Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' API
    (<https://www.elastic.co/elasticsearch/>), including functions for
    setting connection details to 'Elasticsearch' instances, loading bulk data,
    searching for documents with both 'HTTP' query variables and 'JSON' based body
    requests. In addition, 'elastic' provides functions for interacting with API's
    for 'indices', documents, nodes, clusters, an interface to the cat API, and
    more.
Provides functions for fitting the entire
        solution path of the Elastic-Net and also provides functions
        for doing sparse PCA.
This is a (somewhat bizarre) collection of functions written to do
        various sorts of statistical election audits.  There are also
        functions to generate simulated voting data, including methods to simulation different types 
        of voting errors which allow for simulations for checking the characteristics of
        these methods.
Digital simulation of electrochemical processes.
             Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.Update 1.2.0 improved the accuracy of the measurements and allow personalized data resolution for simulation.
             Bibliography regarding this methods can be found in the following texts.
             Dieter Britz, Jorg Strutwolf (2016) <ISBN:978-3-319-30292-8>.
             Allen J. Bard, Larry R. Faulkner (2000) <ISBN:978-0-471-04372-0>.
Functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the 'msm' package using age as the time-scale.
Analyses districted electoral systems of any magnitude by computing district-party conversion ratios and seats-to-votes deviations, decomposing the sources of deviation. Traditional indexes are also computed.
    References:
    Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). <doi:10.1111/ajps.12225>.
    Penades, A and Pavia, J.M. (2025) ''The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''.
    Acknowledgements:
    The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research.
This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <https://www.vanderwalresearch.com/blog/15-elections>.
Offers a set of functions to easily download and clean 
    Brazilian electoral data from the Superior Electoral Court and 'CepespData' websites. 
    Among other features, the package retrieves data on local and
    federal elections for all positions (city councilor, mayor, state deputy,
    federal deputy, governor, and president) aggregated by
    state, city, and electoral zones.
Provides all electivity algorithms (including Vanderploeg and Scavia 
    electivity) that were examined in Lechowicz (1982) <doi:10.1007/BF00349007>, 
    plus the example data that were provided for moth resource utilisation.
Highest averages & largest remainders allocating seats methods and
    several party system scores.
    Implemented highest averages allocating seats methods are D'Hondt, Webster,
    Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague,
    equal proportions and Adams.
    Implemented largest remainders allocating seats methods are Hare, Droop,
    Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders.
    The main advantage of this package is that ties are always reported
    and not incorrectly allocated.
    Party system scores provided are competitiveness, concentration,
    effective number of parties, party nationalization score,
    party system nationalization score and volatility.
    References:
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>.
    Norris (2004, ISBN:0-521-82977-1).
    Consejo Nacional Electoral del Ecuador (2014)<http://cne.gob.ec/documents/Estadisticas/Atlas/ATLAS/CAPITULO%206%20web.pdf>.
    Laakso & Taagepera (1979) <https://journals.sagepub.com/doi/pdf/10.1177/001041407901200101>.
    Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>.
    Pedersen (1979) <https://janda.org/c24/Readings/Pedersen/Pedersen.htm>.
    Golosov (2010) <https://ppq.sagepub.com/content/16/2/171.abstract>.
    Golosov (2014) <https://ppq.sagepub.com/content/early/2014/09/08/1354068814549342.abstract>.
Several web services are available that provide access to elevation
             data. This package provides access to many of those services and 
             returns elevation data either as an 'sf' simple features object 
             from point elevation services or as a 'raster' object from raster 
             elevation services. In future versions, 'elevatr' will drop 
             support for 'raster' and will instead return 'terra' objects. 
             Currently, the package supports access to the Amazon Web Services 
             Terrain Tiles <https://registry.opendata.aws/terrain-tiles/>, 
             the Open Topography Global Datasets 
             API <https://opentopography.org/developers/>, and the USGS 
             Elevation Point Query Service <https://apps.nationalmap.gov/epqs/>.
A method to calculate the distance to the climatic tree line
    for large data sets of coordinates (World Geodetic System 1984) with 
    geographical uncertainty. The default thresholds and the treeline definition
    is based on Paulsen and Körner (2014) <doi:10.1007/s00035-014-0124-0>, users
    are free to decide what climate layers they would like to use.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
Density, distribution function, quantile function
    and random generation for the Kumaraswamy Complementary Weibull
    Geometric (Kw-CWG) lifetime probability distribution proposed
    in Afify, A.Z. et al (2017) <doi:10.1214/16-BJPS322>.
Performs hypothesis testing for general block designs with
    empirical likelihood. The core computational routines are implemented
    using the 'Eigen' 'C++' library and 'RcppEigen' interface, with
    'OpenMP' for parallel computation. Details of the methods are given in
    Kim, MacEachern, and Peruggia (2023)
    <doi:10.1080/10485252.2023.2206919>. This work was supported by the
    U.S. National Science Foundation under Grants No.  SES-1921523 and
    DMS-2015552.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
To run data analysis for enzyme-link immunosorbent assays (ELISAs). 
	Either the five- or four-parameter logistic model will be fitted for data of single ELISA. 
	Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. 
	Feng (2018) <doi:10.1101/483800>.
An alternative to Exploratory Factor Analysis (EFA) for
    metrical data in R. Drawing on characteristics of classical test
    theory, Exploratory Likert Scaling (ELiS) supports the user exploring
    multiple one-dimensional data structures. In common research practice,
    however, EFA remains the go-to method to uncover the (underlying)
    structure of a data set. Orthogonal dimensions and the potential of
    overextraction are often accepted as side effects. As described in
    Müller-Schneider (2001) <doi:10.1515/zfsoz-2001-0404>), ELiS confronts
    these problems. As a result, 'elisr' provides the platform to fully
    exploit the exploratory potential of the multiple scaling approach
    itself.
Create list comprehensions (and other types of comprehension) similar to those in
    'python', 'haskell', and other languages. List comprehension in 'R' converts a 
    regular for() loop into a vectorized lapply() function. Support for looping 
    with multiple variables, parallelization, and across non-standard objects included. Package 
    also contains a variety of functions to help with list comprehension.
Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name "elitism" is an acronym of "e"quipment for "l"ogarithmic and l"i"near "ti"me "s"tepwise "m"ultiple hypothesis testing.
    See Gou, J. (2022), "Quick multiple test procedures and p-value adjustments", Statistics in Biopharmaceutical Research 14(4), 636-650.
Tool for Environment-Wide Association Studies (EnvWAS / EWAS) 
    which are repeated analysis. It includes three functions. One function for
    linear regression, a second for logistic regression and a last one for
    generalized linear models.
Correlation chart of two set (x and y) of data.  
  Using Quantiles.  Visualize the effect of factor.
Contains various routines for drawing
    ellipses and ellipse-like confidence regions, implementing the plots
    described in Murdoch and Chow (1996, <doi:10.2307/2684435>). There are
    also routines implementing the profile plots described in Bates and
    Watts (1988, <doi:10.1002/9780470316757>).
The ellipsis is a powerful tool for extending functions. Unfortunately 
    this power comes at a cost: misspelled arguments will be silently ignored. 
    The ellipsis package provides a collection of functions to catch problems
    and alert the user.
Provides functions for the simulation and
  the nonparametric estimation of elliptical distributions,
  meta-elliptical copulas and trans-elliptical distributions,
  following the article Derumigny and Fermanian (2022) <doi:10.1016/j.jmva.2022.104962>.
A suite of elliptic and related functions including Weierstrass and
 Jacobi forms.  Also includes various tools for manipulating and
 visualizing complex functions.
Given the omnipresence of the assumption of elliptical symmetry, it is essential to be able to test whether that assumption actually holds true or not for the data
            at hand. This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2021) <arXiv:2011.12560v2>.
Chat with large language models from a range of providers
    including 'Claude' <https://claude.ai>, 'OpenAI'
    <https://chatgpt.com>, and more. Supports streaming, asynchronous
    calls, tool calling, and structured data extraction.
Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
An implementation of the algorithm described in "Efficient Large-
    Scale Internet Media Selection Optimization for Online Display Advertising"
    by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below
    for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf>
    for a full-text version of the paper). The algorithm here is designed to 
    allocate budget across a set of online advertising opportunities using a 
    coordinate-descent approach, but it can be used in any resource-allocation 
    problem with a matrix of visitation (in the case of the paper, website page-
    views) and channels (in the paper, websites). The package contains allocation
    functions both in the presence of bidding, when allocation is dependent on 
    channel-specific cost curves, and when advertising costs are fixed at each channel.
Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) <doi:10.1186/s12859-023-05556-x>.
A flexible framework for calculating Elo ratings and resulting
    rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
    etc.). This implementation is capable of evaluating a variety of matchups,
    Elo rating updates, and win probabilities, all based on the basic Elo
    rating system. It also includes methods to benchmark performance,
    including logistic regression and Markov chain models.
Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.
Provides an implementation of the maximum likelihood methods for deriving
    Elo scores as published in Foerster, Franz et al. (2016) <DOI:10.1038/srep35404>.
Provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries' I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.
Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) <doi:10.1111/2041-210X.14021>). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores.
Implements a Markov Chain Monte Carlo algorithm to approximate 
	exact conditional inference for logistic regression models. Exact 
	conditional inference is based on the distribution of the sufficient 
	statistics for the parameters of interest given the sufficient statistics 
	for the remaining nuisance parameters. Using model formula notation, users 
	specify a logistic model and model terms of interest for exact inference.
	See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.
A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.
Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
A generic function for running the Expectation-Maximization (EM) algorithm
  within a maximum likelihood framework, based on Dempster, Laird, and Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>
  is implemented. It can be applied after a model fitting using R's existing functions and packages.
The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) <doi:10.1016/j.fss.2011.05.022>.
Use 'emailjs' API easily in 'R'. This package is not official. <https://www.emailjs.com/docs/rest-api/send/>. You can send e-mail with 'emailjs' with function, based on 'httr'. You can also make a 'shiny' ui and server function. It can be used for making feedback form, inquiry, and so on.
An R client for the 'emailvalidation.io' e-mail verification API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://emailvalidation.io/docs> .
A tool that allows users to generate various indices for evaluating statistical models. The fitstat() function computes indices based on the fitting data. The valstat() function computes indices based on the validation data set. Both fitstat() and valstat() will return 16 indices SSR: residual sum of squares, TRE: total relative error, Bias: mean bias, MRB: mean relative bias, MAB: mean absolute bias, MAPE: mean absolute percentage error, MSE: mean squared	error, RMSE: root mean square error, Percent.RMSE: percentage root mean squared error, R2: coefficient of determination, R2adj: adjusted coefficient of determination, APC: Amemiya's prediction criterion, logL: Log-likelihood, AIC: Akaike information criterion, AICc: corrected Akaike information criterion, BIC: Bayesian information criterion, HQC: Hannan-Quin information criterion. The	lower the better for the SSR, TRE, Bias, MRB, MAB, MAPE, MSE, RMSE, Percent.RMSE, APC, AIC, AICc, BIC and HQC indices. The higher the better for R2 and R2adj indices. Petre Stoica, P., Selén, Y. (2004) <doi:10.1109/MSP.2004.1311138>\n Zhou et al. (2023) <doi:10.3389/fpls.2023.1186250>\n Ogana, F.N., Ercanli, I. (2021) <doi:10.1007/s11676-021-01373-1>\n Musabbikhah et al. (2019) <doi:10.1088/1742-6596/1175/1/012270>.
DNA methylation is essential for human, and environment can change the DNA methylation
    and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find
    potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide.
    For more information on the methods we used, please see the following references:
    Tingley, D. (2014) <doi:10.18637/jss.v059.i05>,
    Turner, S. D. (2018) <doi:10.21105/joss.00731>,
    Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.
A light, simple tool for sending emails with minimal dependencies.
Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'.
Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").
Predictors can be converted to one or more numeric
    representations using a variety of methods. Effect encodings using
    simple generalized linear models <doi:10.48550/arXiv.1611.09477> or 
    nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There 
    are also functions for dimension reduction and other approaches.
Provides a smooth mapping of multidimensional points into
    low-dimensional space defined by a self-organizing map. Designed to work
    with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019)
    <doi:10.12688/f1000research.21642.1>.
Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.
Fit Bayesian (hierarchical) cognitive models
    using a linear modeling language interface using particle metropolis Markov
    chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), 
    linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal
    race model (LNR) are supported. Additionally, users can specify their own likelihood
    function and/or choose for non-hierarchical
    estimation, as well as for a diagonal, blocked or full multivariate normal
    group-level distribution to test individual differences. Prior specification 
    is facilitated through methods that visualize the (implied) prior. 
    A wide range of plotting functions assist in assessing model convergence and
    posterior inference. Models can be easily evaluated using functions
    that plot posterior predictions or using relative model comparison metrics 
    such as information criteria or Bayes factors.    
    References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.
EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.
For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Application of empirical mode decomposition based artificial neural network model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=55&issue=1&article=013>; (ii) Das (2020) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=56&issue=2&article=002>.
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Functions that support estimating, assessing and mapping regional
    disaggregated indicators. So far, estimation methods comprise direct estimation,
    the model-based unit-level approach Empirical Best Prediction (see "Small area
    estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), 
    the area-level model (see "Estimates of income for small places: An 
    application of James-Stein procedures to Census Data" by Fay and Herriot (1979) 
    <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance 
    estimation methods, log and arcsin transformation, spatial, robust and measurement 
    error models), as well as their precision estimates. The assessment of the used model
    is supported by a summary and diagnostic plots. For a suitable presentation of
    estimates, map plots can be easily created. Furthermore, results can easily be
    exported to excel. For a detailed description of the package and the methods used
    see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" 
    by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette 
    "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".
Package providing calculation of Earth Mover's Distance (EMD).
Description: Application of empirical mode decomposition based support vector regression model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <http://krishi.icar.gov.in/jspui/handle/123456789/44873>; (ii) Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/43174>; (iii) Das (2023) <http://krishi.icar.gov.in/jspui/handle/123456789/77772>.
Produce maximum likelihood estimates of common accuracy statistics
 for multiple measurement methods when a gold standard is not available. An R 
 implementation of the expectation maximization algorithms described in Zhou et 
 al. (2011) <doi:10.1002/9780470906514> with additional functions for creating 
 simulated data and visualizing results. Supports binary, ordinal, and 
 continuous measurement methods.
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
Initially designed to distribute code for estimating the Gaussian
  graphical model with Lasso regularization, also known as the graphical lasso
  (glasso), using an Expectation-Maximization (EM) algorithm based on work by
  Städler and Bühlmann (2012) <doi:10.1007/s11222-010-9219-7>. As a byproduct,
  code for estimating means and covariances (or the precision matrix) under a
  multivariate normal (Gaussian) distribution is also available.
Simulate and fitting exponential multivariate Hawkes model. 
    This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.2307/2334319>, with an exponential kernel and fits the parameters from the data. 
    Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. 
    The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with 'maxLik' package.
A C++ implementation of the following evolutionary
    algorithms: Bat Algorithm (Yang, 2010 <doi:10.1007/978-3-642-12538-6_6>),
    Cuckoo Search (Yang, 2009 <doi:10.1109/nabic.2009.5393690>),
    Genetic Algorithms (Holland, 1992, ISBN:978-0262581110),
    Gravitational Search Algorithm (Rashedi et al., 2009 <doi:10.1016/j.ins.2009.03.004>),
    Grey Wolf Optimization (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>),
    Harmony Search (Geem et al., 2001 <doi:10.1177/003754970107600201>),
    Improved Harmony Search (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>),
    Moth-flame Optimization (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>),
    Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959),
    Simulated Annealing (Kirkpatrick et al., 1983 <doi:10.1126/science.220.4598.671>),
    Whale Optimization Algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>).
    'EmiR' can be used not only for unconstrained optimization problems, but also
    in presence of inequality constrains, and variables restricted to be integers.
Various Expectation-Maximization (EM) algorithms are implemented for item 
	response theory (IRT) models. The package includes IRT models for binary and ordinal
	responses, along with dynamic and hierarchical IRT models with binary responses. The
	latter two models are fitted using variational EM.  The package also includes 
	variational network and text scaling models.  The algorithms are described in Imai, Lo, 
	and Olmsted (2016) <DOI:10.1017/S000305541600037X>.
Processing tools to create emissions for use in numerical air 
  quality models. Emissions can be calculated both using emission factors 
  and activity data (Schuch et al 2018) <doi:10.21105/joss.00662> or using 
  pollutant inventories (Schuch et al., 2018) <doi:10.30564/jasr.v1i1.347>. 
  Functions to process individual point emissions, line emissions and 
  area emissions of pollutants are available as well as methods to 
  incorporate alternative data for Spatial distribution of emissions 
  such as satellite images (Gavidia-Calderon et. al, 2018) 
  <doi:10.1016/j.atmosenv.2018.09.026> or openstreetmap data 
  (Andrade et al, 2015) <doi:10.3389/fenvs.2015.00009>.
Provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The 'EmiStatR' package implements modular R methods. This enables 
    to add new functionalities through the R framework.
Implementation of the Mode Jumping Markov Chain Monte Carlo algorithm from Hubin, A., Storvik, G. (2018) <doi:10.1016/j.csda.2018.05.020>, Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., & Frommlet, F. (2020) <doi:10.1214/18-BA1141>, Hubin, A., Storvik, G., & Frommlet, F. (2021) <doi:10.1613/jair.1.13047>, and Hubin, A., Heinze, G., & De Bin, R. (2023) <doi:10.3390/fractalfract7090641>, and Reversible Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Frommlet, F., & Storvik, G. (2021) <doi:10.48550/arXiv.2110.05316>, which allow for estimating posterior model probabilities and Bayesian model averaging across a wide set of Bayesian models including linear, generalized linear, generalized linear mixed, generalized nonlinear, generalized nonlinear mixed, and logic regression models.
Work with Ecological Metadata Language ('EML') files. 
    'EML' is a widely used metadata standard in the ecological and
    environmental sciences, described in Jones et al. (2006),
    <doi:10.1146/annurev.ecolsys.37.091305.110031>.
This is a utility for transforming Ecological Metadata Language
        ('EML') files into 'JSON-LD' and back into 'EML.'  Doing so creates a
        list-based representation of 'EML' in R, so that 'EML' data can easily
        be manipulated using standard 'R' tools. This makes this package an
        effective backend for other 'R'-based tools  working with 'EML.' By
        abstracting away the complexity of 'XML' Schema, developers can
        build around native 'R' list objects and not have to worry about satisfying
        many of the additional constraints of set by the schema (such as element
        ordering, which is handled automatically). Additionally, the 'JSON-LD' 
        representation enables the use of developer-friendly 'JSON' parsing and
        serialization that may facilitate the use of 'EML' in contexts outside of 'R,'
        as well as the informatics-friendly serializations such as 'RDF' and
        'SPARQL' queries.
Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models that represent linear dynamical systems. Currently, one such algorithm is implemented for the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence. The corresponding scientific paper is yet to be published, therefore the relevant reference will be provided later.
End-member modelling analysis of grain-size data is an approach 
    to unmix a data set's underlying distributions and their contribution to 
    the data set. EMMAgeo provides deterministic and robust protocols for 
    that purpose.
Obtain estimated marginal means (EMMs) for many linear, generalized 
  linear, and mixed models. Compute contrasts or linear functions of EMMs,
  trends, and comparisons of slopes. Plots and other displays.
  Least-squares means are discussed, and the term "estimated marginal means"
  is suggested, in Searle, Speed, and Milliken (1980) Population marginal means 
  in the linear model: An alternative to least squares means, The American 
  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Provides unsupervised selection and clustering of microarray data
    using mixture models. Following the methods described in McLachlan, Bean and
    Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected
    based one the likelihood ratio statistic for the test of one versus two
    components when fitting mixtures of t-distributions to the expression data
    for each gene. The dimensionality of this gene subset is further reduced through
    the use of mixtures of factor analyzers, allowing the tissue samples to be
    clustered by fitting mixtures of normal distributions.
We provide functions to fit finite mixtures of multivariate normal or t-distributions to 
    data with various factor analytic structures adopted for the covariance/scale matrices. The 
    factor analytic structures available include mixtures of factor analyzers and mixtures of common 
    factor analyzers. The latter approach is so termed because the matrix of factor loadings is
    common to components before the component-specific rotation of the component factors to 
    make them white noise. Note that the component-factor loadings are not common after 
    this rotation. Maximum likelihood estimators of model parameters are obtained via the 
    Expectation-Maximization algorithm. See descriptions of the algorithms used in
    McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8>
    McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> 
    McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> 
    McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> 
    Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> 
    Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> 
    McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.
The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayes’ rule.
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
        of evolutionary multiobjective optimization algorithms.
Contains data about emojis with relevant metadata, and
    functions to work with emojis when they are in strings.
An implementation of using emoji and fontawesome for using in both
    base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.
The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps    (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) <doi:10.1080/14686996.2019.1620123>, <doi:10.1080/27660400.2021.1899449> <doi:10.1080/27660400.2022.2159753>. The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.
Tools for exploratory analysis of tabular data using colour 
 highlighting. Highlighting is displayed in any console supporting 'ANSI'
 colours, and can be converted to 'HTML', 'typst', 'latex' and 'SVG'. 
 'quarto' and 'rmarkdown' rendering are directly supported. It is 
 also possible to add colour to regular expression matches and 
 highlight differences between two arbitrary R objects.
Evaluates the empirical characteristic function of univariate and multivariate samples.
    This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level.
Routines for performing empirical calibration of observational
  study estimates. By using a set of negative control hypotheses we can
  estimate the empirical null distribution of a particular observational
  study setup. This empirical null distribution can be used to compute a
  calibrated p-value, which reflects the probability of observing an
  estimated effect size when the null hypothesis is true taking both random
  and systematic error into account. A similar approach can be used to
  calibrate confidence intervals, using both negative and positive controls. 
  For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and
  Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.
Empirical likelihood ratio tests and confidence intervals for means/quantiles/hazards
 	from possibly censored and/or truncated data. In particular, the empirical likelihood
        for the Kaplan-Meier/Nelson-Aalen estimator. Now does AFT regression too.
Calculates the empirical likelihood ratio and p-value for a mean-type hypothesis 
            (or multiple mean-type hypotheses) based on two samples with possible censored data.
Test hypotheses and construct confidence intervals for AUC (area under Receiver 
             Operating Characteristic curve) and pAUC (partial area under ROC curve), from the
			 given two samples of test data with disease/healthy subjects. The method used is
			 based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in
			 <https://www.ms.uky.edu/~mai/research/eAUC1.pdf>.
Collection of functions related to benchmark with prediction models
	 for data analysis and editing of clinical and epidemiological data.
Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.
Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM’s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.
Fast and easily computes an Euclidean Minimum Spanning Tree (EMST) from data,
    relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013).
    'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), 
    which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides 
    functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the 
    style of the 'base', 'scatterplot3d', or 'ggplot2' libraries; and functions to export the MST output to shapefiles.
Goodness-of-fit tests for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. Monte Carlo methods are provided to
        make the package capable of solving high-dimensional problems.
Allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a training set
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  The
 package includes functionality to evaluate quadratic forms 
 efficiently.
Provide the EMU Speech Database Management System (EMU-SDMS) with
    database management, data extraction, data preparation and data
    visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/>
    for more details.
We describe fifteen different splice site sequence encoding schemes that have been used in earlier studies for mapping of splice site sequences into numeric feature vectors. These encoding schemes will also be helpful for transforming other nucleotide sequences into numeric forms, provided they are of equal length. These encoding schemes will help the computational biologist working in the field of classification (binary or multiclass) or prediction involving nucleic acid sequences of equal length.
Implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2020) <arXiv:2003.03649>.
Interconverts between ordered lists and compact string notation.  
 Useful for capturing code lists, and pair-wise codes and decodes, for text storage.
 Analogous to factor levels and labels. Generics encode() and decode()
 perform interconversion, while codes() and decodes() extract components of an encoding.
 The function encoded() checks whether something is interpretable as an encoding.
 If a vector has an encoded 'guide' attribute, as_factor() uses it to coerce to factor.
The encompassing test is developed based on multi-step-ahead predictions of two nested models as in Pitarakis, J. (2023) <doi:10.48550/arXiv.2312.16099>. The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output.
Create encrypted html files that are fully self contained and do
  not require any additional software. Using the package you can encrypt
  arbitrary html files and also directly create encrypted 'rmarkdown' html reports.
It is important to ensure that sensitive data is protected. 
    This straightforward package is aimed at the end-user. 
    Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns.
    A public key can be shared to allow others to encrypt data to be sent to you. 
    This is particularly aimed a healthcare settings so patient data can be pseudonymised.
Padroniza endereços brasileiros a partir de diferentes
    critérios. Os métodos de padronização incluem apenas manipulações
    básicas de strings, não oferecendo suporte a correspondências
    probabilísticas entre strings. (Standardizes brazilian addresses using
    different criteria. Standardization methods include only basic string
    manipulation, not supporting probabilistic matches between strings.)
Various recursive two-stage models to address the endogeneity issue of treatment variables in observational study or mediators in experiments. The details of the models are discussed in Peng (2023) <doi:10.1287/isre.2022.1113>.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Maximum likelihood estimation of endogenous switching regression models from Heckman (1979) <doi:10.2307/1912352> and estimation of treatment effects.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy 
             distance, testing for multivariate normality, distance components (disco) for non-parametric 
             analysis of structured data, and other energy statistics/methods are implemented.
Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) <https://github.com/YafeiXu/working_paper/raw/master/CPM102.pdf>.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
Compute energy landscapes using a digital elevation model raster and body mass of animals.
Fully robust versions of the elastic net estimator are introduced for linear and binary and multinomial regression, in particular high dimensional data. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied. A reweighting step is added to improve the statistical efficiency of the proposed estimators. Selecting appropriate tuning parameters for elastic net penalties are done via cross-validation.
Allow numbers to be presented in an English language
        version, one, two, three, ...  Ordinals are also available,
        first, second, third, ... and indefinite article choice, "a" or "an".
Computing economic analysis in civil infrastructure and  
    ecosystem restoration projects is a typical activity. This package contains Standard 
    cost engineering and engineering economics methods that are applied to convert between 
    present, future, and annualized costs. Newnan D. (2020) <ISBN 9780190931919> “Engineering Economic Analysis”.
Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation for nonlinear regression?" by Xinwei Shen and Nicolai Meinshausen (2023). Also supports classification (experimental). 
 <arxiv:2307.00835>.
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.
A set of tools to perform Ecological Niche Modeling with
    presence-absence data. It includes algorithms for data partitioning, 
    model fitting, calibration, evaluation, selection, and prediction. 
    Other functions help to explore signals of ecological niche using univariate
    and multivariate analyses, and model features such as variable response 
    curves and variable importance. Unique characteristics of this package are
    the ability to exclude models with concave quadratic responses, and the
    option to clamp model predictions to specific variables. These tools are
    implemented following principles proposed in
    Cobos et al., (2022) <doi:10.17161/bi.v17i.15985>, 
    Cobos et al., (2019) <doi:10.7717/peerj.6281>, 
    and Peterson et al., (2008) <doi:10.1016/j.ecolmodel.2007.11.008>.
Implements species distribution modeling and ecological niche
	modeling, including: bias correction, spatial cross-validation, model
	evaluation, raster interpolation, biotic "velocity" (speed and
	direction of movement of a "mass" represented by a raster), interpolating
	across a time series of rasters, and use of spatially imprecise records.
	The heart of the package is a set of "training" functions which
	automatically optimize model complexity based number of available
	occurrences. These algorithms include MaxEnt, MaxNet, boosted regression
	trees/gradient boosting machines, generalized additive models,
	generalized linear models, natural splines, and random forests. To enhance
	interoperability with other modeling packages, no new classes are created.
	The package works with 'PROJ6' geodetic objects and coordinate reference
	systems.
Constructing niche models and analyzing patterns of niche evolution.  Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography.  Warren, D.L., R.E. Glor, and M. Turelli (2008) <doi:10.1111/j.1558-5646.2008.00482.x> Glor, R.E., and D.L. Warren (2011) <doi:10.1111/j.1558-5646.2010.01177.x>  Warren, D.L., R.E. Glor, and M. Turelli (2010) <doi:10.1111/j.1600-0587.2009.06142.x> Cardillo, M., and D.L. Warren (2016) <doi:10.1111/geb.12455> D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) <doi:10.1111/ecog.03900>.
An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.
A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 <doi:10.1016/j.isci.2022.104767>) or similar methods.
Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See <https://maayanlab.cloud/Enrichr/> for further details.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.
An implementation of the clustering methods of categorical data
    discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical 
    data via ensembling dissimilarity matrices.  Preprint <arXiv:1506.07930>.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
        from ensemble forecasts and weather observations
 <https://stat.uw.edu/sites/default/files/files/reports/2007/tr516.pdf>.
Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.
Creates ensemble taxonomic assignments of amplicon sequencing data 
  in R using outputs of multiple taxonomic assignment algorithms and/or
	reference databases. Includes flexible algorithms for mapping taxonomic 
	nomenclatures onto one another and for computing ensemble taxonomic 
	assignments.
Gene information from 'Ensembl' genome builds 'GRCh38.p14' and 'GRCh37.p13' to use with the 'topr' package. The datasets were originally downloaded from <https://ftp.ensembl.org/pub/current/gtf/homo_sapiens/Homo_sapiens.GRCh38.111.gtf.gz> and <https://ftp.ensembl.org/pub/grch37/current/gtf/homo_sapiens/Homo_sapiens.GRCh37.87.gtf.gz> and converted into the format required by the 'topr' package. See <https://github.com/totajuliusd/topr?tab=readme-ov-file#how-to-use-topr-with-other-species-than-human> to see the required format.
Elastic net regression models are controlled by two parameters,
  lambda, a measure of shrinkage, and alpha, a metric defining the model's
  location on the spectrum between ridge and lasso regression.  
  glmnet provides tools for selecting lambda via cross
  validation but no automated methods for selection of alpha.  Elastic Net
  SearcheR automates the simultaneous selection of both lambda and alpha.
  Developed, in part, with support by NICHD R03 HD094912.
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>.
  'entropart' provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
  Estimation-bias corrections are available.
Contains methods for data analysis in entropic perspective. These entropic perspective methods are nonparametric, and perform better on non-ordinal data. Currently, the package has a function HeatMap() for visualizing distributional characteristics among multiple populations (groups).
Implements various estimators of entropy for discrete random 
  variables, including the shrinkage estimator by Hausser and Strimmer (2009),
  the maximum likelihood and the Millow-Madow estimator, various Bayesian
  estimators, and the Chao-Shen estimator.  It also offers an R interface to the
  NSB estimator.  Furthermore, the package provides functions for estimating the
  Kullback-Leibler divergence, the chi-squared divergence, mutual information,
  and the chi-squared divergence of independence.  It also computes the
  G statistic and the chi-squared statistic and corresponding p-values.
  Furthermore, there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) <doi:10.1051/ps/2012004> and next articles.
Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991) <https://www.jstor.org/stable/2937655>.
Small toolbox for data analyses in environmental chemistry and
    ecotoxicology. Provides, for example, calibration() to calculate calibration
    curves and corresponding limits of detection (LODs) and  limits of 
    quantification (LOQs) according to German DIN 32645 (2008). texture() makes
    it easy to estimate soil particle size distributions from hydrometer
    measurements (ASTM D422-63, 2007).
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
Prints out information about the R working environment
    (system, R version,loaded and attached packages and versions) from a single
    function "env_doc()".  Optionally adds information on git repository,
    tags, commits and remotes (if available).
A collection of functions for microbial ecology and other
   applications of genomics and metagenomics. Companion package for the
   Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016
   <DOI:10.7287/peerj.preprints.1900v1>).
Estimates an ecological niche using occurrence data, covariates, and kernel
        density-based estimation methods. For a single species with presence and absence data,
        the 'envi' package uses the spatial relative risk function that is estimated using the
        'sparr' package. Details about the 'sparr' package methods can be found in the tutorial:
        Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can
        be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about
        relative risk functions using kernel density estimation can be found in J. F. Bithell
        (1991) <doi:10.1002/sim.4780101112>.
Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.
Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing.
	Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) 
	<doi:10.1021/acs.analchem.5b00941>.
Provides a small set of functions for managing R environments, with defaults designed to encourage usage patterns that scale well to larger code bases. It provides: import_from(), a flexible way to assign bindings that defaults to the current environment; include(), a vectorized alternative to base::source() that also default to the current environment; and attach_eval() and attach_source(), a way to evaluate expressions in attached environments. Together, these (and other) functions pair to provide a robust alternative to base::library() and base::source().
Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.
It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" <doi:10.1016/j.landurbplan.2019.02.005>.
Set of functions to keep track and find objects in user-defined environments
    by identifying environments by name --which cannot be retrieved with the built-in function environmentName().
    The package also provides functionality to obtain simplified information about function calling chains
    and to get an object's memory address.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
Contains utilities for the analysis of protein sequences in a phylogenetic context. 
    Allows   the generation of phylogenetic trees base on protein sequences in an alignment-independent way. 
    Two different methods have been implemented. One approach is based on the frequency analysis of n-grams, 
    previously described in Stuart et al. (2002) <doi:10.1093/bioinformatics/18.1.100>. The other approach is based on the species-specific neighborhood preference around amino acids. Features include the conversion of a protein set into a vector 
    reflecting these neighborhood preferences, pairwise distances (dissimilarity) between these vectors, 
    and the generation of trees based on these distance matrices.
Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).
Runs a series of configurable tests against a user's compute 
  environment. This can be used for checking that things like a specific 
  directory or an environment variable is available before you start an analysis.
  Alternatively, you can use the package's situation report when filing error
  reports with your compute infrastructure.
Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, 
  <doi:10.1007/978-1-4614-8456-1>).
A collection of functions that allows for easy and consistent use
  of environment variables. This includes setting, checking, retrieving,
  transforming, and validating values stored in environment variables.
Evidence of Absence software (EoA) is a user-friendly application for estimating bird and bat fatalities at wind farms and designing search protocols. The software is particularly useful in addressing whether the number of fatalities has exceeded a given threshold and what search parameters are needed to give assurance that thresholds were not exceeded. The models are applicable even when zero carcasses have been found in searches, following Huso et al. (2015) <doi:10.1890/14-0764.1>, Dalthorp et al. (2017) <doi:10.3133/ds1055>, and Dalthorp and Huso (2015) <doi:10.3133/ofr20151227>.
Second and backward-incompatible version of R package 'eodhd' <https://eodhd.com/>, extended with a cache and quota system, 
 also offering functions for cleaning and aggregating the financial data.
Provides wrap functions to export and import graphics and
             data frames in R to 'microsoft' office. And This package also 
             provide write out figures with lots of different formats.
             Since people may work on the platform without GUI support, the package 
             also provide function to easily write out figures
             to lots of different type of formats. Now this package provide function 
             to extract colors from all types of figures and pdf files.
This data management package provides some helper
    classes for publicly available data sources (HMD, DESTATIS) in
    Demography. Similar to ideas developed in the Bioconductor
    project <https://bioconductor.org> we strive to encapsulate data in easy
    to use S4 objects.
    If original data is provided in a text file, the resulting S4
    object contains all information from that text file. But the
    information is somehow structured (header, footer, etc).
    Further the classes provide methods to make a subset for selected
    calendar years or selected regions. The resulting subset
    objects still contain the original header and footer information.
A collection of nice plotting functions directly from a data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Enables simulation of water piping networks using 'EPANET'.
    The package provides functions from the 'EPANET' programmer's toolkit as R
    functions so that basic or customized simulations can be carried out from R.
    The package uses 'EPANET' version 2.2 from Open Water Analytics
    <https://github.com/OpenWaterAnalytics/EPANET/releases/tag/v2.2>.
Reads water network simulation data in 'Epanet' text-based
    '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'.
    Provides basic summary information and plots.  The README file has a 
    quick introduction. See <http://www2.epa.gov/water-research/epanet> 
    for more information on the Epanet software for modeling
    hydraulic and water quality behavior of water piping systems.
Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation.
The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.
EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuída - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. <https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-689/topico-639/NT_Metodologia_4MD_PDE_2032_VF.pdf>.
Tools to download and manipulate the Permanent Household Survey from Argentina
    (EPH is the Spanish acronym for Permanent Household Survey).
    e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets,
    calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology.
    organize_panels() is used to concatenate observations from different periods, and organize_labels()
    adds the official labels to the data. The implemented methods are based on INDEC (2016) <http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf>.
    As this package works with the argentinian Permanent Household Survey and its main audience is from this country,
    the documentation was written in Spanish.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data. In
  particular representation, manipulation, rate estimation and
  simulation for multistate data - the Lexis suite of functions, which
  includes interfaces to 'mstate', 'etm' and 'cmprsk' packages.
  Contains functions for Age-Period-Cohort and Lee-Carter modeling and
  a function for interval censored data and some useful functions for
  tabulation and plotting, as well as a number of epidemiological data
  sets.
Contains elementary tools for analysis of
        common epidemiological problems, ranging from sample size
        estimation, through 2x2 contingency table analysis and basic
        measures of agreement (kappa, sensitivity/specificity).
        Appropriate print and summary statements are also written to
        facilitate interpretation wherever possible.  Source code is 	commented throughout to facilitate modification.  The target audience includes advanced undergraduate and graduate students
        in epidemiology or biostatistics courses, and clinical researchers.
Method and tool for generating time series forecasts using
        an ensemble wavelet-based auto-regressive neural network architecture. This method provides
        additional support of exogenous variables and also generates confidence interval. This 
        package provides EWNet model for time series forecasting based on the algorithm by
        Panja, et al. (2022) and Panja, et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.
Provides methods to simulate and analyse the size and length
    of branching processes with an arbitrary offspring distribution. These
    can be used, for example, to analyse the distribution of chain sizes
    or length of infectious disease outbreaks, as discussed in Farrington
    et al. (2003) <doi:10.1093/biostatistics/4.2.279>.
Offers a tidy solution for epidemiological data. It houses a range of functions for epidemiologists and public health data wizards for data management and cleaning.
Create causal models for use in epidemiological studies, 
    including sufficient-component cause models as introduced by 
    Rothman (1976) <doi:10.1093/oxfordjournals.aje.a112335>.
Provides statistical and visualization tools for the analysis
    of demographic indicators, and spatio-temporal behavior and
    characterization of outbreaks of vector-borne diseases (VBDs) in
    Colombia. It implements travel times estimated in Bravo-Vega C.,
    Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method
    (Bortman, M.  (1999) <https://iris.paho.org/handle/10665.2/8562>).
A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.
Routines for epidemiological contact tracing
    and visualisation of network of contacts.
Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.
The Economic Policy Institute (<https://www.epi.org/>) provides
    researchers, media, and the public with easily accessible, up-to-date, and
    comprehensive historical data on the American labor force. It is compiled
    from Economic Policy Institute analysis of government data sources. Use
    it to research wages, inequality, and other economic indicators over time
    and among demographic groups. Data is usually updated monthly.
The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. <https://github.com/cmu-delphi/delphi-epidata>.
Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<https://medipe.psu.ac.th/epicalc/>'.
Contains utilities and functions for the cleaning, processing and
    management of patient level public health data for surveillance
    and analysis held by the UK Health Security Agency, UKHSA.
Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.
Tools to quantify transmissibility throughout
    an epidemic from the analysis of time series of incidence as described in
    Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) 
    <doi:10.1093/aje/kwh255>.
Analysis and visualization of plant disease progress curve data. Functions for
    fitting two-parameter population dynamics models (exponential, monomolecular, logistic
    and Gompertz) to proportion data for single or multiple epidemics using either linear
    or no-linear regression.  Statistical and visual outputs are provided to aid in model
    selection. Synthetic curves can be simulated for any of the models given the parameters.
    See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>
    for further information on the methods.
Provides functions and classes designed to handle and visualise
    epidemiological flows between locations. Also contains a statistical method
    for predicting disease spread from flow data initially described in
    Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>.
    This package is part of the RECON (<https://www.repidemicsconsortium.org/>)
    toolkit for outbreak analysis.
This is a collection of assorted functions and examples collected 
    from various projects. Currently we have functionalities for simplifying 
    overlapping time intervals, Charlson comorbidity score constructors for 
    Danish data, getting frequency for multiple variables, getting standardized
    output from logistic and log-linear regressions, sibling design linear 
    regression functionalities a method for calculating the confidence intervals 
    for functions of parameters from a GLM, Bayes equivalent for hypothesis 
    testing with asymptotic Bayes factor, and several help functions for 
    generalized random forest analysis using 'grf'.
The interface package to access data from the
    'EpiGraphDB' <https://epigraphdb.org> platform.
    It provides easy access to the 'EpiGraphDB' platform with functions that
    query the corresponding REST endpoints on the API <https://api.epigraphdb.org>
    and return the response data in the 'tibble' data frame format.
Maximum likelihood estimation of nonlinear mixed effects models of
	epidemic growth using Template Model Builder ('TMB').  Enables
	joint estimation for collections of disease incidence time series,
	including time series that describe multiple epidemic waves.
	Supports a set of widely used phenomenological models: exponential,
	logistic, Richards (generalized logistic), subexponential,
	and Gompertz.  Provides methods for interrogating model objects
	and several auxiliary functions, including one for computing basic
	reproduction numbers from fitted values of the initial exponential
	growth rate.
	Preliminary versions of this software were applied
	in Ma et al. (2014) <doi:10.1007/s11538-013-9918-2> and
	in Earn et al. (2020) <doi:10.1073/pnas.2004904117>.
Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.
Using variational techniques we address some epidemiological
  problems as the incidence curve decomposition by inverting the renewal 
  equation as described in Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> 
  and Alvarez et al. (2022) <doi:10.3390/biology11040540> or the estimation of 
  the functional relationship between epidemiological indicators. We also 
  propose a learning method for the short time forecast of the trend 
  incidence curve  as described in 
  Morel et al. (2022) <doi:10.1101/2022.11.05.22281904>.
Contains tools for formatting inline code, renaming redundant
  columns, aggregating age categories, adding survey weights, finding the earliest
  date of an event, plotting z-curves, generating population counts and 
  calculating proportions with confidence intervals. This is part of the 
  'R4Epis' project <https://r4epis.netlify.app/>.
The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. <doi:10.1093/bioinformatics/btad545>.
Estimation of epidemiological parameters with 
 Laplacian-P-splines following the methodology of Gressani et al. (2022)
 <doi:10.1371/journal.pcbi.1010618>.
Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.
Functions, data sets and shiny apps for "Epidemics: Models and Data in R (2nd edition)" by Ottar N. Bjornstad (2022, ISBN: 978-3-031-12055-8) <https://link.springer.com/book/10.1007/978-3-319-97487-3>. The package contains functions to study the Susceptible-Exposed-Infected-Removed SEIR model, spatial and age-structured Susceptible-Infected-Removed SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the 'Coursera' Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.
Tools for simulating mathematical models of infectious disease dynamics.
    Epidemic model classes include deterministic compartmental models, stochastic
    individual-contact models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs)
    from the Statnet suite of software packages in R. Standard templates for epidemic
    modeling include SI, SIR, and SIS disease types. EpiModel features an API for
    extending these templates to address novel scientific research aims. Full
    methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
Allows for forward-in-time simulation of epistatic networks with associated
    phenotypic output.
Estimates the time-varying reproduction number,
    rate of spread, and doubling time using a range of open-source tools
    (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and
    current best practices (Gostic et al. (2020)
    <doi:10.1101/2020.06.18.20134858>).  It aims to help users avoid some
    of the limitations of naive implementations in a framework that is
    informed by community feedback and is actively supported.
A collection of fast and flexible functions for analyzing
    omics data in observational studies. Multiple different approaches for
    integrating multiple environmental/genetic factors, omics data, and/or
    phenotype data are implemented. This includes functions for performing
    omics wide association studies with one or more variables of interest
    as the exposure or outcome; a function for performing a meet in the
    middle analysis for linking exposures, omics, and outcomes (as
    described by Chadeau-Hyam et al., (2010)
    <doi:10.3109/1354750X.2010.533285>); and a function for performing a
    mixtures analysis across all omics features using quantile-based
    g-Computation (as described by Keil et al., (2019)
    <doi:10.1289/EHP5838>).
Classes and helper functions for loading, extracting, converting, 
    manipulating, plotting and aggregating epidemiological parameters for infectious diseases. 
    Epidemiological parameters extracted from the literature are loaded from the 'epiparameterDB'
    R package.
A data package containing a database of epidemiological
    parameters. It stores the data for the 'epiparameter' R package.
    Epidemiological parameter estimates are extracted from the literature.
A toolbox to make it easy to analyze plant disease epidemics. It
    provides a common framework for plant disease intensity data recorded over
    time and/or space. Implemented statistical methods are currently mainly
    focused on spatial pattern analysis (e.g., aggregation indices, Taylor and
    binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See
    Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007)
    <doi:10.1094/9780890545058> for further information on these methods.
    Several data sets that were mainly published in plant disease epidemiology
    literature are also included in this package.
Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.
Drafting an epidemiological report in 'Microsoft Word' format for a given disease,
  similar to the Annual Epidemiological Reports published by the European Centre 
  for Disease Prevention and Control. Through standalone functions, it is specifically 
  designed to generate each disease specific output presented in these reports and includes:
  - Table with the distribution of cases by Member State over the last five years;
  - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Trend plot with the trend and number of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level.
