index (M) introduced to social science literature by
    Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup
    segregation measure that is highly decomposable and that according to Frankel
    and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo
    (2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit
    Decomposability and Strong Group Decomposability properties. This package allows
    computing and decomposing the total index value into its "between" and
    "within" terms. These last terms can also be decomposed into their
    contributions, either by group or unit characteristics. The factors that produce
    each "within" term can also be displayed at the user's request. The results can
    be computed considering a variable or sets of variables that define separate
    clusters.
An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.
Predictive multivariate modelling for metabolomics. 
    Types: Classification and regression. 
    Methods: Partial Least Squares, Random Forest ans Elastic Net 
    Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al. (2009)<doi:10.1002/cem.1225>) 
    Variable selection: Performed internally, through tuning in the inner cross-validation loop.
Functions, data sets, analyses and examples from the book 
  `An Introduction to Applied Multivariate Analysis with R' 
  (Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (<http://www.gnu.org/software/gsl/>) and 'Rcpp'
        (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.
Multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.
Analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.
Implements the multivariate autoregressive distributed lag (ARDL) 
    unit root test proposed by Sam, McNown, Goh, and Goh (2024) 
    <doi:10.1080/03796205.2024.2439101>. The test augments the standard ADF 
    regression with lagged levels of a covariate to improve power when 
    cointegration exists. Bootstrap critical values ensure correct size 
    regardless of nuisance parameters. Provides automatic lag selection via 
    AIC/BIC, diagnostic tests, and comprehensive inference tables following 
    the four-case framework.
Fit, plot, and predict a multivariate response, using an arbitrary 
  univariate Bayesian regression model to independently fit basis components 
  (e.g., principal components) of the response (Francom et al., 2025 <DOI:10.1137/24M1644092>).
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by various packages including debug, offarray, and kinference.
The Cauchy distribution is a special case of the t distribution when the degrees of freedom are equal to 1. The functions are related to the multivariate Cauchy distribution and include simulation, computation of the density, maximum likelihood estimation, contour plot of the bivariate Cauchy distribution, and discriminant analysis. References include: Nadarajah S. and Kotz S. (2008). "Estimation methods for the multivariate t distribution". Acta Applicandae Mathematicae, 102(1): 99--118. <doi:10.1007/s10440-008-9212-8>, and Kanti V. Mardia, John T. Kent and John M. Bibby (1979). "Multivariate analysis", ISBN:978-0124712522. Academic Press, London.
Correlation coefficients for multivariate data, namely the squared correlation coefficient and the RV coefficient (multivariate generalization of the squared Pearson correlation coefficient). References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995<doi:10.1063/1.166141>), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series.
Extends 'multiverse' package
    (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021)
    <doi:10.31219/osf.io/yfbwm>, which allows users perform to create explorable
    multiverse analysis in R. This extension provides an additional level of
    abstraction to the 'multiverse' package with the aim of creating
    user friendly syntax to researchers, educators, and students in statistics.
    The 'mverse' syntax is designed to allow piping and takes hints from
    the 'tidyverse' grammar. The package allows users to define and inspect
    multiverse analysis using familiar syntax in R.
Multivariate estimation and testing, currently a package for testing parametric data. To deal with parametric data, various multivariate normality tests and outlier detection are performed and visualized using the 'ggplot2' package. Homogeneity tests for covariance matrices are also possible, as well as the Hotelling's T-square test and the multivariate analysis of variance test. We are exploring additional tests and visualization techniques, such as profile analysis and randomized complete block design, to be made available in the future and making them easily accessible to users.
Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal  exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 <doi:10.1002/sim.10222>) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) <doi:10.48550/arXiv.2512.19064>.
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.
Generates multivariate subgaussian stable probabilities using the QRSVN
    algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394>
    but by sampling positive stable variates not chi/sqrt(nu).
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>,
    Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by 
	Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.
Methods for estimating and utilizing the multivariate generalized
    propensity score (mvGPS) for multiple continuous exposures described in
    Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow
    estimation of a dose-response surface relating the joint distribution of multiple
    continuous exposure variables to an outcome. Weights are constructed assuming a
    multivariate normal density for the marginal and conditional distribution of
    exposures given a set of confounders. Confounders can be different for different
    exposure variables. The weights are designed to achieve balance across all
    exposure dimensions and can be used to estimate dose-response surfaces.
Tabulate and plot directional and other multivariate histograms.
Hypothesis tests for multivariate data. Tests for one and two mean vectors, multivariate analysis of variance, tests for one, two or more covariance matrices. References include: Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.
Computes regression deletion diagnostics for multivariate linear models and provides some associated
	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
	provided.
Pearson and Spearman correlation coefficients are commonly used to quantify the strength
	of bivariate associations of genomic variables.  For example, correlations of gene-level 
	DNA copy number and gene expression measurements may be used to assess the impact of 
	DNA copy number changes on gene expression in tumor tissue.  'MVisAGe' enables users to 
	quickly compute and visualize the correlations in order to assess the effect of regional 
	genomic events such as changes in DNA copy number or DNA methylation level.  Please see
	Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research 
	<doi:10.1158/0008-5472.CAN-17-3464>.
Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.
Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.
Epistasis, commonly defined as the interaction between genetic
  loci, is known to play an important role in the phenotypic variation of
  complex traits. As a result, many statistical methods have been developed to
  identify genetic variants that are involved in epistasis, and nearly all of
  these approaches carry out this task by focusing on analyzing one trait at a
  time. Previous studies have shown that jointly modeling multiple phenotypes
  can often dramatically increase statistical power for association mapping. In
  this  package, we present the 'multivariate MArginal ePIstasis Test'
  ('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic
  detection method which seeks to detect marginal epistasis or the combined
  pairwise interaction effects between a given variant and all other variants.
  By searching for marginal epistatic effects, one can identify genetic variants
  that are involved in epistasis without the need to identify the exact
  partners with which the variants interact – thus, potentially alleviating
  much of the statistical and computational burden associated with conventional
  explicit search based methods. Our proposed 'mvMAPIT' builds upon this
  strategy by taking advantage of correlation structure between traits to
  improve the identification of variants involved in epistasis.
  We formulate 'mvMAPIT' as a  multivariate linear mixed model and develop a
  multi-trait variance component estimation algorithm for efficient parameter
  inference and P-value computation. Together with reasonable model
  approximations, our proposed approach is scalable to moderately sized
  genome-wide association studies.
  Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>.
  Stamp et al. (2023) <doi:10.1093/g3journal/jkad118>.
  Stamp et al. (2025) <doi:10.1016/j.ajhg.2025.07.004>.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
 Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Offers a general framework of multivariate mixed-effects
        models for the joint analysis of multiple correlated outcomes with clustered 
        data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may 
        depend on the values themselves (missing not at random and non-ignorable), 
        or may depend on only the covariates (missing at random and ignorable), or both.
        This package provides functions for two models: 1) mvMISE_b() 
        allows correlated outcome-specific random intercepts with a factor-analytic 
        structure, and 2) mvMISE_e() allows the correlated outcome-specific 
        error terms with a graphical lasso penalty on the error precision matrix. Both functions 
        are motivated by the multivariate data analysis on data with clustered structures 
        from labelling-based quantitative proteomic studies. These models and functions 
        can also be applied to univariate and multivariate analyses of clustered data 
        with balanced or unbalanced design and no missingness.
Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to
    data generated from a continuous-time multivariate industrial or natural
    process. Employ PCA-based dimension reduction to extract linear combinations
    of relevant features, reducing computational burdens. For a description of 
    ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.
    The multi-state application of ADPCA is from a manuscript under current
    revision entitled "Multi-State Multivariate Statistical Process Control" by
    Odom, Newhart, Cath, and Hering, and is  expected to appear in Q1 of 2018.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood.  See
    Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.
A comprehensive suite for assessing multivariate normality using six statistical tests 
    (Mardia, Henze–Zirkler, Henze–Wagner, Royston, Doornik–Hansen, Energy). 
    Also includes univariate diagnostics, bivariate density visualization, robust outlier 
    detection, power transformations (e.g., Box–Cox, Yeo–Johnson), and imputation strategies 
    ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported 
    for selected tests to improve p-value accuracy in small samples.
    Diagnostic plots are available via both 'ggplot2' and interactive 'plotly' visualizations. See Korkmaz et al. (2014) <https://journal.r-project.org/articles/RJ-2014-031/RJ-2014-031.pdf>.
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.
Tools of Bayesian analysis framework using the method
  suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for
  multivariate normal (MVN) distribution and multivariate normal
  mixture (MixMVN) distribution:
  a) calculating Bayesian posteriori of (Mix)MVN distribution;
  b) generating random vectors of (Mix)MVN distribution;
  c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.
Provides computationally efficient tools related to the
    multivariate normal and Student's t distributions. The main functionalities
    are: simulating multivariate random vectors, evaluating multivariate normal or
    Student's t densities and Mahalanobis distances. These tools are very efficient
    thanks to the use of C++ code and of the OpenMP API.
Package for  moving grid adjustment 
	      in plant breeding field trials.
An implementation of the expectation conditional maximization (ECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in 'gaad_res', and covariates in 'gaad_cov'. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.
Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.
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 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 VišeKriterijumska Optimizacija I Kompromisno Rešenje (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, 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.
Finds the Maximum Likelihood (ML) Estimate of the mean vector
        and variance-covariance matrix for multivariate normal data
        with missing values.
A simple informative powerful test (mvnTest()) for multivariate normality proposed by 
    Zhou and Shao (2014) <doi:10.1080/02664763.2013.839637>, which combines kurtosis 
    with Shapiro-Wilk test that is easy for biomedical researchers to understand and 
    easy to implement in all dimensions. This package also contains some other multivariate 
    normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis 
    test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), 
    Royston’s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical 
    power calculation functions for these tests are also provided. In addition, this package 
    includes some functions to generate several types of multivariate distributions mentioned in 
    Zhou and Shao (2014).
Generalization of Shapiro-Wilk test for multivariate variables.
Given a vector of multivariate normal data, a matrix of
    covariates and the data covariance matrix, generate new multivariate normal
    samples that have the same covariance matrix based on permutations of
    the transformed data residuals.
Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test,  Royston test and Henze-Zirkler test.
Implements the 'MVOPR' (Multi-View Orthogonal Projection Regression) method for robust 
    variable selection and integration of multi-modality data.
A flexible framework for fitting multivariate
    ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.
Detection of multivariate outliers using robust estimates of location and scale. The Minimum Covariance Determinant (MCD) estimator is used to calculate robust estimates of the mean vector and covariance matrix. Outliers are determined based on robust Mahalanobis distances using either an unstructured covariance matrix, a principal components structured covariance matrix, or a factor analysis structured covariance matrix. Includes options for specifying the direction of interest for outlier detection for each variable.
Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.
Fast manipulation of symbolic multivariate polynomials
  using the 'Map' class of the Standard Template Library.  The package
  uses print and coercion methods from the 'mpoly' package but
  offers speed improvements.  It is comparable in speed to the 'spray'
  package for sparse arrays, but retains the symbolic benefits of
  'mpoly'.  To cite the package in publications, use Hankin 2022
  <doi:10.48550/ARXIV.2210.15991>.  Uses 'disordR' discipline.
Generalized Egger tests for detecting publication bias in meta-analysis for diagnostic accuracy test (Noma (2020) <doi:10.1111/biom.13343>, Noma (2022) <doi:10.48550/arXiv.2209.07270>). These publication bias tests are generally more powerful compared with the conventional univariate publication bias tests and can incorporate correlation information between the outcome variables.
Estimates multivariate subgaussian stable densities 
    and probabilities as well as generates random variates using product 
    distribution theory.  A function for estimating the parameters from 
    data to fit a distribution to data is also provided, using the 
    method from Nolan (2013) <doi:10.1007/s00180-013-0396-7>.
Tools for high-dimensional peaks-over-threshold inference and simulation of Brown-Resnick and extremal Student spatial extremal processes. These include optimization routines based on censored likelihood and gradient scoring, and exact simulation algorithms for max-stable and multivariate Pareto distributions based on rejection sampling. Fast multivariate Gaussian and Student distribution functions using separation-of-variable algorithm with quasi Monte Carlo integration are also provided. Key references include de Fondeville and Davison (2018) <doi:10.1093/biomet/asy026>, Thibaud and Opitz (2015) <doi:10.1093/biomet/asv045>, Wadsworth and Tawn (2014) <doi:10.1093/biomet/ast042> and Genz and Bretz (2009) <doi:10.1007/978-3-642-01689-9>.
Tools for estimating multivariate probit models,
   calculating conditional and unconditional expectations,
   and calculating marginal effects on conditional and unconditional
   expectations.
Provides methods to construct multivariate grids, which can be used
    for multivariate quadrature. This grids can be based on different quadrature
    rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
    quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
    multidimensional grid the product-rule or the combination- technique can be
    applied.
Functions used for graphing in multivariate contexts. These 
    functions are designed to support produce reasonable graphs with minimal 
    input of graphing parameters. The motivation for these functions was to 
    support students learning multivariate concepts and R - there may be 
    other functions and packages better-suited to practical data analysis. For
    details about the ellipse methods see Johnson and Wichern (2007, 
    ISBN:9780131877153).
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
            (i) Normalization and/or variance stabilization of the data,
            (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
            (iii) Generation of diverse diagnostic plots,
            (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Compute the coefficient of determination for outcomes in n-dimensions. 
  May be useful for multidimensional predictions (such as a multinomial model) or
  calculating goodness of fit from latent variable models such as probabilistic
  topic models like latent Dirichlet allocation or deterministic topic models 
  like latent semantic analysis. Based on Jones (2019) <arXiv:1911.11061>.
Methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework.
    For technical details on the MVS and stacked penalized logistic regression (StaPLR) methods see Van Loon, Fokkema, Szabo, & De Rooij (2020) <doi:10.1016/j.inffus.2020.03.007> and Van Loon et al. (2022) <doi:10.3389/fnins.2022.830630>.
This package implements the generalization of the Shapiro-Wilk test
        for multivariate normality proposed by Villasenor-Alva and
        Gonzalez-Estrada (2009).
An implementation of the alternating expectation conditional maximization (AECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res, and covariates in gaad_cov. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005> and K. Bartoszek, and J. Tredgett Clarke, J. Fuentes-Gonzalez, V. Mitov,  J. Pienaar, M. Piwczynski, R. Puchalka,  K. Spalik, K. L. Voje (2024) <doi:10.1111/2041-210X.14376>. The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from <https://github.com/venelin/PCMBaseCpp/>.
Estimates the multivariate skew-t and nested models, as described in the
    articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal
    model: a population Monte Carlo approach. Comput. Statist. Data Anal.
    <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017). Objective Bayesian
    analysis for the multivariate skew-t model. Statistical Methods & Applications
    <doi: 10.1007/s10260-017-0404-0>.
Multivariate Surrogate Synchrony ('mvSUSY') estimates the synchrony within datasets that contain more than two time series. 'mvSUSY' was developed from Surrogate Synchrony ('SUSY') with respect to implementing surrogate controls, and extends synchrony estimation to multivariate data. 'mvSUSY' works as described in Meier & Tschacher (2021).
Routines to perform estimation and inference under the multivariate
  t-distribution <doi:10.1007/s10182-022-00468-2>. Currently, the following methodologies 
  are implemented: multivariate mean and covariance estimation, hypothesis testing 
  about equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, 
  QQ-plots with envelopes and random variate generation.
Multivariate hypothesis tests and confidence intervals...
Functions to run fixed effects or random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles, random deviates, 
  and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae 
  parameterised by Cholesky factors of covariance or precision matrices are implemented 
  for interval-censored and exact data, or a mix thereof. Score functions for these 
  log-likelihoods are available. A class representing multiple lower triangular matrices 
  and corresponding methods are part of this package.
A function for plotting multivariate time series data.
Defines predict function that transforms output from a Tweedie
    Generalized Linear Mixed Model (using 'glmmTMB'),
    Generalized Additive Model (using 'mgcv'), or
    spatio-temporal Generalized Linear Mixed Model (using package 'tinyVAST'),
    and returns predicted proportions (and standard errors) across a
    grouping variable from an equivalent multivariate-logit Tweedie model.
    These predicted proportions can then be used for standard plotting
    and diagnostics.  See Thorson et al. 2022 <doi:10.1002/ecy.3637>.
Rudimentary functions for sampling and calculating density from the matrix-variate variance-gamma distribution.
Implementation of Matched Wake Analysis (mwa) for studying causal relationships in spatiotemporal event data, introduced by Schutte and Donnay (2014) <doi:10.1016/j.polgeo.2014.03.001>.
Algorithms for solving various Maximum
    Weight Connected Subgraph Problems, including variants with
    budget constraints, cardinality constraints, weighted edges and signals.
    The package represents an R interface to high-efficient solvers based on
    relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) <doi:10.1016/j.cor.2017.05.015>)
    mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>)
    and simulated annealing.
Handy helper package for cross-referencing lake identifiers 
    among different data sets in the Midwestern United States. There are 
    multiple different state, regional, and federal agencies that have 
    different identifiers on lakes. This package helps you to go between them.
Provides a tidy, high-level interface for creating polished maps
    of Malawi at country, region, district, and Traditional Authority level.
    Functions handle spatial data retrieval, administrative-name matching,
    joins from ordinary data frames, numeric and categorical choropleths,
    labels, highlights, and professional 'ggplot2' styling. Spatial boundary
    data are provided by the companion package 'mwmapdata'.
Provides official spatial boundary datasets for Malawi at multiple
    administrative levels: country (level 0), regions (level 1), districts
    (level 2), and traditional authorities (level 3). Also includes Lake
    Malawi boundary data. Boundary data are the Common Operational Datasets (COD-AB)
    sourced from the National Statistics Office of Malawi and distributed via
    the OCHA Humanitarian Data Exchange (HDX), version 02
    <https://data.humdata.org/dataset/cod-ab-mwi>. Intended for use with the
    'mwmap' package or any spatial analysis workflow requiring Malawi
    administrative boundaries.
Implements random  number generation, plotting, and estimation algorithms for the two-parameter one-sided and two-sided M-Wright (Mainardi-Wright) family.
             The M-Wright  distributions naturally generalize the widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric (Airy and Gaussian or normal) models. 
             These are widely studied in time-fractional differential equations. References: Cahoy and Minkabo (2017) <doi:10.3233/MAS-170388>; Cahoy (2012) <doi:10.1007/s00180-011-0269-x>; Cahoy (2012) <doi:10.1080/03610926.2010.543299>; Cahoy (2011); Mainardi, Mura, and Pagnini (2010) <doi:10.1155/2010/104505>.
A simple function, mwsApp(), that runs a 'shiny' app spanning multiple,
  connected windows. This uses all standard 'shiny' conventions, and depends only on
  the 'shiny' package.
For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.
A minimal-dependency client for the 'Matrix' Client-Server
    HTTP API <https://spec.matrix.org/>, suitable for talking to a
    'Synapse' <https://element-hq.github.io/synapse/> or 'Conduit'
    <https://conduit.rs/> homeserver. Covers login, room management,
    message send and history, media upload or download, and the
    transport endpoints needed to coordinate end-to-end encryption
    (device-key and one-time-key publication, key query and claim,
    to-device events). Encryption itself is out of scope;
    pair with a separate crypto package.
Stateful helpers for building 'Matrix' (<https://matrix.org>)
    chat clients in R. Builds on the low-level 'mx.api' Client-Server API
    bindings, adding local configuration persistence, room resolution,
    sync cursor handling, sync-event extraction, invite acceptance, a
    conservative Markdown-to-HTML converter for formatted messages, and
    'Olm'/'Megolm' end-to-end encryption orchestration over the optional
    'mx.crypto' package.
'Olm' and 'Megolm' encryption ratchet primitives for the
    'Matrix' messaging protocol <https://matrix.org/>, wrapping the
    'vodozemac' Rust crate. Provides device-key generation, one-time-key
    management, 1:1 'Olm' sessions, and 'Megolm' group sessions. Pairs
    with the 'mx.api' package, which handles 'Matrix' HTTP transport.
Computes Control limits, coefficients of control limits, various performance metrics  and depicts control charts for monitoring Maxwell-distributed quality characteristics.
Methods and tools for deriving spatial summary functions from single-cell imaging
  data and performing functional data analyses. Functions can be applied to other single-cell
  technologies such as spatial transcriptomics. Functional regression and functional principal component analysis methods
  are in the 'refund' package <https://cran.r-project.org/package=refund> while calculation of the
  spatial summary functions are from the 'spatstat' package <https://spatstat.org/>.
Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, <doi:10.1080/15598608.2011.10483735>.
Provides a convenient interface in 'OpenMx' for building
    Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of
    Derivatives (MMOD).
Implements methods to normalize multiplexed imaging data, including
    statistical metrics and visualizations to quantify technical variation in 
    this data type. Reference for methods listed here: Harris, C., Wrobel, J., 
    & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging 
    Data. Journal of Open Source Software, 7(71), 4180, 
    <doi:10.21105/joss.04180>.
Provides a 'lavaan'-like syntax for 'OpenMx' models. The syntax supports 
  definition variables, bounds, and parameter transformations. This allows for
  latent growth curve models with person-specific measurement occasions, moderated
  nonlinear factor analysis and much more.
The stepwise variable selection procedure (with iterations
 between the 'forward' and 'backward' steps) can be used to obtain
 the best candidate final regression model in regression analysis.
 All the relevant covariates are put on the 'variable list' to be
 selected. The significance levels for entry (SLE) and for stay
 (SLS) are usually set to 0.15 (or larger) for being conservative.
 Then, with the aid of substantive knowledge, the best candidate
 final regression model is identified manually by dropping the
 covariates with p value > 0.05 one at a time until all regression
 coefficients are significantly different from 0 at the chosen alpha
 level of 0.05.
Implementation of adaptive assessment procedures based
    on Knowledge Space Theory (KST, Doignon & Falmagne, 1999 <ISBN:9783540645016>) and Formal Psychological Assessment
    (FPA, Spoto, Stefanutti & Vidotto, 2010 <doi:10.3758/BRM.42.1.342>) frameworks. An adaptive assessment is a type of evaluation that
    adjusts the difficulty and nature of subsequent questions based on the
    test taker's responses to previous ones. The package contains functions
    to perform and simulate an adaptive assessment. Moreover, it is
    integrated with two 'Shiny' interfaces, making it both accessible and
    user-friendly.  The package has been partially funded by the European Union -
    NextGenerationEU and by the Ministry of University and Research (MUR),
    National Recovery and Resilience Plan (NRRP), Mission 4, Component 2,
    Investment 1.5, project “RAISE - Robotics and AI for Socio-economic
    Empowerment” (ECS00000035).
The chi-squared test for goodness of fit 
    and an independence test. In addition, the chi-squared test for the population variance,
    the function for computing a confidence interval for the population variance, 
    and the function for calculating the population standard deviation are provided.
Handling the microclimatic data in R. The 'myClim' workflow begins
    at the reading data primary from microclimatic dataloggers,
    but can be also reading of meteorological station data from files.
    Cleaning time step, time zone settings and metadata collecting is the next step of the work flow.
    With 'myClim' tools one can crop, join, downscale, and convert microclimatic data formats, sort them into localities,
    request descriptive characteristics and compute microclimatic variables.
    Handy plotting functions are provided with smart defaults.
The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
Colour palettes and helper functions for visualising
    Mycobacterium tuberculosis genomic and epidemiological data with
    'ggplot2' and 'ggtree'. The package provides predefined palettes,
    scale functions, tree/cladogram helpers, and convenient preview tools
    to ensure consistent branding in pathogen-omics visualisations. The
    palettes were developed as part of the 'mycolorsTB' project
    <https://github.com/PathoGenOmics-Lab/mycolorsTB>.
Perform correlation and linear regression test
    among the numeric fields in a data.frame automatically
    and make plots using pairs or lattice::parallelplot.
Plot the daily and cumulative number of downloads of your packages.
    It is designed to be slightly more convenient than the several similar programs. If you want to run this each morning,
    you do not need to keep typing in the names of your packages. Also, this combines the daily and cumulative counts in one
    run, you do not need to run separate programs to get both types of information.
Create interactive 'd3.js' visualizations from R with built-in
    statistical transforms. Computes confidence intervals, regression fits,
    LOESS smoothing, moving averages, error bars, and uncertainty
    visualizations (quantile dot plots and fan charts) in R and renders them
    as composable chart layers via 'htmlwidgets'. Supports 36 chart types
    including boxplots, violin plots, Q-Q diagnostic plots, calendar heatmaps,
    survival curves, and group comparisons with pairwise significance testing.
    Also provides a machine-readable chart specification schema with validators
    so that large language model agents can author and verify charts. Works in
    'RStudio', 'Shiny', and 'R Markdown'.
Calculate MyoScore, a genetically informed muscle health score,
    from bulk RNA sequencing (RNA-seq) raw count data. MyoScore integrates
    results from genome-wide association studies (GWAS) and transcriptome-wide
    association studies (TWAS) across 28 muscle-related phenotypes to quantify
    muscle health along five dimensions (Strength, Mass, LeanMuscle, Youth,
    Resilience), each scored from 0 to 100. The package provides preprocessing
    via counts per million (CPM) normalization, dimension-level and composite
    scoring, and visualization utilities including radar charts and grouped
    boxplots. For more information, see <https://github.com/Hirriririir/MyoScore>.
Provides an 'RStudio' extension with a chat interface for an AI coding agent to help
    users with R programming tasks.
Tools for systematic comparison of data frames, offering
    functionality to identify, quantify, and extract differences. Provides
    functions with user-friendly and interactive console output for
    immediate analysis, while also offering options to export differences
    as structured data frames that can be easily integrated into existing
    workflows.
Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2018) <doi:10.1093/bioinformatics/btx835>). This package aims to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest.
We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.
Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS.  The n1qn1 routine is useful since it allows prespecification of a Hessian.
       If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the
       'Scilab' optimization documentation located at
       <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.
Provides some functions to get Korean text sample from news articles in
             Naver which is popular news portal service <https://news.naver.com/> in Korea.
Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical  Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <doi:10.48550/arXiv.1603.09320>.
Functions for reading cancer record files which follow a format
    defined by the North American Association of Central Cancer Registries
    (NAACCR).
Exact automatic differentiation for R functions. Provides a
    composable derivative operator D that computes gradients, Hessians,
    Jacobians, and arbitrary-order derivative tensors at machine precision.
    D(D(f)) gives Hessians, D(D(D(f))) gives third-order tensors for
    skewness of maximum likelihood estimators, and so on to any order. Works through any R code
    including loops, branches, and control flow.
An R wrapper for 'libnabo', an exact or approximate k nearest
    neighbour library which is optimised for low dimensional spaces (e.g. 3D).
    'libnabo' has speed and space advantages over the 'ANN' library wrapped by
    package 'RANN'. 'nabor' includes a knn function that is designed as a 
    drop-in replacement for 'RANN' function nn2. In addition, objects which 
    include the k-d tree search structure can be returned to speed up repeated 
    queries of the same set of target points.
Incorporating node-level covariates for community detection has gained increasing attention these years. This package provides the function for implementing the novel community detection algorithm known as Network-Adjusted Covariates for Community Detection (NAC), which is designed to detect latent community structure in graphs with node-level information, i.e., covariates. This algorithm can handle models such as the degree-corrected stochastic block model (DCSBM) with covariates. NAC specifically addresses the discrepancy between the community structure inferred from the adjacency information and the community structure inferred from the covariates information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2023) <arXiv:2306.15616>. In addition to NAC, this package includes several other existing community detection algorithms that are compared to NAC in the reference paper. These algorithms are Spectral Clustering On Ratios-of Eigenvectors (SCORE), network-based regularized spectral clustering (Net-based), covariate-based spectral clustering (Cov-based), covariate-assisted spectral clustering (CAclustering) and semidefinite programming (SDP).
NanoString nCounter data are gene expression assays
    where there is no need for the use of enzymes or amplification
    protocols and work with fluorescent barcodes (Geiss et al. (2018)
    <doi:10.1038/nbt1385>). Each barcode is assigned a
    messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its
    target can be counted. As a result each count of a specific barcode
    represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString
    quality Control dasHbOard) is able to analyse the exported NanoString
    nCounter data and facilitates the user in performing a quality
    control. 'NACHO' does this by visualising quality control metrics,
    expression of control genes, principal components and sample specific
    size factors in an interactive web application.
Contains methods described by Dennis Helsel in 
             his book "Nondetects And Data Analysis: Statistics 
             for Censored Environmental Data".
Contains methods described by Dennis Helsel in 
             his book "Statistics for Censored Environmental Data
             using Minitab and R" (2011) and courses and videos at 
             <https://practicalstats.com>. This package incorporates functions 
             of NADA and adds new functionality.
Provides a unified, programmatic interface for searching, browsing,
  and retrieving metadata from various international organization data repositories
  that use the National Data Archive ('NADA') software, such as the World Bank,
  'FAO', and the International Household Survey Network ('IHSN'). Functions allow users to
  discover available data collections, country codes, and access types, perform
  complex searches using keyword and spatial/temporal filters, and retrieve
  detailed study information, including file lists and variable-level data
  dictionaries. It simplifies access to microdata for researchers and policy
  analysts globally.
A functional programming based
    implementation of the super learner algorithm with an emphasis on supporting
    the use of formulas to specify learners. This approach offers several
    improvements compared to past implementations including the ability to 
    easily use random-effects specified in formulas 
    (like y ~ (age | strata) + ...) and construction of new learners is 
    as simple as writing and passing a new function. The 
    super learner algorithm was originally described in van der Laan et al.
    (2007) <https://biostats.bepress.com/ucbbiostat/paper222/>.
Constructs (non)additive genetic relationship matrices, and their
    inverses, from a pedigree to be used in linear mixed effect models (A.K.A.
    the 'animal model'). Also includes other functions to facilitate the use of
    animal models. Some functions have been created to be used in conjunction
    with the R package 'asreml' for the 'ASReml' software, which can be
    obtained upon purchase from 'VSN' international 
    (<https://vsni.co.uk/software/asreml>).
This data package contains the Item Response Theory (IRT) parameters for the National Center for Education Statistics (NCES) items used on the National Assessment of Education Progress (NAEP) from 1990 to 2015. The values in these tables are used along with NAEP data to turn student item responses into scores and include information about item difficulty, discrimination, and guessing parameter for 3 parameter logit (3PL) items. Parameters for Generalized Partial Credit Model (GPCM) items are also included. The adjustments table contains the information regarding the treatment of items (e.g., deletion of an item or a collapsing of response categories), when these items did not appear to fit the item response models used to describe the NAEP data. Transformation constants change the score estimates that are obtained from the IRT scaling program to the NAEP reporting metric. Values from the years 2000 - 2013 were taken from the NCES website <https://nces.ed.gov/nationsreportcard/> and values from 1990 - 1998 and 2015 were extracted from their NAEP data files. All subtest names were reduced and homogenized to one word (e.g. "Reading to gain information" became "information"). The various subtest names for univariate transformation constants were all homogenized to "univariate".
Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
For use in summary functions to omit missing values 
    conditionally using specified checks.
A set of convenience functions as well as geographical/political
  data about Nigeria, aimed at simplifying work with data and information that
  are specific to the country.
A small package designed for interpreting continuous and categorical latent variables. You provide a data set with a latent variable you want to understand and some other explanatory variables. It provides a description of the latent variable based on the explanatory variables. It also provides a name to the latent variable.
An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences.
In this implementation of the Naive Bayes classifier
    following class conditional distributions are available: 'Bernoulli',
    'Categorical', 'Gaussian', 'Poisson', 'Multinomial' and non-parametric
    representation of the class conditional density estimated via Kernel
    Density Estimation. Implemented classifiers handle missing data and
    can take advantage of sparse data.
In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. The naivereg is nonparametric version of 'ivregress' in 'Stata' with IV selection and high dimensional features. The package is based on the paper by Q. Fan and W. Zhong, "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2018), Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991> as well as a series of working papers led by the same authors.
Density, distribution function, quantile function and random 
    generation for the Nakagami distribution of Nakagami (1960) 
    <doi:10.1016/B978-0-08-009306-2.50005-4>.
Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
A system for organizing column names in data. Aimed at supporting a 
  prefix-based and suffix-based column naming scheme. Extends 'dplyr' functionality
  to add ordering by function and more explicit renaming.
Provides 'visual citations' containing the metadata of a
    scientific paper and a 'QR' code.  A 'visual citation' is a banner
    containing title, authors, journal and year of a publication.  This
    package can create such banners based on 'BibTeX' and 'BibLaTeX'
    references or call the reference metadata from 'Crossref'-API. The
    banners include a QR code pointing to the 'DOI'.  The resulting HTML
    object or PNG image can be included in a presentation to point the
    audience to good resources for further reading.  Styling is possible
    via predefined designs or via custom 'CSS'.  This package is not
    intended as replacement for proper reference manager packages, but a
    tool to enrich scientific presentation slides and conference posters.
The Needleman-Wunsch global alignment algorithm can be
  used to find approximate matches between sample names in different
  data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.
It names the 'R Markdown' chunks of files based on the
    filename.
Wraps the 'nametag' library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models.
    Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.
Calculation of molecular number and brightness from
    fluorescence microscopy image series. The software was published in a
    2016 paper <doi:10.1093/bioinformatics/btx434>. The seminal paper for
    the technique is Digman et al. 2008 <doi:10.1529/biophysj.107.114645>.
    A review of the technique was published in 2017
    <doi:10.1016/j.ymeth.2017.12.001>.
Missing values are ubiquitous in data and need to be explored and
    handled in the initial stages of analysis. 'naniar' provides data 
    structures and functions that facilitate the plotting of missing values and 
    examination of imputations. This allows missing data dependencies to be 
    explored with minimal deviation from the common work patterns of 'ggplot2' 
    and tidy data. The work is fully discussed at Tierney & Cook (2023) 
    <doi:10.18637/jss.v105.i07>.
Provides an 'R' interface to the 'nanoarrow' 'C' library and the
  'Apache Arrow' application binary interface. Functions to import and
  export 'ArrowArray', 'ArrowSchema', and 'ArrowArrayStream' 'C' structures
  to and from 'R' objects are provided alongside helpers to facilitate zero-copy
  data transfer among 'R' bindings to libraries implementing the 'Arrow' 'C'
  data interface.