  Two types of datasets can be used:
  - The default dataset of dengue 2015-2019 data;
  - Any dataset specified as described in the vignette.
Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). <doi:10.1038/ejhg.2010.196>. 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011).  <doi:10.1093/bioinformatics/btr218>.)
DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.
Basic sensitivity analysis of the observed relative risks adjusting
    for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Lash T.L, Fox M.P, and Fink A.K.
    "Applying Quantitative Bias Analysis to Epidemiologic Data",
    ('Springer', 2021).
Exploring time series for signal detection. It is specifically designed
    to detect possible outbreaks using infectious disease surveillance data
    at the European Union / European Economic Area or country level.
    Automatic detection tools used are presented in the paper
    "Monitoring count time series in R: aberration detection in public health surveillance",
    by Salmon (2016) <doi:10.18637/jss.v070.i10>.
    The package includes:
    - Signal Detection tool, an interactive 'shiny' application
      in which the user can import external data and perform basic signal detection analyses;
    - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs.
      This report can also be stratified by population characteristics (see 'Population' variable).
    This project was funded by the European Centre for Disease Prevention and Control.
Provides set of functions aimed at epidemiologists. 
 The package includes commands for measures of association and impact for case control studies and cohort studies. 
 It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis.
 The functions for cohort studies include the CS(), CSTable() and CSInter() commands. 
 The functions for case control studies include the CC(), CCTable() and CCInter() commands. 
 References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press.
 Woolf, B. 1955. On estimating the relation between blood group disease. 
 Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. 
 Newton Lower Falls, MA Epidemiology Resources.
 Gilles Desve & Peter Makary, 2007. 'CSTABLE Stata module to calculate summary table for cohort study' Statistical Software Components S456879, Boston College Department of Economics.
 Gilles Desve & Peter Makary, 2007. 'CCTABLE Stata module to calculate summary table for case-control study' Statistical Software Components S456878, Boston College Department of Economics.
Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.
Builds contingency tables that cross-tabulate multiple 
    categorical variables and also calculates various summary measures.
    Export to a variety of formats is supported, including: 'HTML', 
    'LaTeX', and 'Excel'.
Provides functions to test for gene x gene interactions in a
    bi-parental population of inbred lines. The data are fitted with the
    mixed linear model described in Rio et al. (2022) <doi:10.1101/2022.12.18.520958>, 
    that accounts for gene x gene interactions
    at both the fixed effect and variance levels. The package also
    provides graphical tools to display the gene x gene interaction trend at
    the mean level and the variance component analysis.
Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.
It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information is available in the 'epitweetr' peer-review publication (doi:10.2807/1560-7917.ES.2022.27.39.2200177).
A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations.
R 'shiny' web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package 'epiworldR' (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. 'epiworldRShiny' provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.
This extension of the pattern-oriented modeling framework of the 
    'poems' package provides a collection of modules and functions customized 
    for modeling disease transmission on a population scale in a spatiotemporally 
    explicit manner. This includes seasonal time steps, dispersal functions that 
    track disease state of dispersers, results objects that store disease states, 
    and a population simulator that includes disease dynamics.
Collection of ancillary functions and utilities for Partial Linear Single Index Models for Environmental mixture analyses, which currently provides functions for scalar outcomes. The outputs of these functions include the single index function, single index coefficients, partial linear coefficients, mixture overall effect, exposure main and interaction effects, and differences of quartile effects. In the future, we will add functions for binary, ordinal, Poisson, survival, and longitudinal outcomes, as well as models for time-dependent exposures. See Wang et al (2020) <doi:10.1186/s12940-020-00644-4> for an overview.
A rich toolkit of using the whole building
    simulation program 'EnergyPlus'(<https://energyplus.net>), which
    enables programmatic navigation, modification of 'EnergyPlus' models
    and makes it less painful to do parametric simulations and analysis.
Evaluate a function over a data frame of expressions.
Facilitates the aggregation of species' geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) <doi:10.1111/2041-210X.13914>.
Package provides a set of tools for robust estimation and inference for probit model with endogenous covariates. The current version contains a robust two-step estimator. For technical details, see Naghi, Varadi and Zhelonkin (2022), <doi:10.1016/j.ecosta.2022.05.001>.
Implements the Enhanced Portfolio Optimization (EPO) method as 
    described in Pedersen, Babu and Levine (2021)
    <doi:10.2139/ssrn.3530390>.
Clinical coding and diagnosis of patients with kidney using
    clinical practice guidelines. The guidelines used are the evidence-based
    KDIGO guidelines, see <https://kdigo.org/guidelines/> for more information.
    This package covers acute kidney injury (AKI), anemia, and
    chronic kidney disease (CKD).
Provides a set of functions, which facilitates removing objects from an environment. 
             It allows to delete objects specified with regular expression or with other conditions (e.g. if object is numeric),
             using one function call.
Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>.
Extra strength 'glue' for data-driven templates. String
    interpolation for 'Shiny' apps or 'R Markdown' and 'knitr'-powered
    'Quarto' documents, built on the 'glue' and 'whisker' packages.
Performs analysis of polynomial regression in simple designs with quantitative treatments.
Implements the conditional estimation procedure of
  Lee, Sun, Sun and Taylor (2016) <doi:10.1214/15-AOS1371>.
  This procedure allows hypothesis testing on the mean of
  a normal random vector subject to linear constraints.
For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Kim et al. (2020), and Oh and Kim (2020). D. Kim, G. Choi, H.-S. Oh, Ensemble patch transformation: a flexible framework for decomposition and filtering of signal, EURASIP Journal on Advances in Signal Processing 30 (2020) 1-27 <doi:10.1186/s13634-020-00690-7>. H.-S. Oh, D. Kim, Image decomposition by bidimensional ensemble patch transform, Pattern Recognition Letters 135 (2020) 173-179 <doi:10.1016/j.patrec.2020.03.029>.
Provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    The 'epubr' package provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. 
    E-book formatting is not completely standardized across all literature. 
    It can be challenging to curate parsed e-book content across an arbitrary collection of e-books 
    perfectly and in completely general form, to yield a singular, consistently formatted output. 
    Many EPUB files do not even contain all the same pieces of information in their respective metadata. 
    EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. 
    However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. 
    There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with 'epubr'. 
    Text is read 'as is' for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. 
    Substantive changes are expected to be performed subsequently by the user as part of their text analysis. 
    Additional text cleaning can be performed at the user's discretion, such as with functions from packages like 'tm' or 'qdap'.
Query, download climate change projection data from the 'CMIP6' (Coupled
    Model Intercomparison Project Phase 6) project
    <https://pcmdi.llnl.gov/CMIP6/> in the 'ESGF' (Earth System Grid Federation)
    platform <https://esgf.llnl.gov>, and create future 'EnergyPlus'
    <https://energyplus.net> Weather ('EPW') files adjusted from climate changes
    using data from Global Climate Models ('GCM').
An ensemble method for the statistical detection of
    a rare class in two-class classification problems. The method uses an
    ensemble of classifiers where the constituent
    models of the ensemble use disjoint subsets (phalanxes) of explanatory
    variables. We provide an implementation of the phalanx-formation algorithm.
    Please see Tomal et al. (2015) <doi:10.1214/14-AOAS778>,
    Tomal et al. (2016) <doi:10.1021/acs.jcim.5b00663>, and
    Tomal et al. (2019) <arXiv:1706.06971> for more details.
Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), 
    developed by the EuroQol group <https://euroqol.org/>. It assesses five dimensions; mobility, self-care, 
    usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale.
    Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, 
    which are critical in clinical and economic evaluations of healthcare and in population health surveys. 
    The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, 
    including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> 
    (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk 
    by van Hout et al. (2021) <doi:10.1016/j.jval.2021.03.009> (mapping EQ-5D-3L responses
    to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. 
    Additionally, the package provides tools to analyze EQ-5D data according to the recommended 
    guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) <doi:10.1007/978-3-030-47622-9>.
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials).
Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.
The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.
Support functions for R-based 'EQUAL-STATS' software which automatically classifies the data and performs appropriate statistical tests. 'EQUAL-STATS' software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024)<doi:10.5281/zenodo.13354162>.
Functions for examining measurement invariance via equivalence testing are included in this package. The traditionally used RMSEA (Root Mean Square Error of Approximation) cutoff values are adjusted based on simulation results. In addition, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts. For more information, see Yuan, K. H., & Chan, W. (2016) <doi:10.1037/met0000080>, Deng, L., & Yuan, K. H. (2016) <doi:10.1007/s11336-015-9491-8>, and Jiang, G., Mai, Y., & Yuan, K. H. (2017) <doi:10.3389/fpsyg.2017.01823>.
Provides function to transform latex math expressions 
 into format 'HTML' or 'Office Open XML Math'. The 'XML' 
 result can then be included in 'HTML', 'Microsoft Word' 
 documents or 'Microsoft PowerPoint' presentations by using 
 a 'Markdown' document or the R package 'officer'.
Contains methods for observed-score linking
  and equating under the single-group, equivalent-groups,
  and nonequivalent-groups with anchor test(s) designs.
  Equating types include identity, mean, linear, general
  linear, equipercentile, circle-arc, and composites of
  these. Equating methods include synthetic, nominal
  weights, Tucker, Levine observed score, Levine true
  score, Braun/Holland, frequency estimation, and chained
  equating. Plotting and summary methods, and methods for
  multivariate presmoothing and bootstrap error estimation
  are also provided.
Computation of direct, chain and average (bisector) equating coefficients with 
  standard errors using Item Response Theory (IRT) methods for dichotomous items 
  (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, 
  Battauz (2015) <doi:10.18637/jss.v068.i07>). 
  Test scoring can be performed by true score equating and observed score equating methods. 
  DIF detection can be performed using a Wald-type test  
  (Battauz (2019) <doi:10.1007/s10260-018-00442-w>).
  The package includes tests to assess the stability of the equating transformations
  (Battauz(2022) <doi:10.1111/stan.12277>).
The goal of 'equatiomatic' is to reduce the pain
    associated with writing 'LaTeX' formulas from fitted models. The
    primary function of the package, extract_eq(), takes a fitted model
    object as its input and returns the corresponding 'LaTeX' code for the
    model.
In agricultural, post-harvest and processing, engineering and industrial experiments factors are often differentiated with ease with which they can change from experimental run to experimental run. This is due to the fact that one or more factors may be expensive or time consuming to change i.e. hard-to-change factors. These factors restrict the use of complete randomization as it may make the experiment expensive and time consuming. Split plot designs can be used for such situations. In general model estimation of split plot designs require the use of generalized least squares (GLS). However for some split-plot designs ordinary least squares (OLS) estimates are equivalent to generalized least squares (GLS) estimates. These types of designs are known in literature as equivalent-estimation split-plot design. For method details see, Macharia, H. and Goos, P.(2010) <doi:10.1080/00224065.2010.11917833>.Balanced split plot designs are designs which have an equal number of subplots within every whole plot. This package used to construct equivalent estimation balanced split plot designs for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function equivalent_BSPD().
Saturation of ionic substances in urine is calculated based on
  sodium, potassium, calcium, magnesium, ammonia, chloride, phosphate, sulfate,
  oxalate, citrate, ph, and urate.  This program is intended for research use,
  only.  The code within is translated from EQUIL2 Visual Basic code based on
  Werness, et al (1985) "EQUIL2: a BASIC computer program for the calculation
  of urinary saturation" <doi:10.1016/s0022-5347(17)47703-2> to R. The Visual
  Basic code was kindly provided by Dr. John Lieske of the Mayo Clinic.
We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points.
    For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula.
    The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration.
    All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.
Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are “equivalent” to those in the control group. Here, “equivalence” means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>.
Provides statistical tests and graphics for assessing tests
        of equivalence.  Such tests have similarity as the alternative
	hypothesis instead of the null.  Sample data sets are included.
Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results.
Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph
             ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition''
             (Chapman&Hall/CRC).
Implementation of uniformly most powerful invariant equivalence
    tests for one- and two-sample problems (paired and unpaired) as described 
    in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives 
    (non-inferiority and non-superiority tests) are supported. Basically a variant 
    of a t-test with (relaxed) null and alternative hypotheses exchanged.
Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.
Provides a consistent representation of year-based time scales as a
    numeric vector with an associated 'era'. There are built-in era definitions
    for many year numbering systems used in contemporary and historic calendars 
    (e.g. Common Era, Islamic 'Hijri' years); year-based time scales used in 
    archaeology, astronomy, geology, and other palaeosciences (e.g. 
    Before Present, SI-prefixed 'annus'); and support for arbitrary user-defined
    eras. Years can converted from any one era to another using a generalised 
    transformation function. Methods are also provided for robust casting and 
    coercion between years and other numeric types, type-stable arithmetic with 
    years, and pretty-printing in tables.
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) <doi:10.1021/acs.analchem.6b02927>.
Estimates unknown historical or archaeological dates subject to relationships with other dates and absolute constraints, derived as marginal densities from the full joint conditional distribution. Includes rule-based estimation of the production dates of artifact types. Collins-Elliott (2024) <https://volweb.utk.edu/~scolli46/sceGUTChronology.pdf>.
Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together
    with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
Build entity relationship diagrams (ERD) to specify the nature of the relationship between tables in a database.
Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i03> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) <doi:10.1214/12-EJS696> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) <doi:10.1214/16-AOAS1010>.
A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2023) <doi:10.1080/01621459.2023.2242627>.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks. See Krivitsky and Butts (2017) <doi:10.1177/0081175017692623> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM).
    Includes functions to conduct mediation and moderation analyses and to diagnose
    multicollinearity.
    URL: <https://github.com/sduxbury/ergMargins>.
    BugReports: <https://github.com/sduxbury/ergMargins/issues>.
    Duxbury, Scott W (2021) <doi:10.1177/0049124120986178>.
    Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>.
    Mize, Trenton D. (2019) <doi:10.15195/v6.a4>.
    Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>.
    Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>.
    Duxbury, Scott W, Jenna Wertsching (2023) <doi:10.1016/j.socnet.2023.02.003>.
    Huang, Peng, Carter Butts (2023) <doi:10.1016/j.socnet.2023.07.001>.
Provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs).  A number of EGPs are supported, including the families identified in Butts (2023) <doi:10.1080/0022250X.2023.2180001>, as are functions for hazard calculation and timing calibration.
Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.
Simulation and estimation of Exponential Random Graph Models (ERGMs)
  for small networks using exact statistics as shown in Vega Yon et al. (2020)
  <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm'
  package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator
  (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs
  for small networks. As exhaustive enumeration is computationally feasible for
  small networks, this R package takes advantage of this and provides tools for
  calculating likelihood functions, and other relevant functions, directly,
  meaning that in many cases both estimation and simulation of ERGMs for
  small networks can be faster and more accurate than simulation-based
  algorithms.
Provides several validator functions for checking if arguments
    passed by users have valid types, lengths, etc. and for generating
    informative and well-formatted error messages in a consistent style. Also
    provides tools for users to create their own validator functions. The
    error message style used is adopted from
    <https://style.tidyverse.org/error-messages.html>.
Provides a set of functions to solve Erlang-C model. The Erlang C formula was invented by the Danish Mathematician A.K. Erlang and is used to calculate the number of advisors and the service level.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Estimate the effective reproduction number from wastewater
    and clinical data sources.
Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).
A set of user-friendly functions to aid in organizing, plotting
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn
    method to explore ERP data. Should be useful to those without a background
    in computer programming, and to those who are new to ERPs (or new to the
    more advanced ERP software available). Emphasis has been placed on highly
    automated processes using functions with as few arguments as possible.
    Expects processed (cleaned) data.
Computes the probability density and cumulative distribution functions of fourteen distributions used for the probabilistic hazard assessment. Estimates the model parameters of the distributions using the maximum likelihood and reports the goodness-of-fit statistics. The recurrence interval estimations of earthquakes are computed for each distribution.
Simulates and estimates the Exponential Random Partition Model presented 
    in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. 
    It can also be used to estimate longitudinal partitions, following the model 
    proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. 
    The model is an exponential family distribution on the space of partitions 
    (sets of non-overlapping groups) and is called in reference to the Exponential 
    Random Graph Models (ERGM) for networks.
Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.
Errors in data can be located and removed using validation rules from package 
   'validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>,
   chapter 7.
Support for measurement errors in R vectors, matrices and arrays:
    automatic uncertainty propagation and reporting.
    Documentation about 'errors' is provided in the paper by Ucar, Pebesma &
    Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a
    vignette; see 'citation("errors")' for details.
Perform a Bayesian estimation of the exploratory reduced
    reparameterized unified model (ErRUM) described by Culpepper and Chen (2018)
    <doi:10.3102/1076998618791306>.
Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.
Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories.
    Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>.
    Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.
Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges' d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality).
Tools for fitting the Extended Empirical Saddlepoint (EES) density of Fasiolo et al. (2018) <doi:10.1214/18-EJS1433>.
It allows structuring electoral data of different size and structure 
        to calculate various indicators frequently used in the studies of electoral systems and party systems.
        Indicators of electoral volatility, electoral disproportionality, party nationalization and the 
        effective number of parties are included.
Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> 
    as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the 
    expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk 
    of investments. A special feature of the backtests of  Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> 
    is that they only require forecasts of  the expected shortfall, which is in striking contrast to all other existing 
    backtests, making them particularly attractive for practitioners.
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>)
    in R. Based on the input, the effect size can be returned as standardized mean 
    difference, Cohen's f, Hedges' g, Pearson's r or Fisher's
    transformation z, odds ratio or log odds, or eta squared effect size.
Methods for working with dose-finding clinical trials. We provide 
    implementations of many dose-finding clinical trial designs, including the 
    continual reassessment method (CRM) by O'Quigley et al. (1990) 
    <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji
    et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design
    by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal 
    interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox
    by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of
    Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 
    described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs
    are implemented with a common interface. We also offer optional additional 
    classes to tailor the behaviour of all designs, including avoiding skipping 
    doses, stopping after n patients have been treated at the recommended dose,  
    stopping when a toxicity condition is met, or demanding that n patients are 
    treated before stopping is allowed. By daisy-chaining together these classes
    using the pipe operator from 'magrittr', it is simple to tailor the 
    behaviour of a dose-finding design so it behaves how the trialist wants.
    Having provided a flexible interface for specifying designs, we then provide
    functions to run simulations and calculate dose-paths for future cohorts of 
    patients.
A collection of functions and 'jamovi' module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on 'statpsych' and 'metafor'.  This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions.  Data sets are included for all examples from Cumming & Calin-Jageman (2024) <ISBN:9780367531508>.
The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) <arXiv:2210.05802>.
Software of 'esDesign' is developed to implement the adaptive 
    enrichment designs with sample size re-estimation presented in 
    Lin et al. (2021) <doi: 10.1016/j.cct.2020.106216>. In details, 
    three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR),
    AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package 
    also contains several widely used adaptive designs, such as the Marker 
    Sequential Test (MaST) design proposed Freidlin et al. 
    (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs 
    without early stopping (AED or ES), the sample size re-estimation 
    procedure (SSR) based on the conditional power proposed by 
    Proschan and Hunsberger (1995), and some useful functions. 
    In details, we can calculate the futility and/or efficacy stopping 
    boundaries, the sample size required, calibrate the value of the threshold 
    of the difference between subgroup-specific test statistics, conduct the 
    simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.
A tool which allows users to create and evaluate ensembles 
    of species distribution model (SDM) predictions. 
    Functionality is offered through R functions or a GUI (R Shiny app). 
    This tool can assist users in identifying spatial uncertainties and 
    making informed conservation and management decisions. The package is 
    further described in Woodman et al (2019) <doi:10.1111/2041-210X.13283>.
Environmental seismology is a scientific field that studies the  
    seismic signals, emitted by Earth surface processes. This package 
    provides all relevant functions to read/write seismic data files, prepare, 
    analyse and visualise seismic data, and generate reports of the processing 
    history.
Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). <doi:10.48550/arXiv.2206.09639>.
A collection of functions developed to support the tutorial on using Exploratory Structural Equiation Modeling (ESEM) (Asparouhov & Muthén, 2009) <https://www.statmodel.com/download/EFACFA810.pdf>) with Longitudinal Study of Australian Children (LSAC) dataset (Mohal et al., 2023) <doi:10.26193/QR4L6Q>. 
    The package uses 'tidyverse','psych', 'lavaan','semPlot' and provides additional functions to conduct ESEM. 
    The package provides general functions to complete ESEM, including esem_c(), creation of target matrix (if it is used) make_target(), generation of the Confirmatory Factor Analysis (CFA) model syntax esem_cfa_syntax(). 
    A sample data is provided - the package includes a sample data of the Strengths and Difficulties Questionnaire of the Longitudinal Study of Australian Children (SDQ LSAC) in sdq_lsac().  
    'ESEM' package vignette presents the tutorial demonstrating the use of ESEM on SDQ LSAC data.
The nonparametric trend and its derivatives in equidistant time 
    series (TS) with long-memory errors can be estimated. The 
    estimation is conducted via local polynomial regression using an 
    automatically selected bandwidth obtained by a built-in iterative plug-in 
    algorithm or a bandwidth fixed by the user.
    The smoothing methods of the package are described in Letmathe, S., Beran,
    J. and Feng, Y., (2023) <doi:10.1080/03610926.2023.2276049>.
Presents a "Scenarios" class containing
        general parameters, risk parameters and projection results.
        Risk parameters are gathered together into a ParamsScenarios
        sub-object. The general process for using this package is to
        set all needed parameters in a Scenarios object, use the
        customPathsGeneration method to proceed to the projection, then
        use xxx_PriceDistribution() methods to get asset prices.
Computes shrinkage estimators for regression problems. Selects
    penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) <doi:10.1093/aje/kwx225> for more details.
An implementation of extended state-space SIR models developed by
    Song Lab at UM school of Public Health. There are several functions available
    by 1) including a time-varying transmission modifier, 2) adding a time-dependent
    quarantine compartment, 3) adding a time-dependent antibody-immunization compartment.
    Wang L. (2020) <doi:10.6339/JDS.202007_18(3).0003>.
Misc functions programmed by Eduard Szöcs. 
    Provides read_regnie() to read gridded precipitation data from German Weather 
    Service  (DWD, see <http://www.dwd.de/> for more information).
Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.
Tailored explicitly for Experience Sampling Method (ESM)
    data, it contains a suite of functions designed to simplify
    preprocessing steps and create subsequent reporting. It empowers
    users with capabilities to extract critical insights during
    preprocessing, conducts thorough data quality assessments (e.g., design
    and sampling scheme checks, compliance rate, careless responses), and
    generates visualizations and concise summary tables tailored
    specifically for ESM data. Additionally, it streamlines the creation
    of informative and interactive preprocessing reports, enabling
    researchers to transparently share their dataset preprocessing
    methodologies. Finally, it is part of a larger ecosystem which
    includes a framework and a web gallery
    (<https://preprocess.esmtools.com/>).
Exploitation, processing and 2D-3D visualization of DICOM-RT files (structures, dosimetry, imagery) for medical physics and clinical research, in a patient-oriented perspective.
A 'shiny' gadget to create 'ggplot2' figures interactively with drag-and-drop to map your variables to different aesthetics.
    You can quickly visualize your data accordingly to their type, export in various formats,
    and retrieve the code to reproduce the plot.
Simultaneous modeling of the quantile and the expected shortfall of a response variable given 
    a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.
An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis,
	     Michael I Jordan (2013) <arXiv:1301.2267>. The ESS algorithm
	     is used for model selection in decomposable graphical models.
Functions for converting objects to scalars (vectors of length 1) 
    and a more inclusive definition of data that can be interpreted as numbers 
    (numeric and complex alike).
Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.
Este paquete pretende apoyar el proceso enseñanza-aprendizaje de estadística descriptiva e inferencial. Las funciones contenidas en el paquete 'estadistica' cubren los conceptos básicos estudiados en un curso introductorio. Muchos conceptos son ilustrados con gráficos dinámicos o web apps para facilitar su comprensión. This package aims to help the teaching-learning process of descriptive and inferential statistics. The functions contained in the package 'estadistica' cover the basic concepts studied in a statistics introductory course. Many concepts are illustrated with dynamic graphs or web apps to make the understanding easier. See: Esteban et al. (2005, ISBN: 9788497323741), Newbold et al.(2019, ISBN:9781292315034 ), Murgui et al. (2002, ISBN:9788484424673) .
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Estimates item and person parameters for the Continuous Response Model (CRM; Samejima, 1973, <doi:10.1007/BF02291114>), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
An implementation of sequential testing that uses evidence ratios
    computed from the weights of a set of models. These weights correspond either
    to Akaike weights computed from the Akaike Information Criterion (AIC) or the
    Bayesian Information Criterion (BIC) and following Burnham & Anderson
    (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA
    weights computed from the WAIC or the LOO-IC of models fitted
    with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).
Provides tools for determining estimability of linear functions 
  of regression coefficients, and 'epredict' methods that handle 
  non-estimable cases correctly. Estimability theory is discussed in
  many linear-models textbooks including Chapter 3 of Monahan, JF (2008), 
  "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4).
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation.
Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial Durbin (SDM), and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.
Total Time on Test plot and routines for parameter estimation of
    any lifetime distribution implemented in R via maximum likelihood (ML) given
    a data set. It is implemented thinking on parametric survival analysis, but
    it feasible to use in parameter estimation of probability density or mass
    functions in any field. The main routines 'maxlogL' and 'maxlogLreg' are
    wrapper functions specifically developed for ML estimation. There are
    included optimization procedures such as 'nlminb' and 'optim' from base
    package, and 'DEoptim' Mullen (2011) <doi: 10.18637/jss.v040.i06>. Standard
    errors are estimated with 'numDeriv' Gilbert (2011)
    <https://CRAN.R-project.org/package=numDeriv> or the option 'Hessian = TRUE'
    of 'optim' function.
Implements estimation methods for parameters of common
    distribution families. The common d, p, q, r function family for each
    distribution is enriched with the ll, e, and v counterparts, computing
    the log-likelihood, performing estimation, and calculating the asymptotic
    variance - covariance matrix, respectively. Parameter estimation is
    performed analytically whenever possible.
Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance.
Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.
Implements the methods of McGrath et al. (2020) 
    <doi:10.1177/0962280219889080> and Cai et al. (2021) 
    <doi:10.1177/09622802211047348> for estimating the sample mean and standard 
    deviation from commonly reported quantiles in meta-analysis. These methods 
    can be applied to studies that report the sample median, sample size, and 
    one or both of (i) the sample minimum and maximum values and (ii) the first 
    and third quartiles. The corresponding standard error estimators described 
    by McGrath et al. (2023) <doi:10.1177/09622802221139233> are also included.
Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, <doi:10.1214/17-AOAS1026> for more details.
Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. 
    The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. 
    The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>.
    The second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>.
	And the third by Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>.
The new yield tables developed by the Northwest German Forest 
  Research Institute (NW-FVA) provide a forest management tool for the five 
  main commercial tree species oak, beech, spruce, Douglas-fir and pine for 
  northwestern Germany. The new method applied for deriving yield tables 
  combines measurements of growth and yield trials with growth simulations 
  using a state-of-the-art single-tree growth simulator. By doing so, the new 
  yield tables reflect the current increment level and the recommended 
  graduated thinning from above is the underlying management concept. The yield
  tables are provided along with methods for deriving the site index and for 
  interpolating between age and site indices and extrapolating beyond age and 
  site index ranges. The inter-/extrapolations are performed traditionally by 
  the rule of proportion or with a functional approach.
Creates simple to highly customized tables for a wide selection of descriptive statistics, with or without weighting the data.
Fits the space-time Epidemic Type Aftershock Sequence
    ('ETAS') model to earthquake catalogs using a stochastic 'declustering' 
    approach. The 'ETAS' model is a 'spatio-temporal' marked point process
    model and a special case of the 'Hawkes' process. The package is based 
    on a Fortran program by 'Jiancang Zhuang'
    (available at <http://bemlar.ism.ac.jp/zhuang/software.html>),
    which is modified and translated into C++ and C such that it 
    can be called from R. Parallel computing with 'OpenMP' is possible 
    on supported platforms.
The 2-D spatial and temporal Epidemic Type Aftershock Sequence ('ETAS') Model is
 widely used to 'decluster' earthquake data catalogs. Usually, the calculation of standard
 errors of the 'ETAS' model parameter estimates is based on the Hessian matrix derived from
 the log-likelihood function of the fitted model. However, when an 'ETAS' model is fitted to
 a local data set over a time period that is limited or short, the standard errors based on
 the Hessian matrix may be inaccurate. It follows that the asymptotic confidence intervals
 for parameters may not always be reliable. As an alternative, this package allows for the
 construction of bootstrap confidence intervals based on empirical quantiles for the parameters
 of the 2-D spatial and temporal 'ETAS' model. This version improves on Version 0.1.0 of the
 package by enabling the study space window (renamed 'study region') to be polygonal rather
 than merely rectangular. A Japan earthquake data catalog is used in a second example to
 illustrate this new feature.
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017)<doi:10.18637/jss.v076.i03>.
Treatments of a one-way layout, being equivalent to a control, 
 can be selected with this package. Bonferroni adjusted "two one-sided 
 t-tests" (TOST) and related simultaneous confidence intervals are given for 
 both differences or ratios of means of normally distributed data. For the 
 case of equal variances and balanced sample sizes for the treatment groups, 
 the single-step procedure of Bofinger and Bofinger (1995) 
 <doi:10.1111/j.2517-6161.1995.tb02058.x> can be chosen. For non-normal data, 
 the Wilcoxon test is applied.
An implementation of the quantitative ethnobotany indices in R. The goal is to provide an easy-to-use platform for ethnobotanists to assess the cultural significance of plant species based on informant consensus. The package closely follows the paper by Tardio and Pardo-de-Santayana (2008). Tardio, J., and M. Pardo-de-Santayana, 2008. Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain) 1. Economic Botany, 62(1), 24-39. <doi:10.1007/s12231-007-9004-5>.
Reliable and rapid ethnicity annotation from whole exome and targeted sequencing data.
A predictable and pipeable framework for performing ETL 
    (extract-transform-load) operations on publicly-accessible medium-sized data 
    set. This package sets up the method structure and implements generic 
    functions. Packages that depend on this package download specific data sets 
    from the Internet, clean them up, and import them into a local or remote 
    relational database management system.
Provides functions to facilitate the use of the 'ff' package
    in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL',
    'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects
    using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to
    do fast left outer join merging based on 'match', factorisation of data and a
    basic function for re-coding vectors.
The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 <doi:10.18637/jss.v038.i04>). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 <doi:10.1007/s00180-010-0200-x>).
Use R to interface with the 'ETRADE' API <https://developer.etrade.com/home>.
    Functions include authentication, trading, quote requests, account information, and option 
    chains. A user will need an ETRADE brokerage account and 'ETRADE' API approval. See README 
    for authentication process and examples.
Implementation of Energy Trees, a statistical model to perform 
    classification and regression with structured and mixed-type data. The
    model has a similar structure to Conditional Trees, but brings in Energy
    Statistics to test independence between variables that are possibly 
    structured and of different nature. Currently, the package covers functions
    and graphs as structured covariates. It builds upon 'partykit' to
    provide functionalities for fitting, printing, plotting, and predicting with
    Energy Trees. Energy Trees are described in Giubilei et al. (2022) 
    <arXiv:2207.04430>.
Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." arXiv <doi:10.48550/arXiv.2404.01043>. Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway <doi:10.13140/RG.2.2.34500.23685>. Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC.
Provides a collection of functions to perform core tasks within
    Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness 
    forward price curves for electricity and natural gas contracts with flow delivery, as presented in
    F. E. Benth, S. Koekebakker, and F. Ollmar (2007) <doi:10.3905/jod.2007.694791>
    and F. E. Benth,  J. S. Benth,  and S. Koekebakker (2008) <doi:10.1142/6811>.
    Portfolio insurance trading strategies for price risk management in the forward market, see
    F. Black (1976) <doi:10.1016/0304-405X(76)90024-6>, 
    T. Bjork (2009) <https://EconPapers.repec.org/RePEc:oxp:obooks:9780199574742>,  
    F. Black and R. W. Jones (1987) <doi:10.3905/jpm.1987.409131> and
    H. E. Leland (1980) <http://www.jstor.org/stable/2327419>.
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
Convenience functions for implementing extended two-way 
  fixed effect regressions a la Wooldridge (2021, 2023)
  <doi:10.2139/ssrn.3906345>, <doi:10.1093/ectj/utad016>.
Illustrates the concepts developed in Sarkar and Rashid (2019, ISSN:0025-5742) <http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiH4deL3q3xAhWX73MBHR_wDaYQFnoECAUQAw&url=https%3A%2F%2Fwww.indianmathsociety.org.in%2Fmathstudent-part-2-2019.pdf&usg=AOvVaw3SY--3T6UAWUnH5-Nj6bSc>.
  This package helps a user guess four things (mean, MD, scaled MSD, and RMSD) before they get the SD. 
    1) The package displays the Empirical Cumulative Distribution Function (ECDF) of
  the given data. The user must choose the value of the mean by equating the areas of 
  two colored (blue and green) regions. The package gives feedback to improve the choice
  until it is correct. Alternatively, the reader may continue with a different guess for 
  the center (not necessarily the mean).
    2) The user chooses the values of the Mean Deviation (MD) based on the ECDF of the deviations
  by equating the areas of two newly colored (blue and green) regions, with feedback from the 
  package until the user guesses correctly.
    3) The user chooses the Scaled Mean Squared Deviation (MSD) based on the ECDF of the scaled 
  square deviations by equating the areas of two newly colored (blue and green) regions, with 
  feedback from the package until the user guesses correctly.
    4) The user chooses the Root Mean Squared Deviation (RMSD) by ensuring that its intersection 
  with the ECDF of the deviations is at the same height as the intersection between the scaled 
  MSD and the ECDF of the scaled squared deviations. Additionally, the intersection of two
  blue lines (the green dot) should fall on the vertical line at the maximum deviation. 
    5) Finally, if the mean is chosen correctly, only then the user can view the population 
  SD (the same as the RMSD) and the sample SD (sqrt(n/(n-1))*RMSD) by clicking the respective 
  buttons. If the mean is chosen incorrectly, the user is asked to correct it.
An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the “Climatch” algorithm, also referred to as the “Climate” algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). “Climatch user manual”. The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). <doi:10.1111/ddi.12391>.
The remit of the European Clinical Trials Data Base (EudraCT <https://eudract.ema.europa.eu/> ), or ClinicalTrials.gov <https://clinicaltrials.gov/>, is to provide open access to summaries of all registered clinical trial results; thus aiming to prevent non-reporting of negative results and provide open-access to results to inform future research. The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by EudraCT and formats them into the precise requirements to directly upload an XML file into the web portal, with no further data entry by hand.
Analyses 'EuFMDiS' output files in a Shiny App. The distributions of 
  relevant output parameters are described in form of tables (quantiles) and plots. 
  The App is called using eufmdis.adapt::run_adapt().
Contains match results from seven European men's football leagues, namely Premier League (England), Ligue 1 (France), 
	     Bundesliga (Germany), Serie A (Italy), Primera Division (Spain), Eredivisie (The Netherlands), Super Lig (Turkey).
	     Includes Seasons 2010/2011 until 2019/2020 and a set of interesting covariates. Can be used all purposes.
Generate area-proportional Euler diagrams
    using numerical optimization. An Euler diagram is a generalization of a Venn
    diagram, relaxing the criterion that all interactions need to be
    represented. Diagrams may be fit with ellipses and circles via
    a wide range of inputs and can be visualized in numerous ways.
Computes maximum mean discrepancy two-sample test for univariate data using the Laplacian kernel, as described in Bodenham and Kawahara (2023) <doi:10.1007/s11222-023-10271-x>. The p-value is computed using permutations. Also includes implementation for computing the robust median difference statistic 'Q_n' from Croux and Rousseeuw (1992) <doi:10.1007/978-3-662-26811-7_58> based on Johnson and Mizoguchi (1978) <doi:10.1137/0207013>.
The EUNIS habitat classification is a comprehensive pan-European
    system for habitat identification
    <https://www.eea.europa.eu/data-and-maps/data/eunis-habitat-classification-1>.
    This is an R data package providing the EUNIS classification system.
    The classification is hierarchical and covers all types of habitats from
    natural to artificial, from terrestrial to freshwater and marine. The
    habitat types are identified by specific codes, names and descriptions and
    come with schema crosswalks to other habitat typologies.
Facilitates access to sample datasets from the 'EunomiaDatasets' repository (<https://github.com/ohdsi/EunomiaDatasets>).
Access to data on European Union laws and court decisions made easy with pre-defined 'SPARQL' queries and 'GET' requests. See Ovadek (2021) <doi:10.1080/2474736X.2020.1870150> .
Daily 'netCDF' data from e.g. regional climate models (RCMs) are not trivial
    to work with. This package, which relies on 'data.table', makes it easier
    to deal with large data from RCMs, such as from EURO-CORDEX 
    (<https://www.euro-cordex.net/>, <https://cordex.org/data-access/>). It has 
    functions to extract single grid cells from rotated pole grids as well as 
    the whole array in long format. Can handle non-standard calendars (360, 
    noleap) and interpolate them to a standard one. Potentially works with many 
    CF-conform 'netCDF' files.
Interface to Eurostat’s API (SDMX 2.1) with fast data.table-based import of
    data, labels, and metadata. On top of the core functionality, data search and data
    description/comparison functions are also provided.
    Use <https://github.com/alekrutkowski/eurodata_codegen> — a point-and-click app for rapid and
    easy generation of richly-commented R code — to import a Eurostat dataset or its subset
    (based on the eurodata::importData() function).
Unofficial API wrapper for 'Euroleague' and 'Eurocup' basketball API (<https://www.euroleaguebasketball.net/en/euroleague/>), it allows to retrieve real-time and historical standard and advanced statistics about competitions, teams, players and games.
Interact with the Europeana Data Model via a variety of API
 endpoints that contains digital collections from thousands of institutions
 around Europe. This translates to millions of Cultural Heritage Objects in the
 form of image, text, video, sound and 3D, accompanied by rich metadata. The
 Data Model design principles are based on the core principles and best
 practices of the Semantic Web and Linked Data efforts to which Europeana
 contributes (see, e.g., Doerr, Martin, et al. The europeana data model (edm).
 World Library and Information Congress: 76th IFLA general conference
 and assembly. Vol. 10. 2010.). The package also provides methods for bulk
 downloads of specific subsets of items, including both their metadata
 and their associated media files.
An R Client for the Europe PubMed Central RESTful Web Service
    (see <https://europepmc.org/RestfulWebService> for more information). It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
This dataset contains population estimates of all European cities 
    with at least 10,000 inhabitants during the period 1500-1800. These data are
    adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).
Tools to download data from the Eurostat database
    <https://ec.europa.eu/eurostat> together with search and manipulation
    utilities.
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
    Bader, B., Yan, J. & Zhang, X. (2016) <doi:10.1007/s11222-016-9697-3>.
    Bader, B., Yan, J. & Zhang, X. (2018) <doi:10.1214/17-AOAS1092>.
Provides tools for post-process, evaluate and visualize results from 3d Meteorological and Air Quality models against point observations (i.e. surface stations) and grid (i.e. satellite) observations.
Evaluates the performance of binary classifiers.  Computes
    confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR,
    accuracy, F1, DOR, ..), and area under the curve.  Outputs are well
    suited for nested dataframes.
Contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021)<doi:10.1016/j.compbiomed.2021.104753>, and Fahimeh Nezhadmoghadam, et al.(2021)<doi:10.2174/1567205018666210831145825>.
Provides various statistical methods for evaluating
    Individualized Treatment Rules under randomized data. The provided
    metrics include Population Average Value (PAV), Population Average
    Prescription Effect (PAPE), Area Under Prescription Effect Curve
    (AUPEC). It also provides the tools to analyze Individualized
    Treatment Rules under budget constraints. Detailed reference in Imai
    and Li (2019) <arXiv:1905.05389>.
The purpose of this package is to generate trees and validate unverified code. Trees are made by parsing a statement into a verification tree data structure. This will make it easy to port the statement into another language. Safe statement evaluations are done by executing the verification trees.
Parsing and evaluation tools that make it easy to recreate
    the command line behaviour of R.
Implements various quality evaluation statistics to assess the
    value of plant germplasm core collections using qualitative and 
    quantitative phenotypic trait data according to Odong et al. (2015) 
    <doi:10.1007/s00122-012-1971-y>.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
Conducts sensitivity analyses for unmeasured confounding, selection bias, and measurement error (individually or in combination; VanderWeele & Ding (2017) <doi:10.7326/M16-2607>; Smith & VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>; VanderWeele & Li (2019) <doi:10.1093/aje/kwz133>; Smith & VanderWeele (2021) <arXiv:2005.02908>). Also conducts sensitivity analyses for unmeasured confounding in meta-analyses (Mathur & VanderWeele (2020a) <doi:10.1080/01621459.2018.1529598>; Mathur & VanderWeele (2020b) <doi:10.1097/EDE.0000000000001180>) and for additive measures of effect modification (Mathur et al., under review).
An R-based application for exploratory data analysis of global EvapoTranspiration (ET) datasets. 
  'evapoRe' enables users to download, validate, visualize, and analyze multi-source ET data across various spatio-temporal scales.
  Also, the package offers calculation methods for estimating potential ET (PET), including temperature-based approaches described in : Oudin et al., (2005) <doi:10.1016/j.jhydrol.2004.08.026>.
  'evapoRe' supports hydrological modeling, climate studies, agricultural research, and other data-driven fields by facilitating access to ET data and offering powerful analysis capabilities.
  Users can seamlessly integrate the package into their research applications and explore diverse ET data at different resolutions.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
The purpose of this library is to compute the optimal charging cost function for a electric vehicle (EV). It is well known that the charging function of a EV is a concave function that can be approximated by a piece-wise linear function, so bigger the state of charge, slower the charging process is. Moreover, the other important function is the one that gives the electricity price. This function is usually step-wise, since depending on the time of the day, the price of the electricity is different. Then, the problem of charging an EV to a certain state of charge is not trivial. This library implements an algorithm to compute the optimal charging cost function, that is, it plots for a given state of charge r (between 0 and 1) the minimum cost we need to pay in order to charge the EV to that state of charge r. The details of the algorithm are described in González-Rodríguez et at (2023) <https://inria.hal.science/hal-04362876v1>.
Different evidential classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: 
    the evidential K-nearest neighbor rule, the evidential neural 
    network, radial basis function neural networks, logistic regression,
    feed-forward neural networks.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the     
    objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means, 
    Constrained Evidential c-Means, Evidential Clustering, Constrained Evidential 
    Clustering, Evidential K-nearest-neighbor-based Clustering, Bootstrap Model-Based
    Evidential Clustering, Belief Peak Evidential Clustering, Neural-Network-based
    Evidential Clustering.
Combine pieces of evidence in the form of uncertainty representations.
Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.
Provides functions for the Bayesian analysis of extreme
        value models, using Markov chain Monte Carlo methods. Allows
		the construction of both uninformative and informed prior 
		distributions for common statistical models applied to extreme
        event data, including the generalized extreme value distribution.
We quantitatively evaluated the assertion that says if one suit is found to be evenly distributed among the 4 players, the rest of the suits are more likely to be evenly distributed. Our mathematical analyses show that, if one suit is found to be evenly distributed, then a second suit has a slightly elevated probability (ranging between 10% to 15%) of being evenly distributed. If two suits are found to be evenly distributed, then a third suit has a substantially elevated probability (ranging between 30% to 50%) of being evenly distributed.This package refers to methods and authentic data from Ely Culbertson <https://www.bridgebum.com/law_of_symmetry.php>, Gregory Stoll <https://gregstoll.com/~gregstoll/bridge/math.html>, and details of performing the probability calculations from Jeremy L. Martin <https://jlmartin.ku.edu/~jlmartin/bridge/basics.pdf>, Emile Borel and Andre Cheron (1954) "The Mathematical Theory of Bridge",Antonio Vivaldi and Gianni Barracho (2001, ISBN:0 7134 8663 5)  "Probabilities and Alternatives in Bridge", Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>.
Functions for setting up and analyzing event history data.
Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages. Janssenswillen et al. (2020) <http://ceur-ws.org/Vol-2703/paperTD7.pdf>.
A graphical user interface for open source event detection.
Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.
A user friendly, easy to understand way of doing event
    history regression for marginal estimands of interest,
    including the cumulative incidence and the restricted mean
    survival, using the pseudo observation framework for 
    estimation. For a review of the methodology, see Andersen and
    Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs 
    and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The
    interface uses the well known formulation of a generalized
    linear model and allows for features including plotting of 
    residuals, the use of sampling weights, and corrected
    variance estimation.
Functions for analysis of rate changes in sequential events.
Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations.
Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) <doi:10.1186/s12874-016-0117-3>.
Implements event extraction and early classification of events in data streams in R. 
    It has the functionality to generate 2-dimensional data streams with events belonging to 
    2 classes. These events can be extracted and features computed. The event features extracted
    from incomplete-events can be classified using a partial-observations-classifier 
    (Kandanaarachchi et al. 2018) <doi:10.1371/journal.pone.0236331>.