R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ.
    A toolkit for messaging, concurrency and the web. High-performance
    socket messaging over in-process, IPC, TCP, WebSocket and secure TLS
    transports implements 'Scalability Protocols', a standard for common
    communications patterns including publish/subscribe, request/reply and
    survey. A threaded concurrency framework with intuitive 'aio' objects
    that resolve automatically upon completion of asynchronous operations,
    and synchronisation primitives that allow R to wait on events
    signalled by concurrent threads. A unified HTTP server hosting REST
    endpoints, WebSocket connections and streaming on a single port, with
    a built-in HTTP client.
Self-sufficient reader and writer for flat 'Parquet' files.
    Can read most 'Parquet' data types. Can write many 'R' data types,
    including factors and temporal types. See docs for limitations.
Provides quality control (QC), normalization, and batch
    effect correction operations for 'NanoString nCounter' data, Talhouk
    et al. (2016) <doi:10.1371/journal.pone.0153844>.  Various metrics are
    used to determine which samples passed or failed QC.  Gene expression
    should first be normalized to housekeeping genes, before a
    reference-based approach is used to adjust for batch effects.  Raw
    NanoString data can be imported in the form of Reporter Code Count
    (RCC) files.
Full 64-bit resolution date and time functionality with
 nanosecond granularity is provided, with easy transition to and from
 the standard 'POSIXct' type. Three additional classes offer interval,
 period and duration functionality for nanosecond-resolution timestamps.
Conducts Bayesian Hypothesis tests of a point null hypothesis against a two-sided alternative
             using Non-local Alternative Prior (NAP) for one- and two-sample z- and t-tests 
             (Pramanik and Johnson, 2022). Under the alternative, the NAP is assumed on the standardized 
             effects size in one-sample tests and on their differences in two-sample tests. The package 
             considers two types of NAP densities: (1) the normal moment prior, and (2) the composite alternative. 
             In fixed design tests, the functions calculate the Bayes factors and the expected weight of evidence
             for varied effect size and sample size. The package also provides a sequential testing framework using the
             Sequential Bayes Factor (SBF) design. The functions calculate the operating characteristics (OC)
             and the average sample number (ASN), and also conducts sequential tests for a sequentially observed data.
Implements the Network meta-Analytic Predictive (NAP) prior framework to
  accommodate changes in the standard of care (SoC) during ongoing randomized controlled
  trials (RCTs). The method synthesizes pre- and post-change in-trial data by leveraging
  external evidence, particularly head-to-head trials comparing the original and new
  standards of care, to bridge the two evidence periods and enable principled borrowing.
  The package provides utilities to construct NAP-based priors and perform Bayesian
  inference for time-to-event endpoints using summarized trial evidence.
An R wrapper for pulling data from the National Public Transport Access Nodes ('NaPTAN') API (<https://www.api.gov.uk/dft/national-public-transport-access-nodes-naptan-api/#national-public-transport-access-nodes-naptan-api>). This allows users to download 'NaPTAN' transport information, for the full dataset, by ATCO region code, or by name of region.
Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input
    to produce delays in the execution of code and can silence/prevent typical sources of error.
Native rasters are a core R image format which use a compact
    color representation. This color representation closely aligns with
    graphics device internals meaning that these images can be rendered quickly.
    This package provides functions to quickly create, manipulate and composite native
    rasters.
Computes the nonlinear cointegrating autoregressive distributed lag model with automatic bases aic and bic lags selection of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).
Methods and tools for forecasting univariate time series using the NARFIMA (Neural AutoRegressive Fractionally Integrated Moving Average) model. It combines neural networks with fractional differencing to capture both nonlinear patterns and long-term dependencies. The NARFIMA model supports seasonal adjustment, Box-Cox transformations, optional exogenous variables, and the computation of prediction intervals. In addition to the NARFIMA model, this package provides alternative forecasting models including NARIMA (Neural ARIMA), NBSTS (Neural Bayesian Structural Time Series), and NNaive (Neural Naive) for performance comparison across different modeling approaches. The methods are based on algorithms introduced by Chakraborty et al. (2025) <doi:10.48550/arXiv.2509.06697>.
Stacking arrays according to dimension names, subset-aware
    splitting and mapping of functions, intersecting along arbitrary
    dimensions, converting to and from data.frames, and many other helper
    functions.
A toolkit for medical records data analysis. The 'naryn'
    package implements an efficient data structure for storing medical
    records, and provides a set of functions for data extraction,
    manipulation and analysis.
Provides functions to access and download data from various NASA APIs <https://api.nasa.gov/#browseAPI>, including:
    Astronomy Picture of the Day (APOD), Mars Rover Photos, Earth Polychromatic Imaging Camera (EPIC),
    Near Earth Object Web Service (NeoWs), Earth Observatory Natural Event Tracker (EONET), 
    and NASA Earthdata CMR Search. Most endpoints require a NASA API key for access.
    Data is retrieved, cleaned for analysis, and returned in a dataframe-friendly format.
Provides functions to access NASA's Earth Imagery and Assets API
    and the Earth Observatory Natural Event Tracker (EONET) webservice.
An API client for NASA POWER global meteorology, surface
    solar energy and climatology data API.  POWER (Prediction Of Worldwide
    Energy Resources) data are freely available for download with varying
    spatial resolutions dependent on the original data and with several
    temporal resolutions depending on the POWER parameter and community.
    This work is funded through the NASA Earth Science Directorate Applied
    Science Program.  For more on the data themselves, the methodologies
    used in creating, a web-based data viewer and web access, please see
    <https://power.larc.nasa.gov/>.
Tidied data from the ASA 2006 data
    expo, as well as a number of useful other related data sets.
A collection of NASCAR race, driver, owner and manufacturer
    data across the three major NASCAR divisions: NASCAR Cup Series, NXS,
    and NASCAR Craftsman Truck Series. The curated
    data begins with the 1949 season and is updated weekly during the
    racing season. Explore race, season, or career performance for drivers,
    teams, and manufacturers throughout NASCAR's history. Data was sourced
    with permission from DriverAverages.com.
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.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
    biological image data, especially traced neurons. Reads and writes 3D images
    in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
    format. Traced neurons can be imported from and written to SWC and 'Amira'
    LineSet and SkeletonGraph formats. These data can then be visualised in 3D
    via 'rgl', manipulated including applying calculated registrations, e.g.
    using the 'CMTK' registration suite, and analysed. There is also a simple
    representation for neurons that have been subjected to 3D skeletonisation
    but not formally traced; this allows morphological comparison between
    neurons including searches and clustering (via the 'nat.nblast' extension
    package).
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a
    collection of NBLAST-related functions for neuronal morphology comparison (Costa et al. (2016) <doi: 10.1016/j.neuron.2016.06.012>).
Extends package 'nat' (NeuroAnatomy Toolbox) by
    providing objects and functions for handling template brains.
Utility functions that may be of general interest
    but are specifically required by the 'NeuroAnatomy Toolbox' ('nat').
    Includes functions to provide a basic make style system to update
    files based on timestamp information, file locking and 'touch'
    utility. Convenience functions for working with file paths include
    'abs2rel', 'split_path' and 'common_path'. Finally there are utility
    functions for working with 'zip' and 'gzip' files including integrity
    tests.
Fast functions implemented in C++ via 'Rcpp' to support the
    'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large
    speed-ups for basic manipulation of neuronal skeletons over pure R
    functions found in the 'nat' package. The expectation is that end
    users will not use this package directly, but instead the 'nat'
    package will automatically use routines from this package when it is
    available to enable large performance gains.
Provides R native 'ONNX' model inference without requiring 'Python', 'reticulate' bindings, or 'TensorFlow'. This package directly binds the 'ONNX Runtime' C API via 'Rcpp', enabling real-time inference for '.onnx' engines, all within R. Standard CPU execution is supported as well as the 'CoreML' Execution Provider (CEP) for Apple Silicon, all without external bindings. This package handles OS detection, linking 'ONNX' libraries, and inference. For more information about 'ONNX Runtime' see <https://onnxruntime.ai/>.
Provides streamlined installation for packages from the 'natverse',
    a suite of R packages for computational neuroanatomy built on top of the
    'nat' 'NeuroAnatomy Toolbox' package. Installation of the complete
    'natverse' suite requires a 'GitHub' user account and personal access token
    'GITHUB_PAT'. 'natmanager' will help the end user set this up if necessary.
Color palettes for data visualization inspired by National Parks. Currently contains 15 color schemes and checks for colorblind-friendliness of palettes.
Interface to 'NatureServe' (<https://www.natureserve.org/>).
    Includes methods to get data, image metadata, search taxonomic names,
    and make maps.
Natural strata can be used in observational studies to balance
    the distributions of many covariates across any number of treatment
    groups and any number of comparisons. These strata have proportional 
    amounts of units within each stratum across the treatments, allowing 
    for simple interpretation and aggregation across strata. Within each 
    stratum, the units are chosen using randomized rounding of a linear 
    program that balances many covariates. For more details, see Brumberg et al. (2022) <doi:10.1111/rssa.12848> and Brumberg et al.(2023) <doi:10.1093/jrsssc/qlad010>.
    To solve the linear program, the 'Gurobi' commercial optimization software 
    is recommended, but not required. The 'gurobi' R package can be installed by following the instructions at <https://docs.gurobi.com/projects/optimizer/en/current/reference/r/setup.html> after claiming your free academic license at <https://www.gurobi.com/academia/academic-program-and-licenses/>.
Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.
Provides functions related to human natural ordering.
    It handles adjacent digits in a character sequence as a number so that
    natural sort function arranges a character vector by their numbers, not digit
    characters. It is typically seen when operating systems lists file names. For
    example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1
    < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms
    arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth
    characters.
Collection of example animal sounds for bioacoustic analysis.
Implements the non-asymptotically valid and asymptotically exact
  confidence intervals in two cases:
  estimation of the mean, and estimation of (a linear combination of)
  the coefficients in a linear regression model,
  following  (Derumigny, Girard and Guyonvarch, 2025)
  <doi:10.48550/arXiv.2507.16776>.
Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) <doi:10.1109/TIM.2023.3267360> which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data.
             The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. 
             The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions.
Provides a navigation menu to enable pipe-friendly data processing for hierarchical data structures.
    By activating the menu items, you can perform operations on each item while maintaining the overall structure in attributes.
Implements the navigated weighting (NAWT) proposed by Katsumata (2020)
    <arXiv:2005.10998>, which improves the inverse probability weighting by 
    utilizing estimating equations suitable for a specific pre-specified parameter 
    of interest (e.g., the average treatment effects or the average treatment 
    effects on the treated) in propensity score estimation. It includes the 
    covariate balancing propensity score proposed by Imai and Ratkovic (2014) 
    <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in 
    propensity score estimation. The point estimate of the parameter of interest
    as well as coefficients for propensity score estimation and their 
    uncertainty are produced using the M-estimation. The same functions can be 
    used to estimate average outcomes in missing outcome cases.
Model-based clustering of high-dimensional non-negative
             data that follow Generalized Negative Binomial distribution. All functions 
             in this package applies to either continuous or integer data. Correlation
            between variables are allowed, while samples are assumed to be independent.
Provides interface to the online basketball data resources such as
      Basketball reference API <https://www.basketball-reference.com/> and helps
      R users analyze basketball data.
Palettes generated from NBA jersey colorways.
Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.
An implementation of the Naive Bayes Classifier (NBC) algorithm
    used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) 
    <DOI:10.1186/s12916-015-0521-2>.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Three distinct methods are implemented for evaluating the sums of arbitrary negative binomial distributions. These methods are: Furman's exact probability mass function (Furman (2007) <doi:10.1016/j.spl.2006.06.007>), saddlepoint approximation, and a method of moments approximation. Functions are provided to calculate the density function, the distribution function and the quantile function of the convolutions in question given said evaluation methods. Functions for generating random deviates from negative binomial convolutions and for directly calculating the mean, variance, skewness, and excess kurtosis of said convolutions are also provided.
Calls the 'Jupyter' script 'nbconvert' to create vignettes from
  notebooks. Those notebooks ('.ipynb' files) are files containing rich text,
  code, and its output. Code cells can be edited and evaluated interactively.
  See <https://jupyter.org/> for more information.
A suite of functions to work with data from the National Institutes 
  of Health Brain Development Cohorts Data Hub. The package provides tools to 
  create, clean, process, and filter datasets and associated metadata. These 
  utilities are intended to simplify reproducible data-preparation for 
  future research.
The Dirichlet (aka NBD-Dirichlet) model describes the purchase incidence and brand choice of consumer products.  We estimate the model and summarize various theoretical quantities of interest to marketing researchers. Also provides functions for making tables that compare observed and theoretical statistics.
Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint
    with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including 
    (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario.
Catalogue of NBER working papers published between June 1973 and December 2021.
An extension of the Beta Kernel Process model designed to handle negative binomial responses for count data modeling, building upon Zhao, Qing and Xu (2025) <doi:10.48550/arXiv.2508.10447>.
We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version.
Allow users to obtain basketball statistics for 
    the Australian basketball league 'NBL'<https://nbl.com.au/>. 
    Stats include play-by-play, shooting locations, results and 
    box scores for teams and players.
Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) <doi:10.48550/arXiv.2010.03567>), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles.
Conduct inference on the sample average treatment effect for a 
    matched (observational) dataset with a continuous treatment. Equipped with
    calipered non-bipartite matching, bias-corrected sample average treatment
    effect estimation, and covariate-adjusted variance estimation. Matching, 
    estimation, and inference methods are described in 
    Frazier, Heng and Zhou (2024) <doi:10.48550/arXiv.2409.11701>.
Perform non-bipartite matching and matched randomization. A
    "bipartite" matching utilizes two separate groups, e.g. smokers being
    matched to nonsmokers or cases being matched to controls. A "non-bipartite"
    matching creates mates from one big group, e.g. 100 hospitals being
    randomized for a two-arm cluster randomized trial or 5000 children who
    have been exposed to various levels of secondhand smoke and are being
    paired to form a greater exposure vs. lesser exposure comparison. At the
    core of a non-bipartite matching is a N x N distance matrix for N potential
    mates. The distance between two units expresses a measure of similarity or
    quality as mates (the lower the better). The 'gendistance()' and
    'distancematrix()' functions assist in creating this. The 'nonbimatch()'
    function creates the matching that minimizes the total sum of distances
    between mates; hence, it is referred to as an "optimal" matching. The
    'assign.grp()' function aids in performing a matched randomization. Note
    bipartite matching can be performed using the prevent option in
    'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
    regression inferences from RNA-Sequencing Data.
An implementation of network-based statistics in R using mixed effects models.
    Theoretical background for Network-Based Statistics can be found in Zalesky et al. (2010)
    <doi:10.1016/j.neuroimage.2010.06.041>. For Mixed Effects Models check the
    R package <https://CRAN.R-project.org/package=nlme>.
An interactive document on  the topic of naive Bayes classification  analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/NBShiny/>.
Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) <doi:10.1093/ije/dyaa031>.
Performs variable selection in sparse negative binomial GLARMA (Generalised Linear Autoregressive Moving Average) models. For further details we refer the reader to the paper Gomtsyan (2023), <arXiv:2307.00929>.
User-friendly functions for extracting a data
 table (row for each match, column for each group)
 from non-tabular text data using regular expressions,
 and for melting columns that match a regular expression.
 Patterns are defined using a readable syntax
 that makes it easy to build complex patterns
 in terms of simpler, re-usable sub-patterns.
 Named R arguments are translated to column names
 in the output; capture groups without names are used
 internally in order to provide a standard interface
 to three regular expression 'C' libraries
 ('PCRE', 'RE2', 'ICU').
 Output can also include numeric columns via
 user-specified type conversion functions.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52) <doi:10.1177/1094428115584005>.
  NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
  (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g., an effort, a characteristic) is necessary but not sufficient for a (desired or undesired) level of y (e.g., good performance or disease). A quick start guide for using this package can be found here: <https://repub.eur.nl/pub/78323/> or <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2624981>.
Extracts team records/schedules and player statistics for the 
    2020-2025 National Collegiate Athletic Association (NCAA) women's and men's
    divisions I, II, and III volleyball teams from <https://stats.ncaa.org>. 
    Functions can aggregate statistics for teams, conferences, divisions, or 
    custom groups of teams.
A flexible tool that can perform
    (i) traditional non-compartmental analysis (NCA) and
    (ii) Simulation-based posterior predictive checks for population
    pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using 
    NCA metrics. The methods are described in Acharya et al. (2016) 
    <doi:10.1016/j.cmpb.2016.01.013>.
Conduct a noncompartmental analysis with industrial strength.
             Some features are
             1) CDISC SDTM terms
             2) Automatic or manual slope selection
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             5) Produce pdf, rtf, text report files.
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
Makes NCBI taxonomic data locally available and
        searchable as an R object.
Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. 
    The implemented analysis methods are described in: 
    Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, 
    Saville et al. (2022) <doi:10.1177/17407745221112013> and 
    Schmidli et al. (2014) <doi:10.1111/biom.12242>.
Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets.  Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily.  It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files.  This package replaces the former ncdf package, which only worked with netcdf version 3 files.  For various reasons the names of the functions have had to be changed from the names in the ncdf package.  The old ncdf package is still available at the URL given below, if you need to have backward compatibility.  It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem.  However, the ncdf package does not provide an interface for netcdf version 4 files.
Network Common Data Form ('netCDF') files are widely used for 
    scientific data. Library-level access in R is provided through packages 
    'RNetCDF' and 'ncdf4'. Package 'ncdfCF' is built on top of 'RNetCDF' and 
    makes the data and its attributes available as a set of R6 classes that are 
    informed by the Climate and Forecasting Metadata Conventions. Access to the 
    data uses standard R subsetting operators and common function forms.
Tools to create time series and geometry 'NetCDF' files.
Spatial (cross-)covariance and related geostatistical tools: the
        nonparametric (cross-)covariance function , the spline correlogram, the
        nonparametric phase coherence function, local indicators of spatial 
        association (LISA), (Mantel) correlogram, (Partial) Mantel test.
Extract metadata from 'NetCDF' data sources, these can be files, file handles or
 servers. This package leverages and extends the lower level functions of the 'RNetCDF' package 
 providing a consistent set of functions that all return data frames. We introduce named concepts 
 of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 
 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library 
 itself with only the named concepts of 'variables', 'dimensions' and 'attributes'.
A set of handy functions. Includes a versatile one line progress bar, one 
 line function timer with detailed output, time delay function, text histogram, object 
 preview, CRAN package search, simpler package installer, Linux command install check, 
 a flexible Mode function, top function, simulation of correlated data, and more.
Provides tools for fitting the neutral community model (NCM) to 
    assess the role of stochastic processes in community assembly. The package 
    implements the framework of Sloan et al. (2006) <doi:10.1111/j.1462-2920.2005.00956.x>,
    enabling users to evaluate neutral dynamics in ecological and microbial communities.
A set of techniques that can be used to develop, validate, and implement automated classifiers. A powerful tool for transforming raw data into meaningful information, 'ncodeR' (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is designed specifically for working with big data: large document collections, logfiles, and other text data.
Inference and dependence measure for the non-central squared Gaussian, Student, Clayton, Gumbel, and Frank copula models.The description of the methodology is taken from Section 3 of Nasri, Remillard and Bouezmarni (2019) <doi:10.1016/j.jmva.2019.03.007>.
Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more.
    'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering),
    while incorporating the dependencies across multiple types of Omics data.
    (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).
Fits regularization paths for linear regression, GLM, and Cox
  regression models using lasso or nonconvex penalties, in particular the
  minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)
  penalty, with options for additional L2 penalties (the "elastic net" idea).
  Utilities for carrying out cross-validation as well as post-fitting
  visualization, summarization, inference, and prediction are also provided.
  For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388>
  or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.
Non-parametric dimensionality reduction function. Reduction with and without feature selection. Plot functions. Automated feature selections. Kosztyan et. al. (2024) <doi:10.1016/j.eswa.2023.121779>.
Computes various geospatial indices of socioeconomic deprivation 
    and disparity in the United States. Some indices are considered "spatial" 
    because they consider the values of neighboring (i.e., adjacent) census 
    geographies in their computation, while other indices are "aspatial" 
    because they only consider the value within each census geography. Two 
    types of aspatial neighborhood deprivation indices (NDI) are available, 
    including: (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x>
    and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> 
    and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002>
    who use variables chosen by Roux and Mair (2010)
    <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition of multiple
    demographic characteristics from the U.S. Census Bureau American Community 
    Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5
    (2005-2009 onward), the package can also compute indices of racial or ethnic
    residential segregation, including but limited to those discussed in Massey 
    & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of 
    socioeconomic disparity.
Streaming 'JSON' ('ndjson') has one 'JSON' record per-line
        and many modern 'ndjson' files contain large numbers of records.
        These constructs may not be columnar in nature, but it is often
        useful to read in these files and "flatten" the structure out to
        enable working with the data in an R 'data.frame'-like context.
        Functions are provided that make it possible to read in plain
        'ndjson' files or compressed ('gz') 'ndjson' files and either
        validate the format of the records or create "flat" 'data.table'
        structures from them.
Naive discriminative learning implements learning and
    classification models based on the Rescorla-Wagner equations and their
    equilibrium equations.
An interactive presentation on  the topic of normal distribution using 'rmarkdown' and 'shiny' packages. It is helpful to those who want to learn normal distribution quickly and get a hands on experience. The presentation has a template for solving problems on normal distribution. Runtime examples are provided in the package function as well as at  <https://kartikeyastat.shinyapps.io/NormalDistribution/>.
Provides color palettes that approximate the University of
    Notre Dame brand colors, together with 'ggplot2' discrete color and
    fill scales. The default palette leads with six Notre Dame brand
    colors that read clearly on a white background and extends through
    seven former Notre Dame brand colors, interpolating when more colors
    are needed than the brand supplies. The palette is intended for
    statistical visualization and psychometric analysis: the white-safe
    ordering keeps plotted categories legible, and the near-white-to-navy
    ramps suit continuous quantities such as correlations and factor
    loadings. A colorblind-friendly ordering of the Notre Dame colors
    themselves (arranged so the colors stay distinguishable under
    simulated deuteranopia, protanopia, and tritanopia), the former colors
    as a standalone palette, the four near-white brand tints (plus six
    informal soft backgrounds) for backgrounds and sequential
    ramps, a reference table of every color with brand and role labels,
    and a palette-preview helper are also provided. A matching R Markdown
    stylesheet, built from the same colors so a report and its figures
    share one brand palette, themes HTML and 'shiny' output. The
    colors approximate those described in the University's branding
    guidelines (<https://onmessage.nd.edu/university-branding/colors/>).
    This is an independent project and is not affiliated with or endorsed
    by the University of Notre Dame.
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.
Near-far matching is a study design technique for preprocessing observational data to mimic a pair-randomized trial. Individuals are matched to be near on measured confounders and far on levels of an instrumental variable. Methods outlined in further detail in Rigdon, Baiocchi, and Basu (2018) <doi:10.18637/jss.v086.c05>.
Includes functions and examples to compute NEAT, the Network
    Enrichment Analysis Test described in Signorelli et al.  (2016,
    <DOI:10.1186/s12859-016-1203-6>).
Simplify the exploratory data analysis process for multiple network
             data sets with the help of hierarchical clustering, consensus 
             clustering and heatmaps. Multiple network data consists of multiple
             disjoint networks that have common variables (e.g. ego networks). 
             This package contains the necessary tools for exploring such data,
             from the data pre-processing stage to the creation of dynamic
             visualizations.
Utilities for unambiguous, neat and legible
    representation of data (date, time stamp, numbers, percentages and strings) 
    for presentation of analysis , aiming for elegance and consistency.
    The purpose of this package is to format data, that is better 
    for presentation and any automation jobs that reports numbers.
Collapse, partition, combine, fill gaps in and expand date/time ranges.
A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.
Tools to generate Necklaces, Bracelets, Lyndon words and de Bruijn sequences. The generation relies on integer partitions and uses the 'KStatistics' package. Methods used in the package refers to E. Di Nardo and G. Guarino (2022) <doi:10.48550/arXiv.2208.06855>.
Based on Natural Earth <https://www.naturalearthdata.com/>, a subset of countries can
             easily be selected with their administrative boundaries, joined with an external
	     data frame and plotted as a thematic map.
A simple function for easier package loading and auto-installation.
Subsampling methods for big data under different models and assumptions.
    Starting with linear regression and leading to Generalised Linear Models, softmax
    regression, and quantile regression. Specifically, the model-robust subsampling method 
    proposed in Mahendran, A., Thompson, H., and McGree, J. M. (2023) <doi:10.1007/s00362-023-01446-9>, 
    where multiple models can describe the big data, and the subsampling framework for potentially 
    misspecified Generalised Linear Models in Mahendran, A., Thompson, H., and McGree, J. M. (2025)
    <doi:10.48550/arXiv.2510.05902>.
Methods to reduce confounding bias from unmeasured confounders in observational studies of vaccine efficacy using negative control outcomes.
The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.
Estimating the number of essential genes in a genome on
    the basis of data from a random transposon mutagenesis experiment,
    through the use of a Gibbs sampler.
    Lamichhane et al. (2003) <doi:10.1073/pnas.1231432100>.
Researchers often want to evaluate whether there is a negligible
    relationship among variables. The 'negligible' package provides functions that 
    are useful for conducting negligible effect testing (also called
    equivalence testing). For example, there are functions for evaluating the 
    equivalence of means or the presence of a negligible association 
    (correlation or  regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>.
    Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>.
    Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>.
    Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>.
    Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>.
    van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>.
    Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>.
    Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.
A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty.
Implementation of the local approach described in Sola et
    al., 2026 <doi:10.64898/2025.12.05.692507> to identify companion
    species of a bacteria of interest. From several abundance tables of
    metagenomic data, 'NeighborFinder' suggests a shortlist of companion
    species based on the integration of results. A visualization via a
    network is proposed.
Neighbourhood functions are key components of
  local-search algorithms such as Simulated Annealing or
  Threshold Accepting.  These functions take a solution and
  return a slightly-modified copy of it, i.e. a neighbour.
  The package provides a function neighbourfun() that
  constructs such neighbourhood functions, based on
  parameters such as admissible ranges for elements in a
  solution.  Supported are numeric and logical solutions.
  The algorithms were originally created for
  portfolio-optimisation applications, but can be used for
  other models as well.  Several recipes for neighbour
  computations are taken from "Numerical Methods and
  Optimization in Finance" by M. Gilli, D. Maringer and
  E. Schumann (2019, ISBN:978-0128150658).
Provides several direct search optimization algorithms based on the
  simplex method. The provided algorithms are direct search algorithms, i.e.
  algorithms which do not use the derivative of the cost function. They are
  based on the update of a simplex. The following algorithms are available: the
  fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained
  optimization with a fixed shape simplex, 1962) <doi:10.1080/00401706.1962.10490033>, 
  the variable shape simplex method of Nelder and Mead (unconstrained optimization
  with a variable shape simplex made, 1965) <doi:10.1093/comjnl/7.4.308>, and 
  Box's complex method (constrained optimization with a variable shape simplex,
  1965) <doi: 10.1093/comjnl/8.1.42>.
Provides a computational toolkit for analyzing nematode communities in ecological studies. 
    Includes methods to quantify nematode-based ecological indicators such as metabolic footprints, 
    energy flow metrics, and community structure. These tools support assessments of soil health, 
    ecosystem functioning, and trophic interactions, standardizing the use of nematodes as 
    bioindicators.
To study network evolution models and different blockmodeling approaches. Various functions enable generating (temporal) networks with a selected blockmodel type, taking into account selected local network mechanisms. The development of this package is financially supported the Slovenian Research Agency (www.arrs.gov.si) within the research program P5<96>0168 and the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).
Provides helpers for discovering, planning, and downloading
    open-access datasets from the Neuroscience Multi-Omic Archive ('NeMO';
    <https://nemoarchive.org/>). The package builds reproducible file
    manifests that record search parameters, file metadata, download URLs,
    checksums, and local file paths. It supports exploratory 'NeMO' metadata
    queries and provides first-pass bridges from downloaded files into
    'SingleCellExperiment' and 'Seurat' workflows.
Designed to automate the calculation of Emergency Medical
    Service (EMS) quality metrics, 'nemsqar' implements measures defined
    by the National EMS Quality Alliance (NEMSQA). By providing reliable,
    evidence-based quality assessments, the package supports EMS agencies,
    healthcare providers, and researchers in evaluating and improving
    patient outcomes.  Users can find details on all approved NEMSQA
    measures at <https://www.nemsqa.org/measures>.  Full technical
    specifications, including documentation and pseudocode used to develop
    'nemsqar', are available on the NEMSQA website after creating a user
    profile at <https://www.nemsqa.org>.
Calculates a cumulative summation nonparametric extended 
    median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. 
    It then generates a control chart to assess processes and determine if any streams are out of control.
The aim of neo2R is to provide simple and low level connectors
   for querying neo4j graph databases (<https://neo4j.com/>).
   The objects returned by the query functions are either lists or data.frames
   with very little post-processing.
   It allows fast processing of queries returning many records.
   And it let the users handle post-processing according to the data model
   and their needs.
Sends queries to a specified 'Neo4J' graph database, capturing results in a dataframe where appropriate.  
    Other useful functions for the importing and management of data on the 'Neo4J' server and basic local server admin.
A Modern and Flexible 'Neo4J' Driver, allowing you to query
    data on a 'Neo4J' server and handle the results in R. It's modern in
    the sense it provides a driver
    that can be easily integrated in a data analysis workflow, especially by
    providing an API working smoothly with other data analysis and graph
    packages. It's flexible in the  way it returns the results, by
    trying to stay as close as
    possible to the way 'Neo4J' returns data. That way, you have the control
    over the way you will compute the results. At the same time, the result
    is not too complex, so that the "heavy lifting" of data wrangling is not
    left to the user.
Calculating the density, cumulative distribution, quantile, and random number of
    neo-normal distribution. It also interfaces with the 'brms' package,
    allowing the use of the neo-normal distribution as a custom family.
    This integration enables the application of various 'brms' formulas
    for neo-normal regression.  Modified to be Stable as Normal from Burr (MSNBurr), 
    Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), 
    Jones-Faddy Skew-t, Fernandez-Osiewalski-Steel Skew Exponential Power,  
    and Jones Skew Exponential Power distributions are supported.
    References: Choir, A. S. (2020).Unpublished Dissertation, Iriawan, N. (2000).Unpublished Dissertation,
    Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) <doi:10.1201/9780429298547>.
A 'JAGS' extension module provides neo-normal distributions
    family including MSNBurr, MSNBurr-IIa, GMSNBurr, Lunetta Exponential Power,
    Fernandez-Steel Skew t, Fernandez-Steel Skew Normal, Fernandez-Osiewalski-Steel
    Skew Exponential Power, Jones Skew Exponential Power.
    References:
    Choir, A. S. (2020). "The New Neo-Normal Distributions and Their Properties".Unpublished Dissertation.
    Denwood, M.J. (2016) <doi:10.18637/jss.v071.i09>.
    Fernandez, C., Osiewalski, J., & Steel, M. F. (1995) <doi:10.1080/01621459.1995.10476637>.
    Fernandez, C., & Steel, M. F. (1998) <doi:10.1080/01621459.1998.10474117>.
    Iriawan, N. (2000). "Computationally Intensive Approaches to Inference in NeoNormal Linear Models".Unpublished Dissertation.
    Mineo, A., & Ruggieri, M. (2005) <doi:10.18637/jss.v012.i04>.
    Rigby, R. A., & Stasinopoulos, D. M. (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.
    Lunetta, G. (1963). "Di una Generalizzazione dello Schema della Curva Normale".
    Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) <doi:10.1201/9780429298547>.
NEON observational data are provided via the NEON Data Portal <https://www.neonscience.org> 
  and NEON API, and can be downloaded and reformatted by the 'neonUtilities' package. NEON observational data 
  (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and 
  algal chemistry) are published in a format consisting of one or more tabular data files. This package provides 
  tools for performing common operations on NEON observational data, including checking for duplicates and 
  joining tables.
Downloading and organizing plant presence and percent cover data from the National Ecological Observatory Network <https://www.neonscience.org>.
Acquires and synthesizes soil carbon fluxes at sites located in the National Ecological Observatory Network (NEON). Provides flux estimates and associated uncertainty as well as key environmental measurements (soil water, temperature, CO2  concentration) that are used to compute soil fluxes.
The National Ecological Observatory Network (NEON) provides access
  to its numerous data products through its REST API, 
  <https://data.neonscience.org/data-api/>. This package provides a
  high-level user interface for downloading and storing NEON data products.
  Unlike 'neonUtilities', this package will avoid repeated downloading,
  provides persistent storage, and improves performance.  'neonstore' can also
  construct a local 'duckdb' database of stacked tables, making it possible
  to work with tables that are far to big to fit into memory.
NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org>
    or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from
    the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files
    can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting 
    data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and 
    converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.
Access and manipulation of data using the Neotoma Paleoecology Database.
        <https://api.neotomadb.org/api-docs/>.
        Examples in functions that require API access are not executed during CRAN checks.
        Vignettes do not execute as to avoid API calls during CRAN checks.
Set of functions to estimate kidney function and other traits of interest in nephrology.
Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.
An interface to Neptune. A metadata store for MLOps, built for teams that run a lot of experiments.
    It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata.
    Neptune is used for:
    • Experiment tracking: Log, display, organize, and compare ML experiments in a single place.
    • Model registry: Version, store, manage, and query trained models, and model building metadata.
    • Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live
    For more information see <https://neptune.ai/>.
Creates collapsible, expandable 'HTML' tables from hierarchical
    data. Supports data frame input with multi-level grouping, custom column
    formatters, bottom-up rollup aggregation, and CSS-variable-based theming.
    Works in 'Shiny' applications, R Markdown, 'Quarto', and the 'RStudio' Viewer.
Computes effective population size (Ne) and the Ne/N ratio for
    stage-structured populations using the matrix population model framework
    of Yonezawa (2000) <doi:10.1111/j.0014-3820.2000.tb01244.x>. Functions
    are provided for sexually reproducing, clonally reproducing, and mixed
    (sexual + clonal) populations. Includes sensitivity and elasticity
    analyses for Ne/N with respect to vital rates.
Clinical reporting figures require to use consistent colors
    and configurations. As a part of the Roche open-source clinical
    reporting project, namely the NEST project, the 'nestcolor' package
    specifies the color code and default theme with specifying 'ggplot2'
    theme parameters. Users can easily customize color and theme settings
    before using the reset of NEST packages to ensure consistent settings
    in both static and interactive output at the downstream.
Provides functions for specifying and fitting nested
    dichotomy logistic regression models for a multi-category response and
    methods for summarising and plotting those models.  Nested dichotomies are
    statistically independent, and hence provide an additive decomposition
    of tests for the overall 'polytomous' response.  When the dichotomies
    make sense substantively, this method can be a simpler alternative to
    the standard 'multinomial' logistic model which compares response
    categories to a reference level.  See: J. Fox (2016), "Applied
    Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN
    1452205663.
Provides a nested menu widget for usage in 'Shiny'
    applications. This is useful for hierarchical choices (e.g. continent,
    country, city).
Library to plot performance profiles (Dolan and More (2002) <doi:10.1007/s101070100263>) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) <doi:10.19139/soic-2310-5070-679>) for a given data frame.
Implementation of forward selection based on cross-validated
             linear and logistic regression.
Estimate, compare, and analyze dynamic and psychological networks
    using a unified interface. Provides transition network analysis
    estimation (transition, frequency, co-occurrence, attention-weighted)
    Saqr et al. (2025) <doi:10.1145/3706468.3706513>, psychological
    network methods (correlation, partial correlation, 'graphical lasso',
    'Ising') Saqr, Beck, and Lopez-Pernas (2024)
    <doi:10.1007/978-3-031-54464-4_19>,
    and higher-order network methods including higher-order networks,
    higher-order network embedding, hyper-path anomaly, and multi-order
    generative model. Supports bootstrap inference, permutation testing,
    split-half reliability, centrality stability analysis, mixed Markov
    models, multi-cluster multi-layer networks and clustering.
This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem.  Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.
Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax.
Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.
Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of netClust.
Facilitates network clustering and evaluation of cluster
        configurations.
Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package.
Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <doi:10.48550/arXiv.1912.12551>.
The 'NetCoupler' algorithm identifies potential direct effects of 
    correlated, high-dimensional variables formed as a network with an external 
    variable. The external variable may act as the dependent/response variable 
    or as an independent/predictor variable to the network.
Implementation of the NetCutter algorithm described in
    Müller and Mancuso (2008) <doi:10.1371/journal.pone.0003178>. The package
    identifies co-occurring terms in a list of containers. For example, it
    may be used to detect genes that co-occur across genomes.
Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.
Empirical statistical analysis, visualization and simulation of
    diffusion and contagion processes on networks. The package implements algorithms
    for calculating network diffusion statistics such as transmission rate, hazard
    rates, exposure models, network threshold levels, infectiousness (contagion),
    and susceptibility. The package is inspired by work published in Valente,
    et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN:
    9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011)
    <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.
A set of functions providing the implementation of the network meta-analysis model with dose-response relationships, predicted values of the fitted model and dose-response plots in a frequentist way.
Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface;  2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.
A network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our publication by Ahn S and Oh EJ (2026) <doi:10.1093/bioadv/vbag038>.
Carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.
A multi-core R package that contains a set of tools based on copula graphical
             models for accomplishing the three interrelated goals in genetics and genomics in an
			 unified way: (1) linkage map construction, (2) constructing linkage disequilibrium
			 networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-
			 phenotype-environment interactions networks. 
			 The 'netgwas' package can deal with biparental inbreeding and outbreeding species with
			 any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes),
			 tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where 
			 number of variables p is considerably larger than number of sample sizes (p >> n). 
			 The computations is memory-optimized using the sparse matrix output. The 'netgwas' 
			 implements the methodological developments in Behrouzi and Wit (2017)
             <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <doi:10.1093/bioinformatics/bty777>.
The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.
Builds, validates, analyzes, and visualizes network data from
    dyadic, event, matrix, 'igraph', and 'network' inputs. Supports
    cross-sectional, longitudinal, bipartite, and multi-layer networks, with
    conversion helpers for common modeling workflows and plotting utilities
    for exploratory analysis. Network methods are described in Wasserman and
    Faust (1994, ISBN:9780521387071), Cranmer et al. (2021)
    <doi:10.1017/9781316662915>, and Minhas et al. (2022)
    <doi:10.1017/psrm.2021.56>.
Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
Implementation of network integration approaches
    comprising unweighted and weighted integration methods. Unweighted integration
    is performed considering the average, per-edge average, maximum and minimum of
    networks edges. Weighted integration takes into account a weight for each 
    network during the fusion process, where the weights express
    the ''predictiveness strength'' of each network considering a specific predictive
    task. Weights can be learned using a machine learning algorithm able to associate
    the weights to the assessment of the accuracy of the learning algorithm 
    trained on the network itself. The implemented methods can be applied to 
    effectively integrate different biological networks modelling a wide range
    of problems in bioinformatics (e.g. disease gene prioritization, protein
    function prediction, drug repurposing, clinical outcome prediction).
Designed to create interactive and visually compelling network maps using R Shiny. It allows users to quickly analyze CSV files and visualize complex relationships, structures, and connections within data by leveraging powerful network analysis libraries and dynamic web interfaces.
Build and run spatially explicit
    agent-based models using only the R platform. 'NetLogoR' follows the same
    framework as the 'NetLogo' software
    (Wilensky (1999) <https://www.netlogo.org>) and is a translation
    in R of the structure and functions of 'NetLogo'.
    'NetLogoR' provides new R classes to define model agents and functions to
    implement spatially explicit agent-based models in the R environment.
    This package allows benefiting of the fast and easy coding phase from the
    highly developed 'NetLogo' framework, coupled with the versatility, power
    and massive resources of the R software.
    Examples of two models from the NetLogo software repository 
    (Ants <https://ccl.northwestern.edu/netlogo/models/Ants>) and 
    Wolf-Sheep-Predation 
    (<https://ccl.northwestern.edu/netlogo/models/WolfSheepPredation>),
    and a third, Butterfly, from 
    Railsback and Grimm (2012) <https://www.railsback-grimm-abm-book.com/>, all
    written using 'NetLogoR' are available. 
    The 'NetLogo' code of the original version of these
    models is provided alongside.
    A programming guide inspired from the 'NetLogo' Programming Guide
    (<https://docs.netlogo.org/programming.html>) and a dictionary
    of 'NetLogo' primitives (<https://docs.netlogo.org/dictionary.html>)
    equivalences are also available.
    NOTE: To increment 'time', these functions can use a for loop or can be
    integrated with a discrete event simulator, such as 'SpaDES'
    (<https://cran.r-project.org/package=SpaDES>).
Represent 'network' or 'igraph' objects whose vertices can be represented by features in an 'sf' object as a network graph surmising a 'sf' plot. Fits into 'ggplot2' grammar.
Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME).
    URL: <https://github.com/sduxbury/netmediate>.
    BugReports: <https://github.com/sduxbury/netmediate/issues>.
    Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) <doi:10.1086/427322>.
    Snijders, Tom A. B., and Christian E. G. Steglich (2015) <doi:10.1177/0049124113494573>.
    Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) <doi:10.1037/a0020761>.
    Duxbury, Scott (2023) <doi:10.1177/00811750231209040>.
    Duxbury, Scott (2024) <doi:10.1177/00811750231220950>.
Provides measures to describe and manipulate one-mode, two-mode,
    multiplex, and multilevel networks using matrix algebra. Implements
    functions for network centrality, cohesive subgroups, structural holes,
    similarity measures, path distances, signed networks, and random network
    generation. Supports ego-centric and whole-network analyses, including
    dyadic and triadic census, structural balance, and bipartite projections.
    Key references: Bonacich (1972) <doi:10.1080/0022250X.1972.9989806>,
    Breiger (1974) <doi:10.2307/2576011>,
    Kivelä et al. (2014) <doi:10.1093/comnet/cnu016>,
    Espinosa-Rada et al. (2024) <doi:10.1016/j.socnet.2023.11.008>.
The field of immunology benefits from software that can
    predict which peptide sequences trigger an immune response.
    'NetMHCIIpan' is a such a tool: it predicts the
    binding strength of a short peptide to a Major Histocompatibility
    Complex class II (MHC-II) molecule.
    'NetMHCIIpan' can be used from a web server at 
    <https://services.healthtech.dtu.dk/services/NetMHCIIpan-3.2/>
    or from the command-line, using a local installation. This package
    allows to call 'NetMHCIIpan' from R.
Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks,
             incorporating node-level predictors of mixed-membership vectors, as well as 
             dyad-level predictors. For networks observed over time, the model defines a hidden
             Markov process that allows the effects of node-level predictors to evolve in discrete,
             historical periods. In addition, the package offers a variety of utilities for 
             exploring results of estimation, including tools for conducting posterior 
             predictive checks of goodness-of-fit and several plotting functions. The package 
             implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic
             Blockmodel Regression for Social Networks: Application to International Conflicts',
             available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.
Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.
A graph visualization engine that emphasizes on 
  aesthetics at the same time providing default parameters that yield
  out-of-the-box-nice visualizations. The package is built on top of
  'The Grid Graphics Package' and seamlessly work with 'igraph' and 
  'network' objects.
Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.
Implements methods for centrality related analyses of networks. 
    While the package includes the possibility to build more than 20 indices, 
    its main focus lies on index-free assessment of centrality via partial 
    rankings obtained by neighborhood-inclusion or positional dominance. These 
    partial rankings can be analyzed with different methods, including 
    probabilistic methods like computing expected node ranks and relative 
    rank probabilities (how likely is it that a node is more central than another?).
    The methodology is described in depth in the vignettes and in
    Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.
Functions for assessing the replication/preservation of a network 
  module's topology across datasets through permutation testing; Ritchie et al. 
  (2015) <doi: 10.1016/j.cels.2016.06.012>.
Many tools for calculating network, node, or tie
   marks, measures, motifs and memberships of many different types of networks.
   Marks identify structural positions, measures quantify network properties,
   memberships classify nodes into groups, and motifs tabulate substructure participation.
   All functions operate with all classes of network data covered in 'manynet',
   and on directed, undirected, multiplex, multimodal, signed, and other networks.
Statistical entropy analysis of network data as introduced by Frank and Shafie (2016) <doi:10.1177/0759106315615511>, and a in textbook which is in progress.
Calculates network measures commonly used in Network Medicine. Measures such as the Largest Connected Component, the Relative Largest Connected Component, Proximity and Separation are calculated along with their statistical significance. Significance can be computed both using a degree-preserving randomization and non-degree preserving.
Predicting the structure of a graph including new nodes and edges using
    a time series of graphs. Flux balance analysis, a linear and integer programming 
    technique used in biochemistry is used with time series prediction methods to 
    predict the graph structure at a future time point 
    Kandanaarachchi (2025) <doi:10.48550/arXiv.2507.05806>.
Segregation is a network-level property such that edges between 
  predefined groups of vertices are relatively less likely. Network homophily 
  is a individual-level tendency to form relations with people who are similar 
  on some attribute (e.g. gender, music taste, social status, etc.). In general
  homophily leads to segregation, but segregation might arise without 
  homophily. This package implements descriptive indices measuring 
  homophily/segregation. It is a computational companion 
  to Bojanowski & Corten (2014) <doi:10.1016/j.socnet.2014.04.001>.
The network structural equation modeling conducts a network 
    statistical analysis on a data frame of coincident observations of 
    multiple continuous variables [1]. 
    It builds a pathway model by exploring a pool of domain knowledge guided 
    candidate statistical relationships between each of the variable pairs, 
    selecting the 'best fit' on the basis of a specific criteria such as 
    adjusted r-squared value.
    This material is based upon work supported by the U.S. National Science 
    Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, 
    under the NSF Solicitation: 
    NSF 20-570 Industry-University Cooperative Research Centers Program 
    [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, 
    Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) 
    <doi:10.1109/ACCESS.2013.2267611>.
Assists actuaries and other insurance modellers in pricing,
    reserving and capital modelling for non-life insurance and
    reinsurance modelling. Provides functions that help model
    excess levels, capping and pure Incurred but not reported
    claims (pure IBNR).
    Includes capped mean, exposure curves and increased limit
    factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced
    LogNormal-Pareto and Sliced Gamma-Pareto distributions.
    Includes mean, probability density function (pdf), cumulative
    probability function (cdf) and inverse cumulative probability
    function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto
    distributions.
    Includes calculating pure IBNR exposure with LogNormal and
    Gamma distribution for reporting delay.
    Includes three shiny tools, one to simulate insurance claims applying
    reinsurance structures, fit generalised linear models and fit claims
    frequency or severity distributions.
    Methods used in the package refer to
    Free for All by Yiannis Parizas (2023) <https://www.theactuary.com/2023/03/02/free-all>;
    Escaping the triangle by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/06/2019/06/05/escaping-triangle>;
    Take to excess by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/03/2019/03/06/taken-excess>.
R interface for the 'netstat' command line utility used to retrieve and parse 
    commonly used network statistics, including available and in-use 
    transmission control protocol (TCP) ports. Primers offering technical background information 
    on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" 
    by Michael Kerrisk (2014) 
    <https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) 
    <https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.
Implements node subsampling methods for multivariate inference on
    network moments (rescaled motif counts), including: uniform node subsampling
    to approximate the joint distribution of multiple network moments (Algorithm 1);
    externally sparsified moments for density-matched comparisons (Algorithm 2);
    and a two-sample test for unmatchable networks with unequal edge densities
    via a split-and-sparsify subsampling procedure (Algorithm 3). Built-in support
    for V-shape (2-star), triangle, and 3-star motifs, with a user-extensible
    interface for arbitrary additional motifs. Parallel execution is supported
    via 'doParallel' and 'foreach'. Based on Qi, Hua, Li and Zhou (2024)
    <doi:10.48550/arXiv.2409.01599>.
Introduces a novel network-constrained survival
    analysis framework for variable selection and parameter estimation in
    penalized survival models with convex penalties. The package extends two
    classical survival models, the Cox Proportional Hazards (PH) model and the
    Accelerated Failure Time (AFT) model, by incorporating prior biological
    knowledge from curated interaction networks (e.g., KEGG) into a
    double-penalty framework. The first penalty enforces variable selection
    through a LASSO penalty, while the second preserves gene-gene correlations
    by incorporating Laplacian-based constraints, ensuring that biologically
    relevant network structures are maintained. Using censored survival data,
    the method enables the identification of predictive biomarkers and pathways
    with potential relevance for target therapies. Model estimation is
    performed via proximal optimization algorithms combined with
    cross-validation for reliable tuning. To enhance interpretability,
    dedicated utility functions are implemented to consolidate results,
    yielding biologically coherent insights that can support personalized
    medicine and contribute to improved patient outcomes.
Features tools for the network data analysis and community detection. 
    Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. 
    Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models.
    Implements the following: 
    Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138>
    Bickel and Sarkar (2015) <doi:10.1111/rssb.12117>
    Lei (2016) <doi:10.1214/15-AOS1370>
    Wang and Bickel (2017) <doi:10.1214/16-AOS1457>
    Zhang and Amini (2020) <arXiv:2012.15047>
    Le and Levina (2022) <doi:10.1214/21-EJS1971>.
Enables users to retrieve data, meta-data, and codebooks from
    <https://nettskjema.no/>. The data from the API is richer than from
    the online data portal. This package is not developed
    by the University of Oslo IT. Mowinckel (2021) <doi:10.5281/zenodo.4745481>.
Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon 'igraph'.
Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.
Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
Tools to create and modify network objects.  The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.
Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. Version 1.1.0 includes high-performance C++ implementations via 'Rcpp'/'RcppArmadillo' for 5-15x faster MCMC sampling, along with modern 'ggplot2'-based visualizations with colorblind-friendly palettes.
This permutation based hypothesis test, suited for several types of data
    supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018),
    assesses the difference between two networks based on several invariance measures (network
    structure invariance, global strength invariance, edge invariance, several centrality
    measures, etc.). Network structures are estimated with l1-regularization. The Network
    Comparison Test is suited for comparison of independent (e.g., two different groups) and
    dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021),
    available from <doi:10.1037/met0000476>.
A permutation-based hypothesis test for statistical comparison of two networks based on the invariance measures of the R package 'NetworkComparisonTest' by van Borkulo et al. (2022), <doi:10.1037/met0000476>: network structure invariance, global strength invariance, edge invariance, and various centrality measures. Edgelists from dependent or independent samples are used as input. These edgelists are generated from concept maps and summed into two comparable group networks. The networks can be directed or undirected.
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
    graphs from 'R'.
Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) <doi:10.3233/978-1-60750-692-8-227> for an overview on spectral class of inter-graph distance measures.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
Simulates the extinction of species in ecological networks and it analyzes
             its cascading effects, described in Dunne et al. (2002) <doi:10.1073/pnas.192407699>.
This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.
An implementation of some of the core 'network' package functionality based on a 
    simplified data structure that is faster in many research applications. This package is designed 
    for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for
    binary and weighted, directed and undirected, bipartite and unipartite networks; no current 
    support for multigraphs, hypergraphs, or loops.
Collection of functions for fast manipulation, handling, and analysis of large-scale
    networks based on family and social data. Functions are utility functions used to manipulate data
    in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data.
    When possible, the functions should be able to handle millions of data points quickly for use in combination
    with data from large public national registers and databases.
    Kenneth Lange (2003, ISBN:978-8181281135).
Linear regression model and generalized linear models with nonparametric network effects on network-linked observations. The model is originally proposed by Le and Li (2022) <doi:10.48550/arXiv.2007.00803> and is assumed on observations that are connected by a network or similar relational data structure. A more recent work by Wang, Le and Li (2024) <doi:10.48550/arXiv.2410.01163> further extends the framework to generalized linear models. All these models are implemented in the current package. The model does not assume that the relational data or network structure to be precisely observed; thus, the method is provably robust to a certain level of perturbation of the network structure. The package contains the estimation and inference function for the model.
Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.
Provides a variety of Network Scale-up Models for researchers to 
    analyze Aggregated Relational Data, through the use of Stan and 'glmmTMB'.
    Also provides tools for model checking
    In this version, the package implements models from 
    Laga, I., Bao, L., and Niu, X (2023) <doi:10.1080/01621459.2023.2165929>,
    Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>,
    Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., 
    and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and 
    Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and 
    Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.
Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at <https://bigsem.psychstat.org>.
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting.
Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.
Implements univariate continuous probability distributions and associated model
    diagnostics based on the Lindley, Logistic, Half-Cauchy, Half-Logistic, and Poisson families.
    Provides functions for probability density, cumulative distribution, quantile, and hazard
    evaluation, random variate generation, and diagnostic procedures including Q-Q and P-P plots,
    goodness-of-fit tests, and model selection criteria.
An 'R' interface to the 'Julia' package 'NeuralEstimators.jl'. The package facilitates the user-friendly development of neural Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, <doi:10.1080/00031305.2023.2249522>). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, <doi:10.48550/arXiv.2404.12484>). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly through simulated data.
Neural Additive Model framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.
Training of neural networks using backpropagation,
    resilient backpropagation with (Riedmiller, 1994) or without
    weight backtracking (Riedmiller and Braun, 1993) or the
    modified globally convergent version by Anastasiadis et al.
    (2005). The package allows flexible settings through
    custom-choice of error and activation function. Furthermore,
    the calculation of generalized weights (Intrator O & Intrator
    N, 1993) is implemented.
Visualization and analysis tools to aid in the interpretation of
    neural network models.  Functions are available for plotting,
    quantifying variable importance, conducting a sensitivity analysis, and
    obtaining a simple list of model weights.
A small multilayer perceptron implementation for
    'R'. It supports regression and classification, multiple hidden layers,
    mini-batch training, Adam, SGD, momentum, Nesterov, RPROP, GRPROP and
    L-BFGS optimizers, dropout, L2 regularization, early stopping, convergence
    thresholds, gradient clipping, sample and class weights, callback hooks,
    target scaling and robust Huber loss for regression, 'Rcpp' forward-pass
    kernels, formula interfaces, model evaluation with balanced classification
    metrics, cross-validation, compact tuning, permutation importance, model
    persistence helpers, and 'S3' prediction methods. Methods follow
    Rumelhart, Hinton and Williams (1986) <doi:10.1038/323533a0>, with
    optimizers including Riedmiller and Braun (1993)
    <doi:10.1109/ICNN.1993.298623>, Nocedal (1980)
    <doi:10.1090/S0025-5718-1980-0572855-7>, and Kingma and Ba (2014)
    <doi:10.48550/arXiv.1412.6980>.
Analysis functions to quantify inputs importance in neural network models.
  Functions are available for calculating and plotting the inputs importance and obtaining
  the activation function of each neuron layer and its derivatives. The importance of a given
  input is defined as the distribution of the derivatives of the output with respect to that
  input in each training data point <doi:10.18637/jss.v102.i07>.
Base package for 'Neuroconductor', which includes many helper 
    functions that interact with objects of class 'nifti', implemented by
    package 'oro.nifti', for reading/writing and also other manipulation 
    functions.
Annotated neuroblastoma copy number profiles,
 a benchmark data set for change-point detection algorithms,
 as described by Hocking et al. <doi:10.1186/1471-2105-14-164>.
Offers a rich and diverse collection of datasets focused on the brain, nervous system, and related disorders. 
    The package includes clinical, experimental, neuroimaging, behavioral, cognitive, and simulated data on conditions such as 
    Parkinson's disease, Alzheimer's disease, dementia, epilepsy, schizophrenia, autism spectrum disorder, attention deficit, hyperactivity disorder, 
    Tourette's syndrome, traumatic brain injury, gliomas, migraines, headaches, sleep disorders, concussions, encephalitis, 
    subarachnoid hemorrhage, and mental health conditions. Datasets cover structural and functional brain data, cross-sectional and longitudinal 
    MRI imaging studies, neurotransmission, gene expression, cognitive performance, intelligence metrics, sleep deprivation effects, treatment outcomes, 
    brain-body relationships across species, neurological injury patterns, and acupuncture interventions. Data sources include peer-reviewed studies, 
    clinical trials, military health records, sports injury databases, and international comparative studies.
    Designed for researchers, neuroscientists, clinicians, psychologists, data scientists, and students, this package facilitates exploratory data analysis, 
    statistical modeling, and hypothesis testing in neuroscience and neuroepidemiology.
Neural decoding is method of analyzing neural data that  
    uses a pattern classifiers to predict experimental conditions based 
    on neural activity. 'NeuroDecodeR' is a system of objects that 
    makes it easy to run neural decoding analyses. For more information
    on neural decoding see Meyers & Kreiman (2011)
    <doi:10.7551/mitpress/8404.003.0024>.
Downloads and reads data from Human 'Connectome' Project 
    <https://db.humanconnectome.org> using Amazon Web Services ('AWS') 
    'S3' buckets.
A collection of data structures that represent
    volumetric brain imaging data. The focus is on basic data handling for 3D
    and 4D neuroimaging data. In addition, there are function to read and write
    NIFTI files and limited support for reading AFNI files.
A collection of data structures and methods for handling volumetric
    brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient
    representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array
    implementations, memory-mapped file access for large datasets, and
    spatial transformation capabilities. Implements methods for image resampling,
    spatial filtering, region of interest analysis, and connected component labeling. General introduction 
    to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis",
    <ISBN:9781108795760>).
Contains functions to query and visualize the Neuroimaging features 
  associated with genetically regulated gene expression (GReX). The primary utility,
  neuroimaGene(), relies on a list of user-defined genes and returns a table of
  neuroimaging features (NIDPs) associated with each gene. This resource 
  is designed to assist in the interpretation of genome-wide and transcriptome-wide
  association studies that evaluate brain related traits. Bledsoe (2024) 
  <doi:10.1016/j.ajhg.2024.06.002>. In addition there are several visualization 
  functions that generate summary plots and 2-dimensional visualizations of regional 
  brain measures. Mowinckel (2020).
Implements spatial null models and coordinate-space
    transformations for statistical comparison of brain maps, following the
    framework described in Markello et al. (2022)
    <doi:10.1038/s41592-022-01625-w>. Provides variogram-matching
    surrogates (Burt et al. 2020), Moran spectral randomization (Wagner &
    Dray 2015), and spin-based permutation tests (Alexander-Bloch et al.
    2018). Includes an R interface to the 'neuromaps' annotation registry
    for browsing, downloading, and comparing brain map annotations from the
    Open Science Framework ('OSF').
    Integrates with 'ciftiTools' for coordinate-space transforms.
Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.
Tools for the structured processing of PET neuroimaging data 
    in preparation for the estimation of Simultaneous Confidence Corridors (SCCs) 
    for one-group, two-group, or single-patient vs group comparisons. 
    The package facilitates PET image loading, data restructuring, integration 
    into a Functional Data Analysis framework, contour extraction, identification 
    of significant results, and performance evaluation. It bridges established 
    packages (e.g., 'oro.nifti') with novel statistical methodologies (e.g., 
    'ImageSCC') and enables reproducible analysis pipelines, including 
    comparison with Statistical Parametric Mapping ('SPM').
Generates functional Magnetic Resonance Imaging (fMRI) 
             time series or 4D data. Some high-level 
             functions are created for fast data generation with only 
             a few arguments and a diversity of functions to define 
             activation and noise. For more advanced users it is possible 
             to use the low-level functions and manipulate the arguments.
             See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.
Calculate the precision in mean differences (raw or Cohen's D) and 
    correlation coefficients for different sample sizes. Uses permutations of the
    collected functional magnetic resonance imaging (fMRI) region of interest data.
    Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) 
    <doi:10.31234/osf.io/cz32t>.
Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.
Provides methods for Neutrosophic Analysis of Variance
    (NANOVA) for crossover designs and multi-session designs with
    direct and residual effects using interval-valued observations.
    The package computes neutrosophic sums of squares, mean squares,
    interval-valued F-statistics, significance tests, and multiple
    comparisons using Least Significant Difference (LSD) procedures.
    For crisp data, users may enter identical lower and upper response
    values to obtain classical Analysis of Variance (ANOVA) results.
    The basic idea of neutrosophic statistics is obtained from
    Smarandache (2014) <https://fs.unm.edu/NeutrosophicStatistics.pdf>, 
    while the analysis procedures implemented in this package are newly developed.
Provides methods for neutrosophic analysis of variance
    (NANOVA) and neutrosophic analysis of covariance (NANCOVA)
    for interval-valued data arising from incomplete block design
    experiments. The package supports balanced incomplete block
    designs (BIBDs), partially balanced incomplete block designs
    (PBIBDs), and lattice designs. Functions are included for
    treatment comparisons, least significant difference (LSD)
    tests, and interval-based statistical inference under
    neutrosophic environments.
Description: Provides methods for Neutrosophic Analysis of Variance (NANOVA) 
    and Neutrosophic Analysis of Covariance (NANCOVA) for row-column designs, 
    including Latin square designs and Youden square designs, using 
    interval-valued observations. The package computes neutrosophic sums of 
    squares, mean squares, interval-valued F-statistics, significance tests, 
    and multiple comparisons using Least Significant Difference (LSD) 
    procedures. For crisp data, users may enter identical lower and upper 
    values of responses to obtain classical Analysis of Variance (ANOVA) 
    results. Similarly, users may enter identical lower and upper values for 
    both responses and covariates to obtain classical Analysis of Covariance 
    (ANCOVA) results.
Provides methods for Neutrosophic Analysis of Variance
    (NANOVA) for split-plot and strip-plot experimental designs using
    interval-valued observations. The package computes neutrosophic sums
    of squares, mean squares, interval-valued F-statistics, significance
    tests, and Least Significant Difference (LSD) based multiple
    comparisons for main plot, sub plot, horizontal factor, vertical
    factor, and interaction effects. For crisp data, users may provide
    identical lower and upper response values to obtain results equivalent
    to classical analysis of variance. The basic idea of neutrosophic
    statistics is obtained from Smarandache (2014) <https://fs.unm.edu/NeutrosophicStatistics.pdf>, 
    while the analysis procedures implemented in this package are newly developed.
Analyzes data involving imprecise and vague information. Provides summary statistics and describes the characteristics of neutrosophic data, as defined by Florentin Smarandache (2013).<ISBN:9781599732749>.
Apply neutrosophic regression type estimator and performs neutrosophic interval analysis including metric calculations for survey data.
Draw nested extreme value random variables, which are the variables that appear in the latent variable formulation of the nested logit model.
A flexible statistical framework for network-valued data analysis. 
    It leverages the complexity of the space of distributions on graphs by using 
    the permutation framework for inference as implemented in the 'flipr' package. 
    Currently, only the two-sample testing problem is covered and generalization 
    to k samples and regression will be added in the future as well. It is a 
    4-step procedure where the user chooses a suitable representation of the 
    networks, a suitable metric to embed the representation into a metric space, 
    one or more test statistics to target specific aspects of the distributions 
    to be compared and a formula to compute the permutation p-value. Two types 
    of inference are provided: a global test answering whether there is a 
    difference between the distributions that generated the two samples and a 
    local test for localizing differences on the network structure. The latter 
    is assumed to be shared by all networks of both samples. References: Lovato, 
    I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for 
    network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A., 
    Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis 
    testing for network-valued data: Analysis of brain networks of patients with 
    autism" <doi:10.1111/rssc.12463>.
In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.
The aim is to develop an R package, which is the 'new.dist'
    package, for the probability (density) function, the
    distribution function, the quantile function and the
    associated random number generation function for discrete and
    continuous distributions, which have recently been proposed
    in the literature. This package implements the following
    distributions: The Power Muth Distribution, a Bimodal Weibull
    Distribution, the Discrete Lindley Distribution, The
    Gamma-Lomax Distribution, Weighted Geometric Distribution, a
    Power Log-Dagum Distribution, Kumaraswamy Distribution,
    Lindley Distribution, the Unit-Inverse Gaussian Distribution,
    EP Distribution, Akash Distribution, Ishita Distribution,
    Maxwell Distribution, the Standard Omega Distribution,
    Slashed Generalized Rayleigh Distribution, Two-Parameter
    Rayleigh Distribution, Muth Distribution, Uniform-Geometric
    Distribution, Discrete Weibull Distribution.
Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.
Measure the dependence structure between two random variables with a new correlation coefficient and extend it to hypothesis test, feature screening and false discovery rate control.
Extends the classical Newman studentized range statistic
  in various ways that can be applied to genome-scale transcriptomic
  or other expression data.
Implements a four-stage pipeline for probabilistic seismic
  performance analysis of slopes and embankments. The package takes a
  uniform-hazard spectrum at multiple return periods as input (any
  source) and produces: (1) synthetic soil profile generation and
  fundamental period estimation from USCS classification via Ishihara's
  small-strain shear-modulus model and the inhomogeneous truncated
  shear-beam theory of Gazetas and Dakoulas; (2) nonlinear site
  amplification using the Seyhan & Stewart (2014) model
  <doi:10.1193/063013EQS181M>, with inter-period correlation via
  Baker & Jayaram (2008) <doi:10.1193/1.2857544>; (3) Monte Carlo
  ensemble of six empirical Newmark sliding-block displacement models
  (Ambraseys & Menu (1988) <doi:10.1002/eqe.4290160704>,
   Jibson (2007) <doi:10.1016/j.enggeo.2007.01.013>,
   Saygili & Rathje (2008) <doi:10.1061/(ASCE)1090-0241(2008)134:6(790)>,
   Bray & Travasarou (2007) <doi:10.1061/(ASCE)1090-0241(2007)133:4(381)>,
   Bray & Macedo (2017) <doi:10.1016/j.soildyn.2017.05.024>,
   and the Bray and Macedo shallow-crustal update) with coherent
  correlated draws; (4) log-log inversion to the performance-based
  seismic coefficient kmax at user-specified displacement targets. All
  outputs are 'data.table' objects.
Interface to gather news from the 'News API', based on a multilevel query <https://newsapi.org/>. A personal API key is required.
Programmatically collect normalized news from
    (almost) any website. An 'R' clone of the
    <https://github.com/kotartemiy/newscatcher> 'Python' module.
Semissupervised model for geographical document classification (Watanabe 2018) <doi:10.1080/21670811.2017.1293487>. 
    This package currently contains seed dictionaries in English, German, French, Spanish, Italian, Russian, Hebrew, Arabic, Turkish, Japanese and Chinese (Simplified and Traditional).
Adding updates (version or bullet points) to the NEWS.md
    file.
Implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a  model for recurrent event, when the data are correlated or not  correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be  estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.
Novel responsive tools for developing R based 'Shiny' dashboards and applications. The scripts and style sheets are based on 'jQuery' <https://jquery.com/> and 'Bootstrap' <https://getbootstrap.com/>.
Exploration and analysis of compositional data in the
    framework of Aitchison (1986, ISBN: 978-94-010-8324-9). This package
    provides tools for chemical fingerprinting and source tracking of
    ancient materials.
Indices, heuristics, simulations and strategies to help determine the number of factors/components to retain in exploratory factor 
       analysis and principal component analysis.
Provides utility functions to facilitate the use of R within nf-core modules. 
    The package helps parse 'Nextflow' inputs and perform validation checks to ensure 
    correct parameter handling and reproducible execution.
    For more details see Ewels (2020) <doi:10.1038/s41587-020-0439-x>.
This is the R API for the 'nfer' formalism (<http://nfer.io/>).
    'nfer' was developed to specify event stream abstractions for spacecraft 
    telemetry such as the Mars Science Laboratory.  Users write rules using 
    a syntax that borrows heavily from Allen's Temporal Logic that, when 
    applied to an event stream, construct a hierarchy of temporal intervals 
    with data.  The R API supports loading rules from a file or mining them 
    from historical data.  Traces of events or pools of intervals are 
    provided as data frames.
A set of functions to estimate outcomes of fourth down plays
    in the National Football League and obtain fourth down plays from
    <https://www.nfl.com/> and <https://www.espn.com/>.
A set of functions to access National Football League
    play-by-play data from <https://www.nfl.com/>.
A set of functions to visualize National Football League
    analysis in 'ggplot2' plots and 'gt' tables.
A minimal package for downloading data from 'GitHub'
    repositories of the 'nflverse' project.
A set of functions to simulate National Football League
    seasons including the sophisticated tie-breaking procedures.