Estimates linear panel event study models. Plots coefficients following the recommendations in Freyaldenhoven et al. (2021) <doi:10.3386/w29170>. Includes sup-t bands, testing for key hypotheses, least wiggly path through the Wald region. Allows instrumental variables estimation following Freyaldenhoven et al. (2019) <doi:10.1257/aer.20180609>.
Implements the hybrid framework for event prediction described in Fang & Zheng (2011, <doi:10.1016/j.cct.2011.05.013>). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential.
Provides several confidence interval and testing procedures using
    event-specific win ratios for semi-competing risks data with non-terminal
    and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). 
    Compared with conventional methods for survival data, these procedures are 
    designed to utilize more data for improved inference procedures with 
    semi-competing risks data. The event-specific win ratios were introduced in 
    Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, 
    the event-specific win ratios and confidence intervals are obtained for each 
    event type, and several testing procedures are developed for the global null 
    of no treatment effect on either terminal or non-terminal events. Furthermore,
    a test of proportional hazard assumptions, under which the event-specific win 
    ratios converge to the hazard ratios, and a test of equal hazard ratios are 
    provided. For summarizing the treatment effect on all events, confidence 
    intervals for linear combinations of the event-specific win ratios are available
    using pre-determined or data-driven weights. Asymptotic properties of these 
    inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). 
    Also, transformations are used to yield better control of the type one error 
    rates for moderately sized data sets.
Evolutionary relatedness dependent diversification simulation powered by the 'Rcpp' back-end 'SimTable'.
Methods for fitting various extreme value distributions with parameters of 
      generalised additive model (GAM) form are provided. For details of distributions 
      see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>. Details of how evgam works
      and various examples are given in Youngman, B.D. (2022) <doi:10.18637/jss.v103.i03>.
This is an R package implementing the epidemic volatility index (EVI), as 
    discussed by Kostoulas et. al. (2021) and variations by Pateras et. al. (2023). EVI is a new, conceptually simple, early warning tool for oncoming epidemic waves. 
    EVI is based on the volatility of newly reported cases per unit of time,
    ideally per day, and issues an early warning when the volatility change rate exceeds a threshold.
Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package:
        Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) <doi:10.1038/ejhg.2010.47>.
        Strug, L. J., & Hodge, S. E. (2006) <doi:10.1159/000094709>.
        Royall, R. (1997) <ISBN:0-412-04411-0>.
Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach.  The functions in the package also make extensive use of graphical displays for data exploration and model comparison.
Provides tools for integrated sensitivity analysis of evidence factors in observational
	studies. When an observational study allows for multiple independent or nearly
	independent inferences which, if vulnerable, are vulnerable to different biases, we have 
	multiple evidence factors.  This package provides methods that respect type I error rate control. 
	Examples are provided of integrated evidence factors analysis in a longitudinal study with
	continuous outcome and in a case-control study. 
	Karmakar, B., French, B., and Small, D. S. (2019)<DOI:10.1093/biomet/asz003>.
Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. 
  Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.
Contains a collection of examples of evidence factors in observational studies from the book Replication and Evidence Factors in Observational Studies by Paul R. Rosenbaum (2021) <doi:10.1201/9781003039648>.
It allows running 'EViews' (<https://eviews.com>) program from R, R Markdown and Quarto documents. 'EViews' (Econometric Views) is a statistical software for Econometric analysis.  This package integrates 'EViews' and R and also serves as an 'EViews' Knit-Engine for 'knitr' package. Write all your 'EViews' commands in R, R Markdown or Quarto documents. For details,  please consult our peer-review article Mati S., Civcir I. and Abba S.I (2023) <doi:10.32614/RJ-2023-045>.
Checks to see whether a supplied set of dice (their face values) are transitive, returning pair-win and group-roll win probabilities. Expected returns (mean magnitude of win/loss) are presented as well.
Allows users to model and draw inferences from extreme value inflated count data, and to evaluate these models and compare to non extreme-value inflated counterparts. The package is built to be compatible with standard presentation tools such as 'broom', 'tidy', and 'modelsummary'.
Functions for extreme value theory, which may be 
  divided into the following groups; exploratory data analysis, 
  block maxima, peaks over thresholds (univariate and bivariate), 
  point processes, gev/gpd distributions.
Methods to deal with the free antiassociative algebra
      over the reals with an arbitrary number of indeterminates.
      Antiassociativity means that (xy)z = -x(yz).  Antiassociative
      algebras are nilpotent with nilindex four (Remm, 2022,
      <doi:10.48550/arXiv.2202.10812>) and this drives the design and
      philosophy of the package.  Methods are defined to create and
      manipulate arbitrary elements of the antiassociative algebra,
      and to extract and replace coefficients.  A vignette is
      provided.
The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
Runs a genetic algorithm using the 'AlphaSimR' machinery <doi:10.1093/g3journal/jkaa017> and the coalescent simulator 'MaCS' <doi:10.1101/gr.083634.108>.
Dynamic and Interactive Maps with R, powered by 'leaflet' <https://leafletjs.com>. 'evolMap' generates a web page with interactive and dynamic maps to which you can add geometric entities (points, lines or colored geographic areas), and/or markers with optional links between them. The dynamic ability of these maps allows their components to evolve over a continuous period of time or by periods.
Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.
Evolutionary game theory applies game theory to evolving populations 
    in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) 
    or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive 
	set of tools to illustrate the core concepts of evolutionary game theory, 
	such as evolutionary stability or various evolutionary dynamics, for teaching 
	and academic research is provided.
Provides tools for calculating evolvability parameters from estimated G-matrices as defined 
    in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic 
    comparative models that link the rate of evolution of a trait to the state of another evolving 
    trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was 
    released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.
"Evolutionary Virtual Education" - 'evolved' - provides multiple tools to help educators (especially at the graduate level or in advanced undergraduate level courses) apply inquiry-based learning in general evolution classes. In particular, the tools provided include functions that simulate evolutionary processes (e.g., genetic drift, natural selection within a single locus) or concepts (e.g. Hardy-Weinberg equilibrium, phylogenetic distribution of traits). More than only simulating, the package also provides tools for students to analyze (e.g., measuring, testing, visualizing) datasets with characteristics that are common to many fields related to evolutionary biology. Importantly, the package is heavily oriented towards providing tools for inquiry-based learning - where students follow scientific practices to actively construct knowledge. For additional details, see package's vignettes.
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible 
      package providing optimization driven parameter estimation methods using metaheuristics  and 
      evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization 
      for continuous domains, Tabu Search, Evolutionary Strategies, ...)  which could be more efficient and require, 
      in some cases, fewer model evaluations than alternatives relying on experimental design.  Currently there 
      are built in support for models developed with 'Repast Simphony'  Agent-Based framework (<https://repast.github.io/>)  
      and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.
Performs automated morphological character partitioning for 
             phylogenetic analyses and analyze macroevolutionary parameter 
             outputs from clock (time-calibrated) Bayesian inference analyses, following 
             concepts introduced by Simões and Pierce (2021) <doi:10.1038/s41559-021-01532-x>.
Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the 'paleoTS' package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.
Tools for modelling electric vehicle charging sessions into
    generic groups with similar connection patterns called "user profiles",
    using Gaussian Mixture Models clustering. The clustering and profiling
    methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) 
    <doi:10.1016/j.ijepes.2021.107195>.
An implementation of the 'Evidential Neural Network for Regression' model recently introduced in Denoeux (2023) <doi:10.1109/TFUZZ.2023.3268200>. In this model, prediction uncertainty is quantified by Gaussian random fuzzy numbers as introduced in Denoeux (2023) <doi:10.1016/j.fss.2022.06.004>. The package contains functions for training the network, tuning hyperparameters by cross-validation or the hold-out method, and making predictions. It also contains utilities for making calculations with Gaussian random fuzzy numbers (such as, e.g., computing the degrees of belief and plausibility of an interval, or combining Gaussian random fuzzy numbers).
Simulation of Electric Vehicles charging sessions using 
    Gaussian models, together with time-series power demand calculations. 
    The simulation methodology is published in 
    Cañigueral et al. (2023, ISBN:0957-4174) <doi:10.1016/j.eswa.2023.120318>.
The R package proposes extreme value index estimators for heavy tailed models 
             by mean of order p <DOI:10.1016/j.csda.2012.07.019>, peaks over random threshold
             <DOI:10.57805/revstat.v4i3.37> and a bias-reduced estimator 
             <DOI:10.1080/00949655.2010.547196>.
	     The package also computes moment, generalised Hill <DOI:10.2307/3318416> 
	     and mixed moment estimates for the extreme value index.
	     High quantiles and value at risk estimators based on these estimators are implemented.
Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.
Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.
Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.
Implements Escalation With Overdose Control trial designs using two drug combinations described by this paper <doi:10.1002/sim.6961>(Tighiouart et al., 2016). It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics.
Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) <DOI:10.1191/1471082X04st064oa>.
Function and data sets in the book entitled "R ile Temel Ekonometri", S.Guris, E.C.Akay, B. Guris(2020). The book published in Turkish. It is possible to makes Durbin two stage method for autocorrelation, generalized differencing method for correction autocorrelation, Hausman Test for identification and computes LM, LR and Wald test statistics for redundant variable by using the functions written in this package.
The purpose of Early Warning Systems (EWS) is to detect accurately the occurrence of a crisis, which is represented by a binary variable which takes the value of one when the event occurs, and the value of zero otherwise. EWS are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) <doi:10.1162/rest.90.4.777>. Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) <doi:10.1016/j.ijforecast.2014.03.015>. This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. In addition, this package also provides methods for the analysis of the propagation mechanisms of an exogenous shock, as well as robust confidence intervals for these response functions using a block-bootstrap method as in Lajaunie (2021). This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers.
Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) <doi:10.1371/journal.pone.0041010>, Deb et al. (2022) <doi:10.1098/rsos.211475>, Drake and Griffen (2010) <doi:10.1038/nature09389>, Ushio et al. (2018) <doi:10.1038/nature25504> and Weinans et al. (2021) <doi:10.1038/s41598-021-87839-y> for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the 'EWSmethods' website for more information, and tutorials.
Performs unconditional exact tests and power calculations for 2x2 contingency tables.
  For comparing two independent proportions, performs Barnard's test (1945)
  <doi:10.1038/156177a0> using the original CSM test (Barnard, 1947 <doi:10.1093/biomet/34.1-2.123>),
  using Fisher's p-value referred to as Boschloo's test (1970) <doi:10.1111/j.1467-9574.1970.tb00104.x>,
  or using a Z-statistic (Suissa and Shuster, 1985, <doi:10.2307/2981892>).
  For comparing two binary proportions, performs unconditional exact test using McNemar's
  Z-statistic (Berger and Sidik, 2003, <doi:10.1191/0962280203sm312ra>),
  using McNemar's conditional p-value, using McNemar's Z-statistic with continuity correction,
  or using CSM test.  Calculates confidence intervals for the difference in proportion.
  This package interacts with pre-computed data available through the ExactData R package,
  which is available in a 'drat' repository.
  Install the ExactData R package from GitHub at <https://pcalhoun1.github.io/drat/>.
  The ExactData R package is approximately 85 MB.
Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see “Exact samples sizes for clinical trials subject to size and power constraints” by Lloyd, C.J. (2022) Preprint <doi:10.13140/RG.2.2.11828.94085>.
Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or  exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case (Fay, et al, 2015, <DOI:10.1111/biom.12231>). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, <DOI:10.1002/sim.7579>), an unconditional exact test for the situation where the controls are all expected to fail. Gives confidence intervals compatible with exact McNemar's or sign tests (Fay and Lumbard, 2021, <DOI:10.1002/sim.8829>). For review of these kinds of exact tests see Fay and Hunsberger (2021, <DOI:10.1214/21-SS131>).
Researchers often use the bootstrap to understand a sample drawn
    from a population with unknown distribution. The exact bootstrap method is a
    practical tool for exploring the distribution of small sample size data. For
    a sample of size n, the exact bootstrap method generates the entire space of
    n to the power of n resamples and calculates all realizations of the selected
    statistic. The 'exactamente' package includes functions for implementing two
    bootstrap methods, the exact bootstrap and the regular bootstrap. The
    exact_bootstrap() function applies the exact bootstrap method following
    methodologies outlined in Kisielinska (2013) <doi:10.1007/s00180-012-0350-0>.
    The regular_bootstrap() function offers a more traditional bootstrap approach,
    where users can determine the number of resamples. The e_vs_r() function
    allows users to directly compare results from these bootstrap methods. To
    augment user experience, 'exactamente' includes the function exactamente_app()
    which launches an interactive 'shiny' web application. This application
    facilitates exploration and comparison of the bootstrap methods, providing
    options for modifying various parameters and visualizing results.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases (see Fay (2010) <doi:10.32614/rj-2010-008>).
This is a package for exact Confidence Intervals for the difference between two independent or dependent proportions.
Construct the admissible exact intervals for the binomial proportion, the Poisson mean and the total number of subjects with a certain attribute or the total number of the subjects for the hypergeometric distribution. Both one-sided and two-sided intervals are of interest. This package can be used to calculate the intervals constructed methods developed by Wang (2014) <doi:10.5705/ss.2012.257> and Wang (2015) <doi:10.1111/biom.12360>.
Performs the exact test on whether there is a difference between two survival curves. 
  Exact confidence interval for the hazard ratio can also be generated for the Cox model.
Quickly and accurately summarizes raster values over polygonal areas ("zonal statistics").
Life Table Response Experiments (LTREs) are a method of comparative demographic analysis. The purpose is to quantify how the difference or variance in vital rates (stage-specific survival, growth, and fertility) among populations contributes to difference or variance in the population growth rate, "lambda." We provide functions for one-way fixed design and random design LTRE, using either the classical methods that have been in use for several decades, or an fANOVA-based exact method that directly calculates the impact on lambda of changes in matrix elements, for matrix elements and their interactions. The equations and descriptions for the classical methods of LTRE analysis can be found in "Matrix Population Models: Construction, Analysis, and Interpretation (2nd edition)" Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods will be published in a forthcoming paper. We also provide some demographic functions, including generation time from Bienvenu and Legendre (2015) <doi:10.1086/681104>. For implementation of exactLTRE where all possible interactions are calculated, we use an operator matrix presented in Poelwijk, Krishna, and Ranganathan (2016) <doi:10.1371/journal.pcbi.1004771>.
A tool for conducting exact parametric regression-based causal mediation analysis of binary outcomes 
    as described in Samoilenko, Blais and Lefebvre (2018) <doi:10.1353/obs.2018.0013>;
    Samoilenko, Lefebvre (2021) <doi:10.1093/aje/kwab055>; and Samoilenko, Lefebvre (2023) <doi:10.1002/sim.9621>.
Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2023) <doi:10.1080/10618600.2022.2102026>. Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed.
Computes exact conditional p-values and quantiles using an
 implementation of the Shift-Algorithm by Streitberg & Roehmel.
Implements comprehensive test data engineering methods as described in 
    Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for 
    engineering and processing test data: Classical Test Theory (CTT) with 
    reliability coefficients for continuous ability assessment; Item Response 
    Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information 
    functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank 
    Analysis (LRA) for ordinal clustering with automatic determination of cluster 
    numbers; Biclustering methods including infinite relational models for 
    simultaneous clustering of examinees and items without predefined cluster 
    numbers; and Bayesian Network Models (BNM) for visualizing inter-item 
    dependencies. Features local dependence analysis through LRA and biclustering, 
    parameter estimation, dimensionality assessment, and network structure 
    visualization for educational, psychological, and social science research.
Contains all data sets for Exam PA: Predictive Analytics at 
    <https://exampa.net/>.
Take the examples written in your documentation of functions
    and use them to create shells (skeletons which must be manually
    completed by the user) of test files to be tested with the 'testthat'
    package. Sort of like python 'doctests' for R.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single-choice and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include standalone
	files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOlat, ILIAS, TestVision,
	Particify, ARSnova, Kahoot!, Grasple, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format
	(NOPS) is provided that can be printed, scanned, and automatically evaluated.
The main aim is to further facilitate the creation of exercises based on the package 'exams' 
    by Grün, B., and Zeileis, A. (2009) <doi:10.18637/jss.v029.i10>. Creating effective student exercises 
    involves challenges such as creating appropriate data sets and ensuring access to intermediate values 
    for accurate explanation of solutions. The functionality includes the generation of univariate and 
    bivariate data including simple time series, functions for theoretical distributions and their approximation, 
    statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks 
    such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'.
The dataset collection supports Pearson correlation and linear regression analysis, 
    with datasets for n=100,200,400,800,1000, where n is the sum of squared values in x.
    Each dataset has x values summing to zero, with sample sizes (observations in x) ranging 
    from 2 to 10. Additional data frames include variables with German names and measurement levels, 
    and distribution details with R function names, LaTeX names, discreteness, and package origins.
Randomized multiple-select and single-select
  question generation for the 'MyLearn' teaching and learning
  platform. Question templates
  in the form of the R/exams package (see <http://www.r-exams.org/>)
  are transformed into XML format required by 'MyLearn'.
Automatic generation of quizzes or individual questions as (interactive) forms within 'rmarkdown' or 'quarto' documents based on 'R/exams' exercises.
Automatic generation of quizzes or individual questions for 'learnr' tutorials based on 'R/exams' exercises.
Automatic Generation of Exams in R for 'Sakai'.
  Question templates in the form of the 'exams' package (see <https://www.r-exams.org/>)
  are transformed into XML format required by 'Sakai'.
Provides methods for constructing confidence or credible regions
    for exceedance sets and contour lines.
Allows access to data in running instance of Microsoft Excel
    (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
    'xl[a1] = current.graphics()'. Additionally there are function for reading/writing 
    'Excel' files - 'xl.read.file'/'xl.save.file'. They are not fast but able to read/write 
    '*.xlsb'-files and password-protected files. There is an Excel workbook with 
    examples of calling R from Excel in the 'doc' folder. It tries to keep things as
    simple as possible - there are no needs in any additional
    installations besides R, only 'VBA' code in the Excel workbook.
    Microsoft Excel is required for this package.
Split experiment sentences by different experiment design given by the user and the result can be used in 'E-prime' (<https://pstnet.com/products/e-prime/>).
A small group of functions to read in a data dictionary and the
    corresponding data table from 'Excel' and to automate the cleaning, re-coding
    and creation of simple calculated variables. This package was designed to
    be a companion to the macro-enabled 'Excel' template available
    on the GitHub site, but works with any similarly-formatted 'Excel' data.
Implements 'Excel' functions in 'R' for your calculation simplicity.You can use most of the aggregate functions, addressing functions,logical functions and text functions. Helps you a ton in learning how 'R' works as some 'Excel' users might be struggling with the program.
An R interface to 'jExcel' library to create web-based interactive tables and spreadsheets compatible with 'Excel' or any other spreadsheet software.
Implementation of a function which calculates the empirical excess mass 
	for given \eqn{\lambda} and given maximal number of modes (excessm()). Offering 
	powerful plot features to visualize empirical excess mass (exmplot()). This 
	includes the possibility of drawing several plots (with different maximal 
	number of modes / cut off values) in a single graph.
Implementation of method for estimating excess mortality and other health related outcomes from weekly or daily count data described in Acosta and Irizarry (2021) "A Flexible Statistical Framework for Estimating Excess Mortality".
Data that are collected through online sources such as
    Mechanical Turk may require excluding rows because of IP address
    duplication, geolocation, or completion duration. This package
    facilitates exclusion of these data for Qualtrics datasets.
Instead of counting observations before and after a subset() 
    call, the ExclusionTable() function reports the number before and after 
    each subset() call together with the number of observations that have been 
    excluded. This is especially useful in observational studies for keeping 
    track how many observations have been excluded for each in-/ or 
    exclusion criteria. You just need to provide ExclusionTable() with a 
    dataset and a list of logical filter statements.
Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.
Performs frequentist inference for the extremal index of a 
    stationary time series.  Two types of methodology are used.  One type is
    based on a model that relates the distribution of block maxima to the 
    marginal distribution of series and leads to the semiparametric maxima 
    estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and 
    Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>.  Sliding block maxima
    are used to increase precision of estimation. A graphical block size 
    diagnostic is provided.  The other type of methodology uses a model for the 
    distribution of threshold inter-exceedance times (Ferro and Segers (2003) 
    <doi:10.1111/1467-9868.00401>). Three versions of this type of approach are 
    provided: the iterated weight least squares approach of Suveges (2007) 
    <doi:10.1007/s10687-007-0034-2>, the K-gaps model of 
    Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and a similar approach
    of Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3> 
    that we refer to as D-gaps. For the K-gaps and D-gaps models this package 
    allows missing values in the data, can accommodate independent subsets of 
    data, such as monthly or seasonal time series from different years, and can 
    incorporate information from right-censored inter-exceedance times.  
    Graphical diagnostics for the threshold level and the respective tuning
    parameters K and D are provided.
Routines for Bayesian estimation and analysis of dynamic quantile linear models utilizing the extended asymmetric Laplace error distribution, also known as extended dynamic quantile linear models (exDQLM) described in Barata et al (2020) <doi:10.1214/21-AOAS1497>.
Make your 'shiny' application as executable program. 
    Users do not need to install 'R' and 'shiny' on their system.
Presents two methods to estimate the parameters 'mu', 'sigma', and 'tau' of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The 'QMLE' method allows a choice between three different estimation algorithms for these parameters : 'neldermead' ('NEMD'), 'fminsearch' ('FMIN'), and 'nlminb' ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) <doi:10.3758/BF03195527>; McCormack, P. D., & Wright, N. M. (1964) <doi:10.1037/h0083285>; Van Zandt, T. (2000) <doi:10.3758/BF03214357>; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) <doi:10.19139/soic-2310-5070-1251>; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) <doi:10.2307/2986138>.
Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages 'eRm', 'psychotools' or 'pairwise' can be used.
The goal of this package is to provide an easy to use, fast and
    scalable exhaustive search framework. Exhaustive feature selections 
    typically require a very large number of models to be fitted and evaluated. 
    Execution speed and memory management are crucial factors here. This package 
    provides solutions for both. Execution speed is optimized by using a 
    multi-threaded C++ backend, and memory issues are solved by by only storing 
    the best results during execution and thus keeping memory usage constant.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <https://exiftool.org>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Reads, writes, and edits EXIF and other file metadata using
    ExifTool <https://exiftool.org/>, returning read results as a data
    frame. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP
    and ID3, Lyrics3, as well as the maker notes of many digital cameras by
    Canon, Casio, DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf,
    Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson,
    Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo,
    Sigma/Foveon and Sony.
The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) <doi:10.48550/arXiv.2205.15111>.
Pacote para a analise de experimentos havendo duas variaveis
    explicativas quantitativas e uma variavel dependente quantitativa. Os
    experimentos podem ser sem repeticoes ou com delineamento estatistico.
    Sao ajustados 12 modelos de regressao multipla e plotados graficos de
    superficie resposta (Hair JF, 2016) <ISBN:13:978-0138132637>.(Package
    for the analysis of experiments having two explanatory quantitative
    variables and one quantitative dependent variable. The experiments can
    be without repetitions or with a statistical design. Twelve multiple
    regression models are fitted and response surface graphs are plotted
    (Hair JF, 2016) <ISBN:13:978-0138132637>).
Provides a shiny-based front end (the 'ExPanD' app) and
    a set of functions for exploratory data analysis. Run as a web-based 
    app, 'ExPanD' enables users to assess the robustness of empirical evidence 
    without providing them access to the underlying data. You can export a 
    notebook containing the analysis of 'ExPanD' and/or use the functions of the 
    package to support your exploratory data analysis workflow. Refer to the 
    vignettes of the package for more information on how to use 'ExPanD' and/or 
    the functions of this package.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.
The amplitude-dependent autoregressive time series model (EXPAR) proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> was improved by incorporating the moving average (MA) framework for capturing the variability efficiently. Parameters of the EXPARMA model can be estimated using this package. The user is provided with the best fitted EXPARMA model for the data set under consideration.
A SQLite database is designed to store all information 
  of experiment-based data including metadata, experiment design, 
  managements, phenotypic values and climate records. The dataset can be
  imported from an excel file.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test  of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Pacote para análise de delineamentos experimentais (DIC, DBC e DQL), experimentos em esquema fatorial duplo (em DIC e DBC), experimentos em parcelas subdivididas (em DIC e DBC), experimentos em esquema fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em fatorial triplo (em DIC e DBC) e experimentos em esquema fatorial triplo com um tratamento adicional (em DIC e DBC), fazendo analise de variancia e comparacao de multiplas medias (para tratamentos qualitativos), ou ajustando modelos de regressao ate a terceira potencia (para tratamentos quantitativos); analise de residuos (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Expectile and quantile regression of models with nonlinear effects
  e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes
  as well as boosting; also supplies expectiles for common distributions.
Distributes samples in batches while making batches
    homogeneous according to their description. Allows for an arbitrary
    number of variables, both numeric and categorical. For quality control
    it provides functions to subset a representative sample.
Provides convenience functions for researching experiences 
    including user, customer, patient, employee, and other human experiences.
    It provides a suite of tools to simplify data exploration such as 
    benchmarking, comparing groups, and checking for differences. The outputs 
    translate statistical approaches in applied experience research to 
    human readable output.
Provides various statistical methods for
  designing and analyzing randomized experiments. One functionality
  of the package is the implementation of randomized-block and
  matched-pair designs based on possibly multivariate pre-treatment
  covariates. The package also provides the tools to analyze various
  randomized experiments including cluster randomized experiments,
  two-stage randomized experiments, randomized experiments with 
  noncompliance, and randomized experiments with missing data.
Contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at <https://osf.io/b78je>.
Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.
Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) <doi:10.1177/0272989X221150212>.
Three functional modules, including genetic features, differential expression analysis and non-additive expression analysis were integrated into the package. And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being.
The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.
The Australian Regulatory Guidelines for Prescription
    Medicines (ARGPM), guidance on "Stability testing for prescription
    medicines", recommends to predict the shelf life of chemically derived
    medicines from stability data by taking the worst case situation at batch
    release into account. Consequently, if a change over time is observed,
    a release limit needs to be specified. Finding a release limit and the
    associated shelf life is supported, as well as the standard approach
    that is recommended by guidance Q1E "Evaluation of stability data"
    from the International Council for Harmonisation (ICH).
It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.
Generates explanations for classification and regression models and visualizes them.
 Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
 are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
 in the model. The IME method is slower as it samples from all feature subsets.
 For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, 
 and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
 All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided 
 and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.
Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual “hairball” that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be “exploded” to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which “explodes” the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl]<https://github.com/UTMB-DIVA-Lab/epl> that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) <PMID: 28815099>). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Conduct numerous exploratory analyses in an instant with a 
    point-and-click interface. With one simple command, this tool 
    launches a Shiny App on the local machine. Drag and drop variables 
    in a data set to categorize them as possible independent, 
    dependent, moderating, or mediating variables. Then run dozens 
    (or hundreds) of analyses instantly to uncover any statistically 
    significant relationships among variables. Any relationship 
    thus uncovered should be tested in follow-up studies. 
    This tool is designed only to facilitate exploratory 
    analyses and should NEVER be used for p-hacking. Many of 
    the functions used in this package are previous versions of functions
    in the R Packages 'kim' and 'ezr'.
    Selected References:
    Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>.
    Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>.
    Kim (2023) <https://jinkim.science/docs/kim.pdf>.
    Kim (2021) <doi:10.5281/zenodo.4619237>.
    Kim (2020) <https://CRAN.R-project.org/package=ezr>.
    Simmons et al. (2011) <doi:10.1177/0956797611417632>
    Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>.
    Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>.
Interactive data exploration with one line of code, automated
    reporting or use an easy to remember set of tidy functions for low
    code exploratory data analysis.
Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.
Computation of the matrix exponential, logarithm, sqrt,
  and related quantities, using traditional and modern methods.
Easily export 'R' graphs and statistical output to 'Microsoft
    Office' / 'LibreOffice', 'Latex' and 'HTML' Documents, using sensible defaults
    that result in publication-quality output with simple, straightforward commands.
    Output to 'Microsoft Office' is in editable 'DrawingML' vector format for
    graphs, and can use corporate template documents for styling. This enables
    the production of standardized reports and also allows for manual tidy-up
    of the layout of 'R' graphs in 'Powerpoint' before final publication. Export
    of graphs is flexible, and functions enable the currently showing R graph
    or the currently showing 'R' stats object to be exported, but also allow the
    graphical or tabular output to be passed as objects. The package relies on package
    'officer' for export to 'Office' documents,and output files are also fully compatible
    with 'LibreOffice'. Base 'R', 'ggplot2' and 'lattice' plots are supported, as
    well as a wide variety of 'R' stats objects, via wrappers to xtable(), broom::tidy() 
    and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as
    well as matrices and data frames and many more...
A variety of descriptive multivariate analyses with the singular value decomposition,
    such as principal components analysis, correspondence analysis, and multidimensional scaling.
    See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
The EXPOS model uses a digital elevation model (DEM) to estimate
    exposed and protected areas for a given hurricane wind direction and
    inflection angle. The resulting topographic exposure maps can be combined
    with output from the HURRECON model to estimate hurricane wind damage
    across a region. For details on the original version of the EXPOS model
    written in 'Borland Pascal', see: Boose, Foster, and Fluet (1994)
    <doi:10.2307/2937142>, Boose, Chamberlin, and Foster (2001)
    <doi:10.1890/0012-9615(2001)071[0027:LARIOH]2.0.CO;2>, and Boose,
    Serrano, and Foster (2004) <doi:10.1890/02-4057>.
Produces diversity estimates and species lists with associated global distribution for any vascular plant family and genus from 'Plants of the World Online' database <https://powo.science.kew.org/>, by interacting with the source code of each plant taxon page. It also creates global maps of species richness, graphics of species discoveries and nomenclatural changes over time.
A set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. It has been written to accompany the forthcoming paper 'Computing expectations and marginal likelihoods for permutations'. Publication details will be updated as soon as they are finalized.
Allows to calculate the probabilities  of occurrences of an event in a 
 great number of repetitions of Bernoulli experiment, through the application of  
 the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. 
 Gives the possibility to show the results graphically and analytically, and to compare
 the results obtained by the application of the above  theorems with those calculated by
 the direct application of the Binomial formula. Is basically useful for educational
 purposes.
Given a continuous-time dynamic network, this package allows one to fit a stochastic blockmodel where nodes belonging to the same group create interactions and non-interactions of similar lengths. This package implements the methodology described by R. Rastelli and M. Fop (2019) <arXiv:1901.09828>.
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Package computes and displays tables with support for 'SPSS'-style 
        labels, multiple and nested banners, weights, multiple-response variables 
        and significance testing. There are facilities for nice output of tables 
        in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods 
        for labelled variables add value labels support to base R functions and to 
        some functions from other packages. Additionally, the package brings 
        popular data transformation functions from 'SPSS' Statistics and 'Excel': 
        'RECODE', 'COUNT', 'COUNTIF', 'VLOOKUP' and etc. 
        These functions are very useful for data processing in marketing research 
        surveys. Package intended to help people to move data 
        processing from 'Excel' and 'SPSS' to R.
Experiences studies are an integral component of the actuarial 
    control cycle. Regardless of the decrement or policyholder behavior of 
    interest, the analyses conducted is often the same. Ultimately, this 
    package aims to reduce time spent writing the same code used for 
    different experience studies, therefore increasing the time for to uncover
    new insights inherit within the relevant experience.
Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
Estimation for high conditional quantiles based on quantile regression.
A system for batch-marking data analysis to estimate survival probabilities, capture probabilities, and enumerate the population abundance for both marked and unmarked individuals. The estimation of only marked individuals can be achieved through the batchMarkOptim() function. Similarly, the combined marked and unmarked can be achieved through the batchMarkUnmarkOptim() function. The algorithm was also implemented for the hidden Markov model encapsulated in batchMarkUnmarkOptim() to estimate the abundance of both marked and unmarked individuals in the population. The package is based on the paper: "Hidden Markov Models for Extended Batch Data" of Cowen et al. (2017) <doi:10.1111/biom.12701>.
A consistent, unified and extensible
    framework for estimation of parameters for probability distributions, including 
    parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.
Creates family objects identical to stats
    family but for new links.
If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.
Estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. This package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. This package also contains functions for cross validation to select a suitable lambda value given the data. Also provides a function for estimation in fused lasso penalized linear regression. For more details, see Mandal, B. N.(2014). Computational methods for L1 penalized GLM model fitting, unpublished report submitted to Macquarie University, NSW, Australia.
For multiple full/partial ranking lists, R package 'ExtMallows' can (1) detect whether the input ranking lists are over-correlated, and (2) use the Mallows model or extended Mallows model to integrate the ranking lists, and (3) use hierarchical extended Mallows model for rank integration if there are groups of over-correlated ranking lists.
Extract toxicological and chemical information from databases
    maintained by scientific agencies and resources, including the
    Comparative Toxicogenomics Database <https://ctdbase.org/>, the
    Integrated Chemical Environment <https://ice.ntp.niehs.nih.gov/>, the
    Integrated Risk Information System <https://cfpub.epa.gov/ncea/iris/>,
    Provisional Peer-Reviewed Toxicity Values
    <https://www.epa.gov/pprtv/provisional-peer-reviewed-toxicity-values-pprtvs-assessments>,
    the CompTox Chemicals Dashboard Resource Hub
    <https://www.epa.gov/comptox-tools/comptox-chemicals-dashboard-resource-hub>,
    PubChem <https://pubchem.ncbi.nlm.nih.gov/>, and others.
By using a multispectral image and ESRI shapefile (Point/ Line/ Polygon), a data table will be generated for classification, regression or other processing. The data table will be contained by band wise raster values and shapefile ids (User Defined).
Density, distribution function, quantile function
    and random generation for a number of univariate
    and multivariate distributions. This package implements the
    following distributions: Bernoulli, beta-binomial, beta-negative
    binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
    bivariate normal, bivariate Poisson, categorical, Dirichlet,
    Dirichlet-multinomial, discrete gamma, discrete Laplace,
    discrete normal, discrete uniform, discrete Weibull, Frechet,
    gamma-Poisson, generalized extreme value, Gompertz,
    generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t,
    Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy,
    Laplace, location-scale t, logarithmic, Lomax, multivariate
    hypergeometric, multinomial, negative hypergeometric,
    non-standard beta, normal mixture, Poisson mixture, Pareto,
    power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam,
    slash, triangular, truncated binomial, truncated normal,
    truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
    zero-inflated negative binomial, zero-inflated Poisson.
Tools to using fonts other than the standard PostScript fonts.
    This package makes it easy to use system TrueType fonts and with PDF or
    PostScript output files, and with bitmap output files in Windows. extrafont
    can also be used with fonts packaged specifically to be used with, such as
    the fontcm package, which has Computer Modern PostScript fonts with math
    symbols.
Package for holding the database for the extrafont package
Provide estimation and data generation tools for some new multivariate frailty models.
             This version includes the gamma, inverse Gaussian, weighted Lindley, Birnbaum-Saunders,
	     truncated normal, mixture of inverse Gaussian and mixture of Birnbaum-Saunders as the 
             distribution for the frailty terms. For the basal model, it is considered a parametric 
             approach based on the exponential, Weibull and the piecewise exponential distributions 
             as well as a semiparametric approach. 
             For details, see Gallardo and Bourguignon (2022) <doi:10.48550/arXiv.2206.12973> and
             Gallardo et al. (2024) <doi:10.1007/s11222-024-10458-w>.
Speed up common tasks, particularly logical or
  relational comparisons and routine follow up tasks such as finding the
  indices and subsetting. Inspired by mathematics, where something like:
  3 < x < 6 is a standard, elegant and clear way to assert that
  x is both greater than 3 and less than 6
  (see for example <https://en.wikipedia.org/wiki/Relational_operator>),
  a chaining operator is implemented. The chaining operator, %c%,
  allows multiple relational operations to be used in quotes on the right
  hand side for the same object, on the left hand side.
  The %e% operator allows something like set-builder notation 
  (see for example <https://en.wikipedia.org/wiki/Set-builder_notation>) 
  to be used on the right hand side.
  All operators have built in prefixes defined for all, subset, and which
  to reduce the amount of code needed for common tasks, such as return those
  values that are true.
Functions to 'numericise' 'R' objects (coerce to numeric
    objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and
    calculate deviance residuals as well as 'R' translations of some
    'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs
    Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.
Contains additional miscellaneous steps for the 'recipes' package.
    These steps are useful, but doesn't have a good home in other 'recipes'
    packages or its extensions.
Finding life outside the planet Earth several is the ultimate goal of an astrobiologist. Using known astronomical measurements and assumptions the probability of extraterrestrial life existence could be estimated. Equations such as the Drake equation (1961) as stated in the paper of Molina (2019) <arXiv:1912.01783>, Seager (2013) <https://www.space.com/22648-drake-equation-alien-life-seager.html> and Foucher et al, (2017) <doi:10.3390/life7040040> are included in the 'extraterrestrial' package.
Provides summary statistics of local geospatial features within a given geographic area. 
	It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). 
	The geospatial features can be of any type of geospatial data, including point, polygon or line data.
Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) <arXiv:1910.13848>. These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures.
A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) <doi:10.48550/arXiv.1508.05561>, Marcon et al. (2016) <doi:10.1214/16-EJS1162>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Beranger et al. (2021) <doi:10.1007/s10687-019-00364-0>. This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) <doi:10.1007/s10687-020-00376-1>.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Extreme value theory, nonparametric kernel estimation, tail
    conditional probabilities, extreme conditional quantile, adaptive estimation,
    quantile regression, survival probabilities.
An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.
Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) <doi:10.1191/1471082X04st075oa>. FF do Nascimento, D. Gamerman, HF Lopes <doi:10.1007/s11222-011-9270-z>.
A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations.  The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2020) <arxiv:2004.04078>, Padoan and Stupfler (2020) <arxiv:2007.08944>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>.
General functions for performing extreme value analysis.  In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes.  Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping.  Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included.  For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
Fit, plot and compare several (extreme value) distribution functions. 
    Compute (truncated) distribution quantile estimates and plot return periods on a linear scale.
   	On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...]  ISBN 1463508417.
Detect outliers in one-dimensional data.
Conducts inference in statistical models for extreme values (de Carvalho et al (2012), <doi:10.1080/03610926.2012.709905>; de Carvalho and Davison (2014), <doi:10.1080/01621459.2013.872651>; Einmahl et al (2016), <doi:10.1111/rssb.12099>).
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
An implementation of 
              1) the tail pairwise dependence matrix (TPDM) as described in Jiang & Cooley (2020) <doi:10.1175/JCLI-D-19-0413.1>  
              2) the extremal pattern index (EPI) as described in Szemkus & Friederichs ('Spatial patterns and indices for heatwave and droughts over Europe using a decomposition of extremal dependency'; submitted to ASCMO 2023).
Testing for and dating periods of explosive
    dynamics (exuberance) in time series using the univariate and panel
    recursive unit root tests proposed by Phillips et al. (2015)
    <doi:10.1111/iere.12132> and Pavlidis et al. (2016)
    <doi:10.1007/s11146-015-9531-2>.The recursive least-squares
    algorithm utilizes the matrix inversion lemma to avoid matrix
    inversion which results in significant speed improvements. Simulation
    of a variety of periodically-collapsing bubble processes. Details can be 
    found in Vasilopoulos et al. (2022) <doi:10.18637/jss.v103.i10>.
Analysis of trade in value added with international
    input-output tables. Includes commands for easy data extraction,
    matrix manipulation, decomposition of value added in gross exports and
    calculation of value added indicators, with full geographical and
    sector customization.  Decomposition methods include Borin and Mancini
    (2023) <doi:10.1080/09535314.2022.2153221>, Miroudot and Ye (2021)
    <doi:10.1080/09535314.2020.1730308>, Wang et al. (2013)
    <https://econpapers.repec.org/paper/nbrnberwo/19677.htm> and Koopman
    et al. (2014) <doi:10.1257/aer.104.2.459>.
There is no ophthalmic researcher who has not had headaches from 
    the handling of visual acuity entries. Different notations, untidy entries. 
    This shall now be a matter of the past. Eye makes it as easy as pie to work
    with VA data - easy cleaning, easy conversion between 
    Snellen, logMAR, ETDRS letters, and qualitative visual acuity 
    shall never pester you again. The eye 
    package automates the pesky task to count number of patients and eyes, 
    and can help to clean data with easy re-coding for right and left eyes. 
    It also contains functions to help reshaping eye side specific variables 
    between wide and long format. Visual acuity conversion is based on 
    Schulze-Bonsel et al. (2006) <doi:10.1167/iovs.05-0981>, 
    Gregori et al. (2010) <doi:10.1097/iae.0b013e3181d87e04>, 
    Beck et al. (2003) <doi:10.1016/s0002-9394(02)01825-1> and 
    Bach (2007) <http:michaelbach.de/sci/acuity.html>.
Open source data allows for reproducible research and helps advance
    our knowledge. The purpose of this package is to collate open source 
    ophthalmic data sets curated for direct use. This is real life data of 
    people with intravitreal injections with anti-vascular endothelial growth
    factor (anti-VEGF), due to age-related macular degeneration or diabetic 
    macular edema. Associated publications of the data sets: 
    Fu et al. (2020) <doi:10.1001/jamaophthalmol.2020.5044>, 
    Moraes et al (2020) <doi:10.1016/j.ophtha.2020.09.025>,
    Fasler et al. (2019) <doi:10.1136/bmjopen-2018-027441>, 
    Arpa et al. (2020) <doi:10.1136/bjophthalmol-2020-317161>,
    Kern et al. 2020, <doi:10.1038/s41433-020-1048-0>.
Imports plain-text ASC data files from EyeLink eye trackers 
    into (relatively) tidy data frames for analysis and visualization.
Import gaze data from  edf files generated by 
  the SR Research <https://www.sr-research.com/> EyeLink eye tracker. Gaze data,
  both recorded events and samples, is imported per trial. The package allows to extract
  events of interest, such as saccades, blinks, etc. as well as
  recorded variables and custom events (areas of interest, triggers) into separate tables.
  The package requires EDF API library that can be obtained at <https://www.sr-research.com/support/>.
Functions to prepare and analyse eye tracking data of reading
  exercises. The functions allow some basic data preparations and code fixations 
  as first and second pass. First passes can be further devided into forward and 
  reading. The package further allows for aggregating fixation times per AOI or 
  per AOI and per type of pass (first forward, first rereading, second). These 
  methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> 
  and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is 
  also possible to convert between metric length and visual degrees.
Enables the automation of actions across the pipeline, including 
  initial steps of transforming binocular data and gap repair to event-based 
  processing such as fixations, saccades, and entry/duration in Areas of 
  Interest (AOIs). It also offers visualisation of eye movement and AOI 
  entries. These tools take relatively raw (trial, time, x, and y form) data 
  and can be used to return fixations, saccades, and AOI entries and time spent
  in AOIs. As the tools rely on this basic data format, the functions can work 
  with data from any eye tracking device. Implements fixation and saccade 
  detection using methods proposed by Salvucci and Goldberg (2000) 
  <doi:10.1145/355017.355028>.
Addresses tasks along the pipeline from raw
    data to analysis and visualization for eye-tracking data. Offers several
    popular types of analyses, including linear and growth curve time analyses,
    onset-contingent reaction time analyses, as well as several non-parametric
    bootstrapping approaches. For references to the approach see Mirman, 
    Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and
    Barr (2008) <doi:10.1016/j.jml.2007.09.002>.
A set of functions for organising and analysing datasets from
    experiments run using 'Eyelink' eye-trackers. Organising functions help
    to clean and prepare eye-tracking datasets for analysis, and mark up
    key events such as display changes and responses made by participants.
    Analysing functions help to create means for a wide range of standard
    measures (such as 'mean fixation durations'), which can then be fed into
    the appropriate statistical analyses and graphing packages as necessary.
Facilitates easy analysis of factorial experiments, including
    purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss
    designs, and mixed within-and-between-Ss designs. The functions in this package
    aim to provide simple, intuitive and consistent specification of data analysis
    and visualization. Visualization functions also include design visualization for
    pre-analysis data auditing, and correlation matrix visualization. Finally, this
    package includes functions for non-parametric analysis, including permutation
    tests and bootstrap resampling. The bootstrap function obtains predictions
    either by cell means or by more advanced/powerful mixed effects models, yielding
    predictions and confidence intervals that may be easily visualized at any level
    of the experiment's design.
A dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects.
    Johnson WE & Li C (2007)  <doi:10.1093/biostatistics/kxj037> "Adjusting batch effects in microarray expression data using empirical Bayes methods."
    Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.11.024> "Harmonization of cortical thickness measurements across scanners and sites."
    Fortin J-P, Parker D, Tun<e7> B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.08.047> "Harmonization of multi-site diffusion tensor imaging data."
A tool to operate a batch of univariate or multivariate Cox
    models and return tidy result.
Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.
Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.
Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.
Enables users to create visualizations using functions 
    based on the data analysis task rather than on plotting mechanics. It hides
    the details of the individual 'ggplot2' function calls and 
    allows the user to focus on the end goal. Useful for quick preliminary explorations. 
    Provides functions for common exploration patterns. Some of the ideas in this
    package are motivated by Fox (2015, ISBN:1938377052).
Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs.
    The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
    The output variable of the datasets is a scalar (quantitative).
    The optimization of the likelihood function can be chosen by the users (see the documentation of EzGP_fit()).
    The modeling method is published in "EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors" 
    by Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (2022) <doi:10.1137/19M1288462>.
An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.
Serves as a platform for published fluorometric enzyme assay
 protocols. 'ezmmek' calibrates, calculates, and plots enzyme activities as they
 relate to the transformation of synthetic substrates. At present, 'ezmmek'
 implements two common protocols found in the literature, and is modular to accommodate
 additional protocols. Here, these protocols are referred to as the In-Sample
 Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>).
 protocols. By containing multiple protocols, 'ezmmek' aims to stimulate
 discussion about how to best optimize fluorometric enzyme assays. A standardized
 approach would make studies more comparable and reproducible.
Wrapper for the 'ggplot2' package that creates a variety of common
  charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce
  typing.
Runs a Shiny App in the local machine for basic
    statistical and graphical analyses. The point-and-click interface 
    of Shiny App enables obtaining the same analysis outputs (e.g., plots and
    tables) more quickly, as compared with typing the required code in R,
    especially for users without much experience or expertise with coding.
    Examples of possible analyses include tabulating descriptive
    statistics for a variable, creating histograms by experimental groups,
    and creating a scatter plot and calculating the correlation between
    two variables.
Contains two functions that are intended to make
    tuning supervised learning methods easy. The eztune function uses a
    genetic algorithm or Hooke-Jeeves optimizer to find the best 
    set of tuning parameters. The user can choose the optimizer, the 
    learning method, and if optimization will be based on accuracy 
    obtained through validation error, cross validation, or resubstitution. 
    The function eztune.cv will compute a cross validated error rate. The purpose 
    of eztune_cv is to provide a cross validated accuracy or MSE when 
    resubstitution or validation data are used for optimization because 
    error measures from both approaches can be misleading.
Obtain Formula 1 data via the 'Ergast API' <https://ergast.com/mrd/> and the unofficial API <https://www.formula1.com/en/timing/f1-live> via the 'fastf1' 'Python' library <https://docs.fastf1.dev/>.
Allows users to create and deploy the workflow with multiple functions
    in Function-as-a-Service (FaaS) cloud computing platforms.
    The 'FaaSr' package makes it simpler for R developers to use FaaS platforms by providing the following functionality:
    1) Parsing and validating a JSON-based payload compliant to 'FaaSr' schema supporting multiple FaaS platforms
    2) Invoking user functions written in R in a Docker container (derived from rocker), using a list generated from
       the parser as argument
    3) Downloading/uploading of files from/to S3 buckets using simple primitives
    4) Logging to files in S3 buckets
    5) Triggering downstream actions supporting multiple FaaS platforms
    6) Generating FaaS-specific API calls to simplify the registering of a user's workflow with a FaaS platform
    Supported FaaS platforms:
    Apache OpenWhisk <https://openwhisk.apache.org/>
    GitHub Actions <https://github.com/features/actions>
    Amazon Web Services (AWS) Lambda <https://aws.amazon.com/lambda/>
    Supported cloud data storage for persistent storage:
    Amazon Web Services (AWS) Simple Storage Service (S3) <https://aws.amazon.com/s3/>.
Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <DOI:10.1093/biomet/asy009> and  
    'Exact adaptive confidence intervals for linear regression coefficients'
    by Hoff and Yu <DOI:10.1214/18-EJS1517>.
Frequentist assisted by Bayes (FAB) p-values and confidence 
 interval construction. See 
 Hoff (2019) <arXiv:1907.12589> 
 "Smaller p-values via indirect information",
 Hoff and Yu (2019) <doi:10.1214/18-EJS1517> 
 "Exact adaptive confidence intervals for linear regression coefficients", and
 Yu and Hoff (2018) <doi:10.1093/biomet/asy009> 
 "Adaptive multigroup confidence intervals with constant coverage".
Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points.  It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) <arXiv:2103.06347>. For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint.
Provides a collection of commonly used univariate and multivariate
    time series forecasting models including automatically selected exponential 
    smoothing (ETS) and autoregressive integrated moving average (ARIMA) models.
    These models work within the 'fable' framework provided by the 'fabletools'
    package, which provides the tools to evaluate, visualise, and combine models 
    in a workflow consistent with the tidyverse.
Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of
    'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and
    model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016)
    <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides
    innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods.
    Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting.
    It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).
Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.
Provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the 'tscount' package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the 'glarma' package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood.
Provides tools, helpers and data structures for
    developing models and time series functions for 'fable' and extension
    packages. These tools support a consistent and tidy interface for time
    series modelling and analysis.
Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).
Computes and plots prediction intervals for numerical 
  data or prediction sets for categorical data using prior information. 
  Empirical Bayes procedures to estimate the prior information from 
  multi-group data are included. See, e.g.,Bersson and Hoff (2022) 
  <arXiv:2204.08122> "Optimal Conformal Prediction for Small Areas".
The goal of this package is to provide wrapper functions in the 
    data cleaning and cleansing processes. These function helps in messages and 
    interaction with the user, keep track of information in pipelines, help in 
    the wrangling, munging, assessment and visualization of data frame-like 
    material.
Helps you imagine your data before you collect it. Hierarchical data structures
   and correlated data can be easily simulated, either from random number generators or
   by resampling from existing data sources. This package is faster with 'data.table' and
   'mvnfast' installed.
Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. 
    The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes.
    The package relies on the 'fabricjs' 'JavaScript' library. See <http://fabricjs.com/>.
We implement the Fast Covariance Estimation for 
             Sparse Functional Data paper published in Statistics and Computing <doi: 10.1007/s11222-017-9744-8>.
Collect  marketing data from facebook Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
    Use four spaces when indenting paragraphs within the Description.
Collect  your data on digital marketing campaigns from Facebook Leads Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Collect  your data on digital marketing campaigns from 'Facebook Organic' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides an interface to the 'Kairos' Face Recognition API <https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.
Linear cross-section factor model fitting with least-squares 
    and robust fitting the 'lmrobdetMM()' function from 'RobStatTM'; related
    volatility, Value at Risk and Expected Shortfall risk and performance 
    attribution (factor-contributed vs idiosyncratic returns);
    tabular displays of risk and performance reports;
    factor model Monte Carlo. The package authors would like to thank Chicago
    Research on Security Prices,LLC for the cross-section of about 300
    CRSP stocks data (in the data.table object 'stocksCRSP', and S&P GLOBAL MARKET
    INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and
    "factor exposures") fundamental data on the 300 companies in the data.table
    object 'factorSPGMI'.  The 'stocksCRSP' and 'factorsSPGMI' data are not covered by
    the GPL-2 license, are not provided as open source of any kind, and they are
    not to be redistributed in any form.
Supports teaching methods of estimating and testing time series
    factor models for use in robust portfolio construction and analysis. Unique
    in providing not only classical least squares, but also modern robust model
    fitting methods which are not much influenced by outliers. Includes
    returns and risk decompositions, with user choice of  standard deviation,
    value-at-risk, and expected shortfall risk measures. "Robust Statistics
    Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, 
    M. Salibian-Barrera (2019) <doi:10.1002/9781119214656>.
We present 'FACT' (Feature Attributions for ClusTering), a
  framework for unsupervised interpretation methods that can be used with an 
  arbitrary clustering algorithm. The package is capable of re-assigning instances to
  clusters (algorithm agnostic), preserves the integrity of the data and does
  not introduce additional models. 'FACT' is inspired by the principles of
  model-agnostic interpretation in supervised learning. Therefore, some of the
  methods presented are based on 'iml', a R Package for Interpretable Machine
  Learning by Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl (2018)
  <doi:10.21105/joss.00786>.
Implements instrumental variable estimators for 2^K factorial experiments with noncompliance.
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.
Some functions of 'ade4' and 'stats' are combined in order to obtain 
             a partition of the rows of a data table, with columns representing 
             variables of scales: quantitative, qualitative or frequency. 
             First, a principal axes method is performed and then, a combination 
             of Ward agglomerative hierarchical classification and K-means is 
             performed, using some of the first coordinates obtained from the 
             previous principal axes method. 
             In order to permit different weights of the elements to be clustered, 
             the function 'kmeansW', programmed in C++, is included. 
             It is a modification of 'kmeans'. Some graphical functions include 
             the option: 'gg=FALSE'.  When 'gg=TRUE', they  use the 'ggplot2' 
             and 'ggrepel' packages to avoid  the super-position of the labels.
Provides some easy-to-use functions to extract and visualize the
    output of multivariate data analyses, including 'PCA' (Principal Component
    Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence
    Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple
    Factor Analysis) functions from different R packages. It contains also functions
    for simplifying some clustering analysis steps and provides 'ggplot2' - based
    elegant data visualization.
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) <DOI:10.1007/s11222-022-10084-4>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.
Uses raw vectors to minimize memory consumption of categorical 
    variables with fewer than 256 unique values. Useful for analysis of large
    datasets involving variables such as age, years, states, countries, or
    education levels.
Tests for Kaiser-Meyer-Olkin (KMO) and
    communalities in a dataset. It provides a final sample by removing
    variables in a iterable manner while keeping account of the variables
    that were removed in each step. It follows the best practices and assumptions according to
    Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).
Estimation, model selection and goodness-of-fit of (1) factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2021) <doi:10.1111/bmsp.12231>; (2) bi-factor and second-order copula models for item response data in Kadhem and Nikoloulopoulos (2023) <doi:10.1007/s11336-022-09894-2>; (3) factor tree copula models for item response data in Kadhem and Nikoloulopoulos (2022) <arXiv:2201.00339>.
Estimates heterogeneous effects in factorial (and conjoint)
    models. The methodology employs a Bayesian finite mixture of
    regularized logistic regressions, where moderators can affect each
    observation's probability of group membership and a sparsity-inducing
    prior fuses together levels of each factor while respecting
    ANOVA-style sum-to-zero constraints. Goplerud, Imai, and Pashley
    (2024) <doi:10.48550/ARXIV.2201.01357> provide further details.
Used for the design and analysis of a 2x2 factorial trial for
    a time-to-event endpoint.  It performs power calculations and significance
    testing as well as providing estimates of the relevant hazard ratios and the 
    corresponding 95% confidence intervals.  Important reference papers include
    Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609>
    Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507>
    Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020)
    <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.
Functions to estimate a factor model using discrete and continuous proxy variables. The function 'dproxyme' estimates a factor model of discrete proxy variables using an EM algorithm (Dempster, Laird, Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>; Hu (2008) <doi:10.1016/j.jeconom.2007.12.001>; Hu(2017) <doi:10.1016/j.jeconom.2017.06.002> ). The function 'cproxyme' estimates a linear factor model (Cunha, Heckman, and Schennach (2010) <doi:10.3982/ECTA6551>).
The tools herein calculate, print, summarize and plot pairwise differences that result from generalized linear models, general linear hypothesis tests and multinomial logistic regression models. For more information, see Armstrong (2013) <doi:10.32614/RJ-2013-021>.
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Function factories are functions that make functions. They can be 
    confusing to construct. Straightforward techniques can produce functions 
    that are fragile or hard to understand. While more robust techniques exist 
    to construct function factories, those techniques can be confusing. This 
    package is designed to make it easier to construct function factories.
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
Multi-environment genomic prediction for training and test 
    environments using penalized factorial regression. Predictions are made 
    using genotype-specific environmental sensitivities as in Millet et al. 
    (2019) <doi:10.1038/s41588-019-0414-y>.
Allow clients to fetch 'analytics' through API for Portfolio 
    'Analytics'('PA'), Style Performance Risk('SPAR') and 'Vault' products of 
    'FactSet'. Visit 
    <https://github.com/factset/analyticsapi-engines-r-sdk/tree/master/Engines>
    for more information on the usage of package. Visit 
    <https://developer.factset.com/> for more information on products.
Generates 'RProtobuf' classes for 'FactSet' 'STACH V2' tabular 
    format which represents complex multi-dimensional array of data. These 
    classes help in the 'serialization' and 'deserialization' of 'STACH V2' 
    formatted data. See 'GitHub' repository documentation for more 
    information.
Allow clients to convert 'FactSet' 'STACH' formatted data to simpler 
    tabular formats in the form of data frames. This package also provides helper
    methods to extract the meta data from 'FactSet' 'STACH' formatted
    data. See documentation on the 'GitHub' repository for more information.
Compute maximum likelihood estimators of parameters in a Gaussian factor model using
  the the matrix-free methodology described in Dai et al. (2020) <doi:10.1080/10618600.2019.1704296>.
  In contrast to the factanal() function from 'stats' package, fad() can handle high-dimensional datasets where
  number of variables exceed the sample size and is also substantially faster than the EM algorithms.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a  
   factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
    (see Friedman et al. (2010)), sparse linear discriminant analysis (see
    Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
    analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Probability distributions that are sometimes useful in hydrology.
An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).
An easy framework to read FDA Adverse Event Reporting System XML/ASCII files <https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-latest-quarterly-data-files>.
This Shiny application offers researchers a comprehensive tool for performing factor analysis. Users can upload datasets, validate assumptions, manage missing and outlier data, split data for different analyses, and run exploratory and confirmatory factor analyses ("EFA" and "CFA"). The software also offers reliability analysis, exploratory graph analysis, and item weighting. With a user-friendly interface, this tool simplifies the EFA and CFA processes. The main features are data submission and simple data inspection. Data manipulation (excluding variables, splitting data, checking for outliers), assumption checking (Tabachnik & Fidell (2012) <ISBN:978-0-205-84957-4> and Field (2009) <ISBN:978-1-84787-906-6>) for factor analysis, exploratory factor analysis (with various factor number determination methods (Lorenzo-Seva & Ferrando (2021) <doi:10.5964/meth.7185>)), confirmatory factor analysis (model definition and modification suggestions (Kline (2011) <ISBN:978-1-60623-877-6>)), reliability analysis (Cronbach's alpha, McDonald's omega, Armor's theta, structural reliability, stratified alpha), item weighting (Kilic & Dogan (2019) <doi:10.21031/epod.516057>).
Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.
More comfortable interface to work with R data or source files
    in a key-value fashion.
An implementation of the fair data adaptation with quantile
    preservation described in Plecko & Meinshausen (JMLR 2020, 21(242), 1-44).
    The adaptation procedure uses the specified causal graph to pre-process the
    given training and testing data in such a way to remove the bias caused by
    the protected attribute. The procedure uses tree ensembles for quantile
    regression. Instructions for using the methods are further elaborated in 
    the corresponding JSS manuscript, see <doi:10.18637/jss.v110.i04>.
Translates several CSV files with ontological terms and corresponding
    data into RDF triples. These RDF triples are stored in OWL and JSON-LD files,
    facilitating data accessibility, interoperability, and knowledge unification.
    The triples are also visualized in a graph saved as an SVG. The input CSVs
    must be formatted with a template from a public Google Sheet; see README or
    vignette for more information. This is a tool is used by the SDLE Research
    Center at Case Western Reserve University to create and visualize material
    science ontologies, and it includes example ontologies to demonstrate its
    capabilities. This work was supported by the U.S. Department of Energy’s
    Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy
    Technologies Office (SETO) Agreement Numbers E-EE0009353 and DE-EE0009347,
    Department of Energy (National Nuclear Security Administration) under Award
    Number DE-NA0004104 and Contract number B647887, and U.S. National Science
    Foundation Award under Award Number 2133576.
Clustering for categorical and mixed-type of data, to preventing classification biases due to race, 
            gender or others sensitive attributes.
            This algorithm is an extension of the methodology proposed by "Santos & Heras (2020) <doi:10.28945/4643>".
Fair machine learning regression models which take sensitive attributes into account in
  model estimation. Currently implementing Komiyama et al. (2018) 
  <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>, Zafar et al.
  (2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own
  approach from Scutari, Panero and Proissl (2022)
  <https://link.springer.com/content/pdf/10.1007/s11222-022-10143-w.pdf>
  that uses ridge regression to enforce fairness.
Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.
Offers calculation, visualization and comparison of algorithmic fairness metrics. Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) <doi:10.1007/s10618-010-0190-x>, Chouldechova (2017) <doi:10.1089/big.2016.0047>, Feldman et al. (2015) <doi:10.1145/2783258.2783311> , Friedler et al. (2018) <doi:10.1145/3287560.3287589> and Zafar et al. (2017) <doi:10.1145/3038912.3052660>. The package also offers convenient visualizations to help understand fairness metrics.
Allows user to obtain subsets of columns of data or vectors within a list.  These subsets will match the original data in terms of average and variation, but have a consistent length of data per column.  It is intended for use on automated data generation which may not always output the same N per replicate or sample.
This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) <arXiv:2106.02521>).
Use R as a minimal build system. This might come in
    handy if you are developing R packages and can not use a proper build
    system. Stay away if you can (use a proper build system).
Create fake datasets that can be used for prototyping and teaching.
    This package provides a set of functions to generate fake data
    for a variety of data types, such as dates, addresses,
    and names. It can be used for prototyping (notably in 'shiny') or as a tool
    to teach data manipulation and data visualization.
A set of function for clustering data observation with hybrid method Fuzzy ART and K-Means 
            by Sengupta, Ghosh & Dan (2011) <doi:10.1080/0951192X.2011.602362>.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data.  Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.  The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.
Fatty acid metabolic analysis aimed to the estimation of FA import (I), de novo synthesis (S), fractional contribution of the 13C-tracers (D0, D1, D2), elongation (E) and desaturation (Des) based on mass isotopologue data.
Simulates age-at-onset traits associated with a segregating major gene in family data 
     obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment 
     correction is utilized to estimate age-dependent penetrance functions either parametrically from 
     the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm 
     can infer missing genotypes and carrier probabilities estimated from family's genotype and
     phenotype information or from a fitted model. Plot functions include pedigrees of simulated 
     families and predicted penetrance curves based on specified parameter values.
     For more information see Choi, Y.-H., Briollais, L., He, W. and Kopciuk, K. (2021) FamEvent: An 
     R Package for Generating and Modeling Time-to-Event Data in Family Designs, 
     Journal of Statistical Software 97 (7), 1-30.
Provides functionality for testing familial hypotheses. Supports testing centers 
    belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and 
    two-sample tests are supported, as are directional tests. Methods for visualizing output are 
    provided.
Single unified interface for end-to-end modelling of regression, 
    categorical and time-to-event (survival) outcomes. Models created using
    familiar are self-containing, and their use does not require additional
    information such as baseline survival, feature clustering, or feature
    transformation and normalisation parameters. Model performance,
    calibration, risk group stratification, (permutation) variable importance,
    individual conditional expectation, partial dependence, and more, are
    assessed automatically as part of the evaluation process and exported in
    tabular format and plotted, and may also be computed manually using export
    and plot functions. Where possible, metrics and values obtained during the
    evaluation process come with confidence intervals.
An interface to the core 'Familias' functions which are
    programmed in C++. The implementation is described in Egeland, Mostad and Olaisen
    (1997) <doi:10.1016/S1355-0306(97)72202-0> and Simonsson and Mostad
    (2016) <doi:10.1016/j.fsigen.2016.04.005>.
Tools to study lineages, grandparenthood, loss of close relatives, kinship networks and other topics in multi-generation populations.
Grows families of features by selecting features that maximize a weighted score calculated from empirical feature scores and graphical knowledge. The final weighted score for a feature is determined by summing a feature's family-weighted scores across all families in which the feature appears.
Estimate parameters of univariate probability distributions 
  with maximum likelihood and Bayesian methods.
Given a set of parameters describing model dynamics and a corresponding cost function,
    FAMoS performs a dynamic forward-backward model selection on a specified selection
    criterion. It also applies a non-local swap search method. Works on any cost function. 
    For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.
FamSKAT-RC is a family-based association kernel test for both 
    rare and common variants. This test is general and several special cases 
    are known as other methods: famSKAT, which only focuses on rare variants 
    in family-based data, SKAT, which focuses on rare variants in 
    population-based data (unrelated individuals), and SKAT-RC, which focuses 
    on both rare and common variants in population-based data. When one 
    applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes 
    famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the 
    kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one 
    applies famSKAT-RC and set the kinship matrix (fullkins) to the identity 
    matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also 
    include a small sample synthetic pedigree to demonstrate the method with.  
    For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used.  This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
A collection of R functions to perform nonparametric 
    analysis of covariance for regression curves or surfaces. 
    Testing the equality or parallelism of nonparametric curves 
    or surfaces is equivalent to analysis of variance (ANOVA) or 
    analysis of covariance (ANCOVA) for one-sample functional data. 
    Three different testing methods are available in the package, 
    including one based on L-2 distance, one based on an ANOVA 
    statistic, and one based on variance estimators.
Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.
A neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is currently under peer review but an earlier draft is available in Dahl, Johnson, and Andros (2022+) <doi:10.48550/arXiv.2207.13824>.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the 'RStudio IDE' with 'RStudio' package 'manipulate' loaded. 'RStudio' is freely available (<https://rstudio.com/>), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only. For further information on the method see Fruth, J., Roustant, O., Kuhnt, S. (2014) <doi:10.1016/j.jspi.2013.11.007>.
Visualise sequential distributions using a range of plotting
    styles. Sequential distribution data can be input as either simulations or
    values corresponding to percentiles over time. Plots are added to
    existing graphic devices using the fan function. Users can choose from four
    different styles, including fan chart type plots, where a set of coloured
    polygon, with shadings corresponding to the percentile values are layered
    to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.
Counterparts to R string manipulation functions that account for
   the effects of ANSI text formatting control sequences.
Useful functions to translate text for multiple languages using online translators. 
    For example, by translating error messages and descriptive analysis results into a language familiar
    to the user, it enables a better understanding of the information, thereby reducing the barriers caused by language.
    It offers several helper functions to query gene information to help interpretation of interested genes (e.g., marker genes, differential expression genes),
    and provides utilities to translate 'ggplot' graphics. This package is not affiliated with any of the online translators. 
    The developers do not take responsibility for the invoice it incurs when using this package, especially for exceeding the free quota.
Calculation of Evapotranspiration by FAO Penman-Monteith equation based on Allen, R. G., Pereira, L. S., Raes, D., Smith, M. (1998, ISBN:92-5-104219-5) "Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56".
Download Data from the FAOSTAT Database of the Food and Agricultural Organization (FAO) of the United Nations.
    A list of functions to download statistics from FAOSTAT (database of the FAO <https://www.fao.org/faostat/>) 
    and WDI (database of the World Bank <https://data.worldbank.org/>), and to perform some harmonization operations.
Tools for detecting and summarize influential cases that
    can affect exploratory and confirmatory factor analysis models as well as
    structural equation models more generally (Chalmers, 2015, <doi:10.1177/0146621615597894>; 
    Flora, D. B., LaBrish, C. & Chalmers, R. P., 2012, <doi:10.3389/fpsyg.2012.00055>).
Create Frequently Asked Questions page for Shiny application.
Modelizations and previsions functions for
   Functional AutoRegressive processes using
   nonparametric methods: functional kernel,
   estimation of the covariance operator in
   a subspace, ...
Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).
Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.
Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation
    File Format) files are like 'CSV' files, with a little bit of added
    meta information in a header and standardized NA values. They are
    quite often used for machine learning data sets and were introduced
    for the 'WEKA' machine learning 'Java' toolbox. See
    <https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/>
    for further info on 'ARFF' and for
    <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'.
    'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and
    it is at least a faster reader (for bigger files). It uses 'readr' as
    parser back-end for the data section of the 'ARFF' file. Consistency
    with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of
    'ARFF' files from 'OpenML'.
Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.
Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>.
             Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. 
             Extensions to two-sample simultaneous mean comparison problems are also included. 
             As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.
Handy functions and data to support a course book for accounting research. 
    Gow, Ian and Tongqing Ding (2022) 'Accounting Research: An Introductory Course' <https://iangow.github.io/far_book/>.
Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, 
    select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. 
    When the model is executed a set of results are displayed which include efficiency scores, peers' determination, scale efficiencies' evaluation 
    and slacks' calculation. Fore more information about the theoretical background of the package, 
    please refer to Bogetoft & Otto (2011) <doi:10.1007/978-1-4419-7961-2>.
The encoding of colour can be handled in many different ways,
    using different colour spaces. As different colour spaces have
    different uses, efficient conversion between these representations are
    important. The 'farver' package provides a set of functions that gives
    access to very fast colour space conversion and comparisons
    implemented in C++, and offers speed improvements over the
    'convertColor' function in the 'grDevices' package.
The 'FAS' package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) <https://www.jmlr.org/papers/volume22/20-539/20-539.pdf> and Beyhum & Striaukas (2023) <arXiv:2307.13364>.
An implementation of the two-sample multivariate
    Kolmogorov-Smirnov test described by Fasano and Franceschini (1987)
    <doi:10.1093/mnras/225.1.155>. This test evaluates the null hypothesis
    that two i.i.d. random samples were drawn from the same underlying
    probability distribution. The data can be of any dimension, and can be
    of any type (continuous, discrete, or mixed).
Latent process embedding for functional network data with the Functional Adjacency Spectral Embedding. Fits smooth latent processes based on cubic spline bases. Also generates functional network data from three models, and evaluates a network generalized cross-validation criterion for dimension selection. For more information, see MacDonald, Zhu and Levina (2022+) <arXiv:2210.07491>.
It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.
A collection of functions to manage, to investigate and to analyze data sets of financial 
  assets from different points of view.
The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various functions to tidy and screen daily stream discharge data, calculate and visualize various summary statistics and metrics, and compute annual trending and volume frequency analyses. 
     It features useful function arguments for filtering of and handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).
R 'shiny' app to perform data analysis and visualization for the
    Fully Automated Senescence Test (FAST) workflow.
A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in
    Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> 
    and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.
Implements the AdaptiveImpute matrix completion
    algorithm of 'Intelligent Initialization and Adaptive Thresholding for
    Iterative Matrix Completion',
    <https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2018.1518238>.
    AdaptiveImpute is useful for embedding sparsely observed matrices,
    often out performs competing matrix completion algorithms, and
    self-tunes its hyperparameter, making usage easy.
Fast censored linear regression for the accelerated failure time (AFT) model of Huang (2013) <doi:10.1111/sjos.12031>.
The 'fastai' <https://docs.fast.ai/index.html> library 
             simplifies training fast and accurate neural networks 
             using modern best practices. It is based on research 
             in to deep learning best practices undertaken 
             at 'fast.ai', including 'out of the box' support
             for vision, text, tabular, audio, time series, and 
             collaborative filtering models.
Performs backward elimination with similar syntax to 
    the stepAIC() function from the 'MASS' package. A bounding algorithm is 
    used to avoid fitting unnecessary models, making it much faster.
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
A fast method for approximating time-varying infectious disease
	transmission rates from disease incidence time series and other
	data, based on a discrete time approximation of an SEIR model, as
	analyzed in Jagan et al. (2020) <doi:10.1371/journal.pcbi.1008124>.
This is a two-in-one package which provides interfaces to
        both R and 'Python'. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the 'SciPy' package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the 'Python' files, see the file INSTALL in the source distribution.
        Based on the present package, Christoph Dalitz also wrote a pure 'C++'
        interface to 'fastcluster':
        <https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/>.
A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net <doi: 10.32614/RJ-2021-010>.
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) DOI: <doi:10.4310/SII.2013.v6.n2.a1>.
Implements fast change point detection algorithm based on the
    paper "Sequential Gradient Descent and Quasi-Newton's Method for
    Change-Point Analysis" by Xianyang Zhang, Trisha Dawn
    <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is
    based on dynamic programming with pruning and sequential gradient
    descent. It is able to detect change points a magnitude faster than
    the vanilla Pruned Exact Linear Time(PELT). The package includes
    examples of linear regression, logistic regression, Poisson
    regression, penalized linear regression data, and whole lot more
    examples with custom cost function in case the user wants to use their
    own cost function.
For ordinal rating data, consider the accelerated EM algorithm to estimate and test models within the family of
    CUB models (where CUB stands for Combination of a 
	discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis' identity for the observed information matrix. Best-subset variable selection is then implemented since it becomes more feasible from the computational point of view.
A fast and flexible implementation of Callaway and Sant'Anna's (2021)<doi:10.1016/j.jeconom.2020.12.001> staggered Difference-in-Differences (DiD) estimators, 'fastdid' reduces the computation time from hours to seconds, and incorporates extensions such as time-varying covariates and multiple events.
Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)(<doi:10.1214/009053604000000067 >) and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library.
Provides a drop-in replacement for rasterize() from the 'raster'
   package that takes polygon vector or data frame objects, and is much faster. 
   There is support for the main options provided by the rasterize() function, 
   including setting the field used and background value, and options for 
   aggregating multi-layer rasters. Uses the scan line algorithm attributed to
   Wylie et al. (1967) <doi:10.1145/1465611.1465619>.
Processing of large-in-memory/large-on disk rasters and spatial
	vectors using 'GRASS GIS' <https://grass.osgeo.org/>. Most functions in
	the 'terra' package are	recreated. Processing of medium-sized and smaller
	spatial objects will nearly always be faster using 'terra' or 'sf', but
	for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster.
	To use most of the functions, you must have the stand-alone version (not
	the 'OSGeoW4' installer version) of 'GRASS GIS' 8.0 or higher.
Implementation of the fast univariate inference approach (Cui et al. (2022) <doi:10.1080/10618600.2021.1950006>, Loewinger et al. (2023) <doi:10.1101/2023.11.06.565896>) for fitting functional mixed models.
Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes.  See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.
Fast, numerically-stable Gauss-Hermite quadrature rules and
    utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A.
    (1994) <doi:10.2307/2337136> for a reference on these methods.
Two 'Gray Level Co-occurrence Matrix' ('GLCM') implementations are included: The first is a fast 'GLCM' feature texture computation based on 'Python' 'Numpy' arrays ('Github' Repository, <https://github.com/tzm030329/GLCM>). The second is a fast 'GLCM' 'RcppArmadillo' implementation which is parallelized (using 'OpenMP') with the option to return all 'GLCM' features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, <doi:10.1109/ICIP.2019.8803652>.
Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares 
    implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard
    against convergence issues.
Implements fast, scalable optimization algorithms for
    fitting generalized principal components analysis (GLM-PCA) models,
    as described in "A Generalization of Principal Components
    Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE
    (2002, ISBN:9780262271738), and subsequently "Feature Selection 
    and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial
    Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019)
    <doi:10.1186/s13059-019-1861-6>.
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
Implementation of FastICA algorithm to perform Independent
        Component Analysis (ICA) and Projection Pursuit.
TrainFastImputation() uses training data to describe a
    multivariate normal distribution that the data approximates or
    can be transformed into approximating and stores this information
    as an object of class 'FastImputationPatterns'. FastImputation()
    function uses this 'FastImputationPatterns' object to impute (make
    a good guess at) missing data in a single line or a whole data frame
    of data.  This approximates the process used by 'Amelia'
    <https://gking.harvard.edu/amelia> but is much faster when
    filling in values for a single line of data.
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. 
             The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal 
             outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model 
             is estimated using an Expectation Maximization algorithm.
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
A computationally efficient and statistically rigorous fast 
    Kernel Machine method for multi-kernel analysis. The approach is based on 
    a low-rank approximation to the nuisance effect kernel matrices. The 
    algorithm is applicable to continuous, binary, and survival traits and 
    is implemented using the existing single-kernel analysis software 'SKAT' 
    and 'coxKM'. 'coxKM' can be obtained from 
    <https://github.com/lin-lab/coxKM>.
R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 <doi:10.1007/978-3-030-32047-8_16>.
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.
An efficient algorithm to fit and tune kernel quantile regression models based on the majorization-minimization (MM) method. It can also fit multiple quantile curves simultaneously without crossing.
Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data
    and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two 
    datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, 
    tools for preparing, adjusting, and summarizing data merges are included. The package implements methods 
    described in Enamorado, Fifield, and Imai (2019) ''Using a Probabilistic Model to Assist Merging of 
    Large-scale Administrative Records'' <doi:10.1017/S0003055418000783> and is available 
    at <https://imai.fas.harvard.edu/research/linkage.html>.
Efficient computation of the Liu regression coefficient paths, Liu-related statistics and 
    information criteria for a grid of the regularization parameter. 
    The computations are based on the 'C++' library 'Armadillo' through the 'R' package 'Rcpp'.
Provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the 'RcppNumerical' package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference.
Calculates marginal effects based on logistic model objects such as 'glm' or 'speedglm' at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.
Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.
Fast implementation of data structures, including a key-value
    store, stack, and queue. Environments are commonly used as key-value stores
    in R, but every time a new key is used, it is added to R's global symbol
    table, causing a small amount of memory leakage. This can be problematic in
    cases where many different keys are used. Fastmap avoids this memory leak
    issue by implementing the map using data structures in C++.
Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.
Small set of functions to fast computation of some matrices and operations
  useful in statistics and econometrics. Currently, there are functions for efficient
  computation of duplication, commutation and symmetrizer matrices with minimal storage
  requirements. Some commonly used matrix decompositions (LU and LDL), basic matrix
  operations (for instance, Hadamard, Kronecker products and the Sherman-Morrison formula)
  and iterative solvers for linear systems are also available. In addition, the package
  includes a number of common statistical procedures such as the sweep operator, weighted
  mean and covariance matrix using an online algorithm, linear regression (using Cholesky,
  QR, SVD, sweep operator and conjugate gradients methods), ridge regression (with optimal
  selection of the ridge parameter considering several procedures), omnibus tests for 
  univariate normality, functions to compute the multivariate skewness, kurtosis, the 
  Mahalanobis distance (checking the positive defineteness), and the Wilson-Hilferty 
  transformation of gamma variables. Furthermore, the package provides interfaces 
  to C code callable by another C code from other R packages.
A mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138> to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) <doi:10.1093/biomet/asz024>); furthermore, we extend it to tackle datasets in metric spaces.
Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing 
            comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. 
            It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.
This is an extremely fast implementation of a Naive Bayes classifier. This 
    package is currently the only package that supports a Bernoulli distribution, a Multinomial 
    distribution, and a Gaussian distribution, making it suitable for both binary features, 
    frequency counts, and numerical features. Another feature is the support of a mix of 
    different event models. Only numerical variables are allowed, however, categorical variables 
    can be transformed into dummies and used with the Bernoulli distribution. 
    The implementation is largely based on the paper 
    "A comparison of event models for Naive Bayes anti-spam e-mail filtering" 
    written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be 
    submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
We present an implementation of the algorithms required to simulate
  large-scale social networks and retrieve their most relevant metrics. Details 
  can be found in the accompanying scientific paper on the Journal 
  of Statistical Software, <doi:10.18637/jss.v096.i07>.
Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.
The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.
An implementation in 'Rcpp' / 'RcppArmadillo' of Partial Least Square algorithms. This package includes other functions to perform the double cross-validation and a fast correlation.
A full set of fast data manipulation tools with a tidy
    front-end and a fast back-end using 'collapse' and 'cheapr'.
Read and write PNG images with arrays, rasters, native
    rasters, numeric arrays, integer arrays, raw vectors and indexed
    values.  This PNG encoder exposes configurable internal options
    enabling the user to select a speed-size tradeoff.  For example,
    disabling compression can speed up writing PNG by a factor of 50.
    Multiple image formats are supported including raster, native rasters,
    and integer and numeric arrays at color depths of 1, 2, 3 or 4. 16-bit
    images are also supported. This implementation uses the 'libspng' 'C'
    library which is available from
    <https://github.com/randy408/libspng/>.
Finds the critical sample size ("critical point of stability") for a 
    correlation to stabilize in Schoenbrodt and Perugini's definition of 
    sequential stability (see <doi:10.1016/j.jrp.2013.05.009>).
'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.
New and faster implementations for quantile quantile plots. 
    The package also includes a function to prune data for quantile quantile 
    plots. This can drastically reduce the running time for large samples, 
    for 100 million samples, you can expect a factor 80X speedup.
Data sets and utilities to accompany the second edition of
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2017), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet() function.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Provides  templates for reports in 'rmarkdown' and 
     functions to create tables and summaries of data.
Provides hardware-accelerated tools for performing rerandomization
    and randomization testing in experimental research. Using a 'JAX' backend, the
    package enables exact rerandomization inference even for large experiments
    with hundreds of billions of possible randomizations. Key functionalities
    include generating pools of acceptable rerandomizations based on covariate
    balance, conducting exact randomization tests, and performing pre-analysis
    evaluations to determine optimal rerandomization acceptance thresholds. The
    package supports various hardware acceleration frameworks including 'CPU',
    'CUDA', and 'METAL', making it versatile across accelerated computing environments. This
    allows researchers to efficiently implement stringent rerandomization designs and
    conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) <doi:10.48550/arXiv.2310.00861>.
A framework for predicting retention times in liquid
    chromatography. Users can train custom models for specific
    chromatography columns, predict retention times using existing models,
    or adjust existing models to account for altered experimental
    conditions. The provided functionalities can be accessed either via
    the R console or via a graphical user interface. Related work: Bonini
    et al. (2020) <doi:10.1021/acs.analchem.9b05765>.
Samples generalized random product graphs, a generalization of
    a broad class of network models. Given matrices X, S, and Y with with
    non-negative entries, samples a matrix with expectation X S Y^T and
    independent Poisson or Bernoulli entries using the fastRG algorithm of
    Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The
    algorithm first samples the number of edges and then puts them down
    one-by-one.  As a result it is O(m) where m is the number of edges, a
    dramatic improvement over element-wise algorithms that which require
    O(n^2) operations to sample a random graph, where n is the number of
    nodes.
A utility to scrape and load play-by-play data
    and statistics from the Premier Hockey Federation (PHF) <https://www.premierhockeyfederation.com/>, formerly
    known as the National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's
    stats API <https://www.nhl.com/>.
A data package that hosts all models for the
    'nflfastR' package.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.
Computes fast (relative to other implementations) approximate 
    Shapley values for any supervised learning model. Shapley values help to 
    explain the predictions from any black box model using ideas from game 
    theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> 
    for details.
Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.
It implements an improved and computationally faster version 
            of the original Stepwise Gaussian Graphical Algorithm for estimating 
            the Omega precision matrix from high-dimensional data. 
            Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) 
            <doi:10.52933/jdssv.v1i2.11>.
An interface to the 'fastText' <https://github.com/facebookresearch/fastText> library for efficient learning of word representations and sentence classification. The 'fastText' algorithm is explained in detail in (i) "Enriching Word Vectors with subword Information", Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, 2017, <doi:10.1162/tacl_a_00051>; (ii) "Bag of Tricks for Efficient Text Classification", Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov, 2017, <doi:10.18653/v1/e17-2068>; (iii) "FastText.zip: Compressing text classification models", Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, Tomas Mikolov, 2016, <arXiv:1612.03651>.
An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	An example how to use 'fastTextR' can be found in the 'README' file.
Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.
Implements fast, scalable optimization algorithms for
    fitting topic models ("grade of membership" models) and
    non-negative matrix factorizations to count data. The methods
    exploit the special relationship between the multinomial topic
    model (also, "probabilistic latent semantic indexing") and Poisson
    non-negative matrix factorization. The package provides tools to
    compare, annotate and visualize model fits, including functions to
    efficiently create "structure plots" and identify key features in
    topics. The 'fastTopics' package is a successor to the
    'CountClust' package. For more information, see
    <doi:10.48550/arXiv.2105.13440> and
    <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub
    repository for additional vignettes not included in the package on
    CRAN.
An implementation of sparsity-ranked lasso and related methods 
    for time series data. This methodology is especially useful for 
    large time series with exogenous features and/or complex 
    seasonality. Originally described in Peterson and Cavanaugh 
    (2022) <doi:10.1007/s10182-021-00431-7> in the context of variable 
    selection with interactions and/or polynomials, ranked sparsity is 
    a philosophy with methods useful for variable selection in the 
    presence of prior informational asymmetry. This situation exists for time 
    series data with complex seasonality, as shown in Peterson and Cavanaugh 
    (2024) <doi:10.1177/1471082X231225307>, which also describes this package
    in greater detail. The sparsity-ranked penalization methods for time series
    implemented in 'fastTS' can fit large/complex/high-frequency time series
    quickly, even with a high-dimensional exogenous feature set. The method is
    considerably faster than its competitors, while often producing more 
    accurate predictions. Also included is a long hourly series of arrivals 
    into the University of Iowa Emergency Department with concurrent local 
    temperature.
A wide variety of tools for general data analysis, wrangling, spelling, statistics, visualizations, package development, and more. All functions have vectorized implementations whenever possible. Exported names are designed to be readable, with longer names possessing short aliases.
Easy installation, loading and management, of high-performance packages 
             for statistical computing and data manipulation in R. 
             The core 'fastverse' consists of 4 packages: 'data.table', 'collapse', 
             'kit' and 'magrittr', that jointly only depend on 'Rcpp'. 
             The 'fastverse' can be freely and permanently extended with 
             additional packages, both globally or for individual projects. 
             Separate package verses can also be created. Fast packages 
             for many common tasks such as time series, dates and times, strings, 
             spatial data, statistics, data serialization, larger-than-memory 
             processing, and compilation of R code are listed in the README file: 
             <https://github.com/fastverse/fastverse#suggested-extensions>.
A fast 'Rcpp'-based implementation of polynomially-computable
    voting theory methods for committee ranking and scoring. The package
    includes methods such as Approval Voting (AV), Satisfaction Approval
    Voting (SAV), sequential Proportional Approval Voting (PAV), and
    sequential Phragmen's Rule. Weighted variants of these methods are
    also provided, allowing for differential voter influence.
A lightweight package to compute Maximal Overlap Discrete Wavelet 
    Transform (MODWT) and À Trous Discrete Wavelet Transform by leveraging the 
    power of 'Rcpp' to make these operations fast. This package was designed for use in forecasting, and
    allows users avoid the inclusion of future data when performing wavelet decomposition of time series.
    See Quilty and Adamowski (2018) <doi:10.1016/j.jhydrol.2018.05.003>.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
             defined by some design matrix.
Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Kiener distributions K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.
Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.
Provides a replacement for dplyr::na_if().  Allows you to
    specify multiple values to be replaced with NA using a single
    function.
HTTP error helpers. Methods included for general purpose HTTP 
    error handling, as well as individual methods for every HTTP status
    code, both via status code numbers as well as their descriptive names.
    Supports ability to adjust behavior to stop, message or warning.
    Includes ability to use custom whisker template to have any configuration
    of status code, short description, and verbose message. Currently 
    supports integration with 'crul', 'curl', and 'httr'.
Implements the statistic FAVA, an Fst-based Assessment of Variability across 
  vectors of relative Abundances, as well as a suite of helper functions which enable the 
  visualization and statistical analysis of relative abundance data. The 'FAVA' R package 
  accompanies the paper, “Quantifying compositional variability in microbial communities
  with FAVA” by Morrison, Xue, and Rosenberg (2024) <doi:10.1101/2024.07.03.601929>.
Estimate a FAVAR model by a Bayesian method, based on Bernanke et al. (2005) <DOI:10.1162/0033553053327452>.
Easily use 'Font Awesome' icons as 'shiny' 'favicons' (the
    icons that appear on browser tabs).  'Font Awesome' 
    (<https://fontawesome.com/>) is a popular set of icons that can be used in 
    web pages.  'favawesome' provides a simple way to use these icons as 
    'favicons' in 'shiny' applications and other HTML pages.
Finds the URL to the 'favicon' for a website. This is useful if you
  want to display the 'favicon' in an HTML document or web application,
  especially if the website is behind a firewall.
A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos
             (http://pages.bloomsbury.com/favouritenumber).
Provides functions and datasets from the book "Forest Analytics with R".
A toolkit for Flux Balance Analysis and related
    metabolic modeling techniques. Functions are provided for: parsing
    models in tabular format, converting parsed metabolic models to input
    formats for common linear programming solvers, and
    evaluating and applying gene-protein-reaction mappings. In addition, there
    are wrappers to parse a model, select a solver, find the metabolic fluxes,
    and return the results applied to the original model. Compared to other
    packages in this field, this package puts a much heavier focus on
    providing reusable components that can be used in the design of new
    implementation of new techniques, in particular those that involve large
    parameter sweeps. For a background on the theory, see What is Flux Balance 
    Analysis <doi:10.1038/nbt.1614>.
Provides a collection of functions to 
    explore and to investigate basic properties of financial returns 
    and related quantities.
    The covered fields include techniques of explorative data analysis
    and the investigation of distributional properties, including
    parameter estimation and hypothesis testing. Even more there are
    several utility functions for data handling and management.
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
Performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. These trial designs extend the Continual Reassessment Method (CRM) and Bayesian Model Averaging Continual Reassessment Method (BMA-CRM) by allowing the prior toxicity skeleton itself to be random, with posterior distributions obtained from Markov Chain Monte Carlo. On average, the FBCRM and MFBCRM methods outperformed the CRM and BMA-CRM methods in terms of selecting an optimal dose level across thousands of randomly generated simulation scenarios. Details on the methods and results of this simulation study are available on request, and the manuscript is currently under review.
We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
Fit a fractional binomial regression model and
 extended zero-inflated negative binomial regression 
 model to count data with excess zeros using maximum
 likelihood estimation.
 Compare zero-inflated regression models via Vuong 
 closeness test.