The intent here is to enable the simulation of plays/drives and
    evaluate game-play strategies in the National Football League (NFL).
    Built-in strategies include going for it on fourth down and varying the 
    proportion of passing/rushing plays during a drive. The user should be
    familiar with nflscrapR data before trying to write his/her own 
    strategies. This work is inspired by a blog post by Mike Lopez, 
    currently the  Director of Data and Analytics at the NFL, Lopez (2019) <https://statsbylopez.netlify.app/post/resampling-nfl-drives/>.
The 'nflverse' is a set of packages dedicated to data of the
    National Football League. This package is designed to make it easy to
    install and load multiple 'nflverse' packages in a single step. Learn
    more about the 'nflverse' at <https://nflverse.nflverse.com/>.
Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a description of the model at <doi:10.1111/biom.13857>.
Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.
Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.
Posterior distribution of case-control fine-mapping. Specifically, Bayesian variable selection for single-nucleotide polymorphism (SNP) data using the normal-gamma prior. Alenazi A.A., Cox A., Juarez M,. Lin W-Y. and Walters, K. (2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. <doi:10.1002/gepi.22213>.
Next-Generation Clustered Heat Maps (NG-CHMs) allow for
    dynamic exploration of heat map data in a web browser. 'NGCHM' allows
    users to create both stand-alone HTML files containing a
    Next-Generation Clustered Heat Map, and .ngchm files to view in the
    NG-CHM viewer. See Ryan MC, Stucky M, et al (2020)
    <doi:10.12688/f1000research.20590.2> for more details.
Provides an 'htmlwidgets' <https://www.htmlwidgets.org/> interface to 'NGL.js' <http://nglviewer.org/ngl/api/>.
    'NGLvieweR' can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications.
    It includes a set of API functions to manipulate the viewer after creation in Shiny.
Fits and analyzes linear latent non-Gaussian models for
    temporal, spatial, and space-time data. The package provides model
    components for autoregressive and Ornstein-Uhlenbeck processes, random
    walks, Matern fields based on stochastic partial differential equations,
    separable and non-separable space-time models, graph-based Matern models,
    bivariate type-G fields, and user-defined sparse operators. Latent fields
    and observation models can use Gaussian and non-Gaussian noise
    distributions, including normal inverse Gaussian, generalized asymmetric
    Laplace, and skew-t distributions. Functions are included for simulation,
    likelihood-based estimation, prediction, cross-validation, convergence
    diagnostics, stochastic gradient optimization, batch-means confidence
    intervals, and posterior-like sampling. The modeling framework is described
    in Bolin, Jin, Simas and Wallin (2026) "A Unified and Computationally
    Efficient Non-Gaussian Statistical Modeling Framework"
    <doi:10.48550/arXiv.2602.23987>.
An n-gram is a sequence of n "words" taken, in order, from a
    body of text.  This is a collection of utilities for creating,
    displaying, summarizing, and "babbling" n-grams.  The
    'tokenization' and "babbling" are handled by very efficient C
    code, which can even be built as its own standalone library.
    The babbler is a simple Markov chain.  The package also offers
    a vignette with complete example 'workflows' and information about
    the utilities offered in the package.
Retrieve and plot word frequencies through time from the "Google
    Ngram Viewer" <https://books.google.com/ngrams>.
A simple n-gram (contiguous sequences of n items from a
    given sequence of text) tokenizer to be used with the 'tm' package with no
    'rJava'/'RWeka' dependency.
Provides tools for analyzing spatial data, especially non-
    Gaussian areal data. The current version supports the sparse restricted
    spatial regression model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x>,
	the centered autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>,
	and the Bayesian spatial filtering model of Hughes (2017) <arXiv:1706.04651>.
Body Shape and related measurements from the US National Health
    and Nutrition Examination Survey (NHANES, 1999-2004).  See
    http://www.cdc.gov/nchs/nhanes.htm for details.
Utility to retrieve data from the National Health and Nutrition 
	Examination Survey (NHANES) website <https://www.cdc.gov/nchs/nhanes/>.
Instant access to harmonized National Health and Nutrition Examination Survey (NHANES) data spanning 1999-2023. Retrieve pre-processed datasets from reliable cloud storage with automatic type reconciliation and integrated search tools for variables and datasets. Simplifies NHANES data workflows by handling cycle management and maintaining data consistency across survey waves. Data is sourced from <https://www.cdc.gov/nchs/nhanes/>.
Search, preview, and download datasets from the National Health and Nutrition Examination Survey (NHANES) across survey cycles. The package provides functions to identify relevant datasets by keyword, inspect available .XPT files before downloading, and organize retrieved data locally. Data are retrieved from the NHANES web services available at <https://wwwn.cdc.gov/nchs/nhanes/> .
Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'nhdplusTools' are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.
Tools for working with the National Hydrography Dataset, with 
    functions for querying, downloading, and networking both the NHD 
    <https://www.usgs.gov/national-hydrography> 
    and NHDPlus <https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus> datasets.
Each dataset contains scores for every game during a specific season of the NHL.
Scrapes and cleans data from the 'NHL' and 'ESPN' APIs into data.frames and lists. Wraps 125+ endpoints documented in <https://github.com/RentoSaijo/nhlscraper/wiki> from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers' odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays.
Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) <doi:10.1111/j.1541-0420.2010.01550.x>. Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.
Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.
Simulates events from one dimensional nonhomogeneous Poisson point processes (NHPPPs) as per Trikalinos and Sereda (2024, <doi:10.48550/arXiv.2402.00358> and 2024, <doi:10.1371/journal.pone.0311311>). Functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function (Cinlar E, "Theory of stochastic processes" (1975, ISBN:0486497996)); the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme (Lewis PAW, Shedler, GS (1979) <doi:10.1002/nav.3800260304>).
Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using 'NHS Predict'. 
            The time interval for the estimation can be set up to 15 years, with default at 10.
            Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates.
            An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates
            the correct standardised inputs.
            This work is not affiliated with the development of 'NHS Predict' and its underlying statistical model.
            Details on 'NHS Predict' can be found at: <doi:10.1186/bcr2464>.
            The web version of 'NHS Predict': <https://breast.predict.nhs.uk/>.
            A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions,
            and an additional dataset is provided for running example with the dedicated SCAN function.
Provides tools for importing, harmonising, cleaning, analysing,
    benchmarking and visualising National Health Service (NHS) England
    Cancer Waiting Times data. The package supports national performance
    monitoring, provider-level benchmarking and cancer pathway comparisons
    through key performance indicator summaries, provider filtering,
    clustering analyses, mixed-effects regression models, variance
    decomposition, intraclass correlation coefficient estimation, adjusted
    provider performance estimation and sensitivity analyses. Functions are
    included for exploratory analysis, publication-ready visualisations and
    spreadsheet exports, supporting reproducible health services research,
    cancer services evaluation, quality improvement and assessment of
    waiting-time performance across healthcare organisations. Mixed-effects
    modelling functionality is based on Bates et al. (2015)
    <doi:10.18637/jss.v067.i01>. Multilevel modelling concepts and variance
    decomposition follow Gelman and Hill (2007, ISBN:9780521686891).
    Cancer Waiting Times definitions and reporting standards follow NHS
    England
    <https://www.england.nhs.uk/statistics/statistical-work-areas/cancer-waiting-times/>.
Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary <https://datadictionary.nhs.uk/data_elements_overview.html>.
    The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups.
    The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained.
    This package was commissioned by the NHS-R community <https://nhsrcommunity.com/> to provide this consistency of lookups.
    The OpenSafely lookups have now been added <https://www.opencodelists.org/docs/>.
Provides functions for working with NHS number checksums.
    The UK's National Health Service issues NHS numbers to all users of its
    services and this package implements functions for verifying that the
    numbers are valid according to the checksum scheme the NHS use.
    Numbers can be validated and checksums created.
Free United Kingdom National Health Service (NHS) and other healthcare, or population health-related data for education and training purposes. This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data.  This package exists to support skills development in the NHS-R community: <https://nhsrcommunity.com/>.
Provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHS 'Making Data Count' programme, and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached.
Waiting list management using queuing theory to analyse, predict and manage queues, based on the approach described in Fong et al. (2022) <doi:10.1101/2022.08.23.22279117>.  Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included.
Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings.
The implementation of Markov Model Multiple Imputation
  with the application to COVID-19 scale, NIAID OS.
Framework is devoted to mining numerical association rules through the
  utilization of nature-inspired algorithms for optimization. Drawing inspiration
  from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository
  introduces the capability to perform numerical association rule mining in
  the R programming language.
  Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>.
Color palettes based on nature inspired colours in "Sri Lanka".
Get or set UNIX priority (niceness) of running R process.
Provides a robust set of tools for researchers and modelers to
    construct and define virtual ecological niches using ellipsoid geometries.
    It enables the identification and extraction of suitable environmental
    areas, simulation of species occurrence points with various sampling
    strategies, and visualization of niche boundaries and simulated occurrences
    in both environmental and geographic space. Inspired by methodologies in
    'NicheA' and the 'virtualspecies' R package, 'nicheR' aims to streamline
    the process of niche conceptualization and data generation for ecological
    studies. Methodological and theoretical foundations are described in
    Peterson et al. (2011, ISBN:9780691136882),
    Etherington et al. (2009) <doi:10.1111/j.1365-2699.2008.02041.x>,
    Qiao et al. (2015) <doi:10.1111/ecog.01961>,
    Nunez-Penichet et al. (2021) <doi:10.21425/F5FBG52142>,
    Cobos and Peterson (2022) <doi:10.17161/bi.v17i.15985>,
    Alkishe et al. (2022) <doi:10.5194/we-22-33-2022>,
    and Leroy et al. (2015) <doi:10.1111/ecog.01388>.
Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%).  Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions.  It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region.  The article by Swanson et al. (2015) <doi:10.1890/14-0235.1> provides a detailed description of the methodology.  See the package vignette for a worked example using fish stable isotope data.
Provides functions complementary to packages 'nicheROVER' and 'SIBER'
        allowing the user to extract Bayesian estimates from data objects created 
        by the packages 'nicheROVER' and 'SIBER'. Please see the following 
        publications for detailed methods on 'nicheROVER' and 'SIBER' 
        Hansen et al. (2015) <doi:10.1890/14-0235.1>, 
        Jackson et al. (2011) <do i:10.1111/j.1365-2656.2011.01806.x>, and
        Layman et al. (2007) <doi:10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2>,
        respectfully.
A collection of tools that allow users to perform critical steps 
    in the process of assessing ecological niche evolution over phylogenies, with
    uncertainty incorporated explicitly in reconstructions. The method proposed
    here for ancestral reconstruction of ecological niches characterizes species'
    niches using a bin-based approach that incorporates uncertainty in estimations.
    Compared to other existing methods, the approaches presented here reduce risk
    of overestimation of amounts and rates of ecological niche evolution. The
    main analyses include: initial exploration of environmental data in occurrence
    records and accessible areas, preparation of data for phylogenetic analyses,
    executing comparative phylogenetic analyses of ecological niches, and plotting
    for interpretations. Details on the theoretical background and methods used 
    can be found in: Owens et al. (2020) <doi:10.1002/ece3.6359>,
    Peterson et al. (1999) <doi:10.1126/science.285.5431.1265>,
    Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4>,
    Peterson (2011) <doi:10.1111/j.1365-2699.2010.02456.x>,
    Barve et al. (2011) <doi:10.1111/ecog.02671>, 
    Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>,
    Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, 
    Saupe et al. (2018) <doi:10.1093/sysbio/syx084>, and 
    Cobos et al. (2021) <doi:10.1111/jav.02868>.
Provides utility functions and objects for Extreme Value
    Analysis. These include probability functions with their exact
    derivatives w.r.t. the parameters that can be used for estimation
    and inference, even with censored observations. The
    transformations exchanging the two parameterizations of Peaks Over
    Threshold (POT) models: Poisson-GP and Point-Process are also
    provided with their derivatives.
Tools for reading and writing NIfTI-1.1 (NII) files, including optimized voxelwise read/write operations and a simplified method to write dataframes to NII.
    Specification of the NIfTI-1.1 format can be found here <https://nifti.nimh.nih.gov/nifti-1>.
    Scientific publication first using these tools
        Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) <doi:10.1016/j.neuroimage.2020.116764> "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764.
Estimate the correlation between two NIfTI images across random parcellations of the images (Fortea et al., under review). This approach overcomes the problems of both voxel-based correlations (neighbor voxels may be spatially dependent) and atlas-based correlations (the correlation may depend on the atlas used).
Computes and plots the boundary between night and day.
Routines for enumerating all existing nonnegative integer solutions of a linear Diophantine equation. The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem.
Th-U-Pb electron microprobe age dating of monazite, as originally 
             described in <doi:10.1016/0009-2541(96)00024-1>.
Estimators and variance estimators tailored to the NILS hierarchical design (Adler et al. 2020, <https://res.slu.se/id/publ/105630>; Grafström et al. 2023, <https://res.slu.se/id/publ/128235>).
     The National Inventories of Landscapes in Sweden (NILS) is a long-term national monitoring program that collects, analyses and presents data on Swedish nature, covering both common and rare habitats <https://www.slu.se/om-slu/organisation/institutioner/skoglig-resurshushallning/miljoanalys/nils/>.
Miscellaneous R functions developed as collateral damage over the
    course of work in statistical and scientific computing for research. These
    include, for example, utilities that supplement existing idiosyncrasies of
    the R language, extend existing plotting functionality and aesthetics, help
    prepare data objects for imputation, and extend access to command line tools
    and systems-level information.
Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.
A system for writing hierarchical statistical models largely
    compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models
    and do basic R-style math, and compiling both models and nimbleFunctions via
    custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace
    Approximation, deterministic nested approximations, Monte Carlo Expectation
    Maximization, and some other tools.
    The nimbleFunction system makes it easy to do things like implement new MCMC
    samplers from R, customize the assignment of samplers to different parts of
    a model from R, and compile the new samplers automatically via C++ alongside
    the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language
    by making it extensible: New distributions and functions can be added,
    including as calls to external compiled code. Although most people think
    of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models,
    one can use 'NIMBLE' for writing arbitrary other kinds of model-generic
    algorithms as well. A full User Manual is available at <https://r-nimble.org>.
Functions for adaptive parallel tempering (APT) with NIMBLE models. Adapted from 'Lacki' & 'Miasojedow' (2016) <DOI:10.1007/s11222-015-9579-0> and 'Miasojedow, Moulines and Vihola' (2013) <DOI:10.1080/10618600.2013.778779>.
Provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the 'nimble' modelling framework.  It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.
Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. 
  Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models.
  (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).
Provides gradient-based MCMC sampling algorithms for use with the MCMC engine provided by the 'nimble' package.  This includes two versions of Hamiltonian Monte Carlo (HMC) No-U-Turn (NUTS) sampling, and (under development) Langevin samplers.  The `NUTS_classic` sampler implements the original HMC-NUTS algorithm as described in Hoffman and Gelman (2014) <doi:10.48550/arXiv.1111.4246>.  The `NUTS` sampler is a modern version of HMC-NUTS sampling matching the HMC sampler available in version 2.32.2 of Stan (Stan Development Team, 2023). In addition, convenience functions are provided for generating and modifying MCMC configuration objects which employ HMC sampling. Functionality of the 'nimbleHMC' package is described further in Turek, et al (2024) <doi: 10.21105/joss.06745>.
Macros to generate 'nimble' code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the 'nimble' manual: <https://r-nimble.org/html_manual/cha-writing-models.html#subsec:macros>.
A collection of common univariate bounded probability distributions transformed to the unbounded real line, for the purpose of increased MCMC efficiency.
Provides deterministic approximation methods for use with the 'nimble' package. These include Laplace approximation and higher-order extension of Laplace approximation using adaptive Gauss-Hermite quadrature (AGHQ), plus nested deterministic approximation methods related to the 'INLA' approach. Additional information is available in the NIMBLE User Manual and a 'nimbleQuad' tutorial, both available at <https://r-nimble.org/documentation.html>.
Provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.
Includes five particle filtering algorithms for use with state space
    models in the 'nimble' system: 'Auxiliary', 'Bootstrap', 'Ensemble Kalman filter',
    'Iterated Filtering 2', and 'Liu-West', as described in Michaud et al. (2021),
    <doi:10.18637/jss.v100.i03>. A full User Manual is available at
    <https://r-nimble.org>.
A software package to perform Wombling, or boundary analysis, using the 'nimble' Bayesian hierarchical modeling environment. Wombling is used widely to track regions of rapid change within the spatial reference domain. Specific functions in the package implement Gaussian process models for point-referenced spatial data followed by predictive inference on rates of change over curves using line integrals. We demonstrate model based Bayesian inference using posterior distributions featuring simple analytic forms while offering uncertainty quantification over curves. For more details on wombling please see, Banerjee and Gelfand (2006) <doi:10.1198/016214506000000041> and Halder, Banerjee and Dey (2024) <doi:10.1080/01621459.2023.2177166>.
Principal Components Analysis of a matrix using Non-linear
    Iterative Partial Least Squares or weighted Expectation Maximization
    PCA with Gram-Schmidt orthogonalization of the scores and loadings.
    Optimized for speed. See Andrecut (2009) <doi:10.1089/cmb.2008.0221>.
An implementation of the National Information Platforms for 
    Nutrition or NiPN's analytic methods for assessing quality of anthropometric 
    datasets that include measurements of weight, height or length, middle upper 
    arm circumference, sex and age. The focus is on anthropometric status but 
    many of the presented methods could be applied to other variables.
Digital map data of Japan for choropleth mapping, including a circle cartogram.
Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
    Testing.
Provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <doi:10.1177/0962280218786302> for further details.
Datasets for testing nonlinear regression routines.
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for 
    SI (International System of Units) and non-SI units, plus unit conversions
    Based on the data from NIST (National Institute of Standards and Technology, USA)
Contains data, code, and figures from Hill et al. 2018a (Journal of Experimental Marine Biology and Ecology; <DOI: 10.1016/j.jembe.2018.07.006>) and Hill et al. 2018b (Data In Brief <DOI: 10.1016/j.dib.2018.09.133>). Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species.
Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time. For details, see Fay and Follmann, 2016 <DOI:10.1177/1740774516654861>.
Creates interactive bubble chart visualizations for 'Shiny' applications
    using the 'Nivo' circle packing library. Provides an 'htmlwidgets' wrapper around
    the 'Nivo' circle packing chart, enabling hierarchical data visualization with
    customizable colors, labels, and interactive features including click and hover events.
    For more information about 'Nivo', see <https://nivo.rocks/>.
Snow water equivalent is modeled with the process based
    models 'delta.snow' and 'HS2SWE' and empirical regression, which use
    relationships between density and diverse at-site parameters. The
    methods are described in Winkler et al. (2021)
    <doi:10.5194/hess-25-1165-2021>, Magnusson et al. (2025)
    <doi:10.1016/j.coldregions.2025.104435>, Guyennon et al. (2019)
    <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016)
    <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009)
    <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010)
    <doi:10.1175/2010JHM1202.1>.
A Software Development Kit for working with 'Nixtla''s 'TimeGPT', a foundation
    model for time series forecasting. 'API' is an acronym for 'application
    programming interface'; this package allows users to interact with
    'TimeGPT' via the 'API'. You can set and validate 'API' keys and generate forecasts
    via 'API' calls. It is compatible with 'tsibble' and base R. For more details 
    visit <https://www.nixtla.io/docs>.
Optimization for nonlinear objective and constraint functions.  Linear or nonlinear equality and inequality constraints are allowed.  It accepts the input parameters as a constrained matrix.
Nested loop cross validation for classification purposes for misclassification error rate estimation.
  The package supports several methodologies for feature selection: random forest, Student t-test, limma, 
  and provides an interface to the following classification methods in the 'MLInterfaces' package: linear, 
  quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized 
  linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
  the classifier are included: plot of the ranks of the features, scores plot for a specific 
  classification algorithm and number of features, misclassification rate 
  for the different number of features and classification algorithms tested and ROC plot.
  For further details about the methodology, please check:
  Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) 
  <doi:10.2202/1544-6115.1078>.
Solve a system of nonlinear equations using a Broyden or a Newton method
             with a choice of global strategies such as line search and trust region.
             There are options for using a numerical or user supplied Jacobian,
             for specifying a banded numerical Jacobian and for allowing
             a singular or ill-conditioned Jacobian.
Fits nonlinear Bayesian extensions of the Fay-Herriot model for small
    area estimation using area-level direct estimates and corresponding
    sampling variances. The package provides model fitting, prediction,
    uncertainty summaries, and diagnostic tools for nonlinear small area
    estimation workflows.
Interface to the open location server API of 'Publieke Diensten Op de Kaart' (<http://www.pdok.nl>). It offers geocoding, address suggestions and lookup of geographical objects. Included is an utility function for displaying leaflet tiles restricted to the Netherlands.
Six growth models are fitted using non-linear least squares. These are the Richards, the 3, 4 and 5 parameter logistic, the Gompetz and the Weibull growth models. Reference: Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach". BMC medical research methodology, 21(1), 1-11. <doi:10.1186/s12874-020-01165-x>.
Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) <doi:10.1016/0165-1889(80)90069-X>,  and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy <doi:10.1103/PhysRevLett.85.461>, and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors <doi:10.1103/PhysRevE.69.066138>. There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information.
Create and manipulate numeric list ('nlist') objects.  An
    'nlist' is an S3 list of uniquely named numeric objects.  An numeric
    object is an integer or double vector, matrix or array.  An 'nlists'
    object is a S3 class list of 'nlist' objects with the same names,
    dimensionalities and typeofs.  Numeric list objects are of interest
    because they are the raw data inputs for analytic engines such as
    'JAGS', 'STAN' and 'TMB'.  Numeric lists objects, which are useful for
    storing multiple realizations of of simulated data sets, can be
    converted to coda::mcmc and coda::mcmc.list objects.
Estimation of relatively complex nonlinear mixed-effects models, including the Sigmoidal Mixed Model and the Piecewise Linear Mixed Model with abrupt or smooth transition, through a single intuitive line of code and with automated generation of starting values.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Provides functions and datasets to support the book
   by Galecki and Burzykowski (2013),
   'Linear Mixed-Effects Models: A Step-by-Step Approach',
   Springer. Includes functions for power calculations,
   log-likelihood contributions, and data simulation
   for linear mixed-effects models.
Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, <https://www.page-meeting.org/?abstract=1434>).
Fit and compare nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is
    by compiled C code provided in the 'rxode2' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Automated population pharmacokinetic modeling framework for
    data-driven initialisation, model evaluation, and metaheuristic
    optimization. Supports genetic algorithms, ant colony optimization,
    tabu search, and stepwise procedures for automated model selection
    and parameter estimation within the nlmixr2 ecosystem.
Provides automated methods for generating initial parameter estimates 
    in population pharmacokinetic modeling. The pipeline integrates adaptive single-point 
    methods, naive pooled graphic approaches, noncompartmental analysis methods, and parameter 
    sweeping across pharmacokinetic models. It estimates residual unexplained variability 
    using either data-driven or fixed-fraction approaches and assigns pragmatic initial values 
    for inter-individual variability. These strategies are designed to improve model 
    robustness and convergence in 'nlmixr2' workflows. For more details see 
    Huang Z, Fidler M, Lan M, Cheng IL, Kloprogge F, Standing JF (2025) 
    <doi:10.1007/s10928-025-10000-z>.
Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing
    nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is
    by compiled C code provided in the 'rxode2' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for
    support functions like preconditioned fits
    <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate
    selection.
A model library for 'nlmixr2'.  The models include
  (and plan to include) pharmacokinetic, pharmacodynamic, and
  disease models used in pharmacometrics.  Where applicable,
  references for each model are included in the meta-data for
  each individual model.  The package also includes model
  composition and modification functions to make model updates
  easier.
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for
    'ggplot2' plotting methods for 'nlmixr2' objects.
This allows you to generate reporting workflows around 'nlmixr2' analyses with outputs in Word and PowerPoint. You can specify figures, tables and report structure in a user-definable 'YAML' file. Also you can use the internal functions to access the figures and tables to allow their including in other outputs (e.g. R Markdown).
Provides tools to save 'nlmixr2' fitted models in a portable format
    readable outside of 'nlmixr2' and independent of the package version.
    'nlmixr2' fits and compares nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving uses compiled
    C code from the 'rxode2' package (Wang, Hallow, and James 2015
    <doi:10.1002/psp4.12052>).
'nlmixr2' often has long runtimes.  A pipeline toolkit
    tailored to 'nlmixr2' workflows leverages 'targets' and 'nlmixr2' to
    ease reproducible workflows.  'nlmixr2targets' ensures minimal rework
    in model development with 'nlmixr2' and 'targets' by simplifying and
    standardizing models and datasets.
Provides functions to fit linear mixed models
	based on convolutions of the generalized Laplace (GL) distribution.
	The GL mixed-effects model includes four special cases with normal random
	effects and normal errors (NN), normal random effects and Laplace errors (NL),
	Laplace random effects and normal errors (LN), and Laplace random effects
	and Laplace errors (LL). The methods are described in Geraci and Farcomeni (2020,
	Statistical Methods in Medical Research) <doi:10.1177/0962280220903763>.
Replacement for nls() tools for working with nonlinear least squares problems.
      The calling structure is similar to, but much simpler than, that of the nls()
      function. Moreover, where nls() specifically does NOT deal with small or zero
      residual problems, nlmrt is quite happy to solve them. It also attempts to be
      more robust in finding solutions, thereby avoiding 'singular gradient' messages
      that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach
      in nlmrt generally works more reliably to get a solution, though this may be 
      one of a set of possibilities, and may also be statistically unsatisfactory.
      Added print and summary as of August 28, 2012.
Package to select best model  among several linear and nonlinear models. The main function uses the gnls() function from the 'nlme' package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".
It includes four methods: DCOL-based K-profiles clustering, non-linear network reconstruction, non-linear hierarchical clustering, and variable selection for generalized additive model. References:  Tianwei Yu (2018)<DOI: 10.1002/sam.11381>; Haodong Liu and others (2016)<DOI: 10.1371/journal.pone.0158247>; Kai Wang and others (2015)<DOI: 10.1155/2015/918954>; Tianwei Yu and others (2010)<DOI: 10.1109/TCBB.2010.73>.
The purpose of this library is to to call different optimization solvers (such as Gonzalez Rodriguez et al. (2022) <doi:10.1007/s10898-022-01229-w>, Tawarmalani and Sahinidis (2005) <doi:10.1007/s10107-005-0581-8>, and Byrd et al. (2006) <doi:10.1007/0-387-30065-1_4>) to solve problems given by a standard nl file.
Solve optimization problems using an R interface to NLopt. NLopt is a 
    free/open-source library for nonlinear optimization, providing a common
    interface for a number of different free optimization routines available
    online as well as original implementations of various other algorithms.
    See <https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more
    information on the available algorithms. Building from included sources 
    requires 'CMake'. On Linux and 'macOS', if a suitable system build of 
    NLopt (2.7.0 or later) is found, it is used; otherwise, it is built 
    from included sources via 'CMake'. On Windows, NLopt is obtained through 
    'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for
    'R >= 4.2.0'.
Basic classes and methods for Natural Language Processing.
Stanford 'CoreNLP' annotation client.
  Stanford 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/index.html> integrates all 
  NLP tools from the Stanford Natural Language Processing Group, 
  including a part-of-speech (POS) tagger, a named entity recognizer (NER), 
  a parser, and a coreference resolution system, and provides model files 
  for the analysis of English. More information can be found in the README.
Provides efficient methods to compute co-occurrence matrices, pointwise mutual information (PMI) and singular value decomposition (SVD). In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the 'RcppAlgos' package and sparse matrices. Furthermore, the functions can be called on 'SQL' databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.
Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Liu (2025) <doi:10.3758/s13428-025-02596-4>.
Utilities for Natural Language Processing.
Performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.
Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples.
Implements likelihood inference based on higher order approximations for nonlinear models with possibly non constant variance.
Estimate the non-linear odds ratio and plot it against a continuous exposure.
Setup, run and analyze 'NetLogo' (<https://www.netlogo.org>) model simulations in 'R'.
    'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. 
    However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses.
    The user defines all information that is needed in an intuitive framework, using class objects.
    Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user.
    By nesting model calls in future environments, large simulation design with many runs can be executed in parallel.
    This also enables simulating 'NetLogo' experiments on remote high performance computing machines.
    In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.
Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum.
Adds brute force and multiple starting values to nls.
Estimation of structural equation models with nonlinear effects
  and underlying nonnormal distributions.
Non-linear shrinkage estimation of population eigenvalues and covariance
    matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).
We solve non linear least squares problems with optional
    equality and/or inequality constraints. Non linear iterations are
    globalized with back-tracking method. Linear problems are solved by
    dense QR decomposition from 'LAPACK' which can limit the size of
    treated problems. On the other side, we avoid condition number
    degradation which happens in classical quadratic programming approach.
    Inequality constraints treatment on each non
    linear iteration is based on 'NNLS' method (by Lawson and Hanson).
    We provide an original function 'lsi_ln' for solving linear least squares
    problem with inequality constraints in least norm sens. Thus if Jacobian of
    the problem is rank deficient a solution still can be provided.
    However, truncation errors are probable in this case.
    Equality constraints are treated by using a basis of Null-space.
    User defined function calculating residuals must return a list having
    residual vector (not their squared sum) and Jacobian. If Jacobian is
    not in the returned list, package 'numDeriv' is used to calculated
    finite difference version of Jacobian. The 'NLSIC' method was fist
    published in Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.
Data sets and nonlinear regression models dedicated to predictive microbiology.
Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi:10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi:10.1016/j.spl.2011.03.019>.
Provides tools for working with nonlinear least squares problems.
      For the estimation of models reliable and robust tools than nls(), where the
      the Gauss-Newton method frequently stops with 'singular gradient' messages. 
      This is accomplished by using, where possible, analytic derivatives to compute
      the matrix of derivatives and a stabilization of the solution of the estimation
      equations. Tools for approximate or externally supplied derivative matrices
      are included. Bounds and masks on parameters are handled properly.
Set of functions implementing the algorithm described in Fernandez 
    Torvisco et al. (2018) for fitting separable nonlinear regression curves. 
    See Fernandez Torvisco, Rodriguez-Arias Fernandez and Cabello Sanchez (2018)
    <doi:10.2298/FIL1812233T>.
Several tools for assessing the quality of fit of a gaussian nonlinear model are provided.
Utilities and kinship information for behavior genetics and
    developmental research using the National Longitudinal Survey of Youth
    (NLSY; <https://www.nlsinfo.org/>).
Uses a modified lifting algorithm on which it builds the
        nondecimated lifting transform. It has applications in wavelet
        shrinkage.
Fits a non-linear transformation model ('nltm') for
        analyzing survival data, see Tsodikov (2003) <doi:10.1111/1467-9868.00414>. The
        class of 'nltm' includes the following currently supported
        models: Cox proportional hazard, proportional hazard cure,
        proportional odds, proportional hazard - proportional hazard
        cure, proportional hazard - proportional odds cure, Gamma
        frailty, and proportional hazard - proportional odds.
R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.
Provides functions to calculate the normalised Lineage-Through-
    Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures
    the difference between two Lineage-Through-Time curves, where each curve is
    normalised both in time and in number of lineages.
Wald Test for nonlinear restrictions on model parameters and confidence
             intervals for nonlinear functions  of parameters using delta-method.  Applicable
             after ANY model, provided parameters estimates and their covariance matrix are
             available.
Network meta-analysis tools based on contrast-based approach using the multivariate meta-analysis and meta-regression models (Noma et al. (2025) <doi:10.1101/2025.09.15.25335823>). Comprehensive analysis tools for network meta-analysis and meta-regression (e.g., synthesis analysis, ranking analysis, and creating league table) are available through simple commands. For inconsistency assessment, the local and global inconsistency tests based on the Higgins' design-by-treatment interaction model are available. In addition, the side-splitting methods and Jackson's random inconsistency model can be applied. Standard graphical tools for network meta-analysis, including network plots, ranked forest plots, and transitivity analyses, are also provided. For the synthesis analyses, the Noma-Hamura's improved REML (restricted maximum likelihood)-based methods (Noma et al. (2023) <doi:10.1002/jrsm.1652> <doi:10.1002/jrsm.1651>) are adopted as the default methods.
Provides statistical methods for network meta-analysis 
    of diagnostic tests to simultaneously compare multiple tests within a 
    missing data framework, including:
    - Bayesian hierarchical model for network meta-analysis of multiple 
      diagnostic tests 
      (Ma, Lian, Chu, Ibrahim, and Chen (2018) <doi:10.1093/biostatistics/kxx025>)
    - Bayesian Hierarchical Summary Receiver Operating Characteristic Model 
      for Network Meta-Analysis of Diagnostic Tests 
      (Lian, Hodges, and Chu (2019) <doi:10.1080/01621459.2018.1476239>).
Provides customized forest plots for network meta-analysis incorporating direct, indirect, and NMA effects. Includes visualizations of evidence contributions through 
    proportion bars based on the hat matrix and evidence flow decomposition.
Performs network meta-analysis using integrated nested Laplace approximations ('INLA') 
             which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. 
             Includes methods to assess the heterogeneity and inconsistency in the network. 
             Contains more than ten different network meta-analysis dataset. 
             'INLA' package can be obtained from <https://www.r-inla.org>.
A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis :
               - simple outlier and influential detection measures
               - outlier and influential detection measures by considering study deletion (shift the mean)
               - plots for outlier and influential detection measures
	       - Q-Q plot for network meta-analysis
               - Forward Search algorithm in network meta-analysis. 
               - forward plots to monitor statistics in each step of the forward search algorithm
               - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.
A graphical display of results from network meta-analysis (NMA). 
  It is suitable for outcomes like odds ratio (OR), risk ratio (RR), 
  risk difference (RD) and standardized mean difference (SMD). 
  It also has an option to visually display and compare 
  the surface under the cumulative ranking (SUCRA) of different treatments.
Methods to estimate finite-population parameters under nonresponse that is not missing at
    random (NMAR, nonignorable). Incorporates auxiliary information and user-specified response models,
    and supports independent samples and complex survey designs via objects from the 'survey' package.