Implements MJMCMC (mode jumping MCMC) described in Hubin and Storvik (2018) <doi:10.1016/j.csda.2018.05.020> and GMJMCMC (genetically modified MJMCMC) described in Hubin et al. (2021) <doi:10.1613/jair.1.13047> algorithms as well as the subsampling counterpart described in Lachmann et al. (2022) <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.
Normalizes the data from a file containing the raw values of the SNP probes of microarray data by using the FISH probes and their corresponding copy number.
Open-source package for computing likelihood ratios in kinship testing and human identification cases. It has the core function of the software GENis, developed by Fundación Sadosky. It relies on a Bayesian Networks framework and is particularly well suited to efficiently perform large-size queries against databases of missing individuals.
It implements the Nelson-Siegel and the Nelson-Siegel-Svensson
	term structures.
Wrapper functions around the Facebook Marketing 'API' to create,
    read, update and delete custom audiences, images, campaigns, ad sets, ads and
    related content.
Implements a very fast C++ algorithm to quickly bootstrap receiver
    operating characteristics (ROC) curves and derived performance metrics,
    including the area under the curve (AUC) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of one second.
Provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available.
Provides a streamlined, standard evaluation-based approach to multivariate function composition. Allows for chaining commands via a forward-pipe operator, %>%.
Perform various floating catchment area methods to calculate a
    spatial accessibility index (SPAI) for demand point data. The distance
    matrix used for weighting is normalized in a preprocessing step using
    common functions (gaussian, gravity, exponential or logistic).
Provides tools to perform fuzzy formal concept
    analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23>
    and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>.  It
    provides functions to load and save a formal context, extract its
    concept lattice and implications.  In addition, one can use the
    implications to compute semantic closures of fuzzy sets and, thus,
    build recommendation systems.
Provides support for building Feldman-Cousins confidence intervals 
	[G. J. Feldman and R. D. Cousins (1998) <doi:10.1103/PhysRevD.57.3873>].
Provides functions to implement the Flexible cFDR (Hutchinson et al. (2021) <doi:10.1371/journal.pgen.1009853>) and Binary cFDR (Hutchinson et al. (2021) <doi:10.1101/2021.10.21.465274>) methodologies to leverage auxiliary data from arbitrary distributions, for example functional genomic data, with GWAS p-values to generate re-weighted p-values.
Bayesian estimation of forced choice models in Item Response Theory using 'rstan' (See Stan Development Team (2020) <https://mc-stan.org/>).
A financial calculator that provides very fast implementations
    of common financial indicators using 'Rust' code. It includes functions for
    bond-related indicators, such as yield to maturity ('YTM'), modified duration,
    and Macaulay duration, as well as functions for calculating time-weighted
    and money-weighted rates of return (using 'Modified Dietz' method) for multiple portfolios,
    given their market values and profit and loss ('PnL') data. 'fcl' is designed
    to be efficient and accurate for financial analysis and computation. The methods
    used in this package are based on the following references:
    <https://en.wikipedia.org/wiki/Modified_Dietz_method>,
    <https://en.wikipedia.org/wiki/Time-weighted_return>.
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
Provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the 'fcm' package returns a data frame of the concepts' values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.
Provides several functions to create and manipulate fuzzy
    cognitive maps. It is based on 'FCMapper' for Excel, distributed at <http://
    www.fcmappers.net/joomla/>, developed by Michael Bachhofer and Martin Wildenberg.
    Maps are inputted as adjacency matrices. Attributes of the maps and the
    equilibrium values of the concepts (including with user-defined constrained
    values) can be calculated. The maps can be graphed with a function that calls
    'igraph'. Multiple maps with shared concepts can be aggregated.
A toolbox to derive flexible cutoffs for fit indices in 'Covariance-based Structural Equation Modeling' based on the paper by 'Niemand & Mai (2018)' <doi:10.1007/s11747-018-0602-9>. Flexible cutoffs are an alternative to fixed cutoffs - rules-of-thumb - regarding an appropriate cutoff for fit indices such as 'CFI' or 'SRMR'. It has been demonstrated that these flexible cutoffs perform better than fixed cutoffs in grey areas where misspecification is not easy to detect. The package provides an alternative to the tool at <https://flexiblecutoffs.org> as it allows to tailor flexible cutoffs to a given dataset and model, which is so far not available in the tool. The package simulates fit indices based on a given dataset and model and then estimates the flexible cutoffs. Some useful functions, e.g., to determine the 'GoF-' or 'BoF-nature' of a fit index, are provided. So far, additional options for a relative use (is a model better than another?) are provided in an exploratory manner.
Provides a  collection of functions to 
	manage, to investigate and to analyze bivariate financial returns by  
	Copulae. Included are the families of Archemedean, Elliptical, 
	Extreme Value, and Empirical Copulae.
A function composition operator to chain a series of calls into a single function, mimicking the math notion of (f o g o h)(x) = h(g(f(x))). Inspired by 'pipeOp' ('|>') since R4.1 and 'magrittr pipe' ('%>%'), the operator build a pipe without putting data through, which is best for anonymous function accepted by utilities such as apply() and lapply().
Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.
Dynamic prediction in functional concurrent regression with an application to child growth. Extends the pffr() function from the 'refund' package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured.  Leroux et al. (2017), Statistics in Medicine, <doi:10.1002/sim.7582>.
A fold change rank based method is presented to search for genes with changing
             expression and to detect recurrent chromosomal copy number aberrations. This 
             method may be useful for high-throughput biological data (micro-array, sequencing, ...).
             Probabilities are associated with genes or probes in the data set and there is no
             problem of multiple tests when using this method. For array-based comparative genomic
             hybridization data, segmentation results are obtained by merging the significant
             probes detected.
Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages.
    A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$.
    First- and second-order derivatives are also included. These are the mathematically correct ones, no approximations applied.
    As of version 1.1, this package includes B-splines, Fourier bases and polynomials.
Provides a collection of utility functions for manipulating and analyzing factor vectors in R. It offers tools for filtering, splitting, combining, and reordering factor levels based on various criteria. The package is designed to simplify common tasks in categorical data analysis, making it easier to work with factors in a flexible and efficient manner.
Automatically suggests a correction when a typo occurs.
Estimation and inference using the Fractionally Cointegrated 
    Vector Autoregressive (VAR) model. It includes functions for model specification, 
    including lag selection and cointegration rank selection, as well as a comprehensive
    set of options for hypothesis testing, including tests of hypotheses on the 
    cointegrating relations, the adjustment coefficients and the fractional 
    differencing parameters. 
    An article describing the FCVAR model with examples is available on the Webpage 
    <https://sites.google.com/view/mortennielsen/software>.
Computes different multidimensional FD indices.  Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances.  Also contains other useful tools for functional ecology.
These functions were developed to support functional data
 analysis as described in Ramsay, J. O. and Silverman, B. W.
 (2005) Functional Data Analysis. New York: Springer and in 
 Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). 
 Functional Data Analysis with R and Matlab (Springer). 
 The package includes data sets and script files working many examples 
 including all but one of the 76 figures in this latter book.  Matlab versions 
 are available by ftp from 
 <https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
Quantify the serial correlation across lags of a given functional 
    time series using the autocorrelation function and a partial autocorrelation
    function for functional time series proposed in 
    Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>.
    The autocorrelation functions are based on the L2 norm of the lagged covariance 
    operators of the series. Functions are available for estimating the 
    distribution of the autocorrelation functions under the assumption 
    of strong functional white noise.
Implementations of the k-means, hierarchical agglomerative and 
    DBSCAN clustering methods for functional data which allows for jointly 
    aligning and clustering curves. It supports functional data defined on 
    one-dimensional domains but possibly evaluating in multivariate codomains. 
    It supports functional data defined in arrays but also via the 'fd' and 
    'funData' classes for functional data defined in the 'fda' and 'funData' 
    packages respectively. It currently supports shift, dilation and affine 
    warping functions for functional data defined on the real line and uses the 
    SRVF framework to handle boundary-preserving warping for functional data 
    defined on a specific interval. Main reference for the k-means algorithm: 
    Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment 
    for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for 
    the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) 
    "Generative models for functional data using phase and amplitude separation" 
    <doi:10.1016/j.csda.2012.12.001>.
Provides an implementation of concurrent or varying coefficient regression methods for 
    functional data. The implementations are done for both dense and sparsely observed functional
    data. Pointwise confidence bands can be constructed for each case. Further, the influence of
    past predictor values are modeled by a smooth history index function, 
    while the effects on the response are described by smooth varying coefficient functions, 
    which are very useful in analyzing real data such as COVID data.
    References: Yao, F., Müller, H.G., Wang, J.L. (2005) <doi:10.1214/009053605000000660>.
                Sentürk, D., Müller, H.G. (2010) <doi:10.1198/jasa.2010.tm09228>.
An implementation of the methodology described in
    Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional
    data analysis of samples of density functions.  Densities are first
    transformed to their corresponding log quantile densities, followed by
    ordinary Functional Principal Components Analysis (FPCA).  Transformation
    modes of variation yield improved interpretation of the variability in the
    data as compared to FPCA on the densities themselves.  The standard
    fraction of variance explained (FVE) criterion commonly used for functional
    data is adapted to the transformation setting, also allowing for an
    alternative quantification of variability for density data through the
    Wasserstein metric of optimal transport.
Likelihood based analysis of 1-dimension functional data
        in a mixed-effects model framework. Matrix computation are
        approximated by semi-explicit operator equivalents with linear
        computational complexity. Markussen (2013) <doi:10.3150/11-BEJ389>.
Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <arXiv:1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.
Performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) <doi:10.1007/s11749-010-0185-3>, Gorecki and Smaga (2015) <doi:10.1007/s00180-015-0555-0>, Gorecki and Smaga (2017) <doi:10.1080/02664763.2016.1247791>, Zhang et al. (2018) <doi:10.1016/j.csda.2018.05.004>).
A collection of functions for outlier detection in functional data analysis. 
  Methods implemented include directional outlyingness by 
  Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>,
  MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>,
  total variation depth and modified shape similarity index by 
  Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by
  Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection
  tools and depths for functional data like functional boxplot, (modified) band depth etc.,
  are also available.
A versatile package that provides implementation of various
    methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this
    package is Functional Principal Component Analysis (FPCA), a key technique for
    functional data analysis, for sparsely or densely sampled random trajectories
    and time courses, via the Principal Analysis by Conditional Estimation
    (PACE) algorithm. This core algorithm yields covariance and mean functions,
    eigenfunctions and principal component (scores), for both functional data and
    derivatives, for both dense (functional) and sparse (longitudinal) sampling designs.
    For sparse designs, it provides fitted continuous trajectories with confidence bands,
    even for subjects with very few longitudinal observations. PACE is a viable and
    flexible alternative to random effects modeling of longitudinal data. There is also a
    Matlab version (PACE) that contains some methods not available on fdapace and vice
    versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. 
    Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry).
    References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) <doi:10.1111/insr.12444> "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.
Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2022), "Integrated Depth for Partially Observed Functional Data". Journal of Computational and Graphical Statistics. <doi:10.1080/10618600.2022.2070171>.
Provides an implementation of two-dimensional functional principal component analysis (FPCA), Marginal FPCA, and Product FPCA for repeated functional data. Marginal and Product FPCA implementations are done for both dense and sparsely observed functional data. 
    References: Chen, K., Delicado, P., & Müller, H. G. (2017) <doi:10.1111/rssb.12160>.
                Chen, K., & Müller, H. G. (2012) <doi:10.1080/01621459.2012.734196>.
                Hall, P.,  Müller, H.G. and Wang, J.L. (2006) <doi:10.1214/009053606000000272>.
                Yao, F., Müller, H. G., & Wang, J. L. (2005) <doi:10.1198/016214504000001745>.
Provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques.
    See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.
Performs alignment, PCA, and modeling of multidimensional and 
    unidimensional functions using the square-root velocity framework 
    (Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014 
    <DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic 
    analysis of functional data through phase and amplitude separation.
Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
Regression models for functional data, i.e., scalar-on-function,
    function-on-scalar and function-on-function regression models, are fitted
    by a component-wise gradient boosting algorithm. 
	For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.
Provides the probability density function (PDF), cumulative
  distribution function (CDF), the first-order and second-order partial
  derivatives of the PDF, and a fitting function for the diffusion decision
  model (DDM; e.g.,
  Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial
  variability in the drift rate. Because the PDF, its partial derivatives, and
  the CDF of the DDM both contain an infinite sum, they need to be approximated.
  'fddm' implements all published approximations
  (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>;
  Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>;
  Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>;
  Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus
  new approximations. All approximations are implemented purely in 'C++'
  providing faster speed than existing packages.
Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) <https://eprints.soton.ac.uk/474982/1/Thesis_DamianosMichaelides_Final_pdfa_1_.pdf>.
Retrieves financial data from Federal Deposit Insurance Corporation
    (FDIC)-insured institutions and provides access to the FDIC data taxonomy.
Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".
Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.
Forest data quality is a package containing nine methods of analysis for forest databases,
    from databases containing inventory data and growth models, the focus of the analyzes is related to the
    quality of the data present in the database with a focus on consistency , punctuality and completeness of data.
FDR functions for permutation-based estimators, including pi0 as well as FDR
             confidence intervals. The confidence intervals account for dependencies between
             tests by the incorporation of an overdispersion parameter, which is estimated
             from the permuted data. Also included are options for an analog parametric approach.
It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.
The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.
Estimates both tail area-based false 
   discovery rates (Fdr) as well as local false discovery rates (fdr) for a 
   variety of null models (p-values, z-scores, correlation coefficients,
   t-scores).  The proportion of null values and the parameters of the null 
   distribution are adaptively estimated from the data.  In addition, the package 
   contains functions for non-parametric density estimation (Grenander estimator), 
   for monotone regression (isotonic regression and antitonic regression with weights),
   for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), 
   for the half-normal and correlation distributions, and for computing
   empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.
These functions were developed to support statistical analysis on functional covariance operators.
  The package contains functions to:
  - compute 2-Wasserstein distances between Gaussian Processes as in
    Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
  - compute the Wasserstein barycenter (Frechet mean) as in Masarotto,
    Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
  - perform analysis of variance testing procedures for functional
    covariances and tangent space principal component analysis of
    covariance operators as in Masarotto, Panaretos & Zemel (2022)
    <arXiv:2212.04797>.
  - perform a soft-clustering based on the Wasserstein distance where
    functional data are classified based on their covariance structure
    as in Masarotto & Masarotto (2023) <doi:10.1111/sjos.12692>.
Multiple testing procedures for heterogeneous and discrete tests as
    described in Döhler and Roquain (2020) <doi:10.1214/20-EJS1771>. The main 
    algorithms of the paper are available as continuous, discrete and weighted
    versions. They take as input the results of a test procedure from package
    'DiscreteTests', or a set of observed p-values and their discrete support
    under their nulls. A shortcut function to obtain such p-values and supports
    is also provided, along with wrappers allowing to apply discrete procedures
    directly to data.
Finite element modeling of beam structures and 2D geometries using constant strain triangles. Applies material properties and boundary conditions (load and constraint) to generate a finite element model. The model produces stress, strain, and nodal displacements; a heat map is available to demonstrate regions where output variables are high or low.  Also provides options for creating a triangular mesh of 2D geometries. Package developed with reference to: Bathe, K. J. (1996). Finite Element Procedures.[ISBN 978-0-9790049-5-7] -- Seshu, P. (2012). Textbook of Finite Element Analysis. [ISBN-978-81-203-2315-5] -- Mustapha, K. B. (2018). Finite Element Computations in Mechanics with R. [ISBN 9781315144474].
For each feature, a score is computed that can be useful
        for feature selection. Several random subsets are sampled from
        the input data and for each random subset, various linear
        models are fitted using lars method. A score is assigned to
        each feature based on the tendency of LASSO in including that
        feature in the models.Finally, the average score and the models
        are returned as the output. The features with relatively low
        scores are recommended to be ignored because they can lead to
        overfitting of the model to the training data. Moreover, for
        each random subset, the best set of features in terms of global
        error is returned. They are useful for applying Bolasso, the
        alternative feature selection method that recommends the
        intersection of features subsets.
Provides a collection of features, decomposition methods, 
    statistical summaries and graphics functions for the analysing tidy time
    series data. The package name 'feasts' is an acronym comprising of its key
    features: Feature Extraction And Statistics for Time Series.
Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.
Local inferential feature significance for multivariate kernel density estimation.
Uses three different correlation coefficients to
    calculate measurement-level adequate correlations in a feature matrix: 
    Pearson product-moment correlation coefficient,
    Intraclass correlation and Cramer's V.
An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.
Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data.
Feature flags allow developers to turn features of their
    software on and off in form of configuration. This package provides
    functions for creating feature flags in code. It exposes an interface
    for defining own feature flags which are enabled based on custom criteria.
Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: <https://christophm.github.io/interpretable-ml-book/feature-importance.html>.
Discretely-sampled function is first smoothed.  Features
        of the smoothed function are then extracted.  Some of the key
        features include mean value, first and second derivatives,
        critical points (i.e. local maxima and minima), curvature of
        cunction at critical points, wiggliness of the function, noise
        in data, and outliers in data.
The aim is to take in data.frame inputs and utilises methods, such as recursive feature engineering, to enable the features to be removed.
    What this does differently from the other packages, is that it gives you the choice to remove the variables manually, or it automated this process.
    Feature selection is a concept in machine learning, and statistical pipelines, whereby unimportant, or less predictive variables are eliminated from the analysis, see Boughaci (2018) <doi:10.1007/s40595-018-0107-y>.
A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.
Easily analyze relational data from the United States 2016 federal 
    election cycle as reported by the Federal Election Commission.
    This package contains data about candidates, committees, and a
    variety of different financial expenditures. Data is from <https://www.fec.gov/data/browse-data/?tab=bulk-data>.
Functions and example datasets for Fechnerian scaling of discrete
  object sets.  User can compute Fechnerian distances among objects representing
  subjective dissimilarities, and other related information.  See
  package?fechner for an overview.
Estimates causal effects with panel data using the counterfactual methods. It is suitable for panel or time-series cross-sectional analysis with binary treatments under (hypothetically) baseline randomization.It allows a treatment to switch on and off and limited carryover effects. It supports linear factor models, a generalization of gsynth and the matrix completion method. Implementation details can be found in Liu, Wang and Xu (2022) <arXiv:2107.00856>.
Download geospatial data available
    from several federated data sources (mainly sources maintained by the
    US Federal government). Currently, the package enables extraction from
    nine datasets: The National Elevation Dataset digital elevation
    models (<https://www.usgs.gov/3d-elevation-program> 1 and 1/3 arc-second; 
    USGS); The National Hydrography Dataset
    (<https://www.usgs.gov/national-hydrography/national-hydrography-dataset>; 
    USGS); The Soil Survey Geographic (SSURGO) database from the National
    Cooperative Soil Survey (<https://websoilsurvey.sc.egov.usda.gov/>; NCSS), 
    which is led by the Natural Resources
    Conservation Service (NRCS) under the USDA; 
    the Global Historical Climatology Network (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>; GHCN), coordinated by National Climatic Data
    Center at NOAA; the Daymet gridded estimates of daily weather
    parameters for North America, version 4, available from the Oak Ridge
    National Laboratory's Distributed Active Archive Center 
    (<https://daymet.ornl.gov/>; DAAC); the
    International Tree Ring Data Bank; the National Land Cover
    Database (<https://www.mrlc.gov/>; NLCD); the Cropland Data Layer from the National Agricultural 
    Statistics Service (<https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>; NASS); 
    and the PAD-US dataset of protected area boundaries (<https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-data-overview>; USGS).
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly 'shiny' application is included.
Provides a flexible set of tools for matching two un-linked data sets. 
    'fedmatch' allows for three ways to match data: exact matches, fuzzy matches, and multi-variable matches. 
    It also allows an easy combination of these three matches via the tier matching function.
An API for automatic data queries to the fedstat <https://www.fedstat.ru>, using a small set of functions with a common interface.
Flow of funds are financial accounts that are provided by Federal Reserve quarterly. The package contains all
    datasets <https://www.federalreserve.gov/datadownload/Choose.aspx?rel=z1>, tables <https://www.federalreserve.gov/apps/fof/FOFTables.aspx> 
    and descriptions <https://www.federalreserve.gov/apps/fof/Guide/z1_tables_description.pdf> with functions to understand series <https://www.federalreserve.gov/apps/fof/SeriesStructure.aspx> and explore them.
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Provides the function feis() to estimate fixed effects individual 
    slope (FEIS) models. The FEIS model constitutes a more general version of 
    the often-used fixed effects (FE) panel model, as implemented in the 
    package 'plm' by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. 
    In FEIS models, data are not only person demeaned like in conventional 
    FE models, but detrended by the predicted individual slope of each 
    person or group. Estimation is performed by applying least squares lm() 
    to the transformed data. For more details on FEIS models see Bruederl and 
    Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; 
    Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; 
	Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>;
    Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE 
    and random effects estimators against heterogeneous slopes, the package 
    also provides the functions feistest() for an artificial regression test 
    and bsfeistest() for a bootstrapped version of the Hausman test.
Enhance R help system by fuzzy search and preview interface, pseudo-postfix operators, and more.
    The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects.
    The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo).
Upload, download, and edit internet maps with the Felt API 
    (<https://feltmaps.notion.site/Felt-Public-API-reference-c01e0e6b0d954a678c608131b894e8e1>). 
    Allows users to create new maps, edit existing maps, and extract data.
    Provides tools for working with layers, which represent geographic data, and elements,
    which are interactive annotations. Spatial data accessed from the API is 
    transformed to work with 'sf'.
Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported.
A structured profile likelihood algorithm for the logistic fixed effects model and an approximate expectation maximization (EM) algorithm for the logistic mixed effects model. Based on He, K., Kalbfleisch, J.D., Li, Y. and Li, Y. (2013) <doi:10.1007/s10985-013-9264-6>.
R implementations of standard financial engineering codes;
  vanilla option pricing models such as Black-Scholes, Bachelier, CEV, and
  SABR.
Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>.
    Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.
Data from various catalogs of astrophysical gamma-ray sources
    detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071,
    2009 June 1), on board the Fermi gamma-ray satellite. More information on
    Fermi and its data products is available from the Fermi Science Support Center
    (http://fermi.gsfc.nasa.gov/ssc/).
Diagnostic plots for optimisation, with a focus on projection pursuit. These show paths the optimiser 
    takes in the high-dimensional space in multiple ways: by reducing the dimension using principal component analysis, and
    also using the tour to show the path on the high-dimensional space. Several botanical colour palettes are included, reflecting the 
    name of the package. A paper describing the methodology can be found at <https://journal.r-project.org/archive/2021/RJ-2021-105/index.html>.
Quantify variability (such as confidence interval) of fertilizer response curves and optimum fertilizer rates using bootstrapping residuals with several popular non-linear and linear models.
Four fertility models are fitted using non-linear least squares. These are the Hadwiger, the Gamma, the Model1 and Model2, following the terminology of the following paper: Peristera P. and Kostaki A. (2007). "Modeling fertility in modern populations". Demographic Research, 16(6): 141--194. <doi:10.4054/DemRes.2007.16.6>. Model based averaging is also supported.
Processes data from The Social Networks and Fertility Survey,
    downloaded from <https://dataarchive.lissdata.nl>, including correcting 
    respondent errors and transforming network data into network objects to
    facilitate analyses and visualisation.
Original idea was presented in the reference paper. Varghese et al. (2020, 74(1):35-42) "Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery". Marine fisheries governance and management practices are very essential to ensure the sustainability of the marine resources. A widely accepted resource management strategy towards this is to derive sustainable fish harvest levels based on the status of marine fish stock. Various fish stock assessment models that describe the biomass dynamics using time series data on fish catch and fishing effort are generally used for this purpose. In the scenario of complex multi-species marine fishery in which different species are caught by a number of fishing gears and each gear harvests a number of species make it difficult to obtain the fishing effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum of efforts expended by different fishing gears. This necessitates standardisation of fishing effort in unit base.
Contains functions to fetch data from various data sources.
    The user first creates a catalog of objects from a data source,
    then fetches data from the catalog.  The package provides an easy
    way to access data from many different types of sources.
Collect  your data on digital marketing campaigns from Google Analytics using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Collect  your data on digital marketing campaigns from Salesforce using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides functions for analysing
  and modelling extreme events in financial time Series. The
  topics include: (i) data pre-processing, (ii) explorative 
  data analysis, (iii) peak over threshold modelling, (iv) block
  maxima modelling, (v) estimation of VaR and CVaR, and (vi) the
  computation of the extreme index.
Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997, <doi:10.1016/s0167-5877(97)00081-0>). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002, <doi:10.1016/S0167-5877(01)00245-8>). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.
Downloads all the datasets (you can exclude the daily ones or specify a list of those you are targeting specifically) from Kenneth French's Website at <https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>, process them and convert them to list of 'xts' (time series).
General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.
Implements numerical entropy-pooling for portfolio construction and 
    scenario analysis as described in Meucci, Attilio (2008) and Meucci, Attilio (2010) 
    <doi:10.2139/ssrn.1696802>.
Helps access various Fantasy Football APIs by handling
    authentication and rate-limiting, forming appropriate calls, and
    returning tidy dataframes which can be easily connected to other data
    sources.
The 'fftab' package stores Fourier coefficients in a tibble and 
  allows their manipulation in various ways. Functions are available for converting 
  between complex, rectangular ('re', 'im'), and polar ('mod', 'arg') representations, 
  as well as for extracting components as vectors or matrices. Inputs can include 
  vectors, time series, and arrays of arbitrary dimensions, which are restored 
  to their original form when inverting the transform. Since 'fftab' stores Fourier 
  frequencies as columns in the tibble, many standard operations on spectral data 
  can be easily performed using tidy packages like 'dplyr'.
Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described by Phillips, Neth, Woike & Gaissmaier (2017), <doi:10.1017/S1930297500006239>. 
    FFTs are simple and transparent decision trees for solving binary classification problems. 
    FFTs can be preferable to more complex algorithms because they require very little information, are easy to understand and communicate, and are robust against overfitting.
Provides a simple and efficient wrapper around the fastest
        Fourier transform in the west (FFTW) library <http://www.fftw.org/>.
Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
This is a package for implementation of Flury-Gautschi
        algorithms.
Analyze and model heteroskedastic behavior in financial time series.
Read and Parse for Fundamental Geo-Spatial Data (FGD) which downloads XML file 
    from providing site (<https://fgd.gsi.go.jp/download/menu.php>). The JPGIS format file 
    provided by FGD so that it can be handled as an R spatial object such as 'sf' and 
    'raster', 'terra' or 'stars'.
    Supports the FGD version 4.1, and accepts fundamental items and digital elevation models.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to install and load the entire package-collection with a
    single R command, and provides convenient ways to find relevant
    documentation. Most commonly, you should not worry about the
    individual packages that make up the package-collection as you can
    access all features via this package. To learn more about ForestGEO
    visit <http://www.forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to implement analyses of plot species distributions,
    topography, demography, and biomass. It also includes a torus
    translation test to determine habitat associations of tree species as
    described by Zuleta et al. (2018) <doi:10.1007/s11104-018-3878-0>. To
    learn more about ForestGEO visit <https://forestgeo.si.edu/>.
To help you access, transform, analyze, and visualize
    ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to plot ForestGEO data. To learn more about ForestGEO visit
    <https://forestgeo.si.edu/>.
To help you access, transform, analyze, and visualize
    'ForestGEO' data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to easily import, filter, and modify 'ForestGEO' data. To
    learn more about 'ForestGEO' visit <https://forestgeo.si.edu/>.
Access small example datasets from Luquillo, a
    ForestGEO site in Puerto Rico
    (<https://forestgeo.si.edu/sites/north-america/luquillo>).
'FastGit' <https://doc.fastgit.org/> works like a mirror of 'GitHub' to make significant acceleration. 'fgitR' is a package to do git operation with 'FastGit' automatically.
An implementation of the methodologies described in Xi Liu, Afshin A. Divani, and Alexander Petersen (2022) <doi:10.1016/j.csda.2022.107421>, including 
                truncated functional linear and truncated functional logistic regression models.
The function estimates a multivariate regression model for outcomes with network dependence.
Estimates a functional graphical model and a partially separable Karhunen-Loève decomposition for a multivariate Gaussian process. See Zapata J., Oh S. and Petersen A. (2019) <arXiv:1910.03134>.
Growth models and forest production require existing data
    manipulation and the creation of new data, structured from basic forest
    inventory data. The purpose of this package is provide functions to support
    these activities.
A collection of datasets essential for functional genomic analysis. Gene names, gene positions, cytoband information, sourced from Ensembl and phenotypes association graph prepared from GWAScatalog are included. Data is available in both GRCh37 and 38 builds. These datasets facilitate a wide range of genomic studies, including the identification of genetic variants, exploration of genomic features, and post-GWAS functional analysis.
Rapidly create a GUI interface for a function you created by automatically creating widgets for arguments of the function. Automatically parses help routines for context-sensitive help to these arguments. The interface essentially a wrapper to some Tcl/Tk routines to both simplify and facilitate GUI creation. More advanced Tcl/Tk routines/GUI objects can be incorporated into the interface for greater customization for the more experienced.
Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.
Useful tools for conveniently downloading FHIR resources in xml format 
    and converting them to R data.frames. The package uses FHIR-search to download bundles 
    from a FHIR server, provides functions to save and read xml-files containing such bundles 
    and allows flattening the bundles to data.frames using XPath expressions. FHIR® is the registered trademark 
    of HL7 and is used with the permission of HL7. Use of the FHIR trademark does not constitute endorsement of this product by HL7.
Fitting (hierarchical) hidden Markov models to financial data
    via maximum likelihood estimation. See Oelschläger, L. and Adam, T.
    "Detecting Bearish and Bullish Markets in Financial Time Series Using 
    Hierarchical Hidden Markov Models" (2021, Statistical Modelling) 
    <doi:10.1177/1471082X211034048> for a reference on the method. A user guide 
    is provided by the accompanying software paper "fHMM: Hidden Markov Models 
    for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R.
    (2024, Journal of Statistical Software)  <doi:10.18637/jss.v109.i09>.
Functions to compare two or more survival curves with:
             a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
             b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
        Common volumetric equations (Smalian, Newton and Huber) as well
        stacking factor and form
Routines for estimating tree fiber (tracheid) length distributions in the standing tree
           based on increment core samples. Two types of data can be used with the package, increment 
           core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani 
          Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell
          lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells)
           without being able to identify which cells are cut or if they are fines or fibres. The
          microscopy measured data consist of the observed lengths of the uncut fibres in the increment
         core. A censored version of a mixture of the fine and fiber length distributions is proposed to
         fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the
        assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers
        (fines) that at least partially appear in the increment core, being the generalized gamma and 
        the log normal densities.
Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes.  Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models.  The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>. The package is described in Miettinen, Nordhausen and Taskinen (2018) <doi:10.32614/RJ-2018-046>.
Create secure, encrypted, and password-protected static HTML 
  documents that include the machinery for secure in-browser authentication 
  and decryption.
Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'.
A shiny design of experiments (DOE) app that aids in the creation of traditional, 
 un-replicated, augmented and partially-replicated designs applied to agriculture, 
 plant breeding, forestry, animal and biological sciences.
For curve, surface and function fitting with an emphasis
 on splines, spatial data, geostatistics,  and spatial statistics. The major methods
 include cubic, and thin plate splines, Kriging, and compactly supported
 covariance functions for large data sets. The splines and Kriging methods are
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance function parameters by cross
 validation and also by restricted maximum likelihood. For Kriging
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple format can be used for
 spatial prediction. There are also many useful functions for plotting
 and working with spatial data as images. This package also contains
 an implementation of sparse matrix methods for large spatial data
 sets and currently requires the sparse matrix (spam) package. Use
 help(fields) to get started and for an overview.  The fields source
 code is deliberately commented and provides useful explanations of
 numerical details as a companion to the manual pages. The commented
 source code can be viewed by expanding the source code version
 and looking in the R subdirectory. The reference for fields can be generated
 by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
 of this package was supported in part by the National Science Foundation  Grant
 1417857,  the National Center for Atmospheric Research, and Colorado School of Mines.
 See the Fields URL
 for a vignette on using this package and some background on spatial statistics.
Simulates plot data in multi-environment field trials with one or more traits. 
  Its core function generates plot errors that capture spatial trend, random error (noise), 
  and extraneous variation, which are combined at a user-defined ratio. 
  Phenotypes can be generated by combining the plot errors with simulated genetic values that capture 
  genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.
A very flexible framework for building server side logic in
    R. The framework is unopinionated when it comes to how HTTP requests
    and WebSocket messages are handled and supports all levels of app
    complexity; from serving static content to full-blown dynamic
    web-apps. Fiery does not hold your hand as much as e.g. the shiny
    package does, but instead sets you free to create your web app the way
    you want.
A research estimation tool for analysts that work with sample-based
    inventory data from the U.S. Department of Agriculture, Forest Service,
    Forest Inventory and Analysis (FIA) Program.
A set of tools for data wrangling, spatial data analysis,
    statistical modeling (including direct, model-assisted, photo-based, and
    small area tools), and USDA Forest Service data base tools. These tools are
    aimed to help Foresters, Analysts, and Scientists extract and perform
    analyses on USDA Forest Service data.
Work with configs with a source precedence. Either create own R6
  instance or work with convenient functions at a package level.
With the functions in this package you can check the
  validity of the following financial instrument identifiers:
  FIGI (Financial Instrument Global Identifier
  <https://www.openfigi.com/about/figi>),
  CUSIP (Committee on Uniform Security Identification Procedures
  <https://www.cusip.com/identifiers.html#/CUSIP>),
  ISIN (International Securities Identification Number
  <https://www.cusip.com/identifiers.html#/ISIN>),
  SEDOL (Stock Exchange Daily Official List
  <https://www2.lseg.com/SEDOL-masterfile-service-tech-guide-v8.6>).
  You can also calculate the FIGI checksum of 11-character strings,
  which can be useful if you want to create your own FIGI identifiers.
An easy-to-use web client/wrapper for the 'Figma API' <https://www.figma.com/developers/api>. 
  It allows you to bring all data from a 'Figma' file to your 'R' session. This includes the data of all objects
  that you have drawn in this file, and their respective canvas/page metadata.
We view a figure as a collection of graphs/tables assembled on a page and optionally annotated with metadata
    (titles, headers and footers). Functions and supporting documentation are offered to streamline a variety of figure production task.
Transform output files of some tools to the 'microtable' object of 'microtable' class in 'microeco' package. The 'microtable' class is the basic class in 'microeco' package and is necessary for the downstream microbial community data analysis.
Stores large arrays in files to avoid occupying large
    memories. Implemented with super fast gigabyte-level multi-threaded 
    reading/writing via 'OpenMP'. Supports multiple non-character data 
    types (double, float, complex, integer, logical, and raw).
A wrapper for the Filebin API. Filebin implements convenient file
    sharing on the web.
The main functions in this package are with_cache() and cached_read(). 
    The former is a simple way to cache an R object into a file on disk, 
    using 'cachem'. The latter is a wrapper around any standard read function, 
    but caches both the output and the file list info. If the input file list
    info hasn't changed, the cache is used; otherwise, the original files are
    re-read. This can save time if the original operation requires reading from
    many files, and/or involves lots of processing.
Implements a simple key-value style database where character string keys
  are associated with data values that are stored on the disk. A simple interface is provided for inserting,
  retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be
  treated much like environments and lists are already used in R. These utilities are provided to encourage
  interactive and exploratory analysis on large datasets. Three different file formats for representing the
  database are currently available and new formats can easily be incorporated by third parties for use in the
  'filehash' framework.
Simple key-value database using SQLite as the backend.
Place an exclusive or shared lock on a file. It uses
    'LockFile' on Windows and 'fcntl' locks on Unix-like systems.
Interface for working with large matrices stored in files,
        not in computer memory. Supports multiple non-character
        data types (double, integer, logical and raw) of
        various sizes (e.g. 8 and 4 byte real values).
        Access to parts of the matrix is done by indexing, 
        exactly as with usual R matrices.
        Supports very large matrices.
        Tested on multi-terabyte matrices.
        Allows for more than 2^32 rows or columns.
        Allows for quick addition of extra columns to a filematrix.
        Cross-platform as the package has R code only.
Create descriptive file names with ease. New file names are
    automatically (but optionally) time stamped and placed in date stamped
    directories. Streamline your analysis pipeline with input and output file
    names that have informative tags and proper file extensions.
Functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for
  viewing the details (size, time created, time modified, etc.) of a folder's top level contents.
This started out as a package for file and string
    manipulation.  Since then, the 'fs' and 'strex' packages emerged,
    offering functionality previously given by this package (but it's done
    better in these new ones).  Those packages have hence almost pushed
    'filesstrings' into extinction.  However, it still has a small number
    of unique, handy file manipulation functions which can be seen in the
    vignette.  One example is a function to remove spaces from all file
    names in a directory.
A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) <doi:10.1007/BF01441146>. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.
Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.
Adds distinctive yet unobtrusive geometric patterns where solid 
    color fills are normally used. Patterned figures look just as 
    professional when viewed by colorblind readers or when printed in black 
    and white. The dozen included patterns can be customized in terms of scale,
    rotation, color, fill, line type, and line width. Compatible with the 
    'ggplot2' package as well as 'grid' graphics.
Edit vectors to fill missing values, based on the vector itself.
Average rating and number of votes reported by IMDb for films and shorts with over 100 votes in 2022.  The data are analysed in Chapter 3 of the Book 'Getting (more out of) Graphics' (Antony Unwin, CRC Press 2024).
Generate search filters to query scientific bibliographic sources, 
    such as PubMed and Web of Science, for non-human primate related 
    publications.
Enables filtering datasets by a prior specified identifiers
    which correspond to saved filter expressions.
Provides a collection of utility functions 
    to download and manage data sets from the Internet or from other 
    sources.
Generate regression results tables and plots in final 
    format for publication. Explore models and export directly to PDF 
    and 'Word' using 'RMarkdown'.
Calculate the final size of a
    susceptible-infectious-recovered epidemic in a population with
    demographic variation in contact patterns and susceptibility to
    disease, as discussed in Miller (2012)
    <doi:10.1007/s11538-012-9749-6>.
Functions for financial analysis and financial modeling, 
             including batch graphs generation, beta calculation, 
             descriptive statistics, annuity calculation, bond pricing 
             and financial data download.
Infrastructure for defining meta-data and
    relationships for financial instruments.
Contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam, and some topics in the 'Models for Financial Economics' exam.
A programmatic interface to the 'Finnish Biodiversity Information
    Facility' ('FinBIF') API (<https://api.laji.fi>). 'FinBIF' aggregates
    Finnish biodiversity data from multiple sources in a single open access
    portal for researchers, citizen scientists, industry and government.
    'FinBIF' allows users of biodiversity information to find, access, combine
    and visualise data on Finnish plants, animals and microorganisms. The
    'finbif' package makes the publicly available data in 'FinBIF' easily
    accessible to programmers. Biodiversity information is available on taxonomy
    and taxon occurrence. Occurrence data can be filtered by taxon, time,
    location and other variables. The data accessed are conveniently
    preformatted for subsequent analyses.
Learning bipartite and k-component bipartite graphs from financial
     datasets. This package contains implementations of the algorithms described
     in the paper: Cardoso JVM, Ying J, and Palomar DP (2022).
     <https://openreview.net/pdf?id=WNSyF9qZaMd>
     "Learning bipartite graphs: heavy tails and multiple components,
     Advances in Neural Informations Processing Systems" (NeurIPS).
Package for time value of money calculation, time series analysis and computational finance.
Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple
    and Archives. Functionality includes reading and parsing all the
    files in a Darwin Core Archive, including the datasets and metadata;
    read and parse simple Darwin Core files; and validation of Darwin
    Core Archives.
Estimation and regularization for covariance matrix of asset
    returns. For covariance matrix estimation, three major types of factor
    models are included: macroeconomic factor model, fundamental factor model and
    statistical factor model. For covariance matrix regularization, four regularized
    estimators are included: banding, tapering, hard-thresholding and soft-
    thresholding. The tuning parameters of these regularized estimators are selected
    via cross-validation.
Provides tools to estimate the genome size of polyploid species using k-mer frequencies. This package includes functions to process k-mer frequency data and perform genome size estimation by fitting k-mer frequencies with a normal distribution model. It supports handling of complex polyploid genomes and offers various options for customizing the estimation process. The basic method 'findGSE' is detailed in Sun, Hequan, et al. (2018) <doi:10.1093/bioinformatics/btx637>.
Creates a HTML widget which displays the results of searching
    for a pattern in files in a given folder. The results can be viewed in 
    the 'RStudio' viewer pane, included in a 'R Markdown' document or in a 
    'Shiny' application. Also provides a 'Shiny' application allowing to run 
    this widget and to navigate in the files found by the search. Instead of
    creating a HTML widget, it is also possible to get the results of the 
    search in a 'tibble'. The search is performed by the 'grep' command-line 
    utility.
Creates a HTML widget which displays the results of searching for a pattern in files in a given 'git' repository, including all its branches. The results can also be returned in a dataframe.
The heterogeneous treatment effect estimation procedure 
        proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.  
        The proposed method is applicable, for
        example, when selecting a small number of most (or least)
        efficacious treatments from a large number of alternative
        treatments as well as when identifying subsets of the
        population who benefit (or are harmed by) a treatment of
        interest. The method adapts the Support Vector Machine
        classifier by placing separate LASSO constraints over the
        pre-treatment parameters and causal heterogeneity parameters of
        interest. This allows for the qualitative distinction between
        causal and other parameters, thereby making the variable
        selection suitable for the exploration of causal heterogeneity. 	
        The package also contains a class of functions, CausalANOVA, 
        which estimates the average marginal interaction effects (AMIEs)
        by a regularized ANOVA as proposed by Egami and Imai (2019)<DOI:10.1080/01621459.2018.1476246>. 
        It contains a variety of regularization techniques to facilitate 
	analysis of large factorial experiments.
Finds 'CRAN' packages by the topic requested. The topic can be given as a character string or as a regular expression and will help users to locate 'CRAN' packages matching their specified requirement. findPackage(<string>) returns a data frame of packages with description containing the input string.
Package designed to find an acceptable python binary.
Scans all directories and subdirectories of a path for code snippets, R scripts,
    R Markdown, PDF or text files containing a specific pattern.  Files found can be copied to a new folder.
Developed by CDC/ATSDR (Centers for Disease Control and Prevention/
    Agency for Toxic Substances and Disease Registry), 
    Social Vulnerability Index (SVI) serves as a tool to assess the resilience 
    of communities by taking into account socioeconomic and demographic factors. 
    Provided with year(s), region(s) and a geographic level of interest, 
    'findSVI' retrieves required variables from US census data and calculates SVI 
    for communities in the specified area based on CDC/ATSDR SVI documentation. 
    Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, 
    Heitgerd JL, Lewis B (2011) <doi:10.2202/1547-7355.1792>.
A tool to explore wide data sets, by detecting, ranking
    and plotting groups of statistically dependent columns.
Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.
Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.
The ability to tune models is important. 'finetune' enhances
    the 'tune' package by providing more specialized methods for finding
    reasonable values of model tuning parameters.  Two racing methods
    described by Kuhn (2014) <arXiv:1405.6974> are included. An iterative
    search method using generalized simulated annealing (Bohachevsky,
    Johnson and Stein, 1986) <doi:10.1080/00401706.1986.10488128> is also
    included.
Functions to manipulate binary fingerprints
 of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
 which is internally represented a vector of integers, such
 that each element represents the position in the fingerprint that is set to 1.
 The bitwise logical functions in R are overridden so that they can be used directly
 with 'fingerprint' objects. A number of distance metrics are also
 available (many contributed by Michael Fadock). Fingerprints 
 can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
 can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
 handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.
Quantifies the provenance of the sediments in a catchment or study area. Based on a comprehensive characterization of the sediment sources and the end sediment mixtures a mixing model algorithm is applied to the sediment mixtures in order to estimate the relative contribution of each potential source. The package includes several statistical methods such as Kruskal-Wallis test, discriminant function analysis ('DFA'), principal component plot ('PCA') to select the optimal subset of tracer properties. The variability within each sediment source is also considered to estimate the statistical distribution of the sources contribution.
Learning graphs for financial markets with optimization algorithms.
    This package contains implementations of the algorithms described in the paper:
    Cardoso JVM, Ying J, and Palomar DP (2021) <https://papers.nips.cc/paper/2021/hash/a64a034c3cb8eac64eb46ea474902797-Abstract.html>
    "Learning graphs in heavy-tailed markets", Advances in Neural Informations Processing Systems (NeurIPS).
In the Cramér–Lundberg risk process perturbed by a Wiener
    process, this packages provides approximations to the probability of
    ruin within a finite time horizon.  Currently, there are three methods
    implemented: The first one uses saddlepoint approximation (two
    variants are provided), the second one uses importance sampling and
    the third one is based on the simulation of a dual process.  This last
    method is not very accurate and only given here for completeness.