    Provides diagnostics and optional variance estimates. For methodological background see
    Qin, Leung and Shao (2002) <doi:10.1198/016214502753479338> and Riddles, Kim and Im (2016) <doi:10.1093/jssam/smv047>.
Derives the most frequent hierarchies along with their probability of occurrence. One can also define complex hierarchy criteria and calculate their probability. Methodology based on Papakonstantinou et al. (2021) <DOI:10.21203/rs.3.rs-858140/v1>.
Ships statistical and mathematical routines from R internal
    'nmath' ('Mathlib') as 'OpenCL' C sources under directory 'inst/cl/', with R
    wrappers that use the GPU when 'OpenCL' is available at compile time and
    fall back to 'stats' equivalents otherwise. Aimed at package developers
    building custom kernels (for example Bayesian GLMs via suggested package
    'glmbayes') using 'opencltools' kernel loaders and related helpers. Contains
    translated shims, an illustrative GLM-related kernel subsystem, vignettes,
    and optional GPU acceleration. The ported routines are translated from the
    'nmath' ('Mathlib') and 'Rmath' sources of R Core Team (2026) "R: A
    Language and Environment for Statistical Computing"
    <doi:10.32614/R.manuals>. 'OpenCL' GPU execution follows the standard
    described in Stone, Gohara, and Shi (2010) <doi:10.1109/MCSE.2010.69>. The
    likelihood subgradient simulation methodology implemented by the
    illustrative GLM kernel subsystem is described in Nygren and Nygren (2006)
    <doi:10.1198/016214506000000357>.
Calculation and presentation of decision-invariant bias adjustment
    thresholds and intervals for Network Meta-Analysis, as described by 
    Phillippo et al. (2018) <doi:10.1111/rssa.12341>. These describe the 
    smallest changes to the data that would result in a change of decision.
Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.
Efficient tools for preparation, checking and post-processing of data in PK/PD (pharmacokinetics/pharmacodynamics) modeling, with focus on use of Nonmem, including consistency, traceability, and Nonmem compatibility of Data. Rigorously checks final Nonmem datasets. Implemented in 'data.table', but easily integrated with 'base' and 'tidyverse'.
Provides a framework to perform Non-negative Matrix
    Factorization (NMF). The package implements a set of already published algorithms
    and seeding methods, and provides a framework to test, develop and plug
    new/custom algorithms. Most of the built-in algorithms have been optimized
    in C++, and the main interface function provides an easy way of performing
    parallel computations on multicore machines.
Factorize binary matrices into rank-k components using the logistic function in the updating process. See e.g. Tomé et al (2015) <doi:10.1007/s11045-013-0240-9> .
Performs Non-negative Matrix Factorization (NMF)
    with Kernel Covariates. Given an observation matrix and kernel
    covariates, it optimizes both a basis matrix and a parameter matrix.
    Notably, if the kernel matrix is an identity matrix, the method
    simplifies to standard NMF. Also provides NMF with Random Effects
    (NMF-RE) via nmfre(), which estimates a mixed-effects model combining
    covariate-driven scores with unit-specific random effects together
    with wild bootstrap inference, and NMF-based Structural Equation
    Modeling (NMF-SEM) via nmf.sem(), which fits a two-block input-output
    model for blind source separation and path analysis.
    References: Satoh (2025) <doi:10.48550/arXiv.2403.05359>;
    Satoh (2025) <doi:10.48550/arXiv.2510.10375>;
    Satoh (2025) <doi:10.48550/arXiv.2512.18250>;
    Satoh (2026) <doi:10.48550/arXiv.2603.01468>;
    Satoh (2026) <doi:10.1007/s42081-025-00314-0>.
Contains functions useful for debugging, set operations on vectors,
    and 'UTC' date and time functionality. It adds a few vector manipulation 
    verbs to 'purrr' and 'dplyr' packages. It can also generate an R file to 
    install and update packages to simplify deployment into production. The 
    functions were developed at the data science firm 'Numeract LLC' and are 
    used in several packages and projects.
A program for Bayesian analysis of univariate normal mixtures with an unknown number
  of components, following the approach of Richardson and Green (1997) <doi:10.1111/1467-9868.00095>.
  This makes use of reversible jump Markov chain Monte Carlo methods that are capable of jumping
  between the parameter sub-spaces corresponding to different numbers of components in the mixture.
  A sample from the full joint distribution of all unknown variables is thereby generated,
  and this can be used as a basis for a thorough presentation of many aspects of the posterior
  distribution.
Provides residuals and overdispersion metrics to assess the fit of N-mixture models obtained using the package 'unmarked'. 
    Details on the methods are given in Knape et al. (2017) <doi:10.1101/194340>.
We fit inverse probability weighting estimator and the augmented inverse probability weighting for non-monotone missing at random data.
Functions, examples and data from the first and
  the second edition of "Numerical Methods and Optimization in
  Finance" by M. Gilli, D. Maringer and E. Schumann (2019,
  ISBN:978-0128150658).  The package provides implementations
  of optimisation heuristics (Differential Evolution, Genetic
  Algorithms, Particle Swarm Optimisation, Simulated Annealing
  and Threshold Accepting), and other optimisation tools, such
  as grid search and greedy search.  There are also functions
  for the valuation of financial instruments such as bonds and
  options, for portfolio selection and functions that help
  with stochastic simulations.
There are three distinct approaches for phase error correction, they are: a single linear model with a choice of optimization functions, multiple linear models with optimization function choices and a shrinkage-based method. The methodology is based on our new algorithms and various references (Binczyk et al. (2015) <doi:10.1186/1475-925X-14-S2-S5>,Chen et al. (2002) <doi:10.1016/S1090-7807(02)00069-1>, de Brouwer (2009) <doi:10.1016/j.jmr.2009.09.017>, Džakula (2000) <doi:10.1006/jmre.2000.2123>, Ernst (1969) <doi:10.1016/0022-2364(69)90003-1>, Liland et al. (2010) <doi:10.1366/000370210792434350>).
A reproducible workflow for binning and visualizing NMR (nuclear magnetic resonance) spectra from environmental samples. The 'nmrrr' package is intended for post-processing of NMR data, including importing, merging and, cleaning data from multiple files, visualizing NMR spectra, performing binning/integrations for compound classes, and relative abundance calculations. 
    This package can be easily inserted into existing analysis workflows by users to help with analyzing and interpreting NMR data.
A complete and seamless 'Nonmem' simulation interface within R. Turns 'Nonmem' control streams into simulation control streams, executes them with specified simulation input data and returns the results. The simulation is performed by 'Nonmem', eliminating manual work and risks of re-implementation of models in other tools.
A Non-Metric Space Library ('NMSLIB' <https://github.com/nmslib/nmslib>) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' <https://github.com/nmslib/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' <https://github.com/nmslib/nmslib> 'Python' Library.
Perform an exploration and a preliminary analysis on the dose-
    response relationship of nanomaterial toxicity. Several functions are
    provided for data exploration, including functions for creating a subset of
    dataset, frequency tables and plots. Inference for order restricted dose-
    response data is performed by testing the significance of monotonic
    dose-response relationship, using Williams, Marcus, M, Modified M and 
    Likelihood ratio tests. Several methods of multiplicity adjustment 
    are also provided. Description of the methods can be found in <https://github.com/rahmasarina/dose-response-analysis/blob/main/Methodology.pdf>.
Due to 'Rstudio's' status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with 'R'. The 'NMVANOVA' (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice 'Rstudio' users to perform different model variations one-way analysis of variance without downloading  multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.
This shows how 'NONMEM' (Beal SL, Sheiner LB, Boeckmann AJ,
    Bauer RJ.  NONMEM 7.5 Users Guides.  Icon plc, 2020) software works.
    'NONMEM' classical estimation methods such as 'First Order (FO)
    approximation', 'First Order Conditional Estimation (FOCE)', and
    'Laplacian approximation' are explained.  Functions are also provided
    for post-run processing of NONMEM output files, generating PDF
    diagnostic reports including objective function value analysis,
    parameter estimates, prediction and residual diagnostics, empirical
    Bayes estimate (EBE) analysis, input data summary, and individual
    pharmacokinetic parameter distributions.  Helper utilities for
    building NONMEM-ready datasets from SDTM-style source tables are
    also included.
Implements a method that builds the coefficients of a polynomial
    model that performs almost equivalently as a given neural network
    (densely connected). This is achieved using Taylor expansion at the
    activation functions.  The obtained polynomial coefficients can be used
    to explain features (and their interactions) importance  in the neural network,
    therefore working as a tool for interpretability or eXplainable Artificial 
    Intelligence (XAI). See Morala et al. 2021 <doi:10.1016/j.neunet.2021.04.036>,
    and 2023 <doi:10.1109/TNNLS.2023.3330328>.
Calculates spatial pattern analysis using a T-square sample procedure.
  This method is based on two measures "x" and "y".
  "x" - Distance from the random point to the nearest individual.
  "y" - Distance from individual to its nearest neighbor.
  This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species' distribution (random, uniform or clumped patterns).
  Ludwig & Reynolds (1988, ISBN:0471832359).
Provides nearest-neighbors matching and 
    analysis of case-control data. Cui, Z., Marder, E. P., Click, E. S., 
    Hoekstra, R. M., & Bruce, B. B. (2022) <doi:10.1097/EDE.0000000000001504>.
Generates 'LaTeX' code for drawing well-formatted neural network diagrams with 'TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce 'LaTeX' code for drawing activation functions which are crucial in neural network analysis. To make the code work in a 'LaTeX' editor, users need to install and import some 'TeX' packages including 'TikZ' in the setting of 'TeX' file.
Software for feed-forward neural networks with a single
  hidden layer, and for multinomial log-linear models.
An implementation of a Neural Network using the
    Levenberg-Marquardt optimization from 'minpack.lm', ideal for small
    datasets. For more details see Moré (1978) <doi:10.1007/BFb0067700>.
Automatic time series modelling with neural networks. 
    Allows fully automatic, semi-manual or fully manual specification of networks. For details of the
	specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>;
	and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.
Functions to compute the non-negative garrote estimator as 
             proposed by Breiman (1995) <https://www.jstor.org/stable/1269730>
             with the penalized initial estimators extension as proposed by 
             Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.
K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis.
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
Contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN.
An R interface to the Lawson-Hanson implementation of an
        algorithm for non-negative least squares (NNLS).  Also allows
        the combination of non-negative and non-positive constraints.
Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6): 1336-1353 <doi:10.1109/TKDE.2012.51> and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2): 713-730 <doi:10.1137/07069239X>.
Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.
Provides extensions to the 'StMoMo' package by incorporating neural network functionality for Lee-Carter and Poisson Lee-Carter mortality models. Includes tools for constructing mortality datasets from 'demogdata' objects and fitting neural network-based mortality models. Further analysis, such as plotting and forecasting, can be done with 'StMoMo' functions.
Do algebraic operations on neural networks. We seek here to implement
  in R, operations on neural networks and their resulting approximations. Our operations derive
  their descriptions mainly from
  Rafi S., Padgett, J.L., and Nakarmi, U. (2024), "Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials", <doi:10.48550/arXiv.2402.01058>, 
  Grohs P., Hornung, F., Jentzen, A. et al. (2023), "Space-time error estimates for deep neural network approximations for differential equations", <doi:10.1007/s10444-022-09970-2>,
  Jentzen A., Kuckuck B., von Wurstemberger, P. (2023), "Mathematical Introduction to Deep Learning Methods, Implementations, and Theory" <doi:10.48550/arXiv.2310.20360>.
  Our implementation is meant mainly as a pedagogical tool, and proof of concept. Faster implementations with 
  deeper vectorizations may be made in future versions.
NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences.  Designed for real-world data that violates symmetry, linearity, or distributional assumptions, NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic superiority / dominance and Advanced Monte Carlo sampling.  All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995, Second edition: <https://ovvo-financial.github.io/NNS/book/>).
Provides a fast algorithm for solving non-negative least squares 
    problems. It implements the Fast Non-Negative Least Squares algorithm. 
    of Bro and De Jong (1997)<doi:10.1002/(SICI)1099-128X(199709/10)11:53.0.CO;2-L>.
Contains the functions for testing the spatial patterns (of segregation, spatial symmetry, 
      association, disease clustering, species correspondence, and reflexivity) based on nearest neighbor relations, 
      especially using contingency tables such as 
      nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and 
      Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein),
      nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>),
      species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) 
      <doi:10.2436/20.8080.02.72> for two (or higher) dimensional data. 
      The package also contains functions for generating patterns of segregation, association,
      uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>), 
      and various non-random labeling patterns for disease clustering 
      in two dimensional cases (Ceyhan (2014)
      <doi:10.1002/sim.6053>), and for visualization of all these patterns 
      for the two dimensional data.
      The tests are usually (asymptotic) normal z-tests or chi-square tests.
Estimate the NNT using the proposed method in Yang and Yin's paper (2019)
  <doi:10.1371/journal.pone.0223301>, in which the NNT-RMST (number needed to 
  treat based on the restricted mean survival time) is defined as the RMST (restricted 
  mean survival time) in the control group divided by the difference in RMSTs 
  between the treatment and control groups up to a chosen time t.
Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.
Draw samples from truncated multivariate normal distribution using the sequential nearest neighbor (SNN) method introduced in "Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation" <doi:10.48550/arXiv.2406.17307>.
Functions for revealing what happens when effect size estimates 
    from previous studies are taken into account when evaluating each new dataset 
    in a study sequence. The analyses can be conducted for cumulative
    meta-analyses and for Bayesian data analyses. The package contains sample 
    data for a wide selection of research topics. Jointly considering 
    previous findings along with new data is more likely to result in correct 
    conclusions than does the traditional practice of not incorporating previous
    findings, which often results in a back and forth ping-pong of conclusions 
    when evaluating a sequence of studies.
    O'Connor & Ermacora (2021, <doi:10.1037/cbs0000259>).
Fetch data from the National Oceanic and Atmospheric Administration Climate Data Online (NOAA CDO) <https://www.ncdc.noaa.gov/cdo-web/webservices/v2> API including daily, monthly, and yearly climate summaries, radar data, climatological averages, precipitation data, annual summaries, storm events, and agricultural meteorology.
Generate pseudonymous animal names that are delightful and easy to 
    remember like the Likable Leech and the Proud Chickadee. A unique pseudonym 
    can be created for every unique element in a vector or row in a data frame. 
    Pseudonyms can be customized and tracked over time, so that the same input 
    is always assigned the same pseudonym.
A Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. 'NobBS' learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2020) <doi:10.1371/journal.pcbi.1007735>.
Designed to be compatible with the 'R' package 'DBI' (Database Interface)
    when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>.
    To do this the 'R' 'AWS' Software Development Kit ('SDK') 'paws' 
    <https://github.com/paws-r/paws> is used as a driver.
An online app and command-line utility to import, filter and visualise sleep data. Can be used with sleep data collected from any type of device (e.g. radar, sleep diary,...) as long as the data contains sleep onset and wake-up times for each sleep session.
Simplified JSON document database access and manipulation,
     providing a common API across supported 'NoSQL' databases 
     'Elasticsearch', 'CouchDB', 'MongoDB' as well as 
     'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.
Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.
Simulate DNA sequences for the node substitution model.  In
    the node substitution model, substitutions accumulate additionally
    during a speciation event, providing a potential mechanistic
    explanation for substitution rate variation. This package provides
    tools to simulate such a process, simulate a reference process with
    only substitutions along the branches, and provides tools to infer
    phylogenies from alignments. More information can be found in Janzen
    (2021) <doi:10.1093/sysbio/syab085>.
An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. (2014) "Node-based analysis of species distributions." Methods in Ecology and Evolution 5(11): 1225-1235. <doi:10.1111/2041-210X.12283>. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped.
Provides a compact variation of the usual syntax of function
  declaration, in order to support tidyverse-style quasiquotation of a
  function's arguments and body.
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).
Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186.  Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.
Implementation of models for the controlled introduction of errors in 
	classification datasets. This package contains the noise models described in 
	Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, 
	attributes and both simultaneously.
Cross-Entropy optimisation of unconstrained deterministic and noisy
    functions illustrated in Rubinstein and Kroese (2004, ISBN:
    978-1-4419-1940-3) through a highly flexible and customisable function which 
    allows user to define custom variable domains, sampling distributions,
    updating and smoothing rules, and stopping criteria. Several built-in
    methods and settings make the package very easy-to-use under standard
    optimisation problems.
Quantifies and removes technical noise from high-throughput 
        sequencing data. Two approaches are used, one based on the count
        matrix, and one using the alignment BAM files directly.
        Contains several options for every step of the process, as well
        as tools to quality check and assess the stability of output.
Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph 
    and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying 
    binary graph. This procedure comes with a control of the false discovery rate. The method is described
    in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, 
    E. Roquain, F. Villers (2020) <arXiv:1907.10176>.
Provides a suite of tools that can assist in enhancing the 
    processing efficiency of 'SQL' and 'R' scripts. 
    - The 'libr_unused()' retrieves a vector of package names that are called 
    within an 'R' script but are never actually used in the script.
    - The 'libr_used()' retrieves a vector of package names actively utilized 
    within an 'R' script; packages loaded using 'library()' but not actually 
    used in the script will not be included.
    - The 'libr_called()' retrieves a vector of all package names which are 
    called within an 'R' script.
    - 'nolock()' appends 'WITH (nolock)' to all tables in 'SQL' queries. This 
    facilitates reading from databases in scenarios where non-blocking reads are 
    preferable, such as in high-transaction environments.
Provides satellite tracking data from nomadic pectoral
    sandpipers published in Kempenaers and Valcu (2017)
    <doi:10.1038/nature20813>. The data can also serve as benchmark data
    for clustering movement tracks.
Converts numeric vectors to character vectors of English
    number names. Provides conversion to cardinals, ordinals, numerators,
    and denominators. Supports negative and non-integer numbers.
Similarity measures for hierarchical clustering of objects characterized by
    nominal (categorical) variables. Evaluation criteria for nominal data clustering.
Lightweight interface to the 'OpenStreetMap' 'Nominatim' API
    <https://nominatim.org/release-docs/latest/>. Extract coordinates from
    addresses, retrieve addresses from coordinates, look up amenities and
    addresses, and return results as 'tibble' or 'sf' objects.
Functions to use the 'NamePrism' API <https://www.name-prism.com/api> or 'NamSor' API v2 <https://namsor.app/> for classifying names based on gender, 6 U.S. ethnicities, or 39 leaf nationalities. Updated to work with current API endpoints.
Interface to the 'Nomis' database (<https://www.nomisweb.co.uk>), maintained by Durham University on behalf of the Office for National Statistics (ONS). Provides access to UK labour market statistics including census data, benefit claimant counts, and employment surveys. Supports automatic pagination, optional disk caching, spatial data via 'sf', and tidy data output. Independent implementation unaffiliated with ONS or Durham University.
Provides tools for visualizing and analyzing the shape of discrete nominal frequency distributions. The package introduces centered frequency plots, in which nominal categories are ordered from the most frequent category at the center toward less frequent categories on both sides, facilitating the detection of distributional patterns such as uniformity, dominance, symmetry, skewness, and long-tail behavior. In addition, the package supports Pareto charts for the study of dominance and cumulative frequency structure in nominal data. The package is designed for exploratory data analysis and statistical teaching, offering visualizations that emphasize distributional form rather than arbitrary category ordering.
A tool for drawing sassy 'UML' (Unified Modeling Language) diagrams 
  based on a simple syntax, see <https://www.nomnoml.com>. Supports styling, 
  R Markdown and exporting diagrams in the PNG format. Note: you need a chromium based 
  browser installed on your system.
A nomogram can not be easily applied,
    because it is difficult to calculate the points or even the survival probability.
  The package, including a function of nomogramEx(),
    is to extract the polynomial equations to calculate the points of each variable,
    and the survival probability corresponding to the total points.
A nomogram, which can be carried out in 'rms' package, provides a 
    graphical explanation of a prediction process. However, it is not very easy
    to draw straight lines, read points and probabilities accurately. Even, it 
    is hard for users to calculate total points and probabilities for all 
    subjects.
  This package provides formula_rd() and formula_lp() functions to fit the 
    formula of total points with raw data and linear predictors respectively by
    polynomial regression. Function points_cal() will help you calculate the 
    total points. prob_cal() can be used to calculate the probabilities after
    lrm(), cph() or psm() regression. 
  For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() 
    can be used.
Runs several heterogeneity-robust difference-in-differences (DID) event-study estimators
    for non-absorbing (i.e., treatment can switch on and off over time, allowing treatment reversal)
    binary treatments through their respective packages,
    harmonizes their output onto a common time axis and tidy data structure, and overlays them in a single
    'ggplot2' panel for visual comparison. 
    Supported estimators include those provided by 'DIDmultiplegtDYN', 'PanelMatch', and 'fect', with an 
    optional naive two-way fixed-effects reference series via 'fixest'. The underlying methods are respectively described in
    Clement de Chaisemartin and Xavier D'Haultfoeuille. "Difference-in-Differences Estimators of Intertemporal Treatment Effects."
    The Review of Economics and Statistics (2026) <doi:10.1162/rest_a_01414>,
    Kosuke Imai, In Song Kim, and Erik H. Wang. "Matching methods for causal inference with time‐series cross‐sectional data." 
    American Journal of Political Science 67.3 (2023) <doi:10.1111/ajps.12685>,
    Licheng Liu, Ye Wang, and Yiqing Xu. "A practical guide to counterfactual estimators for causal inference
    with time‐series cross‐sectional data." American Journal of Political Science 68.1 (2024) <doi:10.1111/ajps.12723>, and
    Laurent R. Bergé, Kyle Butts, and Grant McDermott. "Fast and user-friendly econometrics estimations: The R package 'fixest'."
    arXiv preprint (2026) <doi:10.48550/arXiv.2601.21749>.
    A single nabs_event_study() wrapper runs any supported estimator with a common interface;
    nabs_event_study_simple() provides a one-line front door for quick exploratory runs;
    the S3 generic as_nabs_event_study() coerces estimator output into a tidy stable schema;
    and nabs_event_plot() overlays multiple methods on a single 'ggplot2' panel, with optional naive two-way
    fixed effects drawn in a neutral color as a reference.
Conduct a noncompartmental analysis with industrial strength.
             Some features are
             1) Use of CDISC SDTM terms
             2) Automatic or manual slope selection
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             5) Installation/Operational Qualification (IQ/OQ) reports in pdf.
             After installation, qualify the package in your own environment:
             run IQNCA() for Installation Qualification and OQNCA() for Operational
             Qualification. Run writeMD5NCA() once after installation so the IQ
             file-integrity check passes. To approve a report, sign it digitally in
             Adobe Acrobat Reader (generate with sigField=TRUE, or run
             addSigFieldNCA(), to add click-to-sign fields), instead of printing and
             scanning; or use signPDFNCA()/verifyPDFNCA() for a scriptable signature.
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
A finite-population significance test of the 'sharp' causal null hypothesis that
    treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers.
    A generalized likelihood ratio test statistic is used, and the resulting p-value is exact.
    Currently, it is assumed that there are only Compliers and Never Takers in the population.
Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.
Supports staggered difference-in-differences designs with
    nonlinear outcomes for both panel and repeated cross-section data.
    Implements estimators for staggered treatment adoption with binary,
    count, and other nonlinear outcomes, extending Callaway and Sant'Anna
    (2021) <doi:10.1016/j.jeconom.2020.12.001> to settings with nonlinear
    outcome models such as logit, probit, and Poisson. For panel data,
    units are followed over time and 'idname' identifies repeated
    observations. For repeated cross-section data, observations are
    independent within each time period; 'idname' is optional and may
    identify survey records or households, but the estimator does not
    require the same units to appear across periods. Repeated cross-section
    estimation includes pooled quasi-maximum likelihood approaches motivated
    by Wooldridge (2023) <doi:10.1093/ectj/utad016>, with optional weighting
    and clustered inference. Methods also draw on Roth and Sant'Anna (2023)
    <doi:10.3982/ECTA19402> and Sant'Anna and Zhao (2020)
    <doi:10.1016/j.jeconom.2020.06.003>.
Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. <doi:10.1109/TVCG.2017.2744018>.
Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.
Functions for nonlinear time series analysis. This package permits
    the computation of the  most-used nonlinear statistics/algorithms
    including generalized correlation dimension, information dimension,
    largest Lyapunov exponent, sample entropy and Recurrence
    Quantification Analysis (RQA), among others. Basic routines
    for surrogate data testing are also included. Part of this work
    was based on the  book "Nonlinear time series analysis" by
    Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Loading NONMEM (NONlinear Mixed-Effect Modeling, <https://www.iconplc.com/solutions/technologies/nonmem/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty.
'NONMEM' has been a tool for running nonlinear mixed
    effects models since the 80s and is still used today (Bauer 2019
    <doi:10.1002/psp4.12404>). This tool allows you to convert
    'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016)
    <doi:10.1002/psp4.12052>) and with simple models 'nlmixr2' syntax
    (Fidler et al (2019) <doi:10.1002/psp4.12445>). The 'nlmixr2'
    syntax requires the residual specification to be included and it
    is not always translated. If available, the 'rxode2' model will
    read in the 'NONMEM' data and compare the simulation for the
    population model ('PRED') individual model ('IPRED') and residual
    model ('IWRES') to immediately show how well the translation is
    performing. This saves the model development time for people who are
    creating an 'rxode2' model manually.  Additionally, this package reads
    in all the information to allow simulation with uncertainty (that is the
    number of observations, the number of subjects, and the covariance
    matrix) with a 'rxode2' model.  This is complementary to the
    'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can
    convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit.
Systematically creates and modifies NONMEM(R) control streams. Harvests
 NONMEM output, builds run logs, creates derivative data, generates diagnostics.
 NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for 
 nonlinear mixed effects modeling. See 'package?nonmemica'.
Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0),
  using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in
  (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).
Testing non-nested models via theory supplied by Vuong (1989) <DOI:10.2307/1912557>.
    Includes tests of model distinguishability and of model fit that can be applied
    to both nested and non-nested models. Also includes functionality to obtain
    confidence intervals associated with AIC and BIC. This material is partially based on
    work supported by the National Science Foundation under Grant Number SES-1061334.
Implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardia’s Pareto of Type I, Logistic, Burr, Cook-Johnson’s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.
Contains the following 5 nonparametric hypothesis tests:
    The Sign Test,
    The 2 Sample Median Test,
    Miller's Jackknife Procedure,
    Cochran's Q Test, &
    The Stuart-Maxwell Test.
Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.
Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.
Plot, process, and analyze NPO files produced by
    'Nonpareil' <http://enve-omics.ce.gatech.edu/nonpareil/>.
Implements the nonparametric causality-in-quantiles test (in mean or variance),
    returning a test object with an S3 plot() method. The current implementation uses one lag
    of each series (first-order Granger causality setup). Methodology is based on 
    Balcilar, Gupta, and Pierdzioch (2016a) <doi:10.1016/j.resourpol.2016.04.004> and 
    Balcilar et al. (2016) <doi:10.1007/s11079-016-9388-x>.
Estimates and plots (as a single plot and as a heat map) the rolling window correlation coefficients between two time series and computes their statistical significance, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) <doi:10.1016/j.ecoinf.2021.101379>.
Implements the nonparametric trend test for one or several samples
    as proposed by Bathke (2009) <doi:10.1007/s00184-008-0171-x>.
    The method provides a unified framework for analyzing trends in both
    independent and dependent data samples, making it a versatile tool for
    various study designs. The package allows for the evaluation of different
    trend alternatives, including two-sided (general trend), monotonic
    increasing, and monotonic decreasing trends. As a nonparametric
    procedure, it does not require the assumption of data normality,
    offering a robust alternative to parametric tests.
Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA).
    The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables.
    Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities.
    The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.
Provides pseudo-weighted estimates of means and
    prevalences for finite population inference from nonprobability
    samples using auxiliary information from one or multiple probability
    reference surveys. The package supports estimation with multiple
    reference surveys, allowing auxiliary information to be combined
    when no single survey contains all variables relevant to
    participation. Optional cumulative precalibration can be applied to
    align weighted totals of shared variables across surveys. Methods
    are based on the generalized estimating equations framework of
    Landsman et al. (2026) <doi:10.1002/sim.70403> for correcting
    participation bias. For a single reference survey, the package
    implements the raking ratio calibration method and includes the
    adjusted logistic propensity (ALP) method of Wang, Valliant, and Li
    (2021) <doi:10.1002/sim.9122>, as well as the Chen-Li-Wu (CLW)
    method of Chen, Li, and Wu (2020) <doi:10.1080/01621459.2019.1677241>.
    Analytic variance estimation uses Taylor linearization and accounts
    for complex sampling designs in the reference surveys via integration
    with the 'survey' package.
Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. The package implements various methods such as inverse probability (propensity score) weighting, mass imputation and doubly robust approach. Details can be found in: Chen et al. (2020) <doi:10.1080/01621459.2019.1677241>, Yang et al. (2020) <doi:10.1111/rssb.12354>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Yang et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm> and Wu (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>. For details on the package and its functionalities see <doi:10.48550/arXiv.2504.04255>.
Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) <doi:10.1080/02664763.2024.2352759>.
Provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" <https://www.researchgate.net/publication/384354932_A_Novel_Nonvisual_Procedure_for_Screening_for_Nonstationarity_in_Time_Series_as_Obtained_from_Intensive_Longitudinal_Designs>.
A non-parametric test for multi-observer concordance and
    differences between concordances in (un)balanced data.
Onedimensional Normal (i.e. Gaussian) Mixture Models (S3) Classes,
   for, e.g., density estimation or clustering algorithms research and teaching;
   providing the widely used Marron-Wand densities.  Efficient random
   number generation and graphics. Fitting to data by efficient ML (Maximum
   Likelihood) or traditional EM estimation.
Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes for use with 'ggplot2' via custom functions.
The Nordklim dataset 1.0 is a unique and useful achievement for climate 
    analysis. It includes observations of twelve different climate elements from 
    more than 100 stations in the Nordic region, in time span over 100 years.
    The project contractors were NORDKLIM/NORDMET on behalf of the National 
    meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), 
    Norway (DNMI) and Sweden (SMHI).
Provides a SQLite-backed cell-level cache that can be used as a
    drop-in backend by the nordstat family of packages ('rKolada', 'rTrafa', and
    'pixieweb'). Designed for multi-user web applications where minimal fetch
    latency and asynchronous writes are required. Individual statistical
    values ("cells") are stored in a gatekeeper schema with a sidecar table
    for arbitrary metadata dimensions, enabling deduplication across
    overlapping queries.
An integrated set of functions for the analysis of 
  multivariate normal datasets with missing values, including implementation of
  the EM algorithm, data augmentation, and multiple imputation.
Enables correction for technical variance in raw quantitative reverse
  transcription polymerase chain reaction (qRT-PCR) data using the least
  squares-based NORMAgene data-driven normalization algorithm originally
  described by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250>.
  Performs normalization of raw crossing threshold values (CT) and also
  calculates relative variability metrics that can be used to assess the impact
  of normalization on variance.
Provides standardized access to a range of re-scaling methods for numerical vectors
    and time-series features calculated within the 'theft' ecosystem.
A toolkit for assessing data normality using a comprehensive collection of statistical methods. 
    It includes descriptive measures and formal hypothesis tests, such as skewness and kurtosis tests, 
    the Anderson–Darling test, the Shapiro–Wilk test, and the D'Agostino–Pearson K2 omnibus test.
Package including an interactive Shiny application for
    testing normality visually.
Provides simple methods for centering and scaling of numeric data. 
    Columns or rows can be ignored when normalizing or be normalized jointly.
Normalize a given Hadamard matrix. A Hadamard matrix is said to be normalized when its first row and first column entries are all 1, see Hedayat, A. and Wallis, W. D. (1978) "Hadamard matrices and their applications. The Annals of Statistics, 1184-1238." <doi:10.1214/aos/1176344370>.
Functions for the normal Laplace distribution.  Currently, it
 provides limited functionality.  Density, distribution and quantile
 functions, random number generation, and moments are provided.
A collection of utilities referred to Exponential Power distribution, also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4).
Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.
It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets.
             Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) <doi:10.1101/251140>.
Cleans and Normalizes FLUOstar DBF and DAT Files obtained from liposome flux assays. Users should verify extended usage of the package on files from other assay types.
Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes.
    Fitting models to data using 'MLE' (maximum likelihood estimation) for
    multivariate normal mixtures via smart parametrization using the 'LDL'
    (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268),
    Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.
Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) <doi:10.1159/000365637> for details.
A toolbox for continuous norming of psychological and educational tests, supporting regression-based norming where norms can vary as a continuous function of age or another norm predictor. Norms are estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), enabling flexible modelling of the full score distribution in a normative sample. The package supports applications in psychometrics and psychological testing, and includes functions for model selection, reliability estimation, norm calculation, including confidence intervals, and sample size planning. For more details, see Timmerman et al. (2021) <doi:10.1037/met0000348>.
Allele frequency databases for 50 forensic short tandem
    repeat (STR) markers, covering Norway and several broader regional
    populations: Europe, Africa, South America, West Asia, Middle Asia,
    and East Asia. Developed and maintained for use at the Department of
    Forensic Sciences, Oslo, Norway.
Five omnibus tests for testing the composite hypothesis of
        normality.
Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.
Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and  Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for  stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.
Calculate NOS (node overlap and segregation) and
    the associated metrics described in Strona and Veech (2015)
    <doi:10.1111/2041-210X.12395> and Strona et al. (2018) 
    <doi:10.1111/ecog.03447>. The functions provided in the 
    package enable assessment of structural patterns ranging from
    complete node segregation to perfect nestedness in a variety 
    of network types. In addition, they provide a measure of 
    network modularity.
Provides functions for classifying sparseness in 2 x 2 categorical data where one or more cells have zero counts. The classification uses three widely applied summary measures: Risk Difference (RD), Relative Risk (RR), and Odds Ratio (OR). Helps in selecting suitable continuity corrections for zero cells in multi-centre or meta-analysis studies. Also supports sensitivity analysis and can detect phenomena such as Simpson's paradox. The methodology is based on Subbiah and Srinivasan (2008) <doi:10.1016/j.spl.2008.06.023>.
Provides a cross-platform interface to prevent the operating system from going to sleep while long-running R tasks are executing.
The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) <doi:10.1111/rssb.12322>.
This is a pure dummy interfaces package which mirrors 'MsSparkUtils' APIs <https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/microsoft-spark-utilities?pivots=programming-language-r> of 'Azure Synapse Analytics' <https://learn.microsoft.com/en-us/azure/synapse-analytics/> for R users,
    customer of Azure Synapse can download this package from CRAN for local development.
Functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.
Enable programmatic interaction with 'Notion' pages,
    databases, blocks, comments, and users through the 'Notion API'
    <https://developers.notion.com/>.  Provides both synchronous and
    asynchronous client interfaces for building workflows and automations
    that integrate with 'Notion' workspaces. Supports all 'Notion API'
    endpoints including content creation, data retrieval, and workspace
    management.
Provides functions to query databases and notes in 'Notion', using the official REST API. To learn more about the functionality of the 'Notion' API, see <https://developers.notion.com/>.
A comprehensive toolkit for analyzing and visualizing neural data 
    outputs, including Principal Component Analysis (PCA) trajectory plotting, 
    Multi-Electrode Array (MEA) heatmap generation, and variable importance 
    analysis. Provides publication-ready visualizations with flexible 
    customization options for neuroscience research applications.
Computes the probability density function, the cumulative density function, quantile function, random numbers and measures of inference for the following families exponentiated generalized gull alpha power family, exponentiated gull alpha powerfamily, gull alpha power family.
The raw dataset and model used in Lai et al. (2021) 
    Decoupled responses of native and exotic tree diversities to 
    distance from old-growth forest and soil phosphorous in 
    novel secondary forests. Applied Vegetation Science, 24, e12548.
In semi-structured interviews that use the 'framework' method, it
    is not always clear how refinements to interview questions affect the
    decision of when to stop interviews. The trend of 'novel' and 'duplicate'
    interview codes (novel codes are information that other interviewees have
    not previously mentioned) provides insight into the richness of qualitative
    information. This package provides tools to visualise when refinements occur
    and how that affects the trends of novel and duplicate codes. These
    visualisations, when used progressively as new interviews are finished, can
    help the researcher to decide on a stopping point for their interviews. For
    context, see Wong et al., (2023) <doi:10.1177/16094069231220773>.
Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Automate the following individual or batch processing: check local source packages;  build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment.
Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Provide additional resources to facilitate the automation of the following individual or batch processing: check local source packages;  build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment. The additional resources include determining the identity and ordering of the packages to process when updating an imported package.
Provides bridge equations with optional autoregressive terms for
    nowcasting low-frequency macroeconomic variables (e.g. quarterly GDP) from
    higher-frequency indicators (e.g. monthly retail sales). Handles the
    ragged-edge problem where different indicators have different publication
    lags via mixed-frequency alignment. Includes pseudo-real-time evaluation
    with expanding or rolling windows, and the Diebold-Mariano test for
    comparing forecast accuracy following Harvey, Leybourne, and Newbold
    (1997) <doi:10.1016/S0169-2070(96)00719-4>. No API calls; designed to
    work with data from any source.
Nowcasting using the Chain-Ladder method. Supports both non-cumulative delay-based estimation and model-based completeness fitting (e.g., using logistic or Gompertz curves) to predict final counts from partially reported data.
The noweb system for source code, implemented in R.
Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.
Nonparametric methods for analysis of covariance (ANCOVA) are
    distribution-free and provide a flexible statistical framework for
    situations where the assumptions of parametric ANCOVA are violated
    or when the response variable is ordinal. This package implements
    several well-known nonparametric ANCOVA procedures, including Quade,
    Puri and Sen, McSweeney and Porter, Burnett and Barr, Hettmansperger
    and McKean, Shirley, and Puri-Sen-Harwell-Serlin. The package provides
    user-friendly functions to apply these methods in practice.
	These methods are described in Olejnik et al. (1985) 
	<doi:10.1177/0193841X8500900104> and Harwell et al. (1988) 
	<doi:10.1037/0033-2909.104.2.268>.
Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) <doi: 10.1016/j.mex.2016.05.006> and van Someren et al. (1999) <doi: 10.3109/07420529908998724>. Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.
Performs nonparametric
        analysis of longitudinal data in factorial experiments.
        Longitudinal data are those which are collected from the same
        subjects over time, and they frequently arise in biological
        sciences. Nonparametric methods do not require distributional
        assumptions, and are applicable to a variety of data types
        (continuous, discrete, purely ordinal, and dichotomous).  Such
        methods are also robust with respect to outliers and for small
        sample sizes.
Analysis of multivariate data with two-way completely randomized
    factorial design. The analysis is based on fully nonparametric, rank-based
    methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda,
    Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate
    response is allowed to be ordinal, quantitative, binary or a mixture of the
    different variable types. The package offers two functions performing the
    analysis, one for small and the other for large sample sizes. The underlying methodology
    is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in 
    Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8> and in 
    Kiefel and Bathke (2022) <doi:10.1515/stat-2022-0112>.
Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.
These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.
Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) <doi:10.1080/03610926.2023.2251623>. This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design.
Construction and evaluation of nested partially balanced bipartite block (NPBBB) designs for comparing a set of test treatments with a set of control treatments under a nested (blocks within blocks) structure. Six systematic construction methods are provided: composing partially balanced bipartite block designs with nested balanced incomplete block designs; augmenting nested partially balanced incomplete block designs with controls; merging rows of group-divisible nested designs; direct construction from group-divisible schemes; and expansion of partially balanced incomplete block designs (Vinayaka et al. 2026: In press). The A-efficiencies of the block and sub-block classifications are computed against the A-optimal completely symmetric reference design, following the test-versus-control optimality framework of Gupta and Parsad (1996) <doi:10.1080/03610929608831743> and Vinayaka et al. (2024) <doi:10.1080/03610926.2023.2251623>. These designs are particularly suited to agricultural, animal husbandry, industrial, and clinical trials involving multiple standard checks under nested experimental conditions, such as multi-environment trials where field heterogeneity (blocks) and within-field variation (sub-blocks) must be controlled simultaneously.
Addressing crucial research questions often necessitates a small
    sample size due to factors such as distinctive target populations, rarity
    of the event under study, time and cost constraints, ethical concerns, or
    group-level unit of analysis. Many readily available analytic methods,
    however, do not accommodate small sample sizes, and the choice of the best
    method can be unclear. The 'npboottprm' package enables the execution of
    nonparametric bootstrap tests with pooled resampling to help fill this gap.
    Grounded in the statistical methods for small sample size studies detailed
    in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the
    package facilitates a range of statistical tests, encompassing independent
    t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests.
    The nonparboot() function undertakes essential computations, yielding
    detailed outputs which include test statistics, effect sizes, confidence
    intervals, and bootstrap distributions. Further, 'npboottprm' incorporates
    an interactive 'shiny' web application, nonparboot_app(), offering intuitive,
    user-friendly data exploration.
Sample sizes are often small due to hard to reach target populations,
    rare target events, time constraints, limited budgets, or ethical considerations.
    Two statistical methods with promising performance in small samples are the
    nonparametric bootstrap test with pooled resampling method, which is the
    focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>,
    and informative hypothesis testing, which is implemented in the 'restriktor'
    package. The 'npboottprmFBar' package uses the nonparametric bootstrap test
    with pooled resampling method to implement informative hypothesis testing.
    The bootFbar() function can be used to analyze data with this method and the
    persimon() function can be used to conduct performance simulations on type-one
    error and statistical power.
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
Statistical tools for analyzing cognitive diagnosis (CD) data collected from small settings using the nonparametric classification (NPCD) framework. The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) <DOI:10.1007/s00357-013-9132-9> and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) <DOI:10.1007/s11336-017-9595-4> and Chiu and Köhn (2019) <DOI:10.1007/s11336-019-09660-x>. An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) <DOI:10.3102/10769986221133088>.  Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation.
Nonparametric smoothing methods for density and regression estimation and inference with circular data. The package provides kernel density estimation along with inferential tools such as circular SiZer for feature significance, mode estimation, and modal clustering. It includes multiple methods for selecting the smoothing parameter, allowing users to optimize the trade-off between bias and variance. Various plotting functions help visualize estimated densities, modes, clusters, and significance features. For regression, the package implements nonparametric estimation of the mean regression function as well as other conditional characteristics, including modal regression and generalized regression. Bandwidth selection is also supported in the regression context, and testing procedures are available to assess structural features or effects in circular regression models.
Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.
An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.
Provides nonparametric CUSUM tests for detecting changes
  in possibly serially dependent univariate or low-dimensional
  multivariate observations. Retrospective tests sensitive to changes
  in the expectation, the variance, the covariance, the
  autocovariance, the distribution function, Spearman's rho, Kendall's
  tau, Gini's mean difference, and the copula are provided, as well as
  a test for detecting changes in the distribution of independent
  block maxima (with environmental studies in mind). The package also
  contains a test sensitive to changes in the autocopula and a
  combined test of stationarity sensitive to changes in the
  distribution function and the autocopula. The latest additions are
  an open-end sequential test based on the retrospective CUSUM
  statistic that can be used for monitoring changes in the mean of
  possibly serially dependent univariate observations, as well as
  closed-end and open-end sequential tests based on empirical
  distribution functions that can be used for monitoring changes in
  the contemporary distribution of possibly serially dependent
  univariate or low-dimensional multivariate observations.
We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).
Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.
Performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.
Provides routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics.
A novel integral estimator for estimating the causal effects with continuous treatments (or dose-response curves) and a localized derivative estimator for estimating the derivative effects. The inference on the dose-response curve and its derivative is conducted via nonparametric bootstrap.
    The reference paper is Zhang, Chen, and Giessing (2024) <doi:10.48550/arXiv.2405.09003>.
Simulate demand and attributes for ready to launch new products 
    during their life cycle, or during their introduction and growth phases.
    You provide the number of products, attributes, time periods and/or other parameters 
    and 'npdsim' can simulate for you the demand for each product during the considered 
    time periods, and the attributes of each product. The simulation for the demand
    is based on the idea that each product has a shape and a level, where the level
    is the cumulative demand over the considered time periods, and the shape is
    the normalized demand across those time periods.
Provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.
Provides tools for non-parametric Fourier deconvolution using the N-Power Fourier Deconvolution (NPFD) method. This package includes methods for density estimation (densprf()) and sample generation (createSample()), enabling users to perform statistical analyses on mixed or replicated data sets.
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
             modeling oriented towards flow-cytometry data preprocessing applications. Method is 
             detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.
Implements the online Bayesian inference framework for joint
    state and parameter estimation in a stochastic
    Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model with a
    time-varying transmission rate. The log-transmission rate is modelled as
    a latent Ornstein-Uhlenbeck (OU) process with exact Gaussian discrete-time
    transitions. Inference is performed via the nested particle filter (NPF) of
    Crisan and Miguez (2018) <doi:10.3150/17-BEJ954>, which maintains an outer
    particle layer over the OU hyperparameters and, for each outer particle, an
    inner bootstrap filter over epidemic states. The Cori-style renewal-equation
    estimator follows Cori et al. (2013) <doi:10.1093/aje/kwt133>. The package
    also provides utilities for simulation, posterior summarisation, and
    forecasting.
Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and 
	to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two
	hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted
	log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the
	separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) 
	<doi:10.1002/pst.2062>.
	Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference
	between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities,
	complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles,
	restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the
	Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate
	normal approximation to the set of selected parameters.
Provides functions and examples for histogram, kernel (classical, variable bandwidth and transformations based), discrete and semiparametric hazard rate estimators.
An R-package for calculating sample size of a survival trial with or without cure fractions.
Performs combination tests and sample size calculation for 
   fixed design with survival endpoints using combination tests under either
   proportional hazards or non-proportional hazards. The combination tests 
   include maximum weighted log-rank test and projection test. The sample 
   size calculation procedure is very flexible, allowing for user-defined
   hazard ratio function and considering various trial conditions like 
   staggered entry, drop-out etc. The sample size calculation also applies to 
   various cure models such as proportional hazards cure model, cure model with
   (random) delayed treatments effects. Trial simulation function is also provided 
   to facilitate the empirical power calculation. The references for 
   projection test and maximum weighted logrank test include Brendel et al. (2014)
   <doi:10.1111/sjos.12059> and Cheng and He (2021) <arXiv:2110.03833>. The 
   references for sample size calculation under proportional hazard include
   Schoenfeld (1981) <doi:10.1093/biomet/68.1.316> and Freedman (1982) <doi:10.1002/sim.4780010204>.
   The references for calculation under non-proportional hazards include 
   Lakatos (1988) <doi:10.2307/2531910> and Cheng and He (2023) <doi:10.1002/bimj.202100403>.
Perform a stratified weighted log-rank test in a randomized controlled trial. Tests can be visualized as a difference in average score on the two treatment arms. These methods are described in Magirr and Burman (2018) <doi:10.48550/arXiv.1807.11097>, Magirr (2020) <doi:10.48550/arXiv.2007.04767>, and Magirr and Jimenez (2022) <doi:10.48550/arXiv.2201.10445>.
Access the United States National Provider Identifier
    Registry API <https://npiregistry.cms.hhs.gov/api/>. Obtain and transform
    administrative data linked to a specific individual or organizational
    healthcare provider, or perform advanced searches based on provider name,
    location, type of service, credentials, and other attributes exposed by
    the API.
An implementation of the Nonparametric Predictive Inference approach in R. It provides tools for quantifying uncertainty via lower and upper probabilities. It includes useful functions for pairwise and multiple comparisons: comparing two groups with and without terminated tails, selecting the best group, selecting the subset of best groups, selecting the subset including the best group.
Implements methods introduced in Chen, Christensen, and Kankanala (2024) <doi:10.1093/restud/rdae025> for estimating and constructing uniform confidence bands for nonparametric structural functions using instrumental variables, including data-driven choice of tuning parameters. All methods in this package apply to nonparametric regression as a special case.
Provides routines for plotting
      linkage and association results along a chromosome,
      with marker names displayed along the top border.
      There are also routines for generating BED and BedGraph
      custom tracks for viewing in the UCSC genome browser.
      The data reformatting program Mega2 uses this
      package to plot output from a variety of
      programs.
Performing drug response analyses and IC50 estimations using n-Parameter logistic regression. Can also be applied to proliferation analyses.
Creation and selection of N-way Partial Least Squares (NPLS) models. Selection of the optimal number of components can be done using ncrossreg(). NPLS was originally described by Rasmus Bro, see <doi:10.1002/%28SICI%291099-128X%28199601%2910%3A1%3C47%3A%3AAID-CEM400%3E3.0.CO%3B2-C>.
Provides functions for manipulating nested data frames in a 
    list-column using 'dplyr' <https://dplyr.tidyverse.org/> syntax. Rather than 
    unnesting, then manipulating a data frame, 'nplyr' allows users to 
    manipulate each nested data frame directly. 'nplyr' is a wrapper for 'dplyr'
    functions that provide tools for common data manipulation steps: filtering 
    rows, selecting columns, summarising grouped data, among others.
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. 
             Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods
             for the conditional-mean and conditional-distribution based models with longitudinal Data.
Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).
Nonparametric maximum likelihood estimation or Gaussian
        quadrature for overdispersed generalized linear models and
        variance component models.
Fit multinomial logistic regression with a penalty on the nuclear
    norm of the estimated regression coefficient matrix, using proximal
    gradient descent.
Performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks' Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable.  The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.
Posterior sampling in several commonly used distributions using
    normalized power prior as described in 
    Duan, Ye and Smith (2006) <doi:10.1002/env.752> and 
    Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. 
    Sampling of the power parameter is achieved via 
    either independence Metropolis-Hastings or random walk Metropolis-Hastings 
    based on transformation.
Calculates phenological cycle and anomalies using a non-parametric
    approach applied to time series of vegetation indices derived from remote sensing data 
    or field measurements. The package implements basic and high-level functions for 
    manipulating vector data (numerical series) and raster data (satellite derived products).
    Processing of very large raster files is supported. For more information, please check 
    the following paper:
    Chávez et al. (2023) <doi:10.3390/rs15010073>.
Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.
Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.
Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.
A method for obtaining nonparametric estimates of regression models
    with or without factor-by-curve interactions using local polynomial kernel
    smoothers or splines. Additionally, a parametric model (allometric model) can be
    estimated.
Nonparametric (and semiparametric) kernel methods that seamlessly
    handle a mix of continuous, unordered, and ordered factor data types. This
    package is a parallel implementation of the 'np' package based on the 'MPI'
    specification that incorporates the 'Rmpi' package (Hao Yu
    <hyu@stats.uwo.ca>) with minor modifications and we are extremely grateful
    to Hao Yu for his contributions to the 'R' community. We would like to
    gratefully acknowledge support from the Natural Sciences and Engineering
    Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the
    Social Sciences and Humanities Research Council of Canada (SSHRC,
    <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic
    Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would
    also like to acknowledge the contributions of the 'GNU GSL' authors. In
    particular, we adapt the 'GNU GSL' B-spline routine 'gsl_bspline.c' adding
    automated support for quantile knots (in addition to uniform knots),
    providing missing functionality for derivatives, and for extending the
    splines beyond their endpoints.
Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>). The package includes 'lprobust()' for local polynomial point estimation and robust bias-corrected inference, 'lpbwselect()' for local polynomial bandwidth selection, 'kdrobust()' for kernel density point estimation and robust bias-corrected inference, 'kdbwselect()' for kernel density bandwidth selection, and 'nprobust.plot()' for plotting results. The main methodological and numerical features are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).
In many binary classification applications, such as disease
    diagnosis and spam detection, practitioners commonly face the need to limit
    type I error (i.e., the conditional probability of misclassifying a class 0
    observation as class 1) so that it remains below a desired threshold. To address
    this need, the Neyman-Pearson (NP) classification paradigm is a natural choice;
    it minimizes type II error (i.e., the conditional probability of misclassifying
    a class 1 observation as class 0) while enforcing an upper bound, alpha, on the
    type I error. Although the NP paradigm has a century-long history in hypothesis
    testing, it has not been well recognized and implemented in classification
    schemes. Common practices that directly limit the empirical type I error to
    no more than alpha do not satisfy the type I error control objective because
    the resulting classifiers are still likely to have type I errors much larger
    than alpha. As a result, the NP paradigm has not been properly implemented
    for many classification scenarios in practice. In this work, we develop the
    first umbrella algorithm that implements the NP paradigm for all scoring-type
    classification methods, including popular methods such as logistic regression,
    support vector machines and random forests. Powered by this umbrella algorithm,
    we propose a novel graphical tool for NP classification methods: NP receiver
    operating characteristic (NP-ROC) bands, motivated by the popular receiver
    operating characteristic (ROC) curves. NP-ROC bands will help choose in a data
    adaptive way and compare different NP classifiers.
Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.
Fits sphere-sphere regression models by estimating locally weighted
    rotations. Simulation of sphere-sphere data according to non-rigid rotation
    models. Provides methods for bias reduction applying iterative procedures
    within a Newton-Raphson learning scheme. Cross-validation is exploited to select
    smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor
    (2018) <doi:10.1080/01621459.2017.1421542>.
Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.
Accompanies the book "Nonparametric Statistical Methods Using R, 2nd Edition" by Kloke and McKean (2024, ISBN:9780367651350).  Includes methods, datasets, and random number generation useful for the study of robust and/or nonparametric statistics.  Emphasizes classical nonparametric methods for a variety of designs --- especially one-sample and two-sample problems.  Includes methods for general scores, including estimation and testing for the two-sample location problem as well as Hogg's adaptive method.
Multidimensional nonparametric spatial (spatio-temporal) geostatistics.
    S3 classes and methods for multidimensional: linear binning,
    local polynomial kernel regression (spatial trend estimation), density and variogram estimation.
    Nonparametric methods for simultaneous inference on both spatial trend
    and variogram functions (for spatial processes).
    Nonparametric residual kriging (spatial prediction).
    For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) 
    <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.
Provides functions to compute the Rank-Based Stability Index (RSI)
    for genotype by environment interaction data, along with a genotype plus
    genotype-by-environment (GGE) style biplot visualization of stability.
Non-parametric survival analysis of exact and
	     interval-censored observations. The methods implemented
	     are developed by Wang (2007)
	     <doi:10.1111/j.1467-9868.2007.00583.x>, Wang (2008)
	     <doi:10.1016/j.csda.2007.10.018>, Wang and Taylor (2013)
	     <doi:10.1007/s11222-012-9341-9> and Wang and Fani (2018)
	     <doi:10.1007/s11222-017-9724-z>.
A number of statistical tests have been proposed to compare two 
  survival curves, including the difference in (or ratio of) t-year 
  survival, difference in (or ratio of) p-th percentile survival, difference in
  (or ratio of) restricted mean survival time, and the weighted log-rank test. 
  Despite the multitude of options, the convention in survival studies is to assume
  proportional hazards and to use the unweighted log-rank test for design and 
  analysis. This package provides sample size and power 
  calculation for all of the above statistical tests with allowance for 
  flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, 
  mixture cure). It is the companion R package to the paper by Yung and Liu (2020)
  <doi:10.1111/biom.13196>. Specific to the weighted log-rank test, users may 
  specify which approximations they wish to use to estimate the large-sample mean 
  and variance. The default option has been shown to provide substantial
  improvement over the conventional sample size and power equations based on Schoenfeld 
  (1981) <doi:10.1093/biomet/68.1.316>.
Robust nonparametric bootstrap and permutation tests for goodness of fit, distribution equivalence, location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package.
Implements a procedure for detecting multiple location-scale change points in a sequence of univariate observations, as described in Ross (2026) "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <doi:10.48550/arXiv.2107.01742>. The method combines Wild Binary Segmentation with a rank-based statistic and provides calibrated thresholds for controlling the probability of incorrectly detecting a change point in a homogeneous sequence.
Facilitates nonresponse bias analysis (NRBA) 
    for survey data.  Such data may arise from a complex
    sampling design with features such as stratification, clustering, or
    unequal probabilities of selection. Multiple types of analyses may be
    conducted: comparisons of response rates across subgroups; comparisons
    of estimates before and after weighting adjustments; comparisons of
    sample-based estimates to external population totals; tests of
    systematic differences in covariate means between respondents
    and full samples; tests of independence between response status
    and covariates; and modeling of outcomes and response status as a 
    function of covariates. Extensive documentation and references are
    provided for each type of analysis. Krenzke, Van de Kerckhove,
    and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>
    and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>
    provide an overview of the methods implemented in this package.
Provides a function designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations.
Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
Provides a set of functions to scrape and analyze rugby data. 
    Supports competitions including the National Rugby League, New South Wales Cup, 
    Queensland Cup, Super League, and various representative and women's competitions. 
    Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. 
    Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. 
    See Woods et al. (2017) <doi:10.1123/ijspp.2016-0187> for an example of rugby league performance analysis methodology.
Provides functions for probability and non-probability sampling
    design, sample selection, and population estimation tailored to natural
    resource management. Probability methods include simple random sampling,
    stratified sampling, systematic sampling, cluster sampling, and
    probability-proportional-to-size sampling. Non-probability methods
    include convenience, judgement-based, and quota sampling. Estimation
    functions cover means, totals, ratio estimators, regression estimators,
    and the unequal-probability estimator of Horvitz and Thompson (1952,
    <doi:10.2307/2280784>) for unequal-probability designs. Utilities support
    biomass, soil-loss, and carbon-stock estimation from field plots. Spatial
    extensions provide random, systematic, stratified, and raster-weighted
    sampling within geographic polygons using the 'sf' and 'terra' packages,
    with extraction of remote-sensing covariates at sample locations.
    Applications include forest inventory, soil erosion monitoring, watershed
    studies, and ecological field surveys.
A comprehensive toolkit for statistical and machine learning-based
    analysis of long-term Natural Resource Management (NRM) datasets. Integrates
    formula-driven approaches, statistical inference, and machine learning (ML)
    models for advanced analytics. Modules cover trend and structural analysis
    (Mann-Kendall test, slope estimation, Chow test, structural break detection),
    multivariate system modelling (Partial Least Squares (PLS), Structural
    Equation Modelling (SEM)), response curve optimisation, time-series
    forecasting (Autoregressive Integrated Moving Average (ARIMA), hybrid
    models), panel data and treatment effects (Difference-in-Differences (DiD),
    causal machine learning), uncertainty and sensitivity analysis (bootstrap,
    Monte Carlo, Bayesian), and automated model selection and performance
    comparison. Designed for long-term datasets covering soil, water, crop, and
    climate domains. Key references: Mann and Kendall (1945)
    <doi:10.2307/1907187>; Sen (1968) <doi:10.1080/01621459.1968.10480934>;
    Bai and Perron (2003) <doi:10.1002/jae.659>; Rosseel (2012)
    <doi:10.18637/jss.v048.i02>; Croissant and Millo (2008)
    <doi:10.18637/jss.v027.i02>.
Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.
Two implementations of canonical correlation analysis
        (CCA) that are based on iterated regression. By choosing the
        appropriate regression algorithm for each data domain, it is
        possible to enforce sparsity, non-negativity or other kinds of
        constraints on the projection vectors. Multiple canonical
        variables are computed sequentially using a generalized
        deflation scheme, where the additional correlation not
        explained by previous variables is maximized. nscancor() is
        used to analyze paired data from two domains, and has the same
        interface as cancor() from the 'stats' package (plus some extra
        parameters). mcancor() is appropriate for analyzing data from
        three or more domains. See
        <https://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/>
        and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more
        details.
Simulation and estimation for Neyman-Scott spatial cluster point
 process models and their extensions, based on the methodology in Tanaka, Ogata,
 and Stoyan (2008) <doi:10.1002/bimj.200610339>. To estimate parameters by the
 simplex method, parallel computation using 'OpenMP' application programming
 interface is available. For more details see Tanaka, Saga and Nakano
 <doi:10.18637/jss.v098.i06>.
Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Count the occurrence of sequences of values in a vector that meets certain conditions of length and magnitude. The method is based on the Run Length Encoding algorithm, available with base R, inspired by A. H. Robinson and C. Cherry (1967) <doi:10.1109/PROC.1967.5493>.
Download Current & Historical Bhavcopy. Get Live Market data from NSE India of Equities and Derivatives (F&O) segment. Data source  <https://www.nseindia.com/>.
Box-constrained multiobjective optimization using the 
        elitist non-dominated sorting genetic algorithm - NSGA-II.
        Fast non-dominated sorting, crowding distance, tournament 
        selection, simulated binary crossover, and polynomial 
        mutation are called in the main program. The methods are 
        described in Deb et al. (2002) <doi:10.1109/4235.996017>.
An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective 
    feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm
    that solves multiple optimization problems simultaneously by applying a non-dominated sorting 
    technique. It uses a reference points based selection operator to explore 
    solution space and preserve diversity. See the original paper by K. Deb and 
    H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.
Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods.  Exact procedures are performed when computationally possible.  Monte Carlo and Asymptotic procedures are performed otherwise.  For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition.
To account for non-stationary multivariate data, this package implements the framework including copula and marginal distributions. In addition to modeling and parameter estimations, it allows the computation and visualization of multivariate quantile curves for given events.
  This package is useful for a variety of disciplines such as finance, climatology and particularly for hydrological applications, where dependence structures and marginal parameters may vary over time.
  This framework, based on Chebana & Ouarda (2021) <doi:10.1016/j.jhydrol.2020.125907>, integrates both multivariate and non-stationary aspects to be more accurate (e.g. for risk assessment) and more realistic (e.g. considering climate changes).
National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access to official data via its API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.
Implementation of Narrowest Significance Pursuit, a general and
    flexible methodology for automatically detecting localised regions in data sequences
    which each must contain a change-point (understood as an abrupt change in the
    parameters of an underlying linear model), at a prescribed global significance level.
    Narrowest Significance Pursuit works with a wide range of distributional assumptions
    on the errors, and yields exact desired finite-sample coverage probabilities,
    regardless of the form or number of the covariates. For details, see P. Fryzlewicz
    (2021) <https://stats.lse.ac.uk/fryzlewicz/nsp/nsp.pdf>.
Mainly for maximum likelihood estimation of nonparametric
             and semiparametric mixture models, but can also be used
             for fitting finite mixtures. The algorithms are
             developed in Wang (2007)
             <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010)
             <doi:10.1007/s11222-009-9117-z>.
Two methods for performing a constrained principal
        component analysis (PCA), where non-negativity and/or sparsity
        constraints are enforced on the principal axes (PAs). The
        function 'nsprcomp' computes one principal component (PC) after
        the other. Each PA is optimized such that the corresponding PC
        has maximum additional variance not explained by the previous
        components. In contrast, the function 'nscumcomp' jointly
        computes all PCs such that the cumulative variance is maximal.
        Both functions have the same interface as the 'prcomp' function
        from the 'stats' package (plus some extra parameters), and both
        return the result of the analysis as an object of class
        'nsprcomp', which inherits from 'prcomp'. See
        <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/>
        and Sigg et al. (2008) <doi:10.1145/1390156.1390277> for more
        details.
Provides access to the 'Native Status Resolver' (NSR) <https://github.com/ojalaquellueva/nsr> API through R. The user supplies plant taxonomic names and political divisions and the package returns information about their likely native status (e.g., native, non-native,endemic), along with information on how those decisions were made.
A collection of statistical tools for objective (non-supervised) applications 
             of the Regional Frequency Analysis methods in hydrology. 
             The package refers to the index-value method and, more precisely, helps the
             hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions 
             with similar growth curves; (3) fit distribution functions to the 
             empirical regional growth curves.
             Most of the methods are those described in the Flood Estimation Handbook 
            (Centre for Ecology & Hydrology, 1999, ISBN:9781906698003).
             Homogeneity tests from Hosking and Wallis (1993) <doi:10.1029/92WR01980> 
             and Viglione et al. (2007) <doi:10.1029/2006WR005095> are available.
Tools for estimating Receiver Operating Characteristic (ROC) curves,
        building confidence bands, comparing several curves both for dependent and 
        independent data, estimating the cumulative-dynamic ROC curve in presence of
        censored data, and performing meta-analysis studies, among others.
To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) <doi:10.1073/pnas.1324044111>. NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) <doi:10.1073/pnas.1904623116>. A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) <doi:10.1016/j.soilbio.2020.108023>. SES is calculated as described in Kraft et al (2011) <doi:10.1126/science.1208584>. RC is calculated as reported by Chase et al (2011) <doi:10.1890/ES10-00117.1> and Stegen et al (2013) <doi:10.1038/ismej.2013.93>. Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) <doi:10.1038/s41467-020-18560-z>.
Geospatial data for creating maps of New South Wales (NSW),
  Australia, and some helpers to work with common problems like normalising
  postcodes. Registers its data with 'cartographer'.
Counts syllables in character vectors for English words.  Imputes syllables as the number of vowel sequences for words not found.
Implements the high-dimensional clustering technique Thresholding After Random Projections (n-TARP). Provides functionality to iteratively decompose larger datasets using contextual variables or within-cluster sum of squares. See Tarun & Boutin (2018) <doi:10.48550/arXiv.1806.05297> and Tarun & Boutin (2018) <doi:10.4231/R74B2ZJV> for the original method and applications.
The 'ntfy' (pronounce: notify) service is a simple HTTP-based pub-sub
    notification service. It allows you to send notifications to your phone or 
    desktop via scripts from any computer, entirely without signup, cost or 
    setup. It's also open source if you want to run your own. 
    Visit <https://ntfy.sh> for more details.
Implements statistical tools for analyzing, simulating, and computing properties of the New Topp-Leone Kumaraswamy Inverse Exponential (NTLKwIEx) distribution. See Atchadé M, Otodji T, and Djibril A (2024) <doi:10.1063/5.0179458> and Atchadé M, Otodji T, Djibril A, and N'bouké M (2023) <doi:10.1515/phys-2023-0151> for details.
Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number
   ,D is the determinant part of the number and I is  the indeterminacy part)
   using the neutrosophic two way anova test keeps the type I error low.
   This algorithm calculates the fisher statistics when we have a neutrosophic
   data, also tests two hypothesizes, first is to test differences between treatments,
   and second is to test differences between sectors. For more information see
    Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022)
    <https://www.americaspg.com/articleinfo/21/show/1058>.
Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).
Provides a collection of datasets related to neutrosophic sets for statistical modeling and analysis.
Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.
Nonparametric test of independence between a pair of spatial objects
    (random fields, point processes) based on random shifts with torus or variance correction. See
    Mrkvička et al. (2021) <doi:10.1016/j.spasta.2020.100430>,
    Dvořák et al. (2022) <doi:10.1111/insr.12503>,
    Dvořák and Mrkvička (2024) <doi:10.1080/10618600.2024.2357626>.
Especially when cross-sectional data are observational, effects of treatment selection
  bias and confounding are best revealed by using Nonparametric and Unsupervised methods to
  "Design" the analysis of the given data ...rather than the collection of "designed data".
  Specifically, the "effect-size distribution" that best quantifies a potentially causal
  relationship between a numeric y-Outcome variable and either a binary t-Treatment or
  continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched
  experimental units (e.g. patients) that have the most similar X-confounder characteristics.
  Since our NU Learning approach will form BLOCKS by "clustering" experimental units in
  confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within
  Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between
  Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is
  Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric
  with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also
  provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute
  information for effect-size inferences when X-Covariates are assumed to influence Treatment
  choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a
  "Most-Like-Me" function provides histograms of effect-size distributions to aid
  Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes.
  Obenchain and Young (2013) <doi:10.1080/15598608.2013.772821>; Obenchain, Young and Krstic
  (2019) <doi:10.1016/j.yrtph.2019.104418>.
Tools for 4D nucleome imaging. 
    Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. 
    See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>.
Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The executable of the model can downloaded from <https://github.com/jeroenpullens/NUCOMBog>.
A collection of colour palettes derived from photographs of nudis and sea slugs I have encountered in intertidal zones and shallow rocky reefs around Sydney, Australia. Palettes can be used in base R or with 'ggplot2'.
A comprehensive toolkit for calculating and visualizing Nitrogen Use Efficiency (NUE) indicators in agricultural research. The package implements 23 parameters categorized into fertilizer-based, plant-based, soil-based, isotope-based, ecology-based, and system-based indicators based on Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Key features include vectorized calculations for paired-plot experimental designs, batch processing capabilities for handling large datasets, and built-in visualization tools using 'ggplot2'. Designed to streamline the workflow from raw agronomic data to publication-ready metrics and plots.