The purpose of this package is to tests whether a given 
             moment of the distribution of a given sample is finite or not. For 
             heavy-tailed distributions with tail exponent b, only moments of 
             order smaller than b are finite. Tail exponent and heavy-
             tailedness are notoriously difficult to ascertain. But the 
             finiteness of moments (including fractional moments) can be tested 
             directly. This package does that following the test suggested by 
             Trapani (2016) <doi:10.1016/j.jeconom.2015.08.006>.
Provides functions to access and retrieve metadata from the 'Finna' API <https://api.finna.fi/>, which aggregates content from Finnish archives, libraries, and museums.
Providing classes, methods, and functions to deal with financial networks. 
    Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as 'rvest' and 'RSelenium'.
    Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, 'igraph', 'network').
    There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis.
R API client package for 'Fingrid Open Data' 
    <https://data.fingrid.fi/> on the electricity market and the power system. 
    get_data() function holds the main application logic to retrieve 
    time-series data. API calls require free user account registration.
    Data is made available by Fingrid Oyj and distributed under
    Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.
Annotates Finnish textual survey responses into CoNLL-U format using Finnish treebanks from <https://universaldependencies.org/format.html> using UDPipe as described in Straka and Straková (2017) <doi:10.18653/v1/K17-3009>. Formatted data is then analysed using single or comparison n-gram plots, wordclouds, summary tables and Concept Network plots. The Concept Network plots use the TextRank algorithm as outlined in Mihalcea, Rada & Tarau, Paul (2004) <https://aclanthology.org/W04-3252/>.
Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time 
    Series Forecasting Framework, aka Finn, can be used to forecast any component of the income 
    statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, 
    Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built 
    to meet the needs of financial analysts to better forecast their businesses within a company, and has 
    a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!
Download and display company financial data from the U.S. Securities
    and Exchange Commission's EDGAR database. It contains a suite of functions with
    web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR 
    in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
    for more information.
R companion to Tsay (2005) Analysis of Financial Time
   Series, second edition (Wiley).  Includes data sets, functions and
   script files required to work some of the examples.  Version 0.3-x
   includes R objects for all data files used in the text and script
   files to recreate most of the analyses in chapters 1-3 and 9 plus
   parts of chapters 4 and 11.
Simplifies the process of importing and managing input-output
  matrices from 'Microsoft Excel' into R, and provides a suite of functions for
  analysis. It leverages the 'R6' class for clean, memory-efficient
  object-oriented programming. Furthermore, all linear algebra computations are
  implemented in 'Rust' to achieve highly optimized performance.
Provides a lightweight suite
             of functions for retrieving information
             about 5-digit or 2-digit US FIPS codes.
Computes implicitly induced quantities from prior/hyperparameter    
    specifications of three Mixtures of Finite Mixtures models: Dirichlet 
    Process Mixtures (DPMs; Escobar and West (1995) 
    <doi:10.1080/01621459.1995.10476550>), Static Mixtures of Finite Mixtures 
    (Static MFMs; Miller and Harrison (2018) 
    <doi:10.1080/01621459.2016.1255636>), and Dynamic Mixtures of Finite 
    Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020)
    <arXiv:2005.09918>). For methodological details, please refer to 
    Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020)
    <arXiv:2012.12337>) as well as the package vignette.
The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets.
    The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.
Authenticate users in 'Shiny' applications using 'Google Firebase'
    with any of the many methods provided; email and password, email link, or
    using a third-party provider such as 'Github', 'Twitter', or 'Google'.
    Use 'Firebase Storage' to store files securely, and leverage 'Firebase Analytics'
    to easily log events and better understand your audience.
Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <DOI:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <DOI:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.
A model-independent factor importance ranking and selection procedure that is based on total Sobol' indices. Please see Huang and Joseph (2024) <arXiv:2401.00800>. This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873.
Proposes non-parametric estimates of the Fisher information measure and the 
              Shannon entropy power. More theoretical and implementation details can be found
              in Guignard et al. <doi:10.3389/feart.2020.00255>. A 'python' version of this
              work is available on 'github' and 'PyPi' ('FiShPy').
Provides raw and curated data on the codes,
    classification and conservation status of freshwater fishes in British
    Columbia. Marine fishes will be added in a future release.
A collection of four datasets 
    based around the population dynamics of migratory fish. Datasets 
    contain both basic size information on a per fish basis, as well as
    otolith data that contains a per day record of fish growth history.
    All data in this package was collected by the author, from 
    2015-2016, in the Wellington region of New Zealand.
The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) <doi:10.1007/s11222-011-9249-9>,
        is an efficient method for the clustering of high-dimensional data. FisherEM models and 
        clusters the data in a discriminative and low-dimensional latent subspace. It also provides
        a low-dimensional representation of the clustered data. A sparse version of Fisher-EM
        algorithm is also provided.
Base maps are transformed to focus on a specific location using an
    azimuthal logarithmic distance transformation.
Fit growth models to otoliths and/or tagging data, using the 'RTMB'
  package and maximum likelihood. The otoliths (or similar measurements of age)
  provide direct observed coordinates of age and length. The tagging data
  provide information about the observed length at release and length at
  recapture at a later time, where the age at release is unknown and estimated
  as a vector of parameters. The growth models provided by this package can be
  fitted to otoliths only, tagging data only, or a combination of the two.
  Growth variability can be modelled as constant or increasing with length.
Dataset of 302 measurements of 11 fish species to accompany the
    manuscript "Length-weight relationships of six freshwater fish species
    from lake Kirkkojarvi, Finland".
Functions for applying a wide range of fisheries stock assessment methods.
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs.
Provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species
   or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) <doi:10.1038/s41586-018-0273-1>.
The proximate composition analysis is the quantification of main components that constitutes nutritional profile of any food and food products including fish, shellfish, fish feed and their ingredients. Understanding this composition is essential for evaluating their nutritional value and for making informed dietary choices. The primary components typically analyzed include; moisture/ water in foods, crude protein, crude fat/ lipid, total ash, fiber and carbohydrates AOAC(2005,ISBN:0-935584-77-3). In case of fish, shellfish and its products, the proximate composition consists of four primary constituents - water, protein, fat, and ash (mostly minerals). Fish exhibit significant variation in their chemical makeup based on age, sex, environment, and season, both within the same species and between individual fish. There is minimal fluctuation in the content of ash and protein. The lipid concentration varies remarkably and is inversely correlated with the water content. In case of fish, carbohydrates are present in minor quantity so that are quantified by subtracting total of other components from 100 to get percentage of carbohydrates.
Calculates metabolic rate of fish and other aquatic organisms measured using
             an intermittent-flow respirometry approach. The tool is used to run a set of graphical
             QC tests of raw respirometry data, correct it for background respiration and chamber
             effect, filter and extract target values of absolute and mass-specific metabolic rate.
             Experimental design should include background respiration tests and measuring of one
             or more metabolic rate traits. The R package is ideally integrated with the pump
             controller 'PumpResp' and the DO meter 'SensResp' (open-source hardware by FishResp).
             Raw respirometry data can be also imported from 'AquaResp' (free software), 'AutoResp'
             ('LoligoSystems'), 'OxyView' ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and
             'Q-box Aqua' ('QubitSystems'). More information about the R package 'FishResp'is
             available in the publication by Morozov et al. (2019) <doi:10.1093/conphys/coz003>.
A bundle of analytics tools for fisheries scientists. A 'shiny' R App is included for a 'no-code' solution for retrieval, analysis, and visualization.
The Food and Agriculture Organization of the United Nations (FAO)
  FishStat database is the leading source of global fishery and aquaculture
  statistics and provides unique information for sector analysis and monitoring.
  This package provides the global production data from all fisheries and
  aquaculture in R format, ready for analysis.
An interface to the Fish Tree of Life API to download taxonomies,
    phylogenies, fossil calibrations, and diversification rate information for
    ray-finned fishes.
Implementation of color palettes based on fish species.
Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. 
  This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software 'FisPro' <https://www.fispro.org>. 
  'FisPro' allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system.
Provides functionality for constructing
    statistical models of transcriptomic dynamics in field conditions.
    It further offers the function to predict expression of a gene given 
    the attributes of samples and meteorological data. Nagano, A. J., Sato, 
    Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and 
    Izawa, T. (2012). <doi:10.1016/j.cell.2012.10.048>. Iwayama, K., Aisaka, Y., 
    Kutsuna, N., and Nagano, A. J. (2017). <doi:10.1093/bioinformatics/btx049>.
The fit.models function and its associated methods (coefficients, print,
  summary, plot, etc.) were originally provided in the robust package to compare robustly
  and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002)
  'Robust Library User's Guide' <http://robust.r-forge.r-project.org/Robust.pdf>). The aim
  of the fit.models package is to separate this fitted model object comparison functionality
  from the robust package and to extend it to support fitting methods (e.g., classical,
  robust, Bayesian, regularized, etc.) more generally.
Many 'Fitbit' users, and R-friendly 'Fitbit' users
    especially, have found themselves curious about their 'Fitbit' data.
    'Fitbit' aggregates a large amount of personal data, much of which is
    interesting for personal research and to satisfy curiosity, and is
    even potentially useful in medical settings. The goal of 'fitbitr' is
    to make interfacing with the 'Fitbit' API as streamlined as possible,
    to make it simple for R users of all backgrounds and comfort levels to
    analyze their 'Fitbit' data and do whatever they want with it!
    Currently, 'fitbitr' includes methods for pulling data on activity,
    sleep, and heart rate, but this list is likely to grow in the future
    as the package gains more traction and more requests for new methods
    to be implemented come in.  You can find details on the 'Fitbit' API
    at <https://dev.fitbit.com/build/reference/web-api/>.
Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official
    API, but instead uses the API that the web dashboard uses to generate the graphs
    displayed on the dashboard after login at <http://www.fitbit.com>.
Connection to the 'Fitbit' Web API <https://dev.fitbit.com/build/reference/web-api/> by including 'ggplot2' Visualizations, 'Leaflet' and 3-dimensional 'Rayshader' Maps. The 3-dimensional 'Rayshader' Map requires the installation of the 'CopernicusDEM' R package which includes the 30- and 90-meter elevation data.
Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an  arbitrary angle and the fit will still succeed. Helper functions are  provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by  least squares" <https://people.cas.uab.edu/~mosya/cl/>; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI,  Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma  (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302.
Extends the fitdistr() function (of the MASS package) with several functions 
  to help the fit of a parametric distribution to non-censored or censored data. 
  Censored data may contain left censored, right censored and interval censored values, 
  with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), 
  the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit 
  estimation (MGE) and maximum spacing estimation (MSE) methods (available only for 
  non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. 
  Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction 
  to parametric estimation.
Estimation of a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods. See Bee, M. (2023) <doi:10.1016/j.csda.2023.107764>.
Robust estimation methods for the mean vector, scatter matrix,
    and covariance matrix (if it exists) from data (possibly containing NAs) 
    under multivariate heavy-tailed distributions such as angular Gaussian 
    (via Tyler's method), Cauchy, and Student's t distributions. Additionally, 
    a factor model structure can be specified for the covariance matrix. The
    latest revision also includes the multivariate skewed t distribution.
    The package is based on the papers: Sun, Babu, and Palomar (2014);
    Sun, Babu, and Palomar (2015); Liu and Rubin (1995);
    Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).
A toolbox for estimating vector fields from intensive
    longitudinal data, and construct potential landscapes thereafter. The
    vector fields can be estimated with two nonparametric methods: the
    Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche
    (2018) <doi:10.1017/S0266466617000305> and the Sparse Vector Field
    Consensus (SparseVFC) algorithm by Ma et al.  (2013)
    <doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be
    constructed with a simulation-based approach with the 'simlandr'
    package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the
    Bhattacharya et al. (2011) method for path integration
    <doi:10.1186/1752-0509-5-85>.
Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell V. (1982) <doi:10.2307/2529885> and Al-Hussaini et al. (2000) <doi:10.1080/00949650008812033>. Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model 
             by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering.
Contains Probability Mass Functions, Cumulative Mass
    Functions, Negative Log Likelihood value, parameter estimation and
    modeling data using Binomial Mixture Distributions (BMD) (Manoj et al
    (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial
    Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>),
    also Journal article to use the package(<doi:10.21105/joss.01505>).
For binomial outcome data Alternate Binomial Distributions
    and Binomial Mixture Distributions are fitted when overdispersion is
    available.
Methods and functions for fitting ordinary differential equations (ODE) model in 'R'. Sensitivity equations are used to compute the gradients of ODE trajectories with respect to underlying parameters, which in turn allows for more stable fitting. Other fitting methods, such as MCMC (Markov chain Monte Carlo), are also available.
Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.
Genotyping assays for bi-allelic markers (e.g. SNPs) produce
	signal intensities for the two alleles. 'fitPoly' assigns genotypes 
	(allele dosages) to a collection of polyploid samples based on these
	signal intensities. 'fitPoly' replaces the older package 'fitTetra' that was
	limited (a.o.) to only tetraploid populations whereas 'fitPoly' accepts any
	ploidy level. Reference: Voorrips RE, Gort G, Vosman B (2011)
	<doi:10.1186/1471-2105-12-172>. 
	New functions added on conversion of data from SNP array software formats, 
	drawing of XY-scatterplots with or without genotype colors,
    checking against expected F1 segregation patterns,
    comparing results from two different assays (probes) for the same SNP,
    recovery from a saveMarkerModels() crash.
Fits Zeta distributions (discrete power laws) to data that arises
    from forensic surveys of clothing on the presence of glass and paint in
    various populations. The general method is described to some extent in
    Coulson, S.A., Buckleton, J.S., Gummer, A.B., and Triggs, C.M. (2001) 
    <doi:10.1016/S1355-0306(01)71847-3>, although the implementation differs.
Convenient classes to model fitness landscapes and fitness
    seascapes. A low-level package with which most users will not interact but
    upon which other packages modeling fitness landscapes and fitness seascapes
    will depend.
Utilities to read and write files in the FITS (Flexible
  Image Transport System) format, a standard format in astronomy (see
  e.g. <https://en.wikipedia.org/wiki/FITS> for more information).
  Present low-level routines allow: reading, parsing, and modifying
  FITS headers; reading FITS images (multi-dimensional arrays);
  reading FITS binary and ASCII tables; and writing FITS images
  (multi-dimensional arrays).  Higher-level functions allow: reading
  files composed of one or more headers and a single (perhaps
  multidimensional) image or single table; reading tables into
  data frames; generating vectors for image array axes; scaling and
  writing images as 16-bit integers.  Known incompletenesses are
  reading random group extensions, as well as
  complex and array descriptor data types in binary tables.
Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a 'shiny' app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.
Extends the fitdist() (from 'fitdistrplus') adding the Anderson-Darling ad.test() (from 'ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from 'stats' package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from 'mclust').
Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.
An easy package for scraping and processing Australia Rules Football (AFL)
    data. 'fitzRoy' provides a range of functions for accessing publicly available data 
    from 'AFL Tables' <https://afltables.com/afl/afl_index.html>, 'Footy Wire' <https://www.footywire.com> and
    'The Squiggle' <https://squiggle.com.au>. Further functions allow for easy processing, 
    cleaning and transformation of this data into formats that can be used for analysis.
Datasets and code published by the data journalism website 
    'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>. 
    Note that while we received guidance from editors at 'FiveThirtyEight', this 
    package is not officially published by 'FiveThirtyEight'.
For functions that take and return vectors (or scalars), this package provides 8 algorithms for finding fixed point vectors (vectors for which the inputs and outputs to the function are the same vector). These algorithms include Anderson (1965) acceleration <doi:10.1145/321296.321305>, epsilon extrapolation methods (Wynn 1962 <doi:10.2307/2004051>) and minimal polynomial methods (Cabay and Jackson 1976 <doi:10.1137/0713060>).
Determine and test the fixed-point property in binary mixture data.
  This package was originally developed in the context of detecting mixture of 
  cognitive processing strategies, based on observed response time distributions. 
  The method is explain in more detail by Van Maanen, De Jong, Van Rijn (2014) 
  <doi:10.1371/journal.pone.0106113> and Van Maanen, Couto, Lebreton, (2016) 
  <doi:10.1371/journal.pone.0167377>.
Distribution functions and test for over-representation of short
    distances in the Liland distribution. Simulation functions are included for
    comparison.
An R client for the "fixer.io" currency conversion and exchange 
  rate API. The API requires registration and some features are only available 
  on paid accounts. The full API documentation is available at 
  <https://fixer.io/documentation>.
Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial.
    The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>.
    Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
A set of functions that facilitate basic data manipulation and cleaning for statistical analysis including functions for finding and fixing duplicate rows and columns, missing values, outliers, and special characters in column and row names and functions for checking data consistency, distribution, quality, reliability, and structure.
Several generalized / directional Fixed Sequence Multiple Testing
    Procedures (FSMTPs) are developed for testing a sequence of pre-ordered
    hypotheses while controlling the FWER, FDR and Directional Error (mdFWER).
    All three FWER controlling generalized FSMTPs are designed under arbitrary
    dependence, which allow any number of acceptances. Two FDR controlling
    generalized FSMTPs are respectively designed under arbitrary dependence and
    independence, which allow more but a given number of acceptances. Two mdFWER
    controlling directional FSMTPs are respectively designed under arbitrary
    dependence and independence, which can also make directional decisions based
    on the signs of the test statistics. The main functions for each proposed
    generalized / directional FSMTPs are designed to calculate adjusted p-values
    and critical values, respectively. For users' convenience, the functions also
    provide the output option for printing decision rules.
Generate mock data in R using YAML configuration.
An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. 
    It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start 
    and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives 
    interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm
    themselves.
Satellite data collected between 2003 and 2022,
	in conjunction with gridded bathymetric data (50-150 m resolution),
	are used to estimate the irradiance reaching the bottom of a series of representative EU Arctic fjords. 
	An Earth System Science Data (ESSD) manuscript, Schlegel et al. (2024),	
	provides a detailed explanation of the methodology.
Analysis of Bayesian adaptive enrichment clinical trial using Free-Knot Bayesian Model Averaging (FK-BMA) method of Maleyeff et al. (2024) for Gaussian data. Maleyeff, L., Golchi, S., Moodie, E. E. M., & Hudson, M. (2024) "An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables" <doi:10.1093/biomtc/ujae141>.
This is a fast and flexible implementation of the Kalman
        filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in
        BLAS and LAPACK. Due to the speed of the filter, the fitting of
        high-dimensional linear state space models to large datasets
        becomes possible. This package also contains a plot function
        for the visualization of the state vector and graphical
        diagnostics of the residuals.
Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter/smoother.
Implements the method of Hofmeyr, D.P. (2021) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations.
  Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods
  for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected
  covariates.
  The package is accompanied by an instructive paper in the Journal of Statistical Software <doi:10.18637/jss.v101.i03>.
Tools and features for "Exploratory Landscape Analysis (ELA)" of
	single-objective continuous optimization problems.
    Those features are able to quantify rather complex properties, such as the
    global structure, separability, etc., of the optimization problems.
Three methods are implemented in R to facilitate the aggregations of flags in official statistics.  
            From the underlying flags the highest in the hierarchy, the most frequent, or with the highest total weight
            is propagated to the flag(s) for EU or other aggregates. Below there are some reference documents for the topic: 
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_v2_1.docx>,
            <https://sdmx.org/wp-content/uploads/CL_CONF_STATUS_1_2_2018.docx>,
            <http://ec.europa.eu/eurostat/data/database/information>,
            <http://www.oecd.org/sdd/33869551.pdf>,
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.pdf>.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.
Efficient implementations of the algorithms in the 
    Almost-Matching-Exactly framework for interpretable matching in causal
    inference. These algorithms match units via a learned, weighted Hamming
    distance that determines which covariates are more important to match on.
    For more information and examples, see the Almost-Matching-Exactly website.
A generative art system for producing tree-like
    images using an L-system to create the structures. The package 
    includes tools for generating the data structures and visualise
    them in a variety of styles.
Provides a variety of original and flexible user-friendly 
    statistical latent variable models for the simultaneous clustering and 
    segmentation of heterogeneous functional data (i.e time series, or more 
    generally longitudinal data, fitted by unsupervised algorithms, including 
    EM algorithms. Functional Latent Data Models for Clustering heterogeneous 
    curves ('FLaMingos') are originally introduced and written in 'Matlab' by
    Faicel Chamroukhi 
    <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. 
    The references are mainly the following ones.
    Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>.
    Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>.
    Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). <doi:10.1109/IJCNN.2011.6033590>.
    Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>.
    Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>.
    Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>.
    Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>.
    Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.
Tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017)  <doi:10.32614/RJ-2017-029>.
The Forecast Linear Augmented Projection (flap) method reduces 
    forecast variance by adjusting the forecasts of multivariate time series to 
    be consistent with the forecasts of linear combinations (components) of the 
    series by projecting all forecasts onto the space where the linear 
    constraints are satisfied. The forecast variance can be reduced 
    monotonically by including more components. For a given number of 
    components, the flap method achieves maximum forecast variance reduction 
    among linear projections.
Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output. For more information, please refer to <https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.
Create a flip over style Flash Card with desired data frame for Shiny application.
Fast implementation of hierarchical clustering
Methods for matrix factorization based on Wang and Stephens (2021) 
    <https://jmlr.org/papers/v22/20-589.html>.
Shed light on black box machine learning models by the help
    of model performance, variable importance, global surrogate models,
    ICE profiles, partial dependence (Friedman J. H. (2001)
    <doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W.
    (2016) <arXiv:1612.08468>), further effects plots, interaction
    strength, and variable contribution breakdown (Gosiewska and Biecek
    (2019) <arxiv:1903.11420>).  All tools are implemented to work with
    case weights and allow for stratified analysis.  Furthermore, multiple
    flashlights can be combined and analyzed together.
A fast and scalable linear mixed-effects model (LMM) estimation algorithm 
    for analysis of single-cell differential expression. The algorithm uses 
    summary-level statistics and requires less computer memory to fit the LMM.
Provides functions for creating flashcard decks of terms and 
    definitions. This package creates HTML slides using 'revealjs' that can be 
    viewed in the 'RStudio' viewer or a web browser.  Users can create 
    flashcards from either existing built-in decks or create their own from CSV
    files or vectors of function names.
Contingency Tables are a pain to work with when you want to run regressions.
  This package takes them, flattens them into a long data frame, so you can more easily analyse them!
  As well, you can calculate other related statistics. All of this is done so in a 'tidy' manner,
  so it should tie in nicely with 'tidyverse' series of packages.
Transformations that allow obtaining a flat table from
    reports in text or Excel format that contain data in the form of pivot
    tables. They can be defined for a single report and applied to a set
    of reports.
On import, the XML information is converted to a dataframe that reflects the hierarchical XML structure. Intuitive functions allow to navigate within this transparent XML data structure (without any knowledge of 'XPath'). 'flatXML' also provides tools to extract data from the XML into a flat dataframe that can be used to perform statistical operations. It also supports converting dataframes to XML.
Streamlines the process of updating changelogs (NEWS.md)
    and versioning R packages developed in git repositories.
Perform variable selection in settings with possibly missing data
    based on extrinsic (algorithm-specific) and intrinsic (population-level)
    variable importance. Uses a Super Learner ensemble to estimate the
    underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2023+) <arXiv:2202.12989>.
The main function kcca implements a general framework for
  k-centroids cluster analysis supporting arbitrary distance measures
  and centroid computation. Further cluster methods include hard
  competitive learning, neural gas, and QT clustering. There are
  numerous visualization methods for cluster results (neighborhood
  graphs, convex cluster hulls, barcharts of centroids, ...), and
  bootstrap methods for the analysis of cluster stability.
Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. 
            Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.
Format for converting an R Markdown document to a grid
    oriented dashboard. The dashboard flexibly adapts the size of it's
    components to the containing web page.
Provides tools to work with the Flexible Dirichlet
    distribution. The main features are an E-M algorithm for computing the maximum
    likelihood estimate of the parameter vector and a function based on conditional
    bootstrap to estimate its asymptotic variance-covariance matrix. It contains
    also functions to plot graphs, to generate random observations and to handle
    compositional data.
Provides tools for flexible non-linear least squares model fitting using general-purpose optimization techniques. The package supports a variety of optimization algorithms, including those provided by the 'optimx' package, making it suitable for handling complex non-linear models. Features include parallel processing support via the 'future' and 'foreach' packages, comprehensive model diagnostics, and visualization capabilities. Implements methods described in Nash and Varadhan (2011, <doi:10.18637/jss.v043.i09>).
Standard generalized additive models assume a response function, 
    which induces an assumption on the shape of the distribution of the 
    response. However, miss-specifying the response function results in biased 
    estimates. Therefore in Spiegel et al. (2017) 
    <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function
    jointly with the covariate effects. This package provides the underlying 
    functions to estimate these generalized additive models with flexible 
    response functions. The estimation is based on an iterative algorithm. In 
    the outer loop the response function is estimated, while in the inner loop 
    the covariate effects are determined. For the response function a strictly
    monotone P-spline is used while the covariate effects are estimated based on
    a modified Fisher-Scoring algorithm. Overall the estimation relies on the 
    'mgcv'-package.
Provides functions to switch the 'BLAS'/'LAPACK' optimized backend
    and change the number of threads without leaving the R session, which needs
    to be linked against the 'FlexiBLAS' wrapper library
    <https://www.mpi-magdeburg.mpg.de/projects/flexiblas>.
Exports 'flextable' objects to 'xlsx' files, 
    utilizing functionalities provided by 'flextable' and 'openxlsx2'.
Application of the filtered monotonic polynomial (FMP) item response
    model to flexibly fit item response models. The package includes tools that
    allow the item response model to be build on any monotonic transformation of
    the latent trait metric, as described by Feuerstahler (2019) <doi:10.1007/s11336-018-9642-9>.
A general framework for finite mixtures of regression
  models using the EM algorithm is implemented. The E-step and all
  data handling are provided, while the M-step can be supplied by the
  user to easily define new models. Existing drivers implement
  mixtures of standard linear models, generalized linear models and
  model-based clustering.
The fitting of mixtures of generalized nonlinear models is implemented as an extension of the existing package 'flexmix'.
A general estimation framework for multi-state Markov processes with flexible specification of the transition intensities.
    The log-transition intensities can be specified through Generalised Additive Models which allow for virtually any type of covariate
    effect. Elementary specifications such as time-homogeneous processes and simple parametric forms are also supported. There are 
    no limitations on the type of process one can assume, with both forward and backward transitions allowed and virtually any number
    of states.
Provides flexible odds ratio curves that enable modeling non-linear relationships between continuous predictors and binary outcomes. This package facilitates a deeper understanding of the impact of each continuous predictor on the outcome by presenting results in terms of odds ratio (OR) curves based on splines. These curves allow for comparison against a specified reference value, aiding in the interpretation of the predictor's effect.
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.
Binding to the C++ implementation of the flexible polyline
    encoding by HERE <https://github.com/heremaps/flexible-polyline>. The
    flexible polyline encoding is a lossy compressed representation of a list of
    coordinate pairs or coordinate triples. The encoding is achieved by:
    (1) Reducing the decimal digits of each value;
    (2) encoding only the offset from the previous point;
    (3) using variable length for each coordinate delta; and
    (4) using 64 URL-safe characters to display the result.
Functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous  responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) <doi:10.1002/sim.8406>) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials),  available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) <doi:10.1002/sim.9005>), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided.
Implementation of the Stochastic Expectation Maximisation (StEM) approach to Record Linkage described in the paper by K. Robach, S. L. van der Pas, M. A. van de Wiel and M. H. Hof (2024, <doi:10.48550/arXiv.2407.06835>); see 'citation("FlexRL")' for details. This is a record linkage method, for finding the common set of records among 2 data sources based on Partially Identifying Variables (PIVs) available in both sources. It includes modelling of dynamic Partially Identifying Variables (e.g. postal code) that may evolve over time and registration errors (missing values and mistakes in the registration). Low memory footprint.
Package for parametric relative survival analyses. It allows to model non-linear and 
        non-proportional effects and both non proportional and non linear effects, using splines (B-spline and truncated power basis), Weighted Cumulative Index of Exposure effect, with correction model for 
        the life table. Both non proportional and non linear effects are described in 
			Remontet, L. et al. (2007) <doi:10.1002/sim.2656> and 
			Mahboubi, A. et al. (2011) <doi:10.1002/sim.4208>.
An easy way to conduct flexible scan.
    Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile.
    A table with formal style and a map with clusters are included in the result report.
    The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
A drop-in replacement for 'flexdashboard' 'Rmd' documents, which
  implements an after-knit-hook to split the generated single page application
  in one document per main section to reduce rendering load in the web browser
  displaying the document. Put all 'JavaScript' stuff needed in all sections 
  before the first headline featuring navigation menu attributes. This package 
  is experimental and maybe replaced by a solution inside 'flexdashboard'.
Flexible parametric models for time-to-event data,
    including the Royston-Parmar spline model, generalized gamma and
    generalized F distributions.  Any user-defined parametric
    distribution can be fitted, given at least an R function defining
    the probability density or hazard. There are also tools for
    fitting and predicting from fully parametric multi-state models,
    based on either cause-specific hazards or mixture models.
Flexible parametric mixture and non-mixture cure models for time-to-event data.
Use a grammar for creating and customizing pretty tables.
    The following formats are supported: 'HTML', 'PDF', 'RTF', 'Microsoft
    Word', 'Microsoft PowerPoint' and R 'Grid Graphics'.  'R Markdown',
    'Quarto' and the package 'officer' can be used to produce the result
    files. The syntax is the same for the user regardless of the type of
    output to be produced. A set of functions allows the creation,
    definition of cell arrangement, addition of headers or footers,
    formatting and definition of cell content with text and or images. The
    package also offers a set of high-level functions that allow tabular
    reporting of statistical models and the creation of complex cross
    tabulations.
Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).
Provides an interface to the Flickr API <https://www.flickr.com/services/api/>
      and allows R users to download data on Flickr.
Functions to create and manipulate 
    FIFO (First In First Out), LIFO (Last In First Out), and NINO (Not In or Never Out) 
    stacks in R.
Provides functionality to plot airplane flight paths on maps. The plotted flight paths follow the great circle of the Earth.
Spatio-temporal locations of an animal are computed 
    from annotated data with a hidden Markov  model via particle
    filter algorithm. The package is relatively robust to varying
    degrees of shading.
    The hidden Markov model is described in Movement Ecology - Rakhimberdiev et al. (2015) <doi:10.1186/s40462-015-0062-5>,
    general package description is in the Methods in Ecology and Evolution - Rakhimberdiev et al. (2017) <doi:10.1111/2041-210X.12765>
    and package accuracy assessed in the Journal of Avian Biology - Rakhimberdiev et al. (2016) <doi:10.1111/jav.00891>.
Download flight and airport data from Brazil’s Civil Aviation Agency
             (ANAC) <https://www.gov.br/anac/pt-br>. The data covers detailed 
             information on aircraft, airports, and airport operations registered 
             with ANAC. It also includes data on airfares, all international 
             flights to and from Brazil, and domestic flights  within the country.
Likelihood-free inference method for stochastic models.
    Uses a deterministic optimizer on simple simulations of the model
    that are performed with a prior drawn randomness by applying the inverse transform method.
    Is designed to work on its own and also by using the Julia package 'Jflimo'
    available on the git page of the project: <https://metabarcoding.org/flimo>.
An R interface to 'FLINT' <https://flintlib.org/>, a C library for
	number theory.  'FLINT' extends GNU 'MPFR' <https://www.mpfr.org/>
	and GNU 'MP' <https://gmplib.org/> with support for arithmetic in
	standard rings (the integers, the integers modulo n, the rational,
	p-adic, real, and complex numbers) as well as vectors, matrices,
	polynomials, and power series over rings.  'FLINT' implements
	midpoint-radius interval arithmetic, also known as ball arithmetic,
	in the real and complex numbers, enabling computation in arbitrary
	precision with rigorous propagation of errors; see Johansson (2017)
	<doi:10.1109/TC.2017.2690633>.  Finally, 'FLINT' provides ball
	arithmetic implementations of many special mathematical functions,
	with high coverage of reference works such as the NIST Digital
	Library of Mathematical Functions <https://dlmf.nist.gov/>.  The R
	interface defines S4 classes, generic functions, and methods for
	representation and basic operations as well as plain R functions
	matching and vectorizing entry points in the C library.
Given a multivariate dataset and some knowledge about the dependencies 
              between its features, it is customary to fit a statistical model to the features 
              to infer parameters of interest. Such a procedure implicitly assumes that the 
              sample is exchangeable. This package provides a flexible non-parametric test 
              of this exchangeability assumption, allowing the user to specify the feature 
              dependencies by hand as long as features can be grouped into disjoint independent sets. 
              This package also allows users to test a dual hypothesis, which is, given that the 
              sample is exchangeable, does a proposed grouping of the features into disjoint sets
              also produce statistically independent sets of features? See Aw, Spence and Song (2023) 
              for the accompanying paper.
It implements many univariate and multivariate permutation (and
    rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear
    models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney,
    Kruskal-Wallis), Sign test and Mc Nemar. Test on Linear Models are
    performed also in presence of covariates (i.e. nuisance parameters).
    The permutation and the rotation methods to get the null distribution of
    the test statistics are available. It also implements methods for
    multiplicity control such as Westfall & Young minP procedure and Closed
    Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed
    effects in mixed effects models.
Flipbooks present code step-by-step and side-by-side with its output.  'flipbookr' helps creators build flipbooks efficiently because code pipelines are automatically parsed and prepped for presentation as flipbooks.
Allows the user to create a countdown in 'RMarkdown' documents and 'shiny' applications. 
    The package is a wrapper of the 'JavaScript' library 'flipdown.js'. See <https://pbutcher.uk/flipdown/> for more info.
Include a countdown <https://github.com/PButcher/flipdown> in all R contexts with the convenience of 'htmlwidgets'.
The lipid scrambling activity of protein extracts and purified
    scramblases is often determined using a fluorescence-based assay involving
    many manual steps. flippant offers an integrated solution for the analysis
    and publication-grade graphical presentation of dithionite scramblase
    assays, as well as a platform for review, dissemination and extension of the
    strategies it employs. The package's name derives from a play on the fact
    that lipid scrambling is also sometimes referred to as 'flipping'.
    The package is originally published as Cotton, R.J., Ploier, B., Goren,
    M.A., Menon, A.K., and Graumann, J. (2017). "flippant–An R package for the
    automated analysis of fluorescence-based scramblase assays." BMC
    Bioinformatics 18, 146. <DOI:10.1186/s12859-017-1542-y>.
A flexible permutation framework for making 
    inference such as point estimation, confidence 
    intervals or hypothesis testing, on any kind of data, 
    be it univariate, multivariate, or more complex such 
    as network-valued data, topological data, functional 
    data or density-valued data.
Provides robust tests for testing in GLMs, by sign-flipping score contributions. The tests are robust against overdispersion, heteroscedasticity and, in some cases, ignored nuisance variables. See Hemerik, Goeman and Finos (2020) <doi:10.1111/rssb.12369>.
R comes with a suite of utilities for linear algebra with "numeric"
    (double precision) vectors/matrices. However, sometimes single precision (or
    less!) is more than enough for a particular task.  This package extends R's
    linear algebra facilities to include 32-bit float (single precision) data.
    Float vectors/matrices have half the precision of their "numeric"-type
    counterparts but are generally faster to numerically operate on, for a
    performance vs accuracy trade-off.  The internal representation is an S4
    class, which allows us to keep the syntax identical to that of base R's.
    Interaction between floats and base types for binary operators is generally
    possible; in these cases, type promotion always defaults to the higher
    precision.  The package ships with copies of the single precision 'BLAS' and
    'LAPACK', which are automatically built in the event they are not available
    on the system.
Converts files to and from flobs.  A flob is a file that was
    read into binary in integer-mode as little endian, saved as the single
    element of a named list (where the name is the name of the original
    file) and then serialized before being coerced into a blob.  Flobs are
    useful for writing and reading files to and from databases.
Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.
Fit occupancy models in 'Stan' via 'brms'. The full variety
    of 'brms' formula-based effects structures are available to use in
    multiple classes of occupancy model, including single-season
    models, models with data augmentation for never-observed species,
    dynamic (multiseason) models with explicit colonization and extinction
    processes, and dynamic models with autologistic occupancy dynamics.
    Formulas can be specified for all relevant distributional terms,
    including detection and one or more of occupancy, colonization,
    extinction, and autologistic depending on the model type. Several
    important forms of model post-processing are provided.  References:
    Bürkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017)
    <doi:10.18637/jss.v076.i01>; Socolar & Mills (2023)
    <doi:10.1101/2023.10.26.564080>.
Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations).
	For details see Kinsvater et al. (2017) <arXiv:1701.06455>.
Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers.
    The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and
    Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).
Provides an efficient 'C++' code for computing an
 optimal segmentation model
 with Poisson loss,
 up-down constraints,
 and label constraints,
 as described by Kaufman et al. (2024) <doi:10.1080/10618600.2023.2293216>.
Tools to quickly compile taxonomic and distribution data from
    the Brazilian Flora 2020.
A collection of functions designed to retrieve, filter and spatialize data from the Flora e Funga do Brasil dataset. For more information about the dataset, please visit <https://floradobrasil.jbrj.gov.br/consulta/>.
Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.
Interactive data visualization for data practitioners. 'flourishcharts' allows users to visualize their data 
  using 'Flourish' graphs that are grounded in data storytelling principles. Users can create racing bar & line
  charts, as well as other interactive elements commonly found in 'D3' graphics, easily in 'R' and 'Python'. 
  The package relies on an enterprise API provided by 'Flourish', a data visualization platform <https://developers.flourish.studio/api/introduction/>.
Visualize as flow diagrams the logic of functions, expressions or 
  scripts in a static way or when running a call, visualize the dependencies between
  functions or between modules in a shiny app, and more.
Creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way.
Computes unidimensional and multidimensional Reciprocity and Inaccuracy indices. These indices are applicable to common heterostylous populations and to any other type of stylar dimorphic and trimorphic populations, such as in enantiostylous and three-dimensional heterostylous plants. Simón-Porcar, V., A. J. Muñoz-Pajares, J. Arroyo, and S. D. Johnson. (in press) "FlowerMate: multidimensional reciprocity and inaccuracy indices for style-polymorphic plant populations."
Create interactive flow maps using 'FlowmapBlue' 'TypeScript'
    library <https://github.com/FlowmapBlue/FlowmapBlue>, which is a free
    tool for representing aggregated numbers of movements between
    geographic locations as flow maps. It is used to visualize urban
    mobility, commuting behavior, bus, subway and air travels, bicycle
    sharing, human and bird migration, refugee flows, freight
    transportation, trade, supply chains, scientific collaboration,
    epidemiological and historical data and many other topics. The package
    allows to either create standalone flow maps in form of 'htmlwidgets'
    and save them in 'HTML' files, or integrate flow maps into 'Shiny'
    applications.
Adds flow maps to 'ggplot2' plots. The flow maps consist of 'ggplot2' layers which visualize the nodes as circles and the bilateral flows between the nodes as bidirectional half-arrows.
Provides functionality to perform machine-learning-based modeling in a computation pipeline.
    Its functions contain the basic steps of machine-learning-based knowledge discovery workflows,
    including model training and optimization, model evaluation, and model testing.
    To perform these tasks, the package builds heavily on existing machine-learning packages,
    such as 'caret' <https://github.com/topepo/caret/> and associated packages.
    The package can train multiple models, optimize model hyperparameters by performing a grid search
    or a random search, and evaluates model performance by different metrics.
    Models can be validated either on a test data set, or in case of a small sample size
    by k-fold cross validation or repeated bootstrapping.
    It also allows for 0-Hypotheses generation by performing permutation experiments.
    Additionally, it offers methods of model interpretation and item categorization
    to identify the most informative features from a high dimensional data space.
    The functions of this package can easily be integrated into computation pipelines
    (e.g. 'nextflow' <https://www.nextflow.io/>) and hereby improve scalability,
    standardization, and re-producibility in the context of machine-learning.
An application to calculate the daily environmental costs of river flow regulation by dams based on García de Jalon et al. 2017 <doi:10.1007/s11269-017-1663-0>.
Screens daily streamflow time series for temporal trends and
    change-points. This package has been primarily developed for assessing the
    quality of daily streamflow time series. It also contains tools for plotting
    and calculating many different streamflow metrics. The package can be used to
    produce summary screening plots showing change-points and significant temporal
    trends for high flow, low flow, and/or baseflow statistics, or it can be used
    to perform more detailed hydrological time series analyses. The package was
    designed for screening daily streamflow time series from Water Survey Canada
    and the United States Geological Survey but will also work with streamflow time
    series from many other agencies.
Useful functions to standardize software outputs from ProteomeDiscoverer, Spectronaut, DIA-NN and MaxQuant on precursor, modified peptide and proteingroup level and to trace software differences for identifications such as varying proteingroup denotations for common precursor.
Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023). <doi:10.1007/978-3-031-27781-8_25>. This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.
Implements a path algorithm for the Fused Lasso Signal Approximator.
    For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>.
Designed to streamline the process of analyzing genotyping data from Fluidigm machines, this package offers a suite of tools for data handling and analysis. It includes functions for converting Fluidigm data to format used by 'PLINK', estimating errors, calculating pairwise similarities, determining pairwise similarity loci, and generating a similarity matrix.
Bindings to 'libfluidsynth' to parse and synthesize MIDI files. It can
    read MIDI into a data frame, play it on the local audio device, or convert into
    an audio file.
Functions to estimate the mortality attributable to 
    influenza and temperature, using distributed-lag nonlinear models
    (DLNMs), as first implemented in 
    Lytras et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.14.1800118>.
    Full descriptions of underlying DLNM methodology in Gasparrini et al. 
    <doi:10.1002/sim.3940> (DLNMs), 
    <doi:10.1186/1471-2288-14-55> (attributable risk from DLNMs) and 
    <doi:10.1002/sim.5471> (multivariate meta-analysis).
Use spectrophotometry measurements performed on insects as a way to infer pathogens 
      virulence. Insect movements cause fluctuations in fluorescence signal, and functions are 
      provided to estimate when the insect has died as the moment when variance in autofluorescence 
      signal drops to zero. The package provides functions to obtain this estimate together with 
      functions to import spectrophotometry data from a Biotek microplate reader. Details of the method 
      are given in Parthuisot et al. (2018) <doi:10.1101/297929>.
A model for leaf fluorescence, reflectance and transmittance spectra. It implements
    the model introduced by Vilfan et al. (2016) <DOI:10.1016/j.rse.2016.09.017>. Fluspect-B
    calculates the emission of ChlF on both the illuminated and shaded side of the leaf.
    Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter 
    and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF 
    quantum efficiency for the two photosystems, PS-I and PS-II.
Functions for the calculation of greenhouse gas flux rates 
	from closed chamber concentration measurements. The package follows 
	a modular concept: Fluxes can be calculated in just two simple steps 
	or in several steps if more control in details is wanted. Additionally 
	plot and preparation functions as well as functions for modelling
	gpp and reco are provided.
Processes the raw data from closed loop flux chamber (or tent)
    setups into ecosystem gas fluxes usable for analysis. It goes from a
    data frame of gas concentration over time (which can contain several
    measurements) and a meta data file indicating which measurement was done
    when, to a data frame of ecosystem gas fluxes including quality diagnostics.
    Functions provided include different models (exponential as described in
    Zhao et al (2018) <doi:10.1016/j.agrformet.2018.08.022>, quadratic and
    linear) to estimate the fluxes from the raw data, quality assessment,
    plotting for visual check and calculation of fluxes based on the setup
    specific parameters (chamber size, plot area, ...).
Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.
Functions for range estimation in birds based on Pennycuick (2008)
    and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008)
    requires manual entry of birds which can be tedious when there are thousands
    of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975)
    and time-marching computation method "constant muscle mass" as in Pennycuick (1998).
    See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>,
    and Pennycuick (2008, ISBN:9780080557816).
Functions for range estimation in birds based on Pennycuick (2008)
    and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008)
    requires manual entry of birds which can be tedious when there are hundreds
    of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975)
    and time-marching computation methods as in Pennycuick (1998) and Pennycuick (2008).
    See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>,
    and Pennycuick (2008, ISBN:9780080557816).
All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.
Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.
The Fill-Mask Association Test ('FMAT')
    <doi:10.1037/pspa0000396>
    is an integrative and probability-based method using
    Masked Language Models to measure conceptual associations
    (e.g., attitudes, biases, stereotypes, social norms, cultural values)
    as propositions in natural language.
    Supported language models include 'BERT'
    <doi:10.48550/arXiv.1810.04805> and its variants available at 'Hugging Face'
    <https://huggingface.co/models?pipeline_tag=fill-mask>.
    Methodological references and installation guidance are provided at
    <https://psychbruce.github.io/FMAT/>.
Generate cost effective minimally changed run sequences 
              for symmetrical as well as asymmetrical factorial 
              designs.
Current status data abounds
            in the field of epidemiology and public health, where the only observable data for a subject is
            the random inspection time and the event status at inspection. Motivated by such a current status
            data from a periodontal study where data are inherently clustered,
            we propose a unified methodology to analyze such complex data.
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC)
         framework for implementing Metropolis-Hastings algorithm in a modular way
         allowing users to specify automatic convergence checker, personalized
         transition kernels, and out-of-the-box multiple MCMC chains using
         parallel computing. Most of the methods implemented in this package can
         be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods
         included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5>
         Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5>
         Robust Adaptive Metropolis, and Thawornwattana et
         al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.