A framework for systematic exploration of
    association rules  (Agrawal et al., 1994, <https://www.vldb.org/conf/1994/P487.PDF>), 
    contrast patterns  (Chen, 2022, <doi:10.48550/arXiv.2209.13556>),
    emerging patterns  (Dong et al., 1999, <doi:10.1145/312129.312191>),
    subgroup  discovery (Atzmueller, 2015, <doi:10.1002/widm.1144>),
    and conditional correlations (Hájek, 1978,  <doi:10.1007/978-3-642-66943-9>).
    User-defined functions may also be supplied to guide custom pattern searches.
    Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions
    expressed as elementary conjunctions, evaluates them on a dataset, and
    inspects the induced sub-data for statistical, logical, or structural
    properties such as  associations, correlations, or contrasts. Includes methods 
    for visualization of logical structures and supports interactive exploration
    through integrated Shiny applications.
Tools for visual inference. Generate null data sets
    and null plots using permutation and simulation. Calculate distance metrics
    for a lineup, and examine the distributions of metrics.
Provides null model algorithms for categorical and quantitative 
    community ecology data. Extends classic binary null models (e.g., 
    'curveball', 'swap') to work with categorical data. Provides a stratified
    randomization framework for continuous data.
A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.
Converts number spellings into their equivalent numbers.
    Supports numbers written in English, French, or Spanish.
The number of distinct alleles observed in a DNA mixture is informative of the number of contributors to the mixture. The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree.
Provides number-theoretic functions for factorization, prime 
    numbers, twin primes, primitive roots, modular logarithm and
    inverses, extended GCD, Farey series and continued fractions.
    Includes Legendre and Jacobi symbols, some divisor functions,
    Euler's Phi function, etc.
Methods for calculating (usually) accurate
	numerical first and second order derivatives. Accurate calculations 
	are done using 'Richardson''s' extrapolation or, when applicable, a
	complex step derivative is available. A simple difference 
	method is also provided. Simple difference is (usually) less accurate
	but is much quicker than 'Richardson''s' extrapolation and provides a 
	useful cross-check. 
	Methods are provided for real scalar and vector valued functions.
High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Format numbers and plots for publication; includes the removal of leading zeros,
           standardization of number of digits, addition of affixes, and a p-value formatter. These
           tools combine the functionality of several 'base' functions such as 'paste()',
           'format()', and 'sprintf()' into specific use case functions that are named in a way
           that is consistent with usage, making their names easy to remember and easy to deploy.
To add the table of numbers at risk below the Kaplan-Meier plot.
Optimizing regular numeric problems in optically stimulated luminescence dating, 
        such as: equivalent dose calculation, dose rate determination, growth curve fitting, decay 
        curve decomposition, statistical age model optimization, and statistical plot visualization.
Tools for detecting numeric persistence ("spells") and rigidity patterns
    in time-ordered numeric data. The package identifies periods of stability,
    computes spell-based rigidity metrics, and provides plain-language
    interpretations suitable for policy and applied analysis.
Segmentation of short text sequences - like hashtags - into the
    separated words sequence, done with the use of dictionary, which may be
    built on custom corpus of texts. Unigram dictionary is used to find most
    probable sequence, and n-grams approach is used to determine possible
    segmentation given the text corpus.
Provides a tool set for food information and dietary assessment. It 
    uses food composition data from several reference databases, including: 'USDA' (United States), 
    'CIQUAL' (France), 'BEDCA' (Spain), 'CNF' (Canada) and 'STFCJ' (Japan). 'NutrienTrackeR' calculates 
    the intake levels for both macronutrient and micronutrients, and compares them with the recommended 
    dietary allowances (RDA). It includes a number of visualization tools, such as time series 
    plots of nutrient intake, and pie-charts showing the main foods contributing to the intake 
    level of a given nutrient. A shiny app exposing the main functionalities of the package is also 
    provided.
Statistical tool for learning the structure of direct associations among variables for 
			 continuous data, discrete data and mixed discrete-continuous data. The package is based
			 on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.
Contains a collection of functions for performing different kinds
    of calculation that are of interest to someone following a diet plan.
    Calculators for the Basal Metabolic Rate are based on Mifflin et al. (1990)
    <doi:10.1093/ajcn/51.2.241> and  McArdle, W. D., Katch, F. I., &
    Katch, V. L. (2010, ISBN:9780812109917).
Motivated by changing administrative boundaries over time,
    the 'nuts' package can convert European regional data with NUTS codes
    between versions (2006, 2010, 2013, 2016 and 2021) and levels (NUTS 1,
    NUTS 2 and NUTS 3). The package uses spatial interpolation as in Lam
    (1983) <doi:10.1559/152304083783914958> based on granular (100m x
    100m) area, population and land use data provided by the European
    Commission's Joint Research Center.
Estimate nonlinear vector autoregression models (also known as the 
    next generation reservoir computing) for nonlinear dynamic systems. The 
    algorithm was described by Gauthier et al. (2021) <doi:10.1038/s41467-021-25801-2>.
Fits Bayesian regularized varying coefficient models with the Nonparametric Varying Coefficient Spike-and-Slab Lasso (NVC-SSL) introduced by Bai et al. (2023) <https://jmlr.org/papers/volume24/20-1437/20-1437.pdf>. Functions to fit frequentist penalized varying coefficients are also provided, with the option of employing the group lasso penalty of Yuan and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, the group minimax concave penalty (MCP) of Breheny and Huang <doi:10.1007/s11222-013-9424-2>, or the group smoothly clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>.
The n-vector framework uses the normal vector to
    the Earth ellipsoid (called n-vector) as a non-singular position
    representation that turns out to be very convenient for practical
    position calculations.  The n-vector is simple to use and gives exact
    answers for all global positions, and all distances, for both
    ellipsoidal and spherical Earth models.  This package is a translation
    of the 'Matlab' library from FFI, the Norwegian Defence Research
    Establishment, as described in Gade (2010)
    <doi:10.1017/S0373463309990415>.
Creates quasi-proportional Venn diagrams with an arbitrary number of sets.
            It is related to the old 'nVennR' package, but the algorithm and use have been reworked.
Functions for working with (grouped) multivariate normal variance mixture
  distributions (evaluation of distribution functions and densities,
  random number generation and parameter estimation), including
  Student's t distribution for non-integer degrees-of-freedom as well as the grouped t
  distribution and copula with multiple degrees-of-freedom parameters.
  See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.
Airline on-time data for all flights departing NYC in 2013.
    Also includes useful 'metadata' on airlines, airports, weather, and
    planes.
Updating the now 10-year-old 'nycflights13' data package. It contains information about all flights that departed from the three main New York City airports in 2023 and metadata on airlines, airports, weather, and planes.
Provides a unified set of helper functions to access datasets 
    from the NYC Open Data platform <https://opendata.cityofnewyork.us/>. 
    Functions return results as tidy tibbles and support optional filtering, 
    sorting, and row limits via the Socrata API. The package includes endpoints 
    for 311 service requests, DOB job applications, juvenile justice metrics, 
    school safety, environmental data, event permitting, and additional citywide datasets.
Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.
'LaBB-CAT' is a web-based language corpus management
 system developed by the New Zealand Institute of Language, Brain
 and Behaviour (NZILBB) - see <https://labbcat.canterbury.ac.nz>.
 This package defines functions for accessing corpus data in a 'LaBB-CAT'
 instance. You must have at least version 20230818.1400 of 'LaBB-CAT'
 to use this package.
 For more information about 'LaBB-CAT', see
 Robert Fromont and Jennifer Hay (2008) <doi:10.3366/E1749503208000142>
 or 
 Robert Fromont (2017) <doi:10.1016/j.csl.2017.01.004>.
Tools to support research on vowel covariation. Methods are provided to
    support Principal Component Analysis workflows (as in Brand et al. (2021) 
    <doi:10.1016/j.wocn.2021.101096> and Wilson Black et al. (2023) 
    <doi:10.1515/lingvan-2022-0086>).
Datasets of driving offences and fines in New Zealand between 2009 and 2017.
    Originally published by the New Zealand Police at
    <http://www.police.govt.nz/about-us/publication/road-policing-driver-offence-data-january-2009-december-2017>.
Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. (2020) <doi:10.1098/rsif.2020.0084>).
Streamlines the post-processing, summarization, and visualization of 'outbreaker2' output via a suite of helper functions. Facilitates tidy manipulation of posterior samples, integration with case metadata, generation of diagnostic plots and summary statistics.
Provides functions to do 'O2PLS-DA' analysis for multiple omics data integration.
            The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" 
            which published by Johan Trygg and Svante Wold at 2003 <doi:10.1002/cem.775>. 
            'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between
            two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 <doi:10.1002/cem.1388>; Löfstedt et al., 2012 <doi:10.1016/j.aca.2013.06.026>) 
            from the systematic sources of variance being specific for each data set separately.
Provides carefully chosen color palettes as used a.o. at OpenAnalytics <http://www.openanalytics.eu>.
A general purpose client to work with any 'OAI-PMH'
    (Open Archives Initiative Protocol for 'Metadata' Harvesting) service.
    The 'OAI-PMH' protocol is described at
    <http://www.openarchives.org/OAI/openarchivesprotocol.html>.
    Functions are provided to work with the 'OAI-PMH' verbs: 'GetRecord',
    'Identify', 'ListIdentifiers', 'ListMetadataFormats', 'ListRecords', and
    'ListSets'.
Harvest metadata using the Open Archives Initiative Protocol for Metadata
  Harvesting (OAI-PMH) version 2.0 (for more information, see
  <https://www.openarchives.org/OAI/openarchivesprotocol.html>).
A comprehensive set of helpers that streamline data transmission
  and processing, making it effortless to interact with the 'OpenAI' API.
Offers a suite of functions for enhancing R plots.
Implements the efficient algorithm by Ortmann and Brandes (2017)
  <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., 'igraph'), the orbit-aware counts are computed respective each of the edges and nodes.
Generalise the starting point of the array index.
A comprehensive set of indexes and tests for social segregation analysis,
              as described in Tivadar (2019) - 'OasisR': An R Package to Bring Some Order
              to the World of Segregation Measurement <doi:10.18637/jss.v089.i07>.
              The package  is the most complete existing tool and it clarifies
              many ambiguities and errors regarding the definition of segregation
              indices. Additionally, 'OasisR' introduces several resampling methods
              that enable testing their statistical significance
              (randomization tests, bootstrapping, and jackknife methods).
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Data processing, visualisation and analysis of Limit Order Book
    event data.
It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <arXiv:2004.04341>.
This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun().
When people make decisions, they may do so using a wide variety of decision rules. The package allows users to easily create obfuscation games to test the obfuscation hypothesis. It provides an easy to use interface and multiple options designed to vary the difficulty of the game and tailor it to the user's needs. For more detail: Chorus et al., 2021, Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence, Mathematical Social Sciences, 109, 28-44, <doi:10.1016/j.mathsocsci.2020.10.002>.
The Open Bodem Index (OBI) is a method to evaluate the quality of soils of agricultural fields in The Netherlands and the sustainability of the current agricultural practices.
    The OBI score is based on four main criteria: chemical, physical, biological and management, which consist of more than 21 indicators. 
    By providing results of a soil analysis and management info the 'OBIC' package can be use to calculate he scores, indicators and derivatives that are used by the OBI.
    More information about the Open Bodem Index can be found at <https://openbodemindex.nl/>.
Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.
An 'RStudio' addin to assist with removing objects from the global environment. Features include removing objects according to name patterns and object type. During the course of an analysis, temporary objects are often created and this tool assists with removing them quickly. This can be useful when memory management within 'R' is important.
A mutable Signal object can report changes to its state,
    clients could register functions so that they are called whenever
    the signal is emitted. The signal could be emitted, disconnected,
    blocked, unblocked, and buffered.
Obtain optimum block from Non-overlapping Block Bootstrap method.
Three-dimensional rendering for 'grid' and 'ggplot2' graphics using cubes and cuboids drawn with an oblique projection.  As a special case also supports primary view orthographic projections.  Can be viewed as an extension to the 'isocubes' package <https://github.com/coolbutuseless/isocubes>.
Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.
Provides access to selected functions and data available through any 'OpenBioMaps' <https://openbiomaps.org> server instance. 'OpenBioMaps' is an open-source biodiversity data management platform designed for conservation professionals and researchers. User authentication and access control are handled through 'OpenBioMaps' login credentials.
Applies an objective Bayesian method to the Mb capture-recapture model to estimate the population size N.  The Mb model is a class of capture-recapture methods used to account for variations in capture probability due to animal behavior.  Under the Mb formulation,  the initial capture of an animal may effect the probability of subsequent captures due to their becoming "trap happy" or "trap shy."
Provides clean, tidy access to data published by the 'Office for
    Budget Responsibility' ('OBR'), the UK's independent fiscal watchdog. Covers
    the Public Finances Databank (outturn for PSNB, PSND, receipts, and
    expenditure since 1946), the Historical Official Forecasts Database (every
    'OBR' forecast since 2010), the Economic and Fiscal Outlook detailed forecast
    tables (five-year projections from the latest Budget), the Welfare Trends
    Report (incapacity benefit spending and caseloads), and the Fiscal Risks and
    Sustainability Report (50-year state pension projections). Data is downloaded
    from the 'OBR' on first use and cached locally for subsequent calls. Data is
    sourced from the 'OBR' website <https://obr.uk>.
An implementation for computing Optimal B-Robust Estimators of two-parameter 
    distribution. The procedure is composed of some equations
    that are evaluated alternatively until the solution is reached. Some tools
    for analyzing the estimates are included. The most relevant is
    covariance matrix computation using a closed formula.
Two-part system for first collecting then managing direct
    observation data, as described by Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
Creates block designs of fixed size J with at least one treated 
    and control unit per block. Blocks larger than pairs better
    distinguish effects caused by a treatment from unmeasured confounding in
    assignment of individuals to treatment. Somewhat counterintuitively,
    blocks larger than pairs can use more units while attaining better
    covariate balance and block homogeneity. A forthcoming manuscript 
    by Brumberg and Rosenbaum details the design.
Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.
Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor.  If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions).  Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change.  This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.
Estimates optimal classification (Poole 2000) <doi:10.1093/oxfordjournals.pan.a029814>
		scores from roll call votes supplied though a 'rollcall'
		object from package 'pscl'.
Estimate the positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fitting methods include the simple 'reference region', 'ordinary least squares' (sometimes known as occupancy plot), and 'restricted maximum likelihood estimation'.
Facilitates the gathering of biodiversity occurrence data 
  from disparate sources. Metadata is managed throughout the process to facilitate 
  reporting and enhanced ability to repeat analyses.
Fits community site occupancy models to environmental DNA
    metabarcoding data collected using spatially-replicated survey design.
    Model fitting results can be used to evaluate and compare the effectiveness
    of species detection to find an efficient survey design.
    Reference: Fukaya et al. (2022) <doi:10.1111/2041-210X.13732>,
    Fukaya and Hasebe (2025) <doi:10.1002/1438-390X.12219>.
The classical and extended occupancy distributions occur in cases where balls are randomly allocated 
    to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating 
    the first four central moments of the distributions are implemented as described in 
    O’Neill (2019) <doi:10.1080/00031305.2019.1699445>.
Perform interactive occupation coding during interviews as
    described in Peycheva, D., Sakshaug, J., Calderwood, L. (2021) <doi:10.2478/jos-2021-0042>
    and Schierholz, M., Gensicke, M., Tschersich, N., Kreuter, F. (2018) <doi:10.1111/rssa.12297>.
    Generate suggestions for occupational categories based on free text
    input, with pre-trained machine learning models in German and a ready-to-use
    shiny application provided for quick and easy data collection.
Implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.
Supports the analysis of Oceanographic data, including 'ADCP'
    measurements, measurements made with 'argo' floats, 'CTD' measurements,
    sectional data, sea-level time series, coastline and topographic data, etc.
    Provides specialized functions for calculating seawater properties such as
    potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state.
    Produces graphical displays that conform to the conventions of the
    Oceanographic literature. This package is discussed extensively by
    Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.
Provides tools for easy exploration of the world ocean atlas of 
  the US agency National Oceanic and Atmospheric Administration (NOAA). It 
  includes functions to extract NetCDF data from the repository and code to 
  visualize several physical and chemical parameters of the ocean. A Shiny app 
  further allows interactive exploration of the data. The methods for data 
  collecting and quality checks are described in several papers, which can be
  found here: <https://www.ncei.noaa.gov/products/world-ocean-atlas>.
Determine the sea area where the fishing boat operates. 
             The latitude and longitude of geographic coordinates are used to match oceanic areas and economic sea areas. 
	    	     You can plot the distribution map with dotplot() function.
		         Please refer to Flanders Marine Institute (2020) <doi:10.14284/403>.
Plotting toolbox for 2D oceanographic data (satellite data, sea surface temperature, chlorophyll, ocean fronts & bathymetry). Recognized classes and formats include netcdf, Raster, '.nc' and '.gz' files.
Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.
Calculate ocean wave height summary statistics and process data 
    from bottom-mounted pressure sensor data loggers. Derived primarily from 
    MATLAB functions provided by U. Neumeier at 
    <http://neumeier.perso.ch/matlab/waves.html>. Wave number 
    calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) 
    "Direct Solution of Wave Dispersion Equation", American Society of Civil 
    Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, 
    Vol 105, pp 457-459.
Estimates win ratio or Mann-Whitney parameter for two group
 comparisons using ordered composite endpoints with right censoring
 as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.
Several Oceanographic data sets are provided for use
    by the 'oce' package and for other purposes.
Picks the suitable cell types in spatial and scRNA-seq data using shrinkage methods.
    The package includes curated reference gene expression profiles for human and mouse cell types, 
    facilitating immediate application to common spatial transcriptomics or scRNA datasets. 
    Additionally, users can input custom reference data to support tissue- or experiment-specific analyses.
Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the 
    conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal 
    and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025)
    <doi:10.1080/07474938.2024.2429596>.
Extends 'flexclust' with an R implementation of order constrained
  solutions in k-means clustering (Steinley and Hubert, 2008, <doi:10.1007/s11336-008-9058-z>).
Provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2024) <doi:10.1007/s00357-024-09473-3>.
Generate and analyze Optimal Channel Networks (OCNs): 
	oriented spanning trees reproducing all scaling features characteristic 
	of real, natural river networks. As such, they can be used in a variety 
	of numerical experiments in the fields of hydrology, ecology and 
	epidemiology. See Carraro et al. (2020) <doi:10.1002/ece3.6479> 
	for a presentation of the package; Rinaldo et al. (2014) 
	<doi:10.1073/pnas.1322700111> for a theoretical overview on the OCN 
	concept; Furrer and Sain (2010) <doi:10.18637/jss.v036.i10> for the 
	construct used.
Implements the Bayesian online changepoint
     detection method by Adams and MacKay (2007) 
     <arXiv:0710.3742> for univariate or multivariate data.
     Gaussian and Poisson probability models are implemented.
      Provides post-processing functions with 
      alternative ways to extract changepoints.
Infers Boolean rules among cis-regulatory regions using
    paired chromatin accessibility and gene expression data at bulk and
    single-cell levels.  Links regulatory regions to target genes,
    providing insights into gene regulation mechanisms.
Provides an Interface to Open Collaboration Services 'OCS' (<https://www.open-collaboration-services.org/>) REST API.
Provides functions to access and download data from the 'Open Case Studies' <https://www.opencasestudies.org/> 
    repositories on 'GitHub' <https://github.com/opencasestudies>. Different functions enable 
    users to grab the data they need at different sections in the case study, as well as 
    download the whole case study repository. All the user needs to do is input the name of 
    the case study being worked on. The package relies on the httr::GET() function to access
    files through the 'GitHub' API. The functions usethis::use_zip() and usethis::create_from_github() 
    are used to clone and/or download the case study repositories. To cite an individual case study,
    please see the respective 'README' file at <https://github.com/opencasestudies/>.
    <https://github.com/opencasestudies/ocs-bp-rural-and-urban-obesity> 
    <https://github.com/opencasestudies/ocs-bp-air-pollution>
    <https://github.com/opencasestudies/ocs-bp-vaping-case-study>
    <https://github.com/opencasestudies/ocs-bp-opioid-rural-urban>
    <https://github.com/opencasestudies/ocs-bp-RTC-wrangling>
    <https://github.com/opencasestudies/ocs-bp-RTC-analysis>
    <https://github.com/opencasestudies/ocs-bp-youth-disconnection>
    <https://github.com/opencasestudies/ocs-bp-youth-mental-health>
    <https://github.com/opencasestudies/ocs-bp-school-shootings-dashboard>
    <https://github.com/opencasestudies/ocs-bp-co2-emissions>
    <https://github.com/opencasestudies/ocs-bp-diet>.
Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.
A database management tool built as a 'shiny' application. Connect to various 
    databases to send queries, upload files, preview tables, and more.
A simple wrapper for the 'Octopus Energy' API
    <https://developer.octopus.energy/docs/api/>. It handles
    authentication, by storing a provided API key and meter details.
    Implemented endpoints include 'products' for viewing tariff details
    and 'consumption' for viewing meter consumption data.
The aim of 'od' is to provide tools and example datasets for
    working with origin-destination ('OD') datasets of the type used to
    describe aggregate urban mobility patterns (Carey et al. 1981)
    <doi:10.1287/trsc.15.1.32>.  The package builds on functions for
    working with 'OD' data in the package 'stplanr', (Lovelace and Ellison
    2018) <doi:10.32614/RJ-2018-053> with a focus on computational
    efficiency and support for the 'sf' class system (Pebesma 2018)
    <doi:10.32614/RJ-2018-009>.  With few dependencies and a simple class
    system based on data frames, the package is intended to facilitate
    efficient analysis of 'OD' datasets and to provide a place for
    developing new functions.  The package enables the creation and
    analysis of geographic entities representing large scale mobility
    patterns, from daily travel between zones in cities to migration
    between countries.
Pure-R implementation of univariate binary-class ODA (UniODA),
    univariate multiclass ODA (MultiODA), and binary Classification Tree
    Analysis (CTA). Supports ordered and categorical attributes, priors-on
    inverse-frequency weighting, MAXSENS / SAMPLEREP / first-identified
    tie-breaking, true leave-one-out cross-validation, and Monte Carlo
    Fisher-randomization p-values. Covered UniODA, MultiODA, and binary CTA
    fixtures are tested for parity against MegaODA.exe and CTA.exe outputs.
Make querying on 'OData' easier. It exposes an 'ODataQuery' object
    that can be manipulated and provides features such as selection, filtering
    and ordering.
A DBI-compatible interface to ODBC databases.
A database resource that is accessible through the Open Database Connectivity ('ODBC') API. This package uses the Resource 
  model, with URL "resolver" and "client", to dynamically discover and make accessible tables stored in a 'MS SQL Server' database.
  For more details see Marcon (2021) <doi:10.1371/journal.pcbi.1008880>.
Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.
Anomaly detection in dynamic, temporal networks. The package 
    'oddnet' uses a feature-based method to identify anomalies. First, it computes 
    many features for each network. Then it models the features using time series 
    methods. Using time series residuals it detects anomalies. This way, the 
    temporal dependencies are accounted for when identifying anomalies 
    (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.
Conversion between the most common odds types for sports betting.
    Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw
    Probability are covered in this package.
Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.
A utility to quickly obtain clean and tidy sports
    odds from The Odds API <https://the-odds-api.com>.
Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot.
    This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.
Simplified odds ratio calculation of GAM(M)s & GLM(M)s.
    Provides structured output (data frame) of all predictors and their
    corresponding odds ratios and confident intervals for further
    analyses.  It helps to avoid false references of predictors and
    increments by specifying these parameters in a list instead of using
    'exp(coef(model))' (standard approach of odds ratio calculation for
    GLMs) which just returns a plain numeric output.  For GAM(M)s, odds
    ratio calculation is highly simplified with this package since it
    takes care of the multiple 'predict()' calls of the chosen predictor
    while holding other predictors constant. Also, this package allows
    odds ratio calculation of percentage steps across the whole predictor
    distribution range for GAM(M)s.  In both cases, confident intervals
    are returned additionally. Calculated odds ratio of GAM(M)s can be
    inserted into the smooth function plot.
We proposes a framework that provides real time support for early detection of
    anomalous series within a large collection of streaming time series data. By definition, anomalies
    are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that
    is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the
    system's typical behaviour using a representative sample of the typical behaviour of the system. An
    approach based on extreme value theory is used for this boundary prediction process. Then a sliding
    window is used to test for anomalous series within the newly arrived collection of series. Feature
    based representation of time series is used as the input to the model. To cope with concept drift,
    the forecast boundary for the system's typical behaviour is updated periodically.  More details
    regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al.
    (2019) <doi:10.1080/10618600.2019.1617160>.
Allows performing forwards prediction for the General Unified 
          Threshold model of Survival using compiled ode code. This package 
          was created to avoid dependency with the 'morse' package that requires 
          the installation of 'JAGS'. This package is based on functions from 
          the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, 
          Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, 
          Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, 
          Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.
An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.
R bindings to 'odiff', a blazing-fast pixel-by-pixel image
    comparison tool <https://github.com/dmtrKovalenko/odiff>. Supports PNG,
    JPEG, WEBP, and TIFF with configurable thresholds, antialiasing detection,
    and region ignoring. Requires system installation of 'odiff'. Ideal for
    visual regression testing in automated workflows.
Generate systems of ordinary differential equations
    (ODE) and integrate them, using a domain specific language
    (DSL).  The DSL uses R's syntax, but compiles to C in order to
    efficiently solve the system.  A solver is not provided, but
    instead interfaces to the packages 'deSolve' and 'dde' are
    generated.  With these, while solving the differential equations,
    no allocations are done and the calculations remain entirely in
    compiled code.  Alternatively, a model can be transpiled to R for
    use in contexts where a C compiler is not present.  After
    compilation, models can be inspected to return information about
    parameters and outputs, or intermediate values after calculations.
    'odin' is not targeted at any particular domain and is suitable
    for any system that can be expressed primarily as mathematical
    expressions.  Additional support is provided for working with
    delays (delay differential equations, DDE), using interpolated
    functions during interpolation, and for integrating quantities
    that represent arrays.
After develop a 'ODK' <https://opendatakit.org/> frame, we can link the frame to 'Google Sheets' <https://www.google.com/sheets/about/> and collect data through 'Android' <https://www.android.com/>. This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'.
OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. 
    In the first layer of the hierarchy, the clusters are separated automatically based on the variation 
    of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy 
    corresponds to the sub clustering process of small clusters based on the distance between the origin 
    and destination of each cluster.
Calculate the optimal sample size allocation that 
    uses the minimum resources to achieve targeted statistical 
    power in experiments.
    Perform power analyses with and without accommodating 
    costs and budget. The designs cover single-level 
    and multilevel experiments detecting main, mediation, 
    and moderation effects (and some combinations). 
    The references for the proposed methods include: 
    (1) Shen, Z., & Kelcey, B. (2020). 
    Optimal sample allocation under unequal costs in 
    cluster-randomized trials.
    Journal of Educational and Behavioral Statistics, 45(4): 446-474.
        <doi:10.3102/1076998620912418>.
    (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for
    three-level multisite cluster-randomized trials. 
    Journal of Research on Educational Effectiveness, 15 (1), 130-150.
    <doi:10.1080/19345747.2021.1953200>.
    (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in
    multisite randomized trials. The Journal of Experimental Education,
    90(3), 693-711. <doi:10.1080/00220973.2020.1830361>.
    (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). 
    Using ant colony optimization to identify optimal sample 
    allocations in cluster-randomized trials. 
    The Journal of Experimental Education, 93(1), 167-185.
    <doi:10.1080/00220973.2024.2306392>.
    (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and 
    optimal design for randomized controlled trials investigating 
    mediation effects. Psychological Methods.
     <doi:10.1037/met0000698>.
    (6) Champely, S. (2020). pwr: Basic functions for power analysis 
    (Version 1.3-0) [Software]. Available from 
    <https://CRAN.R-project.org/package=pwr>.
The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001) <DOI:10.1023/A:1010933404324> respectively.
Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. 
    In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome 
    variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event 
    outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>. 
    Because ODS data has biased sampling nature, standard statistical analysis such as linear regression 
    will lead to biases estimates of the population parameters. This package implements four statistical 
    methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous 
    outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial 
    linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) 
    <doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design 
    (Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary 
    outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.
Implements a tree-based method specifically designed for personalized medicine applications. By using genomic and mutational data, 'ODT' efficiently identifies optimal drug recommendations tailored to individual patient profiles. The 'ODT' algorithm constructs decision trees that bifurcate at each node, selecting the most relevant markers (discrete or continuous) and corresponding treatments, thus ensuring that recommendations are both personalized and statistically robust. This iterative approach enhances therapeutic decision-making by refining treatment suggestions until a predefined group size is achieved. Moreover, the simplicity and interpretability of the resulting trees make the method accessible to healthcare professionals. Includes functions for training the decision tree, making predictions on new samples or patients, and visualizing the resulting tree. For detailed insights into the methodology, please refer to Gimeno et al. (2023) <doi:10.1093/bib/bbad200>.
Extracts covariates from Observational Medical
    Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an
    R-only pipeline. Supports configurable temporal windows,
    domain-specific covariates for drug exposure, drug era (including
    Anatomical Therapeutic Chemical (ATC) groupings), condition
    occurrence, condition era, concept sets and cohorts. Methods are
    based on the Observational Health Data Sciences and Informatics
    (OHDSI) framework described in Hripcsak et al. (2015)
    <doi:10.1038/sdata.2015.35> and "The Book of OHDSI"
    OHDSI (2019, ISBN:978-1-7923-0589-8).
Provides cohort pathway analysis for Observational Medical
    Outcomes Partnership (OMOP) Common Data Model databases,
    including both standard (post-index) and pre-index pathway analyses.
    The pre-index analysis identifies sequences of events occurring in a
    lookback window before the target cohort index date. Built on the
    'CohortPathways' analysis framework originally developed by Christopher
    Knoll and the Observational Health Data Sciences and Informatics
    community through 'WebAPI'. Methodological background and the
    originating implementation are described in
    <https://github.com/OHDSI/CohortPathways>.
Tools to build single-event survival datasets from "OMOP CDM"
    cohorts and estimate survival outcomes. The package supports Kaplan-Meier,
    Cox proportional hazards, and parametric accelerated-failure-time models,
    with optional stratification by gender and age groups.
An interface to the search API of 'HAL'
    <https://hal.science/>, the French open archive for scholarly
    documents from all academic fields. This package provides programmatic
    access to the API <https://api.archives-ouvertes.fr/docs> and allows
    to search for records and download documents.
Search and extract data from the Organization for Economic 
    Cooperation and Development (OECD).
Access and Analyze Official Development Assistance (ODA) data using 
    the OECD API <https://gitlab.algobank.oecd.org/public-documentation/dotstat-migration/-/raw/main/OECD_Data_API_documentation.pdf>.
    ODA data includes sovereign-level aid data such as key aggregates (DAC1), geographical
    distributions (DAC2A), project-level data (CRS), and multilateral contributions (Multisystem).
Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series.  Method is based on  Kubáček (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G.
             and Witkovský, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.
A collection of general-purpose helper functions that I (and maybe 
    others) find useful when developing data science software. Includes tools 
    for simulation, data transformation, input validation, and more.
Solves penalized least squares problems for big tall data
    using the orthogonalizing EM algorithm of Xiong et al. (2016) 
    <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the
    functions cv.oem() and xval.oem() are for cross validation, the latter being an
    accelerated cross validation function for linear models. The big.oem() function
    allows for out of memory fitting. A description of the underlying methods and 
    code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.
Tools to access data from the data web service of the Oesterreichische Nationalbank (OeNB), <https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>.
Developed to help researchers who need to model the kinetics of carbon dioxide (CO2) production in alcoholic fermentation of wines, beers and other fermented products. The following models are available for modeling the carbon dioxide production curve as a function of time: 5PL, Gompertz and 4PL. This package has different functions, which applied can: perform the modeling of the data obtained in the fermentation and return the coefficients, analyze the model fit and return different statistical metrics, and calculate the kinetic parameters: Maximum production of carbon dioxide; Maximum rate of production of carbon dioxide; Moment in which maximum fermentation rate occurs; Duration of the latency phase for carbon dioxide production; Carbon dioxide produced until maximum fermentation rate occurs. In addition, a function that generates graphs with the observed and predicted data from the models, isolated and combined, is available. Gava, A., Borsato, D., & Ficagna, E. (2020)."Effect of mixture of fining agents on the fermentation kinetics of base wine for sparkling wine production: Use of methodology for modeling". <doi:10.1016/j.lwt.2020.109660>.
Contains data from the May 2020 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.
Contains data from the May 2021 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.
Allows production of 'Microsoft' corporate documents from 'R
    Markdown' by reusing formatting defined in 'Microsoft Word' documents.
    You can reuse table styles, list styles but also add column sections,
    landscape oriented pages. Table and image captions as well as
    cross-references are transformed into 'Microsoft Word' fields,
    allowing documents edition and merging without issue with references;
    the syntax conforms to the 'bookdown' cross-reference definition.
    Objects generated by the 'officer' package are also supported in the
    'knitr' chunks.  'Microsoft PowerPoint' presentations also benefit
    from this as well as the ability to produce editable vector graphics
    in 'PowerPoint' and also to define placeholder where content is to be
    added.
Access and manipulate 'Microsoft Word', 'RTF' and 'Microsoft
    PowerPoint' documents from R.  The package focuses on tabular and
    graphical reporting from R; it also provides two functions that let
    users get document content into data objects. A set of functions lets
    add and remove images, tables and paragraphs of text in new or
    existing documents.  The package does not require any installation of
    Microsoft products to be able to write Microsoft files.
Detect the number and locations of change points. The locations can be either exact or in terms of ranges, 
            depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2017) <doi:10.1109/TSP.2017.2711558>.
Extend the 'tidymodels' ecosystem <https://www.tidymodels.org/> to
    enable the creation of predictive models with offset terms. Models with 
    offsets are most useful when working with count data or when fitting an
    adjustment model on top of an existing model with a prior expectation. 
    The former situation is common in insurance where data is often weighted by
    exposures. The latter is common in life insurance where industry mortality 
    tables are often used as a starting point for setting assumptions.
Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.
Generates synthetic Our Future Health cohort datasets for method development,
    including participant, questionnaire, clinic measurements, outpatient,
    inpatient, emergency, mortality, primary care medication, and geography
    outputs. Supports reproducible generation with configurable cohort size and
    user-defined International Classification of Diseases, Tenth Revision
    (ICD-10), Office of Population Censuses and Surveys Classification of
    Interventions and Procedures, version 4 (OPCS-4), and British National
    Formulary (BNF) code pools.
A comprehensive system for designing and implementing on-farm precision field agronomic trials. You provide field data, tell 'ofpetrial' how to design a trial, and get readily-usable trial design files and a report checks the validity and reliability of the trial design.
Consider a data matrix of n individuals with p variates. The objective general index (OGI)
    is a general index that combines the p variates into a univariate index in order to rank the n
    individuals. The OGI is always positively correlated with each of the variates.
    More details can be found in Sei (2016) <doi:10.1016/j.jmva.2016.02.005>.
Provides tools for processing and analyzing data from the 'O-GlcNAcAtlas' database <https://oglcnac.org/>, 
    as described in Ma (2021) <doi:10.1093/glycob/cwab003>. It integrates 'UniProt' <https://www.uniprot.org/> API calls 
    to retrieve additional information. It is specifically designed for research workflows 
    involving 'O-GlcNAcAtlas' data, providing a flexible and user-friendly interface for customizing 
    and downloading processed results. Interactive elements allow users to easily adjust parameters 
    and handle various biological datasets.
Multiple tools are now available for inferring the personalised 
    germ line set from an adaptive immune receptor repertoire. 
    Output from these tools is converted to 
    a single format and supplemented with rich data such as usage and 
    characterisation of 'novel' germ line alleles. This data can be 
    particularly useful when considering the validity of novel inferences. Use
    of the analysis provided is described in <doi:10.3389/fimmu.2019.00435>.
Extract results into R from the Observational Health Data Sciences and Informatics result database (see <https://ohdsi.github.io/Strategus/results-schema/index.html>) and generate reports/presentations via 'quarto' that summarize results in HTML format. Learn more about 'OhdsiReportGenerator' at <https://ohdsi.github.io/OhdsiReportGenerator/>.
Users can build a single 'shiny' app for exploring population characterization, population-level causal effect estimation, and patient-level prediction results generated via the R analyses packages in 'HADES' (see <https://ohdsi.github.io/Hades/>). Learn more about 'OhdsiShinyAppBuilder' at <https://ohdsi.github.io/OhdsiShinyAppBuilder/>.
Supports the modeling of ordinal random variables, 
    like the outcomes of races, via Softmax regression,
    under the Harville <doi:10.1080/01621459.1973.10482425> and
    Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.
Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.
Inference using a class of Hidden Markov models
    (HMMs) called 'oHMMed'(ordered HMM with emission densities 
    <doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify 
    the number of comparably homogeneous regions within observed sequences
    with autocorrelation patterns. These are modelled as discrete hidden
    states; the observed data points are then realisations of continuous
    probability distributions with state-specific means that enable
    ordering of these distributions. The observed sequence is labelled
    according to the hidden states, permitting only neighbouring states
    that are also neighbours within the ordering of their associated
    distributions. The parameters that characterise these state-specific
    distributions are then inferred. Relevant for application to genomic
    sequences, time series, or any other sequence data with serial
    autocorrelation.
Perform a Bayesian estimation of the ordinal exploratory 
    Higher-order General Diagnostic Model (OHOEGDM) for Polytomous Data 
    described by Culpepper, S. A. and Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>.
Ordered homogeneity pursuit lasso (OHPL)
    algorithm for group variable selection proposed in Lin et al. (2017)
    <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
    homogeneity structure in high-dimensional data and enjoys the
    grouping effect to select groups of important variables
    automatically. This feature makes it particularly useful for
    high-dimensional datasets with strongly correlated variables,
    such as spectroscopic data.
A client that grants access to the power of the 'ohsome API'
    from R. It lets you analyze the rich data source of the 
    'OpenStreetMap (OSM)' history. You can retrieve the geometry of 'OSM' 
    data at specific points in time, and you can get aggregated statistics 
    on the evolution of 'OSM' elements and specify your own temporal, 
    spatial and/or thematic filters.
Facilitates the automatic detection of acoustic signals, 
    providing functions to diagnose and optimize the performance of detection 
    routines. Detections from other software can also be explored and optimized. 
    This package has been peer-reviewed by rOpenSci.
    Araya-Salas et al. (2022) <doi:10.1101/2022.12.13.520253>.
Interface with the One Health VBD (vector-borne disease) Hub <https://vbdhub.org/> and related repositories (VectorByte <https://www.vectorbyte.org>, GBIF <https://www.gbif.org> and AREAdata <https://pearselab.github.io/areadata/>) directly to find, download, and subset vector-borne disease data.
Provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers' d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data.
Empirical reservoir water quality modelling using Walker's
    'BATHTUB' Model 1 (second-order available-phosphorus sedimentation)
    from Walker (1985) <https://hdl.handle.net/11681/13884>
    and Walker (1996) <https://hdl.handle.net/11681/4353> as the
    default retention model. The Vollenweider (1976) hydraulic-
    residence form and the equivalent formulation of Larsen and Mercier
    (1976) are available as alternatives. Predicts in-lake total
    phosphorus, total nitrogen, chlorophyll-a, and Secchi depth from
    tributary nutrient and hydraulic loading inputs, and computes
    Carlson (1977) <doi:10.4319/lo.1977.22.2.0361> Trophic State
    Indices. Optional Oklahoma-specific chlorophyll and Secchi
    regression coefficients are provided, calibrated from publicly
    available state lake monitoring data. Supports single-segment and
    multi-segment reservoir configurations and load-reduction scenario
    analysis. Designed to complement watershed loading models such as
    the Soil and Water Assessment Tool ('SWAT';
    <https://swat.tamu.edu>) and the U.S. EPA Hydrologic and Water
    Quality System ('HAWQS'; <https://hawqs.tamu.edu>) in a
    two-model nutrient management workflow.
A collection of aesthetically appealing color palettes for effective data visualization with 'ggplot2'. Palettes support both discrete and continuous data.
Read, validate, and load Open Knowledge Format (OKF) bundles (a
    directory of markdown files with YAML frontmatter) into a portable DuckDB
    catalog, build the concept graph, render to HTML, and optionally embed
    concept bodies for semantic search. Deterministic and agent-free: the same
    bundle always yields the same catalog, graph, and render, with no LLM calls
    in the core. Conformant and permissive per the OKF v0.1 specification.
Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) <DOI:10.1109/ACCESS.2020.3010099>.
An unofficial wrapper for 'okx exchange v5' API <https://www.okx.com/docs-v5/en/>, including 'REST' API and 'WebSocket' API.
Provides lightweight R wrappers for the 'OKX' REST API, covering 
    endpoints for market data, trading, account management, asset balances, 
    and copy trading. The upstream API reference is available at
    <https://www.okx.com/docs-v5/en/>.
We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021)<doi:10.48550/arXiv.2112.13479>.
'Open Location Codes' <http://openlocationcode.com/>
    are a Google-created standard for identifying geographic locations. 'olctools' provides
    utilities for validating, encoding and decoding entries that follow this
    standard.
An implementation of the Rapid Assessment Method for Older People
    or RAM-OP <https://www.helpage.org/resource/rapid-assessment-method-for-older-people-ramop-manual/>.
    It provides various functions that allow the user to design and plan the
    assessment and analyse the collected data. RAM-OP provides accurate and
    reliable estimates of the needs of older people.
A collection of functions to facilitate analysis of proteomic
    data from Olink, primarily NPX data that has been exported from Olink
    Software. The functions also work on QUANT data from
    Olink by log- transforming the QUANT data. The functions are focused
    on reading data, facilitating data wrangling and quality control
    analysis, performing statistical analysis and generating figures to
    visualize the results of the statistical analysis. The goal of this
    package is to help users extract biological insights from proteomic
    data run on the Olink platform.
Exemplifying analysis of large-scale protein
    data from the 'Olink platform', primarily relative protein expression
    data that has been exported from
    'Olink NPX Software', as well as QUANT data from 'Olink'.
    QUANT data is log-transformed. Materials focus on reading data, 
    demonstrating data wrangling and quality control analysis,
    performing statistical analysis and generating
    figures to visualize the results of the statistical analysis. 
    The goal of this package is to guide users extract biological insights 
    from large-scale protein data run on the 'Olink platform'.
    More information on 'Olink' data can be found at <https://olink.com/>.
An interface to easily run local language models with 'Ollama' <https://ollama.com> server
    and API endpoints (see <https://github.com/ollama/ollama/blob/main/docs/api.md> for details). It lets 
    you run open-source large language models locally on your machine.
Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) <doi:10.31801/cfsuasmas.542988> for example.
Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) <doi:10.1080/00949655.2016.1238088>.
The olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictors—regardless of relevance—potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective model—based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome. A Python version is also available at <https://pypi.org/project/olr>.
A transparent, modular, and base-R implemented statistical engine 
    for linear regression (OLS), analysis of variance (ANOVA), and logistic 
    regression (Logit). Designed under the principle of "assisted simplicity", 
    it features an integrated methodological "customs" (Aduana) that automatically 
    audits mathematical assumptions (e.g., multicollinearity, heteroskedasticity, 
    normality, and perfect separation) and outputs publication-ready, APA-formatted 
    tables. It deliberately avoids hidden heuristics and external dependencies, 
    ensuring computational transparency and reproducibility for applied research.
Tools designed to make it easier for users, particularly beginner/intermediate R users 
    to build ordinary least squares regression models. Includes comprehensive regression output, 
    heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, 
    model fit assessment and variable selection procedures.
The 'OLStrajr' package provides comprehensive functions for ordinary
    least squares (OLS) trajectory analysis and case-by-case OLS regression as
    outlined in Carrig, Wirth, and Curran (2004) <doi:10.1207/S15328007SEM1101_9>
    and Rogosa and Saner (1995) <doi:10.3102/10769986020002149>. It encompasses two
    primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies
    the estimation of individual growth curves over time via OLS regression, with
    options for visualizing both group-level and individual-level growth trajectories
    and support for linear and quadratic models. The cbc_lm() function facilitates
    case-by-case OLS estimates and provides unbiased mean population intercept and
    slope estimators by averaging OLS intercepts and slopes across cases. It further
    offers standard error calculations across bootstrap replicates and computation
    of 95% confidence intervals based on empirical distributions from the resampling
    processes.
'Shiny' Application to visualize Olympic Data. From 1896 to
    2016. Even Winter Olympics events are included. Data is from Kaggle at
    <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.
It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. 
    The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) <https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. 
    Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC.
A generalised data structure for fast and efficient loading and data munching of sparse omics data. The 'OmicFlow' requires an up-front validated metadata template from the user,
    which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an 'omics' class. 
    Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated 'autoFlow' method to generate a comprehensive report.
It provides functions to generate a correlation matrix
    from a genetic dataset and to use this matrix to predict the phenotype of an
    individual by using the phenotypes of the remaining individuals through
    kriging. Kriging is a geostatistical method for optimal prediction or best
    unbiased linear prediction. It consists of predicting the value of a
    variable at an unobserved location as a weighted sum of the variable at
    observed locations. Intuitively, it works as a reverse linear regression:
    instead of computing correlation (univariate regression coefficients are
    simply scaled correlation) between a dependent variable Y and independent
    variables X, it uses known correlation between X and Y to predict Y.
A tool for interactive exploration of the results from 'omics'
  experiments to facilitate novel discoveries from high-throughput biology. The
  software includes R functions for the 'bioinformatician' to deposit study
  metadata and the outputs from statistical analyses (e.g. differential
  expression, enrichment). These results are then exported to an interactive
  JavaScript dashboard that can be interrogated on the user's local machine or
  deployed online to be explored by collaborators. The dashboard includes
  'sortable' tables, interactive plots including network visualization, and
  fine-grained filtering based on statistical significance.
Provides an end-to-end workflow for integrative analysis of
	two omics layers using sparse canonical correlation analysis (sCCA),
	including sample alignment, feature selection, network edge construction,
	and visualization of gene-metabolite relationships. The underlying methods
	are based on penalized matrix decomposition and sparse CCA
	(Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008>),
	with design principles inspired by multivariate integrative frameworks
	such as mixOmics (Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>).
A method for the quantitative prediction using omics data. 
    This package provides functions to construct the quantitative prediction 
    model using omics data.
Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset.
    The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data.
    It provides a rigorous and a faster alternative cross-validation method to select the number of components,
    as well as functions to report proportions of explained variation and to construct plots of the results. 
    See the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>, 
    and Trygg and Wold (2003) <doi:10.1002/cem.775>.
    It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.
Provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses.
A method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization.
Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) <doi:10.1038/nprot.2011.335>.
In bulk epigenome/transcriptome experiments, molecular expression
    is measured in a tissue, which is a mixture of multiple types of cells.
    This package tests association of a disease/phenotype with a molecular marker
    for each cell type.
    The proportion of cell types in each sample needs to be given as input.
    The package is applicable to epigenome-wide association study (EWAS) and
    differential gene expression analysis.
    Takeuchi and Kato (submitted)
    "omicwas: cell-type-specific epigenome-wide and transcriptome association study".
Implements the Univariate Bootstrap and the Traditional
    (Naive) Bootstrap for resampling multivariate data while preserving
    covariance structure. Also provides functions for DeFries-Fulker
    behavioral genetics models, including the Rodgers-Kohler formulation
    with robust standard errors.
An assortment of helper functions for managing data (e.g.,
	rotating values in matrices by a user-defined angle, switching from
	row- to column-indexing), dates (e.g., intuiting year from messy date
	strings), handling missing values (e.g., removing elements/rows across
	multiple vectors or matrices if any have an NA), text (e.g.,
	flushing reports to the console in real-time); and combining data frames
	with different schema (copying, filling, or concatenating columns or
	applying functions before combining).
Creates mock data for testing and package development for the
    Observational Medical Outcomes Partnership common data model. The
    package offers functions crafted with pipeline-friendly
    implementation, enabling users to effortlessly include only the
    necessary tables for their testing needs.
Provides functionality to construct standardised tables from health
    care data formatted according to the Observational Medical Outcomes 
    Partnership (OMOP) Common Data Model. The package includes tools to build 
    key tables such as observation period and drug era, among others.
Provides definitions of core classes and methods used by analytic 
    pipelines that query the OMOP (Observational Medical Outcomes Partnership) 
    common data model.
Provides an R interface to the 'OMOPHub' API for accessing
    'OHDSI ATHENA' standardized medical vocabularies. Supports concept search,
    semantic search using neural embeddings, concept similarity, vocabulary
    exploration, hierarchy navigation, relationship queries, concept
    mappings, and FHIR-to-OMOP concept resolution with automatic pagination.
Use health data in the Observational Medical Outcomes Partnership 
    Common Data Model format in 'Spark'. Functionality includes creating all 
    required tables and fields and creation of a single reference to the data. 
    Native 'Spark' functionality is supported.
Summarises key information in data mapped to the Observational 
    Medical Outcomes Partnership (OMOP) common data model. Assess suitability to 
    perform specific epidemiological studies and explore the different domains
    to obtain feasibility counts and trends.
Streamlines the setup and execution of network studies using
    the Observational Medical Outcomes Partnership (OMOP) Common Data
    Model (CDM). Creates standardised project structures with template
    code, manages dependencies with 'renv', provides code review
    utilities, and supports containerised execution with 'Docker' for
    reproducible multi-site studies.  Includes 'GitHub' integration for
    collaboration and version control.
Visualise results obtained from analysing data mapped to the 
    Observational Medical Outcomes Partnership (OMOP) common data model using
    'shiny' applications.
Model mixed integer linear programs in an algebraic way directly in R.
             The model is solver-independent and thus offers the possibility
             to solve a model with different solvers. It currently only supports
             linear constraints and objective functions. See the 'ompr'
             website <https://dirkschumacher.github.io/ompr/> for more information,
             documentation and examples.
A solver for 'ompr' based on the R Optimization Infrastructure ('ROI').
  The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the
  'ompr' website <https://dirkschumacher.github.io/ompr/> and package docs for more information
  and examples on how to use it.
Build 'SVG' components using element-based functions. With
    an 'svg' object, we can modify its graphical elements with a suite of
    transform functions.
Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.
Data sets for network analysis related to People Analytics.  
  Contains various data sets from the book 'Handbook of Graphs and Networks in People Analytics' 
  by Keith McNulty (2021).
Implementation of a likelihood ratio test of differential
    onset of senescence between two groups. Given two groups with
    measures of age and of an individual trait likely to be subjected to
    senescence (e.g. body mass), 'OnAge' provides an asymptotic p-value
    for the null hypothesis that senescence starts at the same age in
    both groups. The package implements the procedure used in 
    Douhard et al. (2017) <doi:10.1111/oik.04421>.
An algorithm for fitting interpretable additive neural
    networks for identifiable and visualizable feature effects using post
    hoc orthogonalization. Fit custom neural networks intuitively using
    established 'R' 'formula' notation, including interaction effects of
    arbitrary order while preserving identifiability to enable a
    functional decomposition of the prediction function. For more details see 
    Koehler et al. (2025) <doi:10.1038/s44387-025-00033-7>.
A database containing the names 
  of the babies born in Ontario between 1917 and 2018. 
  Counts of fewer than 5 names were suppressed for privacy.
Automated reporting in Word and PowerPoint can require customization for each organizational template. This package works around this by adding standard reporting functions and an abstraction layer to facilitate automated reporting workflows that can be replicated across different organizational templates.
Allows you to easily execute expensive compute operations only once, and save the resulting object to disk.
Medication adherence, defined as medication-taking behavior that aligns with the agreed-upon 
    treatment protocol, is critical for realizing the benefits of prescription medications. 
    Medication adherence can be assessed using electronic adherence monitoring devices (EAMDs), 
    pill bottles or boxes that contain a computer chip that records the date and time of each 
    opening (or “actuation”). Before researchers can use EAMD data, they must apply a series of 
    decision rules to transform actuation data into adherence data. 
    The purpose of this R package ('oncmap') is to transform EAMD actuations in the form of a raw .csv file, 
    information about the patient, regimen, and non-monitored periods into two daily adherence values -- 
    Dose Taken and Correct Dose Taken.
Bayesian logistic regression model with optional
  EXchangeability-NonEXchangeability parameter modelling for flexible
  borrowing from historical or concurrent data-sources. The safety model
  can guide dose-escalation decisions for adaptive oncology Phase I
  dose-escalation trials which involve an arbitrary number of
  drugs. Please refer to Neuenschwander et al. (2008)
  <doi:10.1002/sim.3230> and Neuenschwander et al. (2016)
  <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. 
    Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. 
    It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.
Aids in the analysis of genes influencing cancer survival by including a principal function, calculator(), which calculates the P-value for each provided gene under the optimal cutoff in cancer survival studies. Grounded in methodologies from significant works, this package references Therneau's 'survival' package (Therneau, 2024; <https://CRAN.R-project.org/package=survival>) and the survival analysis extensions by Therneau and Grambsch (2000, ISBN 0-387-98784-3). It also integrates the 'survminer' package by Kassambara et al. (2021; <https://CRAN.R-project.org/package=survminer>), enhancing survival curve visualizations with 'ggplot2'.
Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota.
    There are 3 main functions within this package.
    (1) calcPhenotype() is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model.
    (2) GLDS() is used to calculate the general level of drug sensitivity, which can improve biomarker discovery.
    (3) IDWAS() can take the results from calcPhenotype() and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers.
    Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function.
    The package is described in Maeser et al. (2021) "oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data" <doi:10.1093/bib/bbab260>.
    calcPhenotype() - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.
    GLDS() - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.
    IDWAS() -  Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.
Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data.
Construct and evaluate directed tree structures that
   model the process of occurrence of genetic alterations during carcinogenesis
   as described in Szabo, A. and Boucher, K (2002)  <doi:10.1016/S0025-5564(02)00086-X>.
Single-cell datasets are growing in size, posing challenges
    as well as opportunities for genomics researchers. 'ondisc' is an R
    package that facilitates analysis of large-scale single-cell data
    out-of-core on a laptop or distributed across tens to hundreds of
    processors on a cluster or cloud. In both of these settings, 'ondisc'
    requires only a few gigabytes of memory, even if the input data are tens
    of gigabytes in size. 'ondisc' mainly is oriented toward single-cell
    CRISPR screen analysis, but also can be used for single-cell differential
    expression and single-cell co-expression analyses. 'ondisc' is powered by
    several new, efficient algorithms for manipulating and querying large,
    sparse expression matrices.
Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a 'CSV' or 'Excel' file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.
Two-stage design for single-arm phase II 
		trials with time-to-event endpoints (e.g., clinical trials on 
		immunotherapies among cancer patients) can be calculated using this package. 
		Two notable advantages of the package: 1) It provides flexible choices from 
		three design methods (optimal, minmax, and admissible), and 2) the power of
		the design is more accurately calculated using the exact variance in the
		one-sample log-rank test. The package can be used for 1) planning the sample
		sizes and other design parameters, and 2) conducting the interim and final
		analyses for the Go/No-go decisions. More details about the design method
		can be found in: Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020).
		<doi:10.1002/pst.1983>.
Get operating characteristics of one-arm clinical trial designs for time-to-event endpoint through simulation and perform analysis with time-to-event data.
Maximum homogeneity clustering algorithm for one-dimensional data
    described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479>
    via dynamic programming.
Quickly create numeric matrices for machine learning algorithms
    that require them. It converts factor columns into onehot vectors.
Estimates one-inflated positive Poisson (OIPP) and
    one-inflated zero-truncated negative binomial (OIZTNB) regression
    models. A suite of ancillary statistical tools are also provided,
    including: estimation of positive Poisson (PP) and zero-truncated
    negative binomial (ZTNB) models; marginal effects and their standard
    errors; diagnostic likelihood ratio and Wald tests; plotting;
    predicted counts and expected responses; and random variate
    generation. The models and tools, as well as four applications, are
    shown in Godwin, R. T. (2024). "One-inflated zero-truncated count
    regression models" arXiv preprint <doi:10.48550/arXiv.2402.02272>.
The identity provider ['OneLogin']<http://onelogin.com> is used for 
  authentication via Single Sign On (SSO). This package provides an R interface to their API.
Analysis of molecular marker data from model and non-model systems. 
    For the later, it allows statistical analysis by simultaneously estimating 
    linkage and linkage phases (genetic map construction) according to Wu and 
    colleagues (2002)
    <doi:10.1006/tpbi.2002.1577>. All analysis are based on multi-point 
    approaches using hidden Markov models.
An R wrapper for the 'OneMap.Sg' API <https://www.onemap.gov.sg/docs/>. 
    Functions help users query data from the API and return raw JSON data
    in "tidy" formats. Support is also available for users to retrieve data from multiple API calls 
    and integrate results into single dataframes, without
    needing to clean and merge the data themselves. This package is best suited for users who would 
    like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.
Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.
The log-rank test is performed to assess the survival outcomes between two group. 
             When there is no proper control group or obtaining such data is cumbersome, one sample
             log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the 
             test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package
             generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.
Useful functions for one-sample (individual level data)
    Mendelian randomization and instrumental variable analyses. The
    package includes implementations of; the Sanderson and Windmeijer
    (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic,
    the multiplicative structural mean model Hernán and Robins (2006)
    <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor
    substitution and two-stage residual inclusion estimators explained by
    Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.
This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).
Provide principally an eponymic function that numerically computes 
  the Le Cam's one-step estimator for an independent and identically distributed sample. 
  One-step estimation is asymptotically 
  efficient (see L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) 
  and can be computed faster than the maximum likelihood estimator for large observation 
  samples, see e.g. Brouste et al. (2021) <doi:10.32614/RJ-2021-044>.
Allows code to be run only once on a given computer, using
  lockfiles. Typical use cases include startup messages shown only when a
  package is loaded for the very first time.
We introduce an R function
        one_two_sample() which can deal with one and two (normal)
        samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. 
        For one normal sample x, the function reports
        descriptive statistics, plot, interval estimation and test of
        hypothesis of x. For two normal samples x and y, the function
        reports descriptive statistics, plot, interval estimation and
        test of hypothesis of x and y, respectively. It also reports
        interval estimation and test of hypothesis of mu1-mu2 (the
        difference of the means of x and y) and sigma1^2 / sigma2^2
        (the ratio of the variances of x and y), tests whether x and y
        are from the same population, finds the correlation coefficient
        of x and y if x and y have the same length.
Performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. These are one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test, Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests, Alvandi's F test, Alvandi's generalized p-value, approximate F test, B square test, Cochran test, Weerahandi's generalized F test, modified Brown-Forsythe test, adjusted Welch's heteroscedastic F test, Welch-Aspin test, Permutation F test. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, <https://journal.r-project.org/archive/2018/RJ-2018-022/RJ-2018-022.pdf>).
Quaternions and Octonions are four- and eight- dimensional
  extensions of the complex numbers.  They are normed division
  algebras over the real numbers and find applications in spatial
  rotations (quaternions), and string theory and relativity
  (octonions).  The quaternions are noncommutative and the octonions
  nonassociative.  See the package vignette for more details.
It implements the online Bayesian methods for change point analysis. It can
            also perform missing data imputation with methods from 'VIM'. The reference 
            is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>. 
            The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.
Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.
A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "onlineforecast: An R package for adaptive and recursive forecasting" <https://journal.r-project.org/articles/RJ-2023-031/>.
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Transactions occurring for a UK-based and registered, non-store online 
    retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, <doi:10.1145/1835804.1835882>). 
    This dataset is included in this package with the donor's permission, Dr. Daqing Chen.
Tools for surrogate evaluation in longitudinal studies using state-space models as proposed in Santos Jr. and Parast (2026)<doi:10.48550/arXiv.2604.12882>. The package estimates treatment effects over time with and without adjustment for surrogate information, summarizes the proportion of treatment effect explained by a longitudinal surrogate, quantifies uncertainty via bootstrap resampling, and provides plotting and summary utilities for fitted models.
Fits two-dimensional data by means of orthogonal nonlinear least-squares using Levenberg-Marquardt minimization and provides functionality for fit diagnostics and plotting. Delivers the same results as the 'ODRPACK' Fortran implementation described in Boggs et al. (1989) <doi:10.1145/76909.76913>, but is implemented in pure R.
The Ontario Marginalization Index is a socioeconomic model that is built on Statistics Canada census data.
    The model consists of four dimensions: In 2021, these dimensions were updated to "Material Resources" (previously called "Material Deprivation"), "Households and Dwellings" (previously called "Residential Instability"), "Age and Labour Force" (previously called "Dependency"), and "Racialized and Newcomer Populations" (previously called "Ethnic Concentration").
    This update reflects a movement away from deficit-based language. 2021 data will load with these new dimension names, wheras 2011 and 2016 data will load with the historical dimension names.
    Each of these dimensions are imported for a variety of geographic levels (DA, CD, etc.) for the 2021, 2011 and 2016 administrations of the census.
    These data sets contribute to community analysis of equity with respect to Ontario's Anti-Racism Act.
    The Ontario Marginalization Index data is retrieved from the Public Health Ontario website: <https://www.publichealthontario.ca/en/data-and-analysis/health-equity/ontario-marginalization-index>.
    The shapefile data is retrieved from the Statistics Canada website: <https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-eng.cfm>.
R Interface to 'ONNX' - Open Neural Network Exchange <https://onnx.ai/>. 
             'ONNX' provides an open source format for machine learning models. 
             It defines an extensible computation graph model, as well as definitions
             of built-in operators and standard data types.
Provides native access to the 'Open Neural Network Exchange' (ONNX) 
    Runtime <https://onnxruntime.ai/>, which is a performant engine for running
    machine learning models that are saved to a standardized format. Rather than
    interfacing with 'ONNX' via 'Python', as in the official 'onnx' package, 
    'onnxr' directly interfaces with the runtime's 'C++' API via 'cpp11'. Models
    saved to '.onnx' files can be loaded and run on various backends, including
    CPUs and Apple's 'CoreML' library.
Different measures which can be used to quantify similarities between regions. These measures are isonymy, isonymy between, Lasker distance, coefficients of Hedrick and Nei. In addition, it calculates biodiversity indices such as Margalef, Menhinick, Simpson, Shannon, Shannon-Wiener, Sheldon, Heip, Hill Numbers, Geometric Mean and Cressie and Read statistics.
Growing collection of helper functions for point pattern analysis. Most functions
  are designed to work with the 'spatstat' (<http://spatstat.org>) package. The focus of 
  most functions are either null models or summary functions for spatial point patterns. 
  For a detailed description of all null models and summary functions, see 
  Wiegand and Moloney (2014, ISBN:9781420082548).
Provides functions to download and tidy statistical data
    published by the 'Office for National Statistics'
    <https://www.ons.gov.uk>. Covers GDP, inflation (CPI, CPIH, RPI),
    unemployment, employment, wages, trade, retail sales, house prices,
    productivity, population, and public sector finances. Most series are
    fetched from the 'ONS' website using its CSV time series endpoint.
    House price data is sourced from 'HM Land Registry'
    <https://www.gov.uk/government/organisations/land-registry>.
    Data is cached locally between sessions.
Client for the 'Office of National Statistics'
    ('ONS') API <https://api.beta.ons.gov.uk/v1>.
Helps to create 'ggplot2' charts in the style used by the National 
    Road Safety Observatory (ONSV). The package includes functions to 
    customize 'ggplot2' objects with new theme and colors.
Wrapper functions for customizing HTML 
  tables from the 'gt' package to the ONSV style.
Tools for annotating characters (character matrices) with anatomical and phenotype ontologies. Includes functions for visualising character annotations and creating simple queries using ontological relationships.
Functions for reading ontologies into R as lists and manipulating sets of ontological terms - 'ontologyX: A suite of R packages for working with ontological data', Greene et al 2017 <doi:10.1093/bioinformatics/btw763>.
Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Provides new tools for analyzing discrete trait data integrating bio-ontologies and phylogenetics. It expands on the previous work of Tarasov et al. (2019) <doi:10.1093/isd/ixz009>. The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps.
  Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest.
Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package.
Order-of-addition experiments are often conducted to address research questions in many real-world studies. This package provides a comprehensive toolbox for researchers and practitioners to design and analyze order-of-addition experiments. Detailed comparisons and summary of all statistical methods in this package can be found in Tsai (2026), "Order-of-addition experiments in R using OofAExp", Journal of Quality Technology (to appear).
Intended to create standard human-in-the-loop validity tests for typical automated content analysis such as topic modeling and dictionary-based methods. This package offers a standard workflow with functions to prepare, administer and evaluate a human-in-the-loop validity test. This package provides functions for validating topic models using word intrusion, topic intrusion (Chang et al. 2009,  <https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>) and word set intrusion (Ying et al. 2021) <doi:10.1017/pan.2021.33> tests. This package also provides functions for generating gold-standard data which are useful for validating dictionary-based methods. The default settings of all generated tests match those suggested in Chang et al. (2009) and Song et al. (2020) <doi:10.1080/10584609.2020.1723752>.
Provides the class unions that must be
  preloaded in order for the basic tools in the OOMPA (Object-Oriented 
  Microarray and Proteomics Analysis) project to be defined and loaded.
  It also includes vectorized operations for row-by-row means,
  variances, and t-tests. Finally, it provides new color schemes.
  Details on the packages in the OOMPA project can be found at
  <http://oompa.r-forge.r-project.org/>.
This is a data-only package to provide example data for
  other packages that are part of the "Object-Oriented Microrray and
  Proteomics Analysis" suite of packages. These are described in more
  detail at the package URL.
Implementation of optimistic optimization methods for global optimization of deterministic or stochastic functions. The algorithms feature guarantees of the convergence to a global optimum. They require minimal assumptions on the (only local) smoothness, where the smoothness parameter does not need to be known. They are expected to be useful for the most difficult functions when we have no information on smoothness and the gradients are unknown or do not exist. Due to the weak assumptions, however, they can be mostly effective only in small dimensions, for example, for hyperparameter tuning.
Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.
Implements the out-of-treatment testing from Kuelpmann and Kuzmics (2020) <doi:10.2139/ssrn.3441675> based on the Vuong Test introduced in Vuong (1989) <doi:10.2307/1912557>. 
    Out-of treatment testing allows for a direct, pairwise likelihood comparison of theories, calibrated with pre-existing data.
Quantifies hypothesis to data fit for repeated measures 
    and longitudinal data, as described by Thorngate (1987) 
    <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) 
    <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
    pairwise relative orderings which are then compared to determine the
    percentage of orderings in the data that are matched by the hypothesis.
Data integration Web application for biobanks by 'OBiBa'. 'Opal' is
    the core database application for biobanks. Participant data, once
    collected from any data source, must be integrated and stored in a central
    data repository under a uniform model. 'Opal' is such a central repository.
    It can import, process, validate, query, analyze, report, and export data.
    'Opal' is typically used in a research center to analyze the data acquired at
    assessment centres. Its ultimate purpose is to achieve seamless
    data-sharing among biobanks. This 'Opal' client allows to interact with 'Opal'
    web services and to perform operations on the R server side. 'DataSHIELD'
    administration tools are also provided.
The online principal component method can process the online data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.
The online principal component regression method can process the online data set. 'OPCreg' implements the online principal component regression method, which is specifically designed to process online datasets efficiently. This method is particularly useful for handling large-scale, streaming data where traditional batch processing methods may be computationally infeasible.The philosophy of the package is described in 'Guo' (2025) <doi:10.1016/j.physa.2024.130308>.
An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data <doi:10.1371/journal.pone.0255838>.
Several function related to Experimental Design are implemented here, see
  "Optimal Experimental Design with R" by Rasch D. et. al (ISBN 9781439816974).
This contains functions and data used by the Open Visualization Academy classes on data processing and visualization. The tutorial included with this package requires the 'gradethis' package which can be installed using "remotes::install_github('rstudio/gradethis')".
An R wrapper of OpenAI API endpoints (see
    <https://platform.openai.com/docs/introduction> for details). This package
    covers Models, Completions, Chat, Edits, Images, Embeddings, Audio, Files,
    Fine-tunes, Moderations, and legacy Engines endpoints.