Functions for performing (external) multidimensional unfolding.
             Restrictions (fixed coordinates or model restrictions) are available for both row and column coordinates in all combinations.
Provides functions to help in fitting models to data, to
  perform Monte Carlo, sensitivity and identifiability analysis. It is
  intended to work with models be written as a set of differential
  equations that are solved either by an integration routine from
  package 'deSolve', or a steady-state solver from package
  'rootSolve'. However, the methods can also be used with other types of
  functions.
Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and 'fmeffects' computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.
Heterogeneity pursuit methodologies for regularized finite mixture regression by effects-model formulation proposed by Li et al. (2021) <arXiv:2003.04787>.
Generate planar and spherical triangle meshes,
    compute finite element calculations for 1- and 2-dimensional flat and curved
    manifolds with associated basis function spaces, methods for lines and
    polygons, and transparent handling of coordinate reference systems and
    coordinate transformation, including 'sf' and 'sp' geometries. The core
    'fmesher' library code was originally part of the 'INLA' package, and
    implements parts of "Triangulations and Applications" by
    Hjelle and Daehlen (2006) <doi:10.1007/3-540-33261-8>.
A fast class noise detector which provides noise score for each observations. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
Provides a collection of functions to fit and explore single, multi-component and restricted Frequency Modulated Moebius (FMM) models. 'FMM' is a nonlinear parametric regression model capable of fitting non-sinusoidal shapes in rhythmic patterns. Details about the mathematical formulation of 'FMM' models can be found in Rueda et al. (2019) <doi:10.1038/s41598-019-54569-1>.
Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.
Provides a flexible interface to the 'Financial Modeling Prep' API 
    <https://site.financialmodelingprep.com/developer/docs>. The package 
    supports all available endpoints and parameters, enabling R users 
    to interact with a wide range of financial data.
Use R to access to the 'FMP Cloud' API <https://fmpcloud.io/> and 
    'Financial Modeling Prep' API <https://financialmodelingprep.com/developer/docs/>.
    Data available includes stock prices, market indexes, company fundamentals,
    13F holdings data, and much more. A valid API token must be set to enable
    functions.
Functions that support stable prediction and classification with radiomics data through factor-analytic modeling. For details, see Peeters et al. (2019) <arXiv:1903.11696>.
Contains R-functions to perform an fMRI analysis as described in
             Polzehl and Tabelow (2019) <DOI:10.1007/978-3-030-29184-6>,
             Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>,
             Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>,
             Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Methods for performing fMRI quality assurance (QA) measurements of
  test objects. Heavily  based on the fBIRN procedures detailed by Friedman and 
  Glover (2006) <doi:10.1002/jmri.20583>.
Data-driven fMRI denoising with projection scrubbing (Pham et al 
    (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for 
    DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) 
    <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al 
    (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical 
    Components Correction) (Muschelli et al (2014)
    <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance
    regression. Projection scrubbing is also applicable to other
    outlier detection tasks involving high-dimensional data.
Supports fMRI (functional magnetic resonance imaging) 
    analysis tasks including reading in 'CIFTI', 'GIFTI' and 
    'NIFTI' data, temporal filtering, nuisance regression, and 
    aCompCor (anatomical Components Correction) (Muschelli et al.
    (2014) <doi:10.1016/j.neuroimage.2014.03.028>).
Several utility functions for the book entitled 
	"Practices of Medical and Health Data Analysis using R"
	(Pearson Education Japan, 2007) with Japanese demographic
	data and some demographic analysis related functions.
Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.
Some basic procedures for dealing
        with log maximally skew stable distributions, which are also
        called finite moment log stable distributions.
The FMT method computes posterior residual variances to be used in
  the denominator of a moderated t-statistic from a linear model analysis of
  gene expression data. It is an extension of the moderated t-statistic
  originally proposed by Smyth (2004) <doi:10.2202/1544-6115.1027>.
  LOESS local regression and empirical Bayesian method are used to estimate
  gene specific prior degrees of freedom and prior variance based on average
  gene intensity levels. The posterior residual variance in the denominator is
  a weighted average of prior and residual variance and the weights are prior
  degrees of freedom and residual variance degrees of freedom. The degrees of
  freedom of the moderated t-statistic is simply the sum of prior and residual
  variance degrees of freedom.
Contains a set of functions that can be used to apply
  formats to data frames or vectors.  The package aims to provide 
  functionality similar to that of SAS® formats. Formats are assigned to
  the format attribute on data frame columns.  Then when the fdata() 
  function is called, a new data frame is created with the column data
  formatted as specified.  The package also contains a value() function
  to create a user-defined format, similar to a SAS® user-defined format.
A collection of functions inspired by Venables and Ripley (2002) <doi:10.1007/978-0-387-21706-2>
             and Azzalini and Capitanio (1999) <arXiv:0911.2093> to manage, investigate and analyze
             bivariate and multivariate data sets of financial returns.
A parametrization framework for finite mixture
       distribution using S4 objects. Density, cumulative
       density, quantile and simulation functions are defined.
       Currently normal, Tukey g-&-h, skew-normal and skew-t
       distributions are well tested. The gamma, negative
       binomial distributions are being tested.
Implements methods for network estimation and forecasting of high-dimensional time series 
    exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model.
    See Barigozzi, Cho and Owens (2024+) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and 
    Owens, Cho and Barigozzi (2024+) <arXiv:2301.11675> accompanying the R package.
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
        including KNN classification, regression and information measures are implemented.
Provides a collection of functions for testing various aspects of
	univariate time series including independence and neglected
	nonlinearities. Further provides functions to investigate the chaotic
	behavior of time series processes and to simulate different types of chaotic
	time series maps.
Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.
Provides a 'C++' API for routinely used numerical tools such as integration,
    root-finding, and optimization, where function arguments are given as
    lambdas. This facilitates 'Rcpp' programming, enabling the development of
    'R'-like code in 'C++' where functions can be defined on the fly and use
    variables in the surrounding environment.
Feature Ordering by Conditional Independence (FOCI) is a variable selection algorithm based on the measure of conditional dependence.
    For more information, see the paper: Azadkia and Chatterjee (2019),"A simple measure of conditional dependence" <arXiv:1910.12327>.
Takes a distance matrix and plots it as an 
    interactive graph. One point is focused at the center of the graph,
    around which all other points are plotted in their exact distances as
    given in the distance matrix. All other non-focus points are plotted 
    as best as possible in relation to one another. Double click on any 
    point to choose a new focus point, and hover over points to see their
    ID labels. If color label categories are given, hover over colors in 
    the legend to highlight only those points and click on colors to 
    highlight multiple groups. For more information on the rationale 
    and mathematical background, as well as an interactive introduction,
    see <https://lea-urpa.github.io/focusedMDS.html>.
The CRAN check results and where your package stands in the
    CRAN submission queue in your R terminal.
A novel forward stepwise discriminant analysis framework 
    that integrates Pillai's trace with Uncorrelated Linear Discriminant Analysis (ULDA), 
    providing an improvement over traditional stepwise LDA methods that rely on Wilks' Lambda. 
    A stand-alone ULDA implementation is also provided, offering a more general solution 
    than the one available in the 'MASS' package. It automatically handles missing values and 
    provides visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2409.03136>.
Supports the use of standardized folder names.
Datasets for teaching quantitative approaches and modeling in
    archaeology and paleontology. This package provides several types of
    data related to broad topics (cultural evolution, radiocarbon dating,
    paleoenvironments, etc.), which can be used to illustrate statistical
    methods in the classroom (multivariate data analysis, compositional
    data analysis, diversity measurement, etc.).
Extend 'shiny.semantic' with extra 'Fomantic UI' components.
    Create pages in a format similar to 'shiny', form validation and more.
Easily and flexibly insert 'Font Awesome' icons into 'R Markdown'
    documents and 'Shiny' apps. These icons can be inserted into HTML content
    through inline 'SVG' tags or 'i' tags. There is also a utility function for
    exporting 'Font Awesome' icons as 'PNG' images for those situations where
    raster graphics are needed.
Provides fonts licensed under the 'Bitstream Vera Fonts'
    license for the 'fontquiver' package.
Computer Modern font for use with extrafont package
A placeholder for the Liberation fontset intended for the
    `fontquiver` package. This fontset covers the 12 combinations of
    families (sans, serif, mono) and faces (plain, bold, italic, bold
    italic) supported in R graphics devices.
Provides a set of fonts with permissive licences. This is
    useful when you want to avoid system fonts to make sure your
    outputs are reproducible.
Displays a weighted undirected food graph from an adjacency matrix.
    Can perform confidence-interval bootstrap inference with mutual information
    or maximal information coefficient.
    Based on my Master 1 internship at the Bordeaux Population Health center.
    References : Reshef et al. (2011) <doi:10.1126/science.1205438>,
    Meyer et al. (2008) <doi:10.1186/1471-2105-9-461>,
    Liu et al. (2016) <doi:10.1371/journal.pone.0158247>.
Aids in analysing data from a food frequency questionnaire known as 
    the Harvard Service Food Frequency Questionnaire (HSFFQ).
    Functions from this package use answers from the HSFFQ to generate 
    estimates of daily consumed micronutrients, calories, macronutrients on 
    an individual level.
    The package also calculates food quotients on individual and group levels. 
    Foodquotient calculation is an often tedious step in the calculation of 
    total human energy expenditure (TEE) using the doubly labeled water method, 
    which is the gold standard for measuring TEE.
Enhances the functionality of the mvbutils::foodweb() program. The matrix-format output of the original program contains identical row names and column names, each name representing a retrieved function. This format is enhanced by using the find_funs() program [see Sebastian (2017) <https://sebastiansauer.github.io/finds_funs/>] to concatenate the package name to the function name. Each package is assigned a unique color, that is used to color code the text naming the packages and the functions. This color coding is extended to the entries of value "1" within the matrix, indicating the pattern of ancestor and descendent functions.
Basic analysis of all penalties taken in the German men's Bundesliga
  between the start of its inaugural season and May 2017. The main functions are
  suitable printing and plotting functions. Flexible selection of a player is
  supported via grep. Missed penalties can easily be included or excluded, depending
  on the user's wishes.
This is the first package allowing for the estimation,
             visualization and prediction of the most well-known 
             football models: double Poisson, bivariate Poisson,
             Skellam, student_t, diagonal-inflated bivariate Poisson, and
             zero-inflated Skellam. The package allows Hamiltonian
             Monte Carlo (HMC) estimation through the underlying Stan
             environment and Maximum Likelihood estimation (MLE, for 
             'static' models only). The model construction relies on
             the most well-known football references, such as 
             Dixon and Coles (1997) <doi:10.1111/1467-9876.00065>,
             Karlis and Ntzoufras (2003) <doi:10.1111/1467-9884.00366> and
             Egidi, Pauli and Torelli (2018) <doi:10.1177/1471082X18798414>.
A handy tool to calculate carbon footprints from
    air travel based on three-letter International Air Transport Association (IATA) airport codes or latitude and longitude.
    footprint first calculates the great-circle distance between departure and arrival 
    destinations. It then uses the Department of Environment, Food & Rural Affairs (DEFRA) 
    greenhouse gas conversion factors for business air travel to estimate the carbon footprint.
    These conversion factors consider trip length, flight class (e.g. economy, business), and emissions 
    metric (e.g. carbon dioxide equivalent, methane).
Tools for quickly processing and analyzing 
	field observation data and air quality data. This 
	tools contain functions that facilitate analysis 
	in atmospheric chemistry (especially in ozone 
	pollution). Some functions of time series are also 
	applicable to other fields. For detail please view 
	homepage<https://github.com/tianshu129/foqat>.
	Scientific Reference:
	1. The Hydroxyl Radical (OH) Reactivity: Roger Atkinson and Janet Arey (2003) <doi:10.1021/cr0206420>.
	2. Ozone Formation Potential (OFP): <http://ww2.arb.ca.gov/sites/default/files/barcu/regact/2009/mir2009/mir10.pdf>, Zhang et al.(2021) <doi:10.5194/acp-21-11053-2021>.
	3. Aerosol Formation Potential (AFP): Wenjing Wu et al. (2016) <doi:10.1016/j.jes.2016.03.025>.
	4. TUV model: <https://www2.acom.ucar.edu/modeling/tropospheric-ultraviolet-and-visible-tuv-radiation-model>.
Several functions to compute indicators for organization and efficiency in visual foraging, multi-target visual search, and cancellation tasks. The current version of this package includes the following indicators: best-r, mean Inter-target Distance, Percentage Above Optimal (PAO) scan path, and intersections in the scan path. For more detailed descriptions, see Mark et al. (2004) <doi:10.1212/01.WNL.0000131947.08670.D4>.
SHE, FORAM Index and ABC Method analyses and custom plot
        functions for community data.
Helpers for reordering factor levels (including moving
    specified levels to front, ordering by first appearance, reversing,
    and randomly shuffling), and tools for modifying factor levels
    (including collapsing rare levels into other, 'anonymising', and
    manually 'recoding').
Process raw force-plate data (txt-files) by segmenting them into trials and, if needed, calculating (user-defined) descriptive 
    statistics of variables for user-defined time bins (relative to trigger onsets) for each trial. When segmenting the data a baseline 
    correction, a filter, and a data imputation can be applied if needed. Experimental data can also be processed and combined with the 
    segmented force-plate data. This procedure is suggested by Johannsen et al. (2023) <doi:10.6084/m9.figshare.22190155> and some of the 
    options (e.g., choice of low-pass filter) are also suggested by Winter (2009) <doi:10.1002/9780470549148>.
For cleaning and analysis of graphs, such as animal closing force 
    measurements. 
    'forceR' was initially written and optimized to deal with insect bite force 
    measurements, but can be used for any time series. Includes a full workflow 
    to load, plot and crop data, correct amplifier and baseline drifts, 
    identify individual peak shapes (bites), rescale (normalize) peak curves, 
    and find best polynomial fits to describe and analyze force curve shapes.
Support for the foreach looping construct.  Foreach is an
        idiom that allows for iterating over elements in a collection,
        without the use of an explicit loop counter.  This package in
        particular is intended to be used for its return value, rather
        than for its side effects.  In that sense, it is similar to the
        standard lapply function, but doesn't require the evaluation
        of a function.  Using foreach without side effects also
        facilitates executing the loop in parallel.
Implementation of Forecastable Component Analysis ('ForeCA'),
    including main algorithms and auxiliary function (summary, plotting, etc.) to
    apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension
    reduction (DR) technique for temporally dependent signals. Contrary to other
    popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency
    explicitly into account and searches for the most ''forecastable'' signal.
    The measure of forecastability is based on the Shannon entropy of the spectral
    density of the transformed signal.
Methods and tools for displaying and analysing
             univariate time series forecasts including exponential smoothing
             via state space models and automatic ARIMA modelling.
Provides geometric- and regression-based forecast
    combination methods under a unified user interface for the packages 'ForecastCombinations'
    and 'GeomComb'. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity). 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>, 
    and Clemen RT (1989). <doi:10.1016/0169-2070(89)90012-5>.
Aim: Supports the most frequently used methods to combine forecasts. Among others: Simple average, Ordinary Least Squares, Least Absolute Deviation, Constrained Least Squares, Variance-based, Best Individual model, Complete subset regressions and Information-theoretic (information criteria based).
A web application for displaying, analysing and forecasting univariate time series. Includes basic methods such as mean, naïve, seasonal naïve and drift, as well as more complex methods such as Holt-Winters Box,G and Jenkins, G (1976) <doi:10.1111/jtsa.12194> and ARIMA Brockwell, P.J. and R.A.Davis (1991) <doi:10.1007/978-1-4419-0320-4>.
Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights
    based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>),
    or cross-validated weights. Cross validation for time series data with user-supplied models
    and forecasting functions is also supported to evaluate model accuracy.
Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.
The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
Methods to compute linear h-step ahead prediction coefficients based
    on localised and iterated Yule-Walker estimates and empirical mean squared
    and absolute prediction errors for the resulting predictors. Also, functions
    to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time
    series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics,
    forthcoming. Preprint <arXiv:1611.04460>.
Provides a test bench for the comparison of forecasting
    methods in uni-variate time series. Forecasting methods are compared using 
    different error metrics. Proposed forecasting methods and alternative error 
    metrics can be used. Detailed discussion is provided in the vignette.
Classical (bottom-up and top-down), optimal combination and heuristic  
    point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and 
    probabilistic (Girolimetto et al. 2023 <doi:10.1016/j.ijforecast.2023.10.003>) 
    forecast reconciliation procedures for linearly constrained time series 
    (e.g., hierarchical or grouped time series) in cross-sectional, temporal, 
    or cross-temporal frameworks.
Offers a set of tools for visualizing and analyzing size and power properties of the test for equal predictive accuracy, the Diebold-Mariano test that is based on heteroskedasticity and autocorrelation-robust (HAR) inference. A typical HAR inference is involved with non-parametric estimation of the long-run variance, and one of its tuning parameters, the truncation parameter, trades off a size and power. Lazarus, Lewis, and Stock (2021)<doi:10.3982/ECTA15404> theoretically characterize the size-power frontier for the Gaussian multivariate location model. 'ForeComp' computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute the finite-sample size and power, it works with the best approximating ARMA process to the given dataset. It informs the user how their choice of the truncation parameter performs and how robust the testing outcomes are.
Routines for forecasting univariate time series using Theta Models.
Reading and writing data stored by some versions of
	'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
	and for reading and writing some 'dBase' files.
Computes likelihood ratios based on pigmentation traits. Also, it allows computing conditional probabilities for unidentified individuals based on missing person characteristics. A set of tailored plots are incorporated to analyze likelihood ratio distributions.
The 'forensIT' package is a comprehensive statistical toolkit tailored for handling missing person cases. By leveraging information theory metrics, it enables accurate assessment of kinship, particularly when limited genetic evidence is available. With a focus on optimizing statistical power, 'forensIT' empowers investigators to effectively prioritize family members, enhancing the reliability and efficiency of missing person investigations.
Include assessing site classes based on the stand height growth and establishing a nonlinear mixed-effect biomass model under different site classes based on the whole stand model to achieve more accurate estimation of carbon sequestration. In particular, a carbon sequestration potential productivity calculation method based on the potential mean annual increment is proposed. This package is applicable to both natural forests and plantations. It can quantitatively assess stand’s potential productivity, realized productivity, and possible improvement under certain site, and can be used in many aspects such as site quality assessment, tree species suitability evaluation, and forest degradation evaluation. Reference: Lei X, Fu L, Li H, et al (2018) <doi:10.11707/j.1001-7488.20181213>. Fu L, Sharma R P, Zhu G, et al (2017) <doi:10.3390/f8040119>.
Approximate false positive rate control in selection frequency for
    random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>.
    Methods for calculating the selection frequency threshold at false positive rates
    and selection frequency false positive rate feature selection.
Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.
Code for fitting and assessing models for the growth of trees. In 
    particular for the Bayesian neighborhood competition linear regression model 
    of Allen (2020): methods for model fitting and generating fitted/predicted 
    values, evaluating the effect of competitor species identity using 
    permutation tests, and evaluating model performance using spatial 
    cross-validation.
A small subset of plots in Washington State are sampled and 
     assessed "on-the-ground" as forested or non-forested by the U.S. 
     Department of Agriculture, Forest Service, Forest Inventory and Analysis
     (FIA) Program, but the FIA also has access to remotely sensed data for 
     all land in the state. The 'forested' package contains a data frame by the
     same name intended for use in predictive modeling applications
     where the more easily-accessible remotely sensed data can be used to 
     predict whether a plot is forested or non-forested.
Provides generic data structures and algorithms for use with forest
    mensuration data in a consistent framework. The functions and objects
    included are a collection of broadly applicable tools. More specialized 
    applications should be implemented in separate packages that build on this
    foundation. Documentation about 'ForestElementsR' is provided by three
    vignettes included in this package. For an introduction to the field of 
    forest mensuration, refer to the textbooks by Kershaw et al. (2017)
    <doi:10.1002/9781118902028>, and van Laar and Akca (2007)
    <doi:10.1007/978-1-4020-5991-9>.
Estimates the conditional error distributions of random forest
    predictions and common parameters of those distributions, including
    conditional misclassification rates, conditional mean squared prediction
    errors, conditional biases, and conditional quantiles, by out-of-bag
    weighting of out-of-bag prediction errors as proposed by Lu and Hardin
    (2021). This package is compatible with several existing packages that
    implement random forests in R.
Developed for the following tasks. 1 ) Computing the probability density function,
             cumulative distribution function, random generation, and estimating the parameters
 			 of the eleven mixture models. 2 ) Point estimation of the parameters of two - 
			 parameter Weibull distribution using twelve methods and three - parameter Weibull 
			 distribution using nine methods. 3 ) The Bayesian inference for the three - 
			 parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter
			 Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to
			 grouped data using three methods including approximated maximum likelihood, 
			 expectation maximization, and maximum likelihood. 5 ) Estimating the parameters
			 of the gamma, log-normal, and Weibull mixture models fitted to the grouped data
			 through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve
			 fitted to the height - diameter observation, 7 ) Estimating parameters, computing
			 probability density function, cumulative distribution function, and generating
			 realizations from gamma shape mixture model introduced by Venturini et al. (2008)
			 <doi:10.1214/07-AOAS156> , 8 ) The Bayesian inference, computing probability
			 density function, cumulative distribution function, and generating realizations
			 from univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear
			 regression analysis when error term follows skewed t distribution, 10 ) Estimating 
			 parameters of a given distribution fitted to grouped data using method of maximum
			 likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through 
			 the Bayesian, method of moment, conditional maximum likelihood, and two - percentile 
			 method.
Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps. Details were published in Silva and others (2019) <doi:10.1111/2041-210X.13211>.
The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.
Assessing forest ecosystem health is an effective way for forest resource management.The national forest health evaluation system at the forest stand level using analytic hierarchy process, has a high application value and practical significance. The package can effectively and easily realize the total assessment process, and help foresters to further assess and management forest resources.
Extensive global and small-area estimation procedures for multiphase
    forest inventories under the design-based Monte-Carlo approach are provided.
    The implementation has been published in the Journal of Statistical Software (<doi:10.18637/jss.v097.i04>) 
    and includes estimators for simple and cluster sampling
    published by Daniel Mandallaz in 2007 (<doi:10.1201/9781584889779>),
    2013 (<doi:10.1139/cjfr-2012-0381>, <doi:10.1139/cjfr-2013-0181>,
    <doi:10.1139/cjfr-2013-0449>, <doi:10.3929/ethz-a-009990020>)
    and 2016 (<doi:10.3929/ethz-a-010579388>). It provides point estimates,
    their external- and design-based variances and confidence intervals, as well as a
    set of functions to analyze and visualize the produced estimates.
    The procedures have also been optimized for the use of remote sensing data
    as auxiliary information, as demonstrated in 2018 by Hill et al. (<doi:10.3390/rs10071052>).
Interactive forest plot for clinical trial safety analysis
    using 'metalite', 'reactable', 'plotly', and Analysis Data Model (ADaM)
    datasets. Includes functionality for adverse event filtering,
    incidence-based group filtering, hover-over reveals, and search and sort
    operations. The workflow allows for metadata construction, data preparation,
    output formatting, and interactive plot generation.
Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>.
Produces forest plots using 'ggplot2' from models produced by functions
    such as stats::lm(), stats::glm() and survival::coxph().
Allows the creation of forest plots with advanced features, such as 
    multiple confidence intervals per row, customizable fonts for individual text 
    elements, and flexible confidence interval drawing. It also supports mixing 
    text with mathematical expressions. The package extends the application of 
    forest plots beyond traditional meta-analyses, offering a more general version 
    of the original 'rmeta' package’s forestplot() function. It relies heavily on 
    the 'grid' package for rendering the plots.
Create a forest plot based on the layout of the data. Confidence intervals in multiple columns by groups can be done easily. Editing the plot, inserting/adding text, applying a theme to the plot, and much more.
Analysis of forest population structure and quantitative dynamics is the research and evaluation of the composition, distribution, age structure and changes in quantity over time of various populations in the forest. By deeply understanding these characteristics of forest populations, scientific basis can be provided for the management, protection and sustainable utilization of forest resources. This R package conducts a systematic analysis of forest population structure and quantitative dynamics through analyzing age structure, compiling life tables, population quantitative dynamic change indices and time series models, in order to provide support for forest population protection and sustainable management. References: Zhang Y, Wang J, Wang X, et al(2024)<doi:10.3390/plants13070946>. Yuan G, Guo Q, Xie N, et al(2023)<doi:10.1007/s11629-022-7429-z>.
Provides a toolkit for calculating forest and canopy structural complexity metrics from
    terrestrial LiDAR (light detection and ranging). References:  Atkins et al. 2018 <doi:10.1111/2041-210X.13061>; Hardiman et al. 2013 <doi:10.3390/f4030537>;
    Parker et al. 2004 <doi:10.1111/j.0021-8901.2004.00925.x>.
Provides functions that calculates common types of splitting
    criteria used in random forests for classification problems, as well as 
    functions that make predictions based on a single tree or a Forest-R.K. model; 
    the package also provides functions to generate importance plot for a 
    Forest-R.K. model, as well as the 2D multidimensional-scaling plot of 
    data points that are colour coded by their predicted class types by the 
    Forest-R.K. model. This package is based on: 
    Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) 
    "Forest-R.K.: A New Random Forest Induction Method", 
    Fourth International Conference on Intelligent Computing,
    September 2008, Shanghai, China, pp.430-437.
A series of utility functions to help with 
    reshaping hierarchy of data tree, and reform the structure of data tree.
Recent years have seen significant interest in neighborhood-based structural parameters that effectively represent the spatial characteristics of tree populations and forest communities, and possess strong applicability for guiding forestry practices. This package provides valuable information that enhances our understanding and analysis of the fine-scale spatial structure of tree populations and forest stands. Reference: Yan L, Tan W, Chai Z, et al (2019) <doi:10.13323/j.cnki.j.fafu(nat.sci.).2019.03.007>.
Tools for analyzing remote sensing forest data, including functions for detecting treetops from canopy models, outlining tree crowns, and calculating textural metrics.
Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class 
    Discrete Weibull Models.This package is based on Fader and Hardie (2007) 
    <doi:10.1002/dir.20074> and Fader and Hardie et al. (2018) <doi:10.1016/j.intmar.2018.01.002>.
Helper functions with a consistent interface to coerce and verify
    the types and shapes of values for input checking.
Reads cell contents plus formatting from a spreadsheet file and creates an editable 'gt' object with the same data and formatting. Supports the most commonly-used cell and text styles including colors, fills, font weights and decorations, and borders.
The goal of this collection of functions is to provide an easy to use tool for the measurement of foraminifera and other unicellulars organisms size. With functions developed to guide foraminiferal test biovolume calculations and cell biomass estimations. The volume function includes several microalgae models geometric adaptations based on Hillebrand et al. (1999) <doi:10.1046/j.1529-8817.1999.3520403.x>, Sun and Liu (2003) <doi:10.1093/plankt/fbg096> and Vadrucci, Cabrini and Basset (2007) <doi:10.1285/i1825229Xv1n2p83>.
Tabacchi et al. (2011) published a very detailed study producing a uniform system of functions to estimate tree volume and
    phytomass components (stem, branches, stool). The estimates of the 2005 Italian forest inventory (<https://www.inventarioforestale.org/it/>) 
    are based on these functions. The study documents the domain of applicability of each function and the equations to quantify estimates
    accuracies for individual estimates as well as for aggregated estimates. This package makes the functions available in the R environment. 
    Version 2 exposes two distinct functions for individual and summary estimates. To facilitate access to the functions, tree species 
    identification is now based on EPPO species codes (<https://data.eppo.int/>).
Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the 'ForLion' algorithm and 'EW ForLion' algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.
Format BibTeX entries and files in an opinionated way.
Provides a small set of tools for formatting numbers in R-markdown 
    documents. Convert a numerical vector to character strings in power-of-ten 
    form, decimal form, or measurement-units form; all are math-delimited for 
    rendering as inline equations. Can also convert text into math-delimited 
    text to match the font face and size of math-delimited numbers. Useful for 
    rendering single numbers in inline R code chunks and for rendering columns 
    in tables.
Provides a function tidy_source() to format R source code. Spaces
    and indent will be added to the code automatically, and comments will be
    preserved under certain conditions, so that R code will be more
    human-readable and tidy. There is also a Shiny app as a user interface in
    this package (see tidy_app()).
Provides functions to create formattable vectors and data frames.
    'Formattable' vectors are printed with text formatting, and formattable
    data frames are printed with multiple types of formatting in HTML
    to improve the readability of data presented in tabular form rendered in
    web pages.
We provide a framework for rendering complex tables to ASCII,
    and a set of formatters for transforming values or sets of values into
    ASCII-ready display strings.
'Shiny' apps can often make use of the same key elements, this package provides modules for common tasks (data upload, wrangling data, figure generation and saving the app state), and also a framework for developing. These modules can react and interact as well as generate code to create reproducible analyses.
Provides analytics directly from 'R'. It requires:
    'FormShare App': <https://github.com/qlands/FormShare >= 2.22.0> .
    Analytics plugin: <https://github.com/qlands/formshare_analytics_plugin> .
    Remote SQL plugin: <https://github.com/qlands/formshare_sql_plugin> .
Infrastructure for extended formulas with multiple parts on the
             right-hand side and/or multiple responses on the left-hand side
	     (see <doi:10.18637/jss.v034.i01>).
These utilities facilitate the programmatic manipulations of
    formulas, expressions, calls, assignments and other R language objects. 
    These objects all share the same structure: a left-hand side, operator and 
    right-hand side. This packages provides methods for accessing and 
    modifying this structures as well as extracting and replacing names and 
    symbols from these objects.
Many statistical models and analyses in R are implemented through formula objects. The formulaic package creates a unified approach for programmatically and dynamically generating formula objects. Users may specify the outcome and inputs of a model directly, search for variables to include based upon naming patterns, incorporate interactions, and identify variables to exclude. A wide range of quality checks are implemented to identify issues such as misspecified variables, duplication, a lack of contrast in the inputs, and a large number of levels in categorical data.  Variables that do not meet these quality checks can be automatically excluded from the model.  These issues are documented and reported in a manner that provides greater accountability and useful information to guide an investigation of the data.
Perform mathematical operations on R formula (add, subtract, multiply, etc.) and substitute parts of formula.
This presents a comprehensive set of tools for the analysis and visualization of drug formulation data. It includes functions for statistical analysis, regression modeling, hypothesis testing, and comparative analysis to assess the impact of formulation parameters on drug release and other critical attributes. Additionally, the package offers a variety of data visualization functions, such as scatterplots, histograms, and boxplots, to facilitate the interpretation of formulation data. With its focus on usability and efficiency, this package aims to streamline the drug formulation process and aid researchers in making informed decisions during formulation design and optimization.
Simplifies the creation and customization of forest plots (alternatively called dot-and-whisker plots). Input classes accepted by 'forplo' are data.frame, matrix, lm, glm, and coxph. 'forplo' was written in base R and does not depend on other packages.
Forensic applications of pedigree analysis, including
    likelihood ratios for relationship testing, general relatedness
    inference, marker simulation, and power analysis. 'forrel' is part of
    the 'pedsuite', a collection of packages for pedigree analysis,
    further described in the book 'Pedigree Analysis in R' (Vigeland,
    2021, ISBN:9780128244302). Several functions deal specifically with
    power analysis in missing person cases, implementing methods described
    in Vigeland et al. (2020) <doi:10.1016/j.fsigen.2020.102376>. Data
    import from the 'Familias' software (Egeland et al. (2000)
    <doi:10.1016/S0379-0738(00)00147-X>) is supported through the
    'pedFamilias' package.
Identifies potential data outliers and their impact on estimates and 
         analyses. Tool for evaluation of study credibility. Uses the forward 
         search approach of Atkinson and Riani, "Robust Diagnostic Regression 
         Analysis", 2000,<ISBN: o-387-95017-6> to prepare descriptive statistics 
         of a dataset that is to be analyzed by functions lm {stats}, glm {stats}, 
         nls {stats}, lme {nlme}, or coxph {survival}, or their equivalent in
         another language.  Includes graphics functions to display the 
         descriptive statistics.
The goal of 'forstringr' is to enable complex string
    manipulation in R especially to those more familiar with LEFT(),
    RIGHT(), and MID() functions in Microsoft Excel. The package combines
    the power of 'stringr' with other manipulation packages such as
    'dplyr' and 'tidyr'.
Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, <doi:10.1016/j.envsoft.2022.105337>).
A collection of fortunes from the R community.
Robust analysis using forward search in linear and generalized linear regression models, as described in Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer.
Anonymized data from surveys conducted by Forwards <https://forwards.github.io/>, the R Foundation task force on women and other under-represented groups. Currently, a single data set of responses to a survey of attendees at useR! 2016 <https://www.r-project.org/useR-2016/>, the R user conference held at Stanford University, Stanford, California, USA, June 27 - June 30 2016.
A set of analytical tools useful in analysing ecological and geographical data sets, both ancient and modern. The package includes functions for estimating species richness (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta diversity, species area curves and geographic distances and areas.
Functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database.
Simulating and plotting taxonomy and fossil data on phylogenetic trees under mechanistic
    models of speciation, preservation and sampling.
A shiny application based on 'FossilSim'. Used for simulating
    tree, taxonomic and fossil data under mechanistic models of speciation,
    preservation and sampling.
A tool to use a principal component analysis on radially averaged
  two dimensional Fourier spectra to characterize image texture. The method
  within the context of ecology was first described by Couteron et al. (2005)
  <doi:10.1111/j.1365-2664.2005.01097.x> and expanded upon by
  Solorzano et al. (2018) <doi:10.1117/1.JRS.12.036006>
  using a moving window approach.
Interface to 'Palantir Foundry', including
    reading and writing structured or unstructured datasets, and more
    <https://www.palantir.com/platforms/foundry/>.
The four-gamete test is based on the infinite-sites model which assumes that the probability of the same mutation occurring twice (recurrent or parallel mutations) and the probability of a mutation back to the original state (reverse mutations) are close to zero. Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility.
Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation.
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> 
    four parameter IRT model with lower and upper asymptotes using Bayesian
    formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.
Implements parsimonious hidden Markov models for four-way data via expectation-
    conditional maximization algorithm, as described in Tomarchio et al. (2020) <arXiv:2107.04330>.
    The matrix-variate normal distribution is used as emission distribution. For each hidden
    state, parsimony is reached via the eigen-decomposition of the covariance matrices of the
    emission distribution. This produces a family of 98 parsimonious hidden Markov models.
Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.
Computes the power and sample size (PASS) required to test for the
    difference in the mean function between two groups under a repeatedly measured longitudinal 
    or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> 
    for details of the PASS formula and computational details. The details of the testing
    procedure for univariate and multivariate response are presented in
    Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) 
    <arXiv:2302.05612> respectively.
Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Standardisation of cluster validation statistics by random clusterings and 
  comparison between many clustering methods and numbers of clusters based on
  this.  
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.
Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.
Functions to represent functional objects under a Reproducing Kernel Hilbert Space (RKHS) framework as described 
  in Muñoz & González (2010). Autoregressive Hilbertian Model for functional time series using RKHS and predictive confidence bands construction 
  as proposed in Hernández et al (2021).
We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    'The R Inferno' <https://www.burns-stat.com/pages/Tutor/R_inferno.pdf>).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.
Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets.
Tools to help text files importation. It can return 
 the number of lines; print the first and last lines; convert 
 encoding. Operations are made without reading the entire file 
 before starting, resulting in good performances with large files. 
 This package provides an alternative to a simple use of the 
 'head', 'tail', 'wc' and 'iconv' programs that are not always 
 available on machine where R is installed.
Given the values of sampled units and selection probabilities 
             the desraj function  in the package computes the estimated value of the total
             as well as estimated variance.
This data contains a large variety of information on players and their
  current attributes on Fantasy Premier League
  <https://fantasy.premierleague.com/>. In particular, it contains a
  `next_gw_points` (next gameweek points) value for each player
  given their attributes in the current week. Rows represent player-gameweeks,
  i.e. for each player there is a row for each gameweek. This
  makes the data suitable for modelling a player's next gameweek points, given
  attributes such as form, total points, and cost at the current gameweek.
  This data can therefore be used to create Fantasy Premier League bots that
  may use a machine learning algorithm and a linear programming solver
  (for example) to return the best possible transfers and team to pick for
  each gameweek, thereby fully automating the decision making process in
  Fantasy Premier League. This function simply supplies the required data
  for such a task.
Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and box plots. See <https://lrberge.github.io/fplot/> for more information.
Read and process a large delimited file block by
    block. A block consists of all the contiguous rows that have the same value
    in the first field. The result can be returned as a list or a data.table,
    or even directly printed to an output file.
A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles.
  The 'fpop'  package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017
  <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated 
  in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error 
  for a penalty linear in the number of changepoints.
Weighted-L2 FPOP Maidstone et al. (2017) <doi:10.1007/s11222-016-9636-3> and pDPA/FPSN Rigaill (2010) <arXiv:1004.0887> algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) <doi:10.1016/j.sigpro.2004.11.012> and the 'capsushe' package.
A collection of functions to optimize portfolios and to analyze them from different points of view.
Returns the noncentrality parameter of the noncentral F
        distribution if probability of type I and type II error,
        degrees of freedom of the numerator and the denominator are
        given.  It may be useful for computing minimal detectable
        differences for general ANOVA models.  This program is
        documented in the paper of A. Baharev, S. Kemeny, On the
        computation of the noncentral F and noncentral beta
        distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
        the book "Forecasting: principles and practice" by Rob J
        Hyndman and George Athanasopoulos. All packages required to run
        the examples are also loaded.
All data sets required for the examples and exercises
  in the book "Forecasting: principles and practice" (2nd ed, 2018)
  by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>.
  All packages required to run the examples are also loaded.
All data sets required for the examples and exercises in the book
    "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos
    <https://OTexts.com/fpp3/>. All packages required to run the examples are also
    loaded. Additional data sets not used in the book are also included.
Efficient approximation of first passage time densities for diffusion processes based on the First Passage Time Location (FPTL) function.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, see Filzmoser and Viertl (2004) <doi:10.1007/s001840300269>, (2) testing fuzzy hypotheses based on crisp data, see Parchami et al. (2010) <doi:10.1007/s00362-008-0133-4>, and (3) testing fuzzy hypotheses based on fuzzy data, see Parchami et al. (2012) <doi:10.1007/s00362-010-0353-2>. In all cases, the fuzziness of data or / and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
A collection of functions for calculating Floristic Quality
    Assessment (FQA) metrics using regional FQA databases that have been
    approved or approved with reservations as ecological planning models
    by the U.S. Army Corps of Engineers (USACE). For information on FQA
    see Spyreas (2019) <doi:10.1002/ecs2.2825>. These databases are stored
    in a sister R package, 'fqadata'. Both packages were developed for the
    USACE by the U.S. Army Engineer Research and Development Center’s
    Environmental Laboratory.
Contains regional Floristic Quality Assessment databases that
    have been approved or approved with reservations by the U.S. Army
    Corps of Engineers (USACE). Paired with the 'fqacalc' R package, these
    data sets allow for Floristic Quality Assessment metrics to be
    calculated. For information on FQA see Spyreas (2019)
    <doi:10.1002/ecs2.2825>. Both packages were developed for the USACE by
    the U.S.  Army Engineer Research and Development Center's
    Environmental Laboratory.
Tools for downloading and analyzing floristic quality assessment data. 
  See Freyman et al. (2015) <doi:10.1111/2041-210X.12491> for more information 
  about floristic quality assessment and the associated database.
A "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.
Methods to "add" two R tables; also an alternative
     interpretation of named vectors as generalized R tables, so that
     c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3).  Uses
     'disordR' discipline (Hankin, 2022,
     <doi:10.48550/arXiv.2210.03856>).  Extraction and replacement
     methods are provided.  The underlying mathematical structure is
     the Free Abelian group, hence the name.  To cite in publications
     please use Hankin (2023) <doi:10.48550/arXiv.2307.13184>.
Implements fractional differencing with Autoregressive Moving Average models to analyse long-memory 	time series data. Traditional ARIMA models typically use integer values for differencing, which are 	suitable for time series with short memory or anti-persistent behaviour. In contrast, the Fractional ARIMA 	model allows fractional differencing, enabling it to effectively capture long memory characteristics in 	time series data. The ‘fracARMA’ package is user-friendly and allows users to manually input the 	fractional differencing parameter, which can be obtained using various estimators such as the GPH 	estimator, Sperio method, or Wavelet method and many. Additionally, the package enables users to directly 	feed the time series data, AR order, MA order, fractional differencing parameter, and the proportion of 	training data as a split ratio, all in a single command. The package is based on the reference from the 	paper of Irshad and others (2024, <doi:10.22271/maths.2024.v9.i6b.1906>).
Maximum likelihood estimation of the parameters of a fractionally
   differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989);
   including inference and basic methods.  Some alternative algorithms to estimate "H".
Calculate numerical asymptotic distribution functions of likelihood ratio 
    statistics for fractional unit root tests and tests of cointegration rank. 
    For these distributions, the included functions calculate critical values 
    and P-values used in unit root tests, cointegration tests, and rank tests 
    in the Fractionally Cointegrated Vector Autoregression (FCVAR) model.
    The functions implement procedures for tests described in the following articles:
    Johansen, S. and M. Ø. Nielsen (2012) <doi:10.3982/ECTA9299>,
    MacKinnon, J. G. and M. Ø. Nielsen (2014) <doi:10.1002/jae.2295>.
Aim is to provide fractional Brownian vector field generation algorithm, Hurst parameter estimation method and fractional kriging model for multivariate data modeling.
Functions for fitting data to a quiescent growth model,
        i.e. a growth process that involves members of the population
        who stop dividing or propagating.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data <doi:10.1214/11-STS370>.
The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) <doi:10.1002/sim.6497>.
Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) <doi:10.1007/BF02065874>.
Turn numeric,data.frame,matrix into fraction form.
The main function of this package allows numerical vector objects to
  be displayed with their values in vulgar fractional form.  This is convenient if
  patterns can then be more easily detected.  In some cases replacing the components
  of a numeric vector by a rational approximation can also be expected to remove
  some component of round-off error.  The main functions form a re-implementation
  of the functions 'fractions' and 'rational' of the MASS package, but using a
  radically improved programming strategy.
Provides functions for converting decimals to a
    matrix of numerators and denominators or a character vector of
    fractions.  Supports mixed or improper fractions, finding common
    denominators for vectors of fractions, limiting denominators to powers
    of ten, and limiting denominators to a maximum value.  Also includes
    helper functions for finding the least common multiple and greatest
    common divisor for a vector of integers.  Implemented using C++ for
    maximum speed.
A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 <doi:10.1016/j.jclinepi.2013.10.019>; Lin, 2021 <doi:10.1111/jep.13428>), conventional pairwise meta-analyses (Atal et al., 2019 <doi:10.1016/j.jclinepi.2019.03.012>), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 <doi:10.1016/j.jclinepi.2020.07.003>). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 <doi:10.1016/j.ajog.2022.08.053>) and detailed in Lin and Chu (2022 <doi:10.1371/journal.pone.0268754>).
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).
Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.
Implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.
The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers,
    Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>,
    Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.
Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.
Tools to support sensible statistics for functional response analysis.
Provides a friendly interface for modifying data frames with a sequence of piped commands built upon the 'tidyverse' Wickham et al., (2019) <doi:10.21105/joss.01686> . The majority of commands wrap 'dplyr' mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via 'tidyselect'.
Point and interval estimation in dual frame surveys. In contrast
    to classic sampling theory, where only one sampling frame is considered,
    dual frame methodology assumes that there are two frames available for
    sampling and that, overall, they cover the entire target population. Then,
    two probability samples (one from each frame) are drawn and information
    collected is suitably combined to get estimators of the parameter of
    interest.
With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.
Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.
Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.
Perform robust inference based on applying Fast and Robust Bootstrap 
    on robust estimators (Van Aelst and Willems (2013) <doi:10.18637/jss.v053.i03>). 
    This method constitutes an alternative to ordinary bootstrap or asymptotic inference. 
    procedures when using robust estimators such as S-, MM- or GS-estimators. 
    The available methods are multivariate regression, principal component analysis 
    and one-sample and two-sample Hotelling tests. It provides both the robust point 
    estimates and uncertainty measures based on the fast and robust bootstrap.
Generating fractional binomial random variables and computing density, cumulative distribution, and quantiles of fractional binomial distributions. (Lee, J. (2023) <arXiv:2209.01516>.)
An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. 
    FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
    representing the reasoning of human experts in a set of IF-THEN rules, to
    handle real-life problems in, e.g., control, prediction and inference, data
    mining, bioinformatics data processing, and robotics. FRBSs are also known
    as fuzzy inference systems and fuzzy models. During the modeling of an
    FRBS, there are two important steps that need to be conducted: structure
    identification and parameter estimation. Nowadays, there exists a wide
    variety of algorithms to generate fuzzy IF-THEN rules automatically from
    numerical data, covering both steps. Approaches that have been used in the
    past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
    methods, genetic algorithms, squares methods, etc. Furthermore, in this
    version we provide a universal framework named 'frbsPMML', which is adopted
    from the Predictive Model Markup Language (PMML), for representing FRBS
    models. PMML is an XML-based language to provide a standard for describing
    models produced by data mining and machine learning algorithms. Therefore,
    we are allowed to export and import an FRBS model to/from 'frbsPMML'.
    Finally, this package aims to implement the most widely used standard
    procedures, thus offering a standard package for FRBS modeling to the R
    community.
Contains the core functions associated with Fast
        Regularized Canonical Correlation Analysis. Please see the following for
        details: Raul Cruz-Cano, Mei-Ling Ting Lee, 
        Fast regularized canonical correlation analysis, 
        Computational Statistics & Data Analysis, 
        Volume 70, 2014, Pages 88-100, 
        ISSN 0167-9473 <doi:10.1016/j.csda.2013.09.020>.
Provides implementation of statistical methods for random objects 
    lying in various metric spaces, which are not necessarily linear spaces. 
    The core of this package is Fréchet regression for random objects with 
    Euclidean predictors, which allows one to perform regression analysis 
    for non-Euclidean responses under some mild conditions. 
    Examples include distributions in 2-Wasserstein space, 
    covariance matrices endowed with power metric (with Frobenius metric 
    as a special case), Cholesky and log-Cholesky metrics, spherical data.  
    References: Petersen, A., & Müller, H.-G. (2019) <doi:10.1214/17-AOS1624>.
An R client for the 'Federal Reserve Economic Data'
    ('FRED') API <https://research.stlouisfed.org/docs/api/>.  Functions
    to retrieve economic time series and other data from 'FRED'.
Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) <doi:10.48550/arXiv.2110.11074>.
The free algebra in R with non-commuting indeterminates.
     Uses 'disordR' discipline
     (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>).  To cite the
     package in publications please use Hankin (2022)
     <doi:10.48550/ARXIV.2211.04002>.
Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) 
    <doi:10.1093/biomet/asaa016>. This package relies on the optimization software 'MOSEK', 
    <https://www.mosek.com>.
An R client for the 'freecurrencyapi.com' currency conversion API. The API requires registration of an API key. You can find the full API documentation at <https://freecurrencyapi.com/docs> .
Implements the formulae required to calculate freedom
    from disease according to Cameron and Baldock (1998)
    <doi:10.1016/S0167-5877(97)00081-0>. These are the
    methods used at the Swedish national veterinary institute (SVA) to
    evaluate the performance of our nation animal disease
    surveillance programmes.
The free group in R; juxtaposition is represented by a
  plus.  Includes inversion, multiplication by a scalar,
  group-theoretic power operation, and Tietze forms.  To cite the
  package in publications please use Hankin (2022)
  <doi:10.48550/ARXIV.2212.05883>.
Provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
Wrapper functions that interface with 'Freesurfer' 
    <https://surfer.nmr.mgh.harvard.edu/>, a powerful and 
    commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with
    'Freesurfer' completely in R, where you pass R objects of class 'nifti',
    implemented by package 'oro.nifti', and the function executes an 'Freesurfer'
    command and returns an R object of class 'nifti' or necessary output.
Provides functions to read and write neuroimaging data in various file formats, with a focus on 'FreeSurfer' <http://freesurfer.net/> formats. This includes, but is not limited to, the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.
This tree-based method deals with high dimensional longitudinal 
	data with correlated features through the use of a piecewise random effect 
	model. FREE tree also exploits the network structure of the features, by 
	first clustering them using Weighted Gene Co-expression Network Analysis 
	('WGCNA'). It then conducts a screening step within each cluster of features 
	and a selecting step among the surviving features, which provides a relatively
	unbiased way to do feature selection. By using dominant principle components 
	as regression variables at each leaf and the original features as splitting 
	variables at splitting nodes, FREE tree delivers easily interpretable results
	while improving computational efficiency.
Creates dynamic grid layouts of images that can be included in 
   'Shiny' applications and R markdown documents.
A collection of functions for linear and non-linear regression 
  modelling. It implements a wrapper for several regression models available 
  in the base and contributed packages of R.
Functions for finding smooth interpolating curves connecting
    a series of points in the plane.  Curves may be open or closed,
    that is, with the first and last point of the curve at the initial point.
Download data sets from Kenneth's French finance data library site <http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>, reads all the data subsets from the file. Allows R users to collect the data as
    'tidyverse'-ready data frames.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
Generates a frequency distribution. The frequency
    distribution includes raw frequencies, percentages in each category, and
    cumulative frequencies. The frequency distribution can be stored as a data
    frame.
Input has to be in the form of vectors of lower class limits and upper class limits and frequencies; the output will give a cumulative frequency distribution table with cumulative frequency plot.
Implementation of dynamic principal component
    analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. 
    These frequency domain methods for dimensionality reduction of multivariate time series
    were introduced by David Brillinger in his book Time Series (1974). We follow implementation
    guidelines as described in Hormann, Kidzinski and Hallin (2016),
    Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
Implementations of functional dynamic principle components analysis. Related graphic tools and frequency domain methods.
  These methods directly use multivariate dynamic principal components implementation,
  following the guidelines from Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
New approaches to parallel coordinates plots for
   multivariate data visualization, including applications to clustering,
   outlier hunting and regression diagnostics.  Includes general functions
   for multivariate nonparametric density and regression estimation, 
   using parallel computation.
Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.
Tools for generating an informative type of line graph, the frequency profile, 
    which allows single behaviors, multiple behaviors, or the specific behavioral patterns 
    of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
Quickly make tables of descriptive statistics (i.e., counts, 
    percentages, confidence intervals) for categorical variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to Microsoft Word ® with minimal pain.
Generate 'SPSS'/'SAS' styled frequency tables. Frequency tables are 
    generated with variable and value label attributes where applicable with optional
    html output to quickly examine datasets.
Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
Implement frequent-directions algorithm for efficient matrix sketching. 
  (Edo Liberty (2013) <doi:10.1145/2487575.2487623>).
A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the median survival time) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. Though The survival endpoint is often encountered in phase II trials, the existing SSD methods cannot deal with the survival endpoint. Furthermore, the existing SSD won’t control the type I error rate.  The proposed designs can “partially” control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.
A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the response rate) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. The proposed designs can “partially” control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal_2arm_binary() or optimal_3arm_binary() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.
Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
Customize 'Bootstrap' and 'Bootswatch' themes, like colors, fonts, grid layout, 
  to use in 'Shiny' applications, 'rmarkdown' documents and 'flexdashboard'.
Procedure for solving the maximin problem for identical design across heterogeneous data groups. Particularly efficient when the design matrix is either orthogonal or has tensor structure. Orthogonal wavelets can be specified for 1d, 2d or 3d data simply by name. For tensor structured design the tensor components (two or three) may be supplied. The package also provides an efficient implementation of the generic magging estimator.
A simple way to unload none-base packages and remove all global
    variables.
Regular and non-regular Fractional Factorial 2-level designs 
        can be created. Furthermore, analysis tools for Fractional
        Factorial designs with 2-level factors are offered (main
        effects and interaction plots for all factors simultaneously,
        cube plot for looking at the simultaneous effects of three
        factors, full or half normal plot, alias structure in a more
        readable format than with the built-in function alias).
Catalogues of resolution IV regular
        fractional factorial designs in 128 runs are provided 
        for up to 33 2-level factors. The catalogues are complete, 
        excluding resolution IV designs without 5-letter words, 
        because these do not add value for a search for unblocked 
        clear designs. The previous package version
        1.0 with complete catalogues up to 24 runs (24 runs and a
        namespace added later) can be downloaded from the authors
        website.
Computes relative importance of main and interaction effects. Also, sum of the modified generalized weights is computed. Ibrahim et al. (2022) <doi:10.1134/S1064229322080051>.
Read and write Frictionless Data Packages. A 'Data Package'
    (<https://specs.frictionlessdata.io/data-package/>) is a simple
    container format and standard to describe and package a collection of
    (tabular) data. It is typically used to publish FAIR
    (<https://www.go-fair.org/fair-principles/>) and open datasets.
The complete scripts from the American sitcom Friends in tibble 
    format. Use this package to practice data wrangling, text analysis and 
    network analysis.
Miscellaneous utilities, tools and helper
    functions for finding and searching files on disk, searching for and
    removing R objects from the workspace.
    Does not import or depend on any third party package, but on core R 
    only (i.e. it may depend on packages with priority 'base').
A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.
When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz/regression/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.
Streamlines 'Quarto' workflows by providing tools for consistent project setup and documentation. Enables portability through reusable metadata, automated project structure creation, and standardized templates. Features include enhanced project initialization, pre-formatted 'Quarto' documents, comprehensive data protection settings, custom styling, and structured documentation generation. Designed to improve efficiency and collaboration in R data science projects by reducing repetitive setup tasks while maintaining consistent formatting across multiple documents. There are many valuable resources providing in-depth explanations of customizing 'Quarto' templates and theme styling by the Posit team: <https://quarto.org/docs/output-formats/html-themes.html#customizing-themes> & <https://quarto.org/docs/output-formats/html-themes-more.html>, and at the Bootstrap community's GitHub at <https://github.com/twbs/bootstrap/blob/main/scss/_variables.scss>.
Provides a set of helper functions for constructing file paths 
    relative to the root of various types of projects, such as R packages, Git
    repositories, and more. File paths are specified with function arguments,
    or `$` to navigate into folders to specific files supported by 
    auto-completion.
Fast, numerically robust computation of weighted moments via 'Rcpp'. 
   Supports computation on vectors and matrices, and Monoidal append of moments. 
   Moments and cumulants over running fixed length windows can be computed, 
   as well as over time-based windows.
   Moment computations are via a generalization of Welford's method, as described
   by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.
Maximum Likelihood Estimation of
   Stochastic Frontier Production and Cost Functions.
   Two specifications are available:
   the error components specification with time-varying efficiencies
   (Battese and Coelli, 1992, <doi:10.1007/BF00158774>)
   and a model specification in which the firm effects are directly 
   influenced by a number of variables (Battese and Coelli, 1995,
   <doi:10.1007/BF01205442>).
It calculates the alpha-quantile proposed by Daouia and Simar (2007) <doi:10.1016/j.jeconom.2006.07.002> and order-m efficiency score in multi-dimension proposed by Daouia and Gijbels (2011) <doi:10.1016/j.jeconom.2010.12.002> and computes several summaries and representation of the associated frontiers in 2d and 3d.
An R API to MET Norway's 'Frost' API <https://frost.met.no/index.html> 
    to retrieve data as data frames. The 'Frost' API, and the underlying data, is 
    made available by the Norwegian Meteorological Institute (MET Norway). The data
    and products are distributed under the 
    Norwegian License for Open Data 2.0 (NLOD) <https://data.norge.no/nlod/en/2.0>
    and Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.
Emulates a 'Forth' programming environment with added features to 
  interface between R and 'Forth'. Implements most of the functionality described 
  in the original "Starting Forth" textbook <https://www.forth.com/starting-forth/>.
Functions for automatically performing 
             a reanalysis series
             on a data set using CNA, and for calculating the fit-robustness
             of the resulting models, as described in 
             Parkkinen and Baumgartner (2021) <doi:10.1177/0049124120986200>.
Climate is a critical component limiting growing range of plant species, which
    also determines cultivar adaptation to a region. The evaluation of climate influence on
    fruit production is critical for decision-making in the design stage of orchards and 
    vineyards and in the evaluation of the potential consequences of future climate. Bio-
    climatic indices and plant phenology are commonly used to describe the suitability of 
    climate for growing quality fruit and to provide temporal and spatial information about 
    regarding ongoing and future changes. 'fruclimadapt' streamlines the assessment of 
    climate adaptation and the identification of potential risks for grapevines and fruit 
    trees. Procedures in the package allow to i) downscale daily meteorological variables
    to hourly values (Forster et al (2016) <doi:10.5194/gmd-9-2315-2016>),
    ii) estimate chilling and forcing heat accumulation (Miranda et al (2019)
    <https://ec.europa.eu/eip/agriculture/sites/default/files/fg30_mp5_phenology_critical_temperatures.pdf>),
    iii) estimate plant phenology (Schwartz (2012) <doi:10.1007/978-94-007-6925-0>), iv) 
    calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr 
    et al (2017) <doi:10.3354/cr01532>), v) estimate the incidence of weather-related disorders 
    in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi)
    estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)).
A cross-platform interface to file system operations, built
    on top of the 'libuv' C library.
A variety of simple fish stock assessment methods.
The datasets to support the Fish Stock Assessment ('FSA') package.
Provides high-level access to neuroimaging data from standard software packages like 'FreeSurfer' <http://freesurfer.net/> on the level of subjects and groups. Load morphometry data, surfaces and brain parcellations based on atlases. Mask data using labels, load data for specific atlas regions only, and visualize data and statistical results directly in 'R'.
Manages a file system cache. Regular files can be moved or copied to the cache folder. Sub-folders can be created in order to organize the files. Files can be located inside the cache using a glob function. Text contents can be easily stored in and retrieved from the cache using dedicated functions. It can be used for an application or a package, as a global cache, or as a per-user cache, in which case the standard OS user cache folder will be used (e.g.: on Linux $HOME/.cache/R/my_app_or_pkg_cache_folder).
Automated feature selection using variety of models
        provided by 'caret' package.
        This work was funded by Poland-Singapore bilateral cooperation
        project no 2/3/POL-SIN/2012.
FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints.
Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis
    (FSDA)' which is comprehensive and computationally efficient
    software package for robust statistics in regression, multivariate
    and categorical data analysis. The current R version implements tools
    for regression: (forward search, S- and MM-estimation, least trimmed
    squares (LTS) and least median of squares (LMS)), for multivariate analysis
    (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression.
    The distinctive feature of our package is the possibility of
    monitoring the statistics of interest as a function of breakdown point,
    efficiency or subset size, depending on the estimator. This is
    accompanied by a rich set of graphical features, such as dynamic
    brushing, linking, particularly useful for exploratory data analysis.
Functions for selecting attributes from a given
    dataset. Attribute subset selection is the process of identifying and
    removing as much of the irrelevant and redundant information as
    possible.
'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection 
 algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
 In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf>
 with a sparse matrix support.
Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. These models include the functional single-index model, the semi-functional partial linear model, and the semi-functional partial linear single-index model. Additionally, the package offers algorithms for handling scalar covariates with linear effects that originate from the discretization of a curve. This functionality is applicable in the context of the linear model, the multi-functional partial linear model, and the multi-functional partial linear single-index model.
Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from <http://sourceforge.net/projects/formscanner/>. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects.
Performs fast detection of interactions in large-scale data using the
    method of random intersection trees introduced in
    Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. 
    The algorithm finds potentially high-order interactions in high-dimensional binary
    two-class classification data, without requiring lower order interactions
    to be informative.  The search is particularly fast when the matrices of
    predictors are sparse.  It can also be used to perform market basket analysis
    when supplied with a single binary data matrix.  Here it will find collections
    of columns which for many rows contain all 1's.
Functions for importing, creating, editing and
    exporting 'FSK' files <https://foodrisklabs.bfr.bund.de/fskx-food-safety-knowledge-exchange-format/>
    using the 'R' programming environment. Furthermore, it enables users
    to run simulations contained in the 'FSK' files and visualize the results.
Wrapper functions that interface with 'FSL' 
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with 'FSL'
    completely in R, where you pass R objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' if desired.
Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.
Estimation of Rosenthal's fail safe number including confidence intervals. The relevant papers are the following. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2014). "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number". International Scholarly Research Notices, Volume 2014. <doi:10.1155/2014/825383>. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2017). "Exploring the distribution for the estimator of Rosenthal's fail-safe number of unpublished studies in meta-analysis". Communications in Statistics-Theory and Methods, 46(11):5672--5684. <doi:10.1080/03610926.2015.1109664>.
Fuzzy set ordination is a multivariate analysis used in ecology to
    relate the composition of samples to possible explanatory variables.  While
    differing in theory and method, in practice, the use is similar to 'constrained
    ordination.'  The package contains plotting and summary functions as well as
    the analyses.
Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.
Support for fuzzy spatial objects, their operations, and fuzzy spatial inference models based on Spatial Plateau Algebra. 
    It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. 
    It mainly implements underlying concepts defined in the following research papers: 
    (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" <doi:10.1109/FUZZ-IEEE.2018.8491565>; 
    (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" <doi:10.1109/FUZZ-IEEE.2019.8858878>; 
    (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" <doi:10.1109/FUZZ48607.2020.9177620>;
    (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" <doi:10.1109/FUZZ-IEEE.2017.8015707>;
    (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" <doi:10.1109/FUZZ-IEEE55066.2022.9882658>.
Social Relations Analysis with roles ("Family SRM") are computed,
    using a structural equation modeling approach. Groups ranging from three members
    up to an unlimited number of members are supported and the mean structure can
    be computed. Means and variances can be compared between different groups of
    families and between roles.
An optimizer of Fused-Sparse Structural Equation Models, which is 
 the state of the art jointly fused sparse maximum likelihood function 
 for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 
 <doi:10.1101/466623>).
Provides three methods proposed by Shang and Apley (2019) <doi:10.1080/00224065.2019.1705207> to generate fully-sequential space-filling designs inside a unit hypercube. A 'fully-sequential space-filling design' means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design.
Multithreaded serialization of compressed data frames using the 'fst' format. The
    'fst' format allows for full random access of stored data and a wide range of compression
    settings using the LZ4 and ZSTD compressors.
Provides efficient methods to compute local and genome wide
    genetic distances (corresponding to the so called Hudson Fst
    parameters) through moment method, perform chromosome segmentation
    into homogeneous Fst genomic regions, and selection sweep detection
    for multi-population comparison. When multiple profile segmentation is
    required, the procedure can be parallelized using the future package.
Has two functions to help with calculating feature selection stability. 'Lump' is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length.
    'ASM' is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection.
    The calculation for 'asm' uses the Adjusted Stability Measure proposed in: 'Lustgarten', 'Gopalakrishnan', & 'Visweswaran' (2009)<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815476/>.
The 'fstlib' library provides multithreaded serialization of compressed data frames using the
    'fst' format. The 'fst' format allows for random access of stored data and compression with the 'LZ4' and 'ZSTD'
    compressors.
A program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.
Functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.
Calculation and plotting of instantaneous unavailabilities of basic events along with the top event of fault trees are issues important in reliability analysis of complex systems. Here, a fault tree is provided in terms of its minimal cut sets, along with reliability and maintainability distribution functions of the basic events. All the methods are derived from Horton (2002, ISBN: 3-936150-21-4), Niloofar and Lazarova-Molnar (2022).
A wrapper for the API of the Danish Parliament. It makes it 
    possible to get data from the API easily into a data frame. Learn more at 
    <http://www.ft.dk/dokumenter/aabne_data>.
Build display tables easily by extending the functionality of the
    'flextable' package. Features include spanning header, grouping rows,
    parsing markdown and so on.
A collection of functions for trading and rebalancing financial
	instruments. It implements various technical indicators to analyse time series such
	as moving averages or stochastic oscillators.
Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) <DOI:10.1093/bib/bbz041>. 'iFeature' 'Z. Chen et al.' (2018) <DOI:10.1093/bioinformatics/bty140>. <https://CRAN.R-project.org/package=rDNAse>. 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) <DOI:10.1093/bioinformatics/btw564>. 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) <DOI:10.1016/j.ygeno.2018.01.005>. 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) <DOI:10.1073/pnas.92.19.8700>. 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) <DOI:10.1038/srep13859>.
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
It offers comprehensive tools for the analysis of functional
    time series data, focusing on white noise hypothesis testing and
    goodness-of-fit evaluations, alongside functions for
    simulating data and advanced visualization techniques, such as 3D
    rainbow plots. These methods are described in Kokoszka, Rice, and Shang (2017)  <doi:10.1016/j.jmva.2017.08.004>, 
    Yeh, Rice, and Dubin (2023) <doi:10.1214/23-EJS2112>, Kim, Kokoszka, and Rice (2023) <doi:10.1214/23-ss143>, and 
    Rice, Wirjanto, and Zhao (2020) <doi:10.1111/jtsa.12532>.
Functions for estimating spectral density operator of functional
    time series (FTS) and comparing the spectral density operator of two
    functional time series, in a way that allows detection of differences of
    the spectral density operator in frequencies and along the curve length.
Full Consistency Method (FUCOM) for multi-criteria decision-making (MCDM), developed by Dragam Pamucar in 2018 (<doi:10.3390/sym10090393>). The goal of the method is to determine the weights of criteria such that the deviation from full consistency is minimized. Users provide a character vector specifying the ranking of each criterion according to its significance, starting from the criterion expected to have the highest weight to the least significant one. Additionally, users provide a numeric vector specifying the priority values for each criterion. The comparison is made with respect to the first-ranked (most significant) criterion. The function returns the optimized weights for each criterion (summing to 1), the comparative priority (Phi) values, the mathematical transitivity condition (w) value, and the minimum deviation from full consistency (DFC).
Provides utilities to facilitate handling of Fude Polygon data 
    downloadable from the Ministry of Agriculture, Forestry and Fisheries 
    website <https://open.fude.maff.go.jp>.
Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package 'fuel' implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.
Fuel economy data from the EPA, 1985-2015,
    conveniently packaged for consumption by R users.
As in music, a fugue statistic repeats a theme in small variations.  Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes.  The main functions in the package are sen() and senCI().  For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.
Provides core functions and utilities for packages and other code
    developed by Jordan Mark Barbone.
We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples.
Creates a full rank matrix out of a given matrix.
  The intended use is for one-hot encoded design matrices that should be used in linear models to ensure that significant associations can be correctly interpreted. However, 'fullRankMatrix' can be applied to any matrix to make it full rank.
  It removes columns with only 0's, merges duplicated columns and discovers linearly dependent columns and replaces them with linearly independent columns that span the space of the original columns. Columns are renamed to reflect those modifications.
  This results in a full rank matrix that can be used as a design matrix in linear models. The algorithm and some functions are inspired by Kuhn, M. (2008) <doi:10.18637/jss.v028.i05>.
Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). 
  References: Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". <doi:10.13140/RG.2.2.20415.94885/1>  ,
              Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. <doi:10.1177/1745691620902426>.
This is a collection of R games and other funny stuff, such as the
    classic Mine sweeper and sliding puzzles.
Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, 
    protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' 
    from gene set over-expression analysis. Performs clustering of pathway based on similarity 
    of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) 
    when up and down regulated genes are known. The methods are described in a paper currently
    submitted by Orecchioni et al, 2020 in Nanoscale.
The FunCC algorithm allows to apply the FunCC algorithm to simultaneously cluster the rows and the columns of a data matrix whose inputs are functions.
Compute alpha and beta contributional diversity metrics,
    which is intended for linking taxonomic and functional microbiome
    data. See 'GitHub' repository for the tutorial:
    <https://github.com/gavinmdouglas/FuncDiv/wiki>. Citation: Gavin M.
    Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023)
    <doi:10.1093/bioinformatics/btac809>.
Provides functional control charts 
    for statistical process monitoring of functional data, 
    using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507>, 
    Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>,
    Capezza et al. (2024) <doi:10.1080/00401706.2024.2327346>,
    Capezza et al. (2024) <doi:10.1080/00224065.2024.2383674>,
    Centofanti et al. (2022) <doi:10.48550/arXiv.2205.06256>.
    The package is thoroughly illustrated in the paper of 
    Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.
YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. stale_package_check() tries to identify any library() calls to unused packages.
Statistical hypothesis testing methods for
 inferring model-free functional dependency using asymptotic
 chi-squared or exact distributions. Functional test
 statistics are asymmetric and functionally optimal, unique
 from other related statistics. Tests in this package reveal
 evidence for causality based on the causality-by-
 functionality principle. They include asymptotic functional
 chi-squared tests (Zhang & Song 2013) <doi:10.48550/arXiv.1311.2707>,
 an adapted functional chi-squared test (Kumar & Song 2022) 
 <doi:10.1093/bioinformatics/btac206>, 
 and an exact functional test (Zhong & Song 2019)
 <doi:10.1109/TCBB.2018.2809743> (Nguyen et al. 2020)
 <doi:10.24963/ijcai.2020/372>. The normalized functional
 chi-squared test was used by Best Performer 'NMSUSongLab'
 in HPN-DREAM (DREAM8) Breast Cancer Network Inference
 Challenges (Hill et al. 2016) <doi:10.1038/nmeth.3773>. A
 function index (Zhong & Song 2019)
 <doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018)
 <doi:10.1109/BIBM.2018.8621502> derived from the
 functional test statistic offers a new effect size measure
 for the strength of functional dependency, a better
 alternative to conditional entropy in many aspects. For
 continuous data, these tests offer an advantage over
 regression analysis when a parametric functional form
 cannot be assumed; for categorical data, they provide a
 novel means to assess directional dependency not possible
 with symmetrical Pearson's chi-squared or Fisher's exact
 tests.
Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers).  Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank.  FuncMap should be useful for comparing packages at a high level for their overall design.  Plus, it's just plain fun.  The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package.  Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.
A collection of functions which fit functional neural network models. In
            other words, this package will allow users to build deep learning models 
            that have either functional or scalar responses paired with functional and 
            scalar covariates. We implement the theoretical discussion found 
            in Thind, Multani and Cao (2020) <arXiv:2006.09590> through the help of a main fitting and 
            prediction function as well as a number of helper functions to assist with 
            cross-validation, tuning, and the display of estimated functional weights.
An implementation of revised functional regression models for multiple genetic variation data, such as single nucleotide polymorphism (SNP) data, which provides revised functional linear regression models, partially functional interaction regression analysis with penalty-based techniques and corresponding drawing functions, etc.(Ruzong Fan, Yifan Wang, James L. Mills, Alexander F. Wilson, Joan E. Bailey-Wilson, and Momiao Xiong (2013) <doi:10.1002/gepi.21757>).
Curry, Compose, and other higher-order functions
Find functions in an unstructured directory and explore their dependencies. 
    Sourcing of R source files is performed without side-effects: from R scripts that have 
    executable code and function definitions only functions are sourced.
S4 classes for univariate and multivariate functional data with
    utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description 
    of the package functionalities and its interplay with the MFPCA package for 
    multivariate functional principal component analysis 
    <https://CRAN.R-project.org/package=MFPCA>.
Computes six functional diversity indices. These are namely,
  Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness
  (FRic), Functional Richness intersections (FRic_intersect), Functional
  Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008
  <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions
  to compute functional diversity indices
  (preprint: <doi:10.32942/osf.io/dg7hw>).
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>.
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights.
    Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices:
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568.
    Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF)
    covariance matrix of standardized regression coefficients: theoretical extensions
    and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>.
    Waller, N. G.  (2018).  Direct Schmid-Leiman transformations and rank-deficient loadings matrices.  Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.
Construction and smart selection of Gaussian process models
	for analysis of computer experiments
	with emphasis on treatment of functional inputs that are regularly sampled. This package
	offers: (i) flexible modeling of functional-input regression
	problems through the fairly general Gaussian process model; (ii)
	built-in dimension reduction for functional inputs; (iii)
	heuristic optimization of the structural parameters of the model
	(e.g., active inputs, kernel function, type of distance).
	An in-depth tutorial in the use of funGp is provided in
	Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and
	Metamodeling background is provided in
	Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
	The algorithm for structural parameter optimization is described
	in <https://hal.science/hal-02532713>.
Similar to base's unique function, only optimized for working with 
    data frames, especially those that contain date-time columns.
Provides four addons for analyzing trends and
    unit roots in financial time series: (i) functions for the density
    and probability of the augmented Dickey-Fuller Test, (ii) functions 
    for the density and probability of MacKinnon's unit root test 
    statistics, (iii) reimplementations for the ADF and MacKinnon
    Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's
    unit root test suite.
Compare variables of interest between (potentially large
    numbers of) spatial interactions and meta-variables. Spatial variables
    are summarized using K, or other, functions, and projected for use in
    a modified random forest model. The model allows comparison of
    functional and non-functional variables to each other and to noise,
    giving statistical significance to the results. Included are
    preparation, modeling, and interpreting tools along with example
    datasets, as described in VanderDoes et al., (2023)
    <doi:10.1101/2023.07.18.549619>.
Allows generating heatmap-like visualisations for data 
  frames. Funky heatmaps can be fine-tuned by providing annotations of the 
  columns and rows, which allows assigning multiple palettes or geometries
  or grouping rows and columns together in categories.
  Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.
The funLBM algorithm allows to simultaneously cluster the rows and the columns of a data matrix where each entry of the matrix is a function or a time series.
Fits a functional mediation model with a scalar distal outcome. The method is described in detail by Coffman, Dziak, Litson, Chakraborti, Piper & Li (2021) <arXiv:2112.03960>. The model is similar to that of Lindquist (2012) <doi:10.1080/01621459.2012.695640> although allowing a binary outcome as an alternative to a numerical outcome.  The current version is a minor bug fix in the vignette. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
Time-based joins to analyze sequence of events, 
    both in memory and out of memory. after_join() joins two 
    tables of events, while funnel_start() and funnel_step() 
    join events in the same table. With the type argument, you
    can switch between different funnel types, like first-first
    and last-firstafter.
An implementation of methods presented by Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots for comparing institutional performance, for standardised ratios, ratios of counts and proportions with additive overdispersion adjustment.
A set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.
Functional principal component analysis under the Linear Mixed Models representation of smoothing splines. The method utilizes the Demmler-Reinsch basis and assumes error independence. For more details see: F. Rosales (2016) <https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-87F9-6>.
High-order functions for data manipulation : sort or group data, given one
    or more auxiliary functions. Functions are inspired by other pure
    functional programming languages ('Haskell' mainly). The package also 
    provides built-in function operators for creating compact anonymous
    functions, as well as the possibility to use the 'purrr' package syntax.
A small utility which wraps Rscript and provides access to all R
    functions from the shell.
Computes functional rarity indices as proposed by Violle et al.
    (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed
    using both regional and local information. Functional Rarity combines both
    the functional aspect of rarity as well as the extent aspect of rarity.
    'funrar' is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.
Performs functional regression, and some related
    approaches, for intensive longitudinal data (see the book by Walls & Schafer, 
    2006, Models for Intensive Longitudinal Data, Oxford) when such data is not
    necessarily observed on an equally spaced grid of times.  The
    approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
    Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but
    with some modifications to make it more feasible to use with long rather
    than wide, non-rectangular longitudinal datasets with unequal and
    potentially random measurement times.  It also allows easy plotting of the
    correlation between the smoothed covariate and the outcome as a function of
    time, which can add additional insights on how to interpret a functional
    regression.  Additionally, it also provides several permutation tests for
    the significance of the functional predictor.  The heuristic interpretation
    of ``time'' is used to describe the index of the functional predictor, but
    the same methods can equally be used for another unidimensional continuous
    index, such as space along a north-south axis.  Note that most of the functionality
	of this package has been superseded by added features after 2016 in the 'pfr' function by
    Jonathan Gellar, Mathew W. McLean, Jeff Goldsmith, and Fabian Scheipl, in the 
	'refund' package built by Jeff Goldsmith and co-authors and maintained by Julia Wrobel.
	The development of the funreg package in 2015 and 2016 was part of a research 
	project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 
	DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the
    authors and does not necessarily represent the official views of the
    National Institute on Drug Abuse, the National Cancer Institute, or the
    National Institutes of Health.
Estimation of functional spaces based on traits of organisms.
    The package includes functions to impute missing trait values (with or
    without considering phylogenetic information), and to create,
    represent and analyse two dimensional functional spaces based on
    principal components analysis, other ordination methods, or raw
    traits. It also allows for mapping a third variable onto the
    functional space.  See 'Carmona et al. (2021)' 
    <doi:10.1038/s41586-021-03871-y>, 'Puglielli et al.  (2021)'
    <doi:10.1111/nph.16952>, 'Carmona et al. (2021)'
    <doi:10.1126/sciadv.abf2675>, 'Carmona et al. (2019)'
    <doi:10.1002/ecy.2876> for more information.
Helpers for parsing out the R functions
  and packages used in R scripts and notebooks.
Implementation of two sample comparison procedures based on
    median-based statistical tests for functional data, introduced in
    Smida et al (2022) <doi:10.1080/10485252.2022.2064997>.  Other
    competitive state-of-the-art approaches proposed by Chakraborty and
    Chaudhuri (2015) <doi:10.1093/biomet/asu072>, Horvath et al (2013)
    <doi:10.1111/j.1467-9868.2012.01032.x> or Cuevas et al (2004)
    <doi:10.1016/j.csda.2003.10.021> are also included in the package, as
    well as procedures to run test result comparisons and power analysis
    using simulations.
Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.
Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.
A collection of toys to do things like generate Collatz and other interesting sequences, calculate a fraction which is a close approximation to some value (e.g., 22/7 or 355/113 for pi), and so on.
Contains four main functions (i.e., four pieces of furniture):
    table1() which produces a well-formatted table of descriptive statistics common as Table 1
    in research articles, tableC() which produces a well-formatted table of correlations,
    tableF() which provides frequency counts, and washer() which 
    is helpful in cleaning up the data. These furniture-themed functions are designed 
    to simplify common tasks in quantitative analysis. Other data summary and cleaning tools 
    are also available.
Implementations of the family of map() functions from 'purrr'
    that can be resolved using any 'future'-supported backend, e.g.
    parallel on the local machine or distributed on a compute cluster.
Implementation of fused Markov graphical model (FMGM; Park and Won, 2022). The functions include building mixed graphical model (MGM) objects from data, inference of networks using FMGM, stable edge-specific penalty selection (StEPS) for the determination of penalization parameters, and the visualization. For details, please refer to Park and Won (2022) <doi:10.48550/arXiv.2208.14959>.
Recent technological advances have enable the simultaneous collection
    of multi-omics data i.e., different types or modalities of molecular data, 
    presenting challenges for integrative prediction modeling due to the heterogeneous,
    high-dimensional nature and possible missing modalities of some individuals. 
    We introduce this package for late integrative prediction modeling, enabling 
    modality-specific variable selection and prediction modeling, followed by the 
    aggregation of the modality-specific predictions to train a final meta-model. 
    This package facilitates conducting late integration predictive modeling in a 
    systematic, structured, and reproducible way.
Use Rmarkdown First method to build your package. Start your
    package with documentation, functions, examples and tests in the same
    unique file. Everything can be set from the Rmarkdown template file
    provided in your project, then inflated as a package. Inflating the
    template copies the relevant chunks and sections in the appropriate
    files required for package development.
Enables high-dimensional penalized regression across heterogeneous 
    subgroups. Fusion penalties are used to share information about the linear 
    parameters across subgroups. The underlying model is described 
    in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.
FusionCharts provides awesome and minimalist functions to make beautiful interactive charts <https://www.fusioncharts.com/>.
Provides the Big Merge Tracker and COSCI algorithms for convex clustering and 
    feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and 
    T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.
The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.
Provides a simple yet powerful logging utility. Based loosely on
    log4j, futile.logger takes advantage of R idioms to make logging a
    convenient and easy to use replacement for cat and print statements.
A scoped options management framework. Used in other packages.
Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results.
The purpose of this package is to provide a lightweight and
    unified Future API for sequential and parallel processing of R
    expression via futures.  The simplest way to evaluate an expression
    in parallel is to use `x %<-% { expression }` with `plan(multisession)`.
    This package implements sequential, multicore, multisession, and
    cluster futures.  With these, R expressions can be evaluated on the
    local machine, in parallel a set of local machines, or distributed
    on a mix of local and remote machines.
    Extensions to this package implement additional backends for
    processing futures via compute cluster schedulers, etc.
    Because of its unified API, there is no need to modify any code in order
    switch from sequential on the local machine to, say, distributed
    processing on a remote compute cluster.
    Another strength of this package is that global variables and functions
    are automatically identified and exported as needed, making it
    straightforward to tweak existing code to make use of futures.
Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework <doi:10.32614/RJ-2021-048>.
Implementation of the Future API on top of the 'batchtools' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.
Implementation of the Future API on top of the 'callr' package.  This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine.  Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes.
Implementation of the 'Future' API <doi:10.32614/RJ-2021-048> on top of the 'mirai' package <doi:10.5281/zenodo.7912722>. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local machine or across remote machines. Contrary to back-ends relying on the 'parallel' package (e.g. 'multisession') and socket connections, 'mirai_cluster' and 'mirai_multisession', provided here, can run more than 125 parallel R processes.
Backends implementing the 'Future' API, as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API.  The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines.
The 'Futureverse' is a set of packages for parallel and distributed process with the 'future' package at its core, cf. Bengtsson (2021) <doi:10.32614/RJ-2021-048>. This package is designed to make it easy to install and load multiple 'Futureverse' packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each 'Futureverse' package as a dependency as needed.
Test function arguments with a wide array of inputs, and produce
  reports summarizing messages, warnings, errors, and returned values.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Calculation of AHP (Analytic Hierarchy Process -
    <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>)
    with classic and fuzzy weights based on Saaty's pairwise
    comparison method for determination of weights.
It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) <doi:10.1007/s40815-020-00936-4>.
An interface for training Fuzzy DBScan with both Fuzzy Core and Fuzzy Border.
    Therefore, the package provides a method to initialize and run the algorithm and a
    function to predict new data w.t.h. of 'R6'.
    The package is build upon the paper "Fuzzy Extensions of the DBScan algorithm"
    from Ienco and Bordogna (2018) <doi:10.1007/s00500-016-2435-0>.
    A predict function assigns new data according to the same criteria as the algorithm itself.
    However, the prediction function freezes the algorithm to preserve the trained
    cluster structure and treats each new prediction object individually.
Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
    See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
    for further details.
Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.
Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.
Provides methods to solve Fuzzy Linear Programming Problems with
             fuzzy constraints (following different approaches proposed by 
             Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy
             technological matrix.
Contains functions for operations with fuzzy cognitive maps using t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay and George Metcalfe (2007) <doi:10.1007/s00153-007-0047-1>. System indicators are described by Cox, Earl D. (1995) <isbn:1886801010>. Executable examples are provided in the "inst/examples" folder.
S4 classes and methods
    to deal with fuzzy numbers. They allow for computing any arithmetic
    operations (e.g., by using the Zadeh extension principle),
    performing approximation of arbitrary fuzzy numbers by trapezoidal
    and piecewise linear ones, preparing plots for publications, computing
    possibility and necessity values for comparisons, etc.
One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.
Estimates fuzzy measures of poverty and deprivation. It also estimates the sampling variance of these measures using bootstrap or jackknife repeated replications.
Fuzzy clustering of species in an ecological community as common or
    rare based on their abundance and occupancy. It also includes functions to
    compute confidence intervals of classification metrics and plot results. See
    Balbuena et al. (2020, <doi:10.1101/2020.08.12.247502>).
Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic.
    This toolkit includes with graphical user interface (GUI) and an adaptive neuro-
    fuzzy inference system (ANFIS). This toolkit is a continuation from the previous
    package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA)
    and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. 
    A big thank you to the many people who have contributed to the development/evaluation of the toolbox.
    Please cite the toolbox and the corresponding paper <doi:10.1109/FUZZ48607.2020.9177780> when using it.
    More related papers can be found in the NEWS.
Does fuzzy tests and confidence intervals (following Geyer
    and Meeden, Statistical Science, 2005, <doi:10.1214/088342305000000340>)
    for sign test and Wilcoxon signed rank and rank sum tests.
Estimators for fuzzy linear regression. The functions estimate parameters of 
    fuzzy linear regression models with crisp or fuzzy independent variables (triangular
    fuzzy numbers are supported). Implements multiple methods for parameter estimation and 
    algebraic operations with triangular fuzzy numbers. Includes functions for 
    summarising, printing and plotting the model fit. Calculates predictions from the 
    model and total error of fit. Individual methods are described in
    Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>,
	Hung & Yang (2006) <doi:10.1016/j.fss.2006.08.004>,
	Lee & Tanaka (1999) <doi:10.15807/jorsj.42.98>,
	Nasrabadi, Nasrabadi & Nasrabady (2005) <doi:10.1016/j.amc.2004.02.008>,
	Skrabanek, Marek & Pozdilkova (2021) <doi:10.3390/math9060685>,
	Tanaka, Hayashi & Watada (1989) <doi:10.1016/0377-2217(89)90431-1>,
	Zeng, Feng & Li (2017) <doi:10.1016/j.asoc.2016.09.029>.
The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317)  "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data.
 The main aim of the algorithms implemented in this package  is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample.
 To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. <doi:10.2991/eusflat-19.2019.68>, Grzegorzewski et al. (2020) <doi:10.2991/ijcis.d.201012.003>, Grzegorzewski et al. (2020) <doi:10.34768/amcs-2020-0022>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-030-95929-6_3>,  Romaniuk and Hryniewicz (2019) <doi:10.1007/s00500-018-3251-5>).
 Some additional procedures related to these resampling methods are also provided,
 like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers")
 and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, <doi:10.1016/j.ejor.2015.11.016>)).
 Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions.
Functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) <doi:10.1111/2041-210X.12372>.
Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective 
 procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, 
 see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details).
 Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk
 (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>)
 together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap.
 The package also includes a real-life data set of epistemic fuzzy triangular numbers.
 The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package.
An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
        from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley 
        in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities.
The aim of the package is to provide some basic functions
 for doing statistics with trapezoidal fuzzy numbers. In particular,
  the package contains several functions for simulating trapezoidal 
  fuzzy numbers, as well as for calculating some central tendency 
  measures (mean and two types of median), some scale measures 
  (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and 
  one diversity index and one inequality index. Moreover, 
  functions for calculating the 1-norm distance, the mid/spr 
  distance and the (phi,theta)-wabl/ldev/rdev distance between 
  fuzzy numbers are included, and a function to calculate the 
  value phi-wabl given a sample of trapezoidal fuzzy numbers.
The main goal of this package is to present various fuzzy statistical tools. It intends to provide an implementation of the theoretical and empirical approaches presented in the book entitled "The signed distance measure in fuzzy statistical analysis. Some theoretical, empirical and programming advances" <doi: 10.1007/978-3-030-76916-1>. For the theoretical approaches, see Berkachy R. and Donze L. (2019) <doi:10.1007/978-3-030-03368-2_1>. For the empirical approaches, see Berkachy R. and Donze L. (2016) <ISBN: 978-989-758-201-1>). Important (non-exhaustive) implementation highlights of this package are as follows: (1) a numerical procedure to estimate the fuzzy difference and the fuzzy square. (2) two numerical methods of fuzzification. (3) a function performing different possibilities of distances, including the signed distance and the generalized signed distance for instance with all its properties. (4) numerical estimations of fuzzy statistical measures such as the variance, the moment, etc. (5) two methods of estimation of the bootstrap distribution of the likelihood ratio in the fuzzy context. (6) an estimation of a fuzzy confidence interval by the likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data. (9) a method of estimation of global and individual evaluations of linguistic questionnaires. (10) numerical estimations of multi-ways analysis of variance models in the fuzzy context. The unbalance in the considered designs are also foreseen.
Fuzzy string matching implementation of the 'fuzzywuzzy' <https://github.com/seatgeek/fuzzywuzzy> 'python' package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.
A Bayesian Nonparametric model for the study of time-evolving frequencies, which has become renowned in the study of population genetics.
      The model consists of a Hidden Markov Model (HMM) in which the latent signal is a distribution-valued stochastic process that takes the form of a finite mixture of Dirichlet Processes, indexed by vectors that count how many times each value is observed in the population.
      The package implements methodologies presented in Ascolani, Lijoi and Ruggiero (2021) <doi:10.1214/20-BA1206> and Ascolani, Lijoi and Ruggiero (2023) <doi:10.3150/22-BEJ1504> that make it possible to study the process at the time of data collection or to predict its evolution in future or in the past.
An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in
   the 'boot' package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing
   weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020)
   <doi:10.1080/00031305.2020.1731599> for details.
A simple method
    to select the best model or best subset of variables using
    different types of data (binary, Gaussian or Poisson) and
    applying it in different contexts (parametric or non-parametric).
Creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the 'fixest' package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <doi:10.48550/arXiv.2307.00369>.
Fresh biomass determination is the key to evaluating crop genotypes' response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight.
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
The 'fxl' Charting package is used to prepare and design single case design figures that are typically prepared in spreadsheet software. With 'fxl', there is no need to leave the R environment to prepare these works and many of the more unique conventions in single case experimental designs can be performed without the need for physically constructing features of plots (e.g., drawing annotations across plots). Support is provided for various different plotting arrangements (e.g., multiple baseline), annotations (e.g., brackets, arrows), and output formats (e.g., svg, rasters).
Exchange rate regression and structural change tools
             for estimating, testing, dating, and monitoring
	     (de facto) exchange rate regimes.
The goal of this package is to provide an improved version of 
    WA-PLS (Weighted Averaging Partial Least Squares) by including the 
    tolerances of taxa and the frequency of the sampled climate variable. 
    This package also provides a way of leave-out cross-validation that 
    removes both the test site and sites that are both geographically 
    close and climatically close for each cycle, to avoid the risk of 
    pseudo-replication.
In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June
     of the following calendar year. As such, many databases need to represent and 
     validate financial years efficiently. While the use of integer years with a convention that  
     they represent the year ending is common, it may lead to ambiguity with calendar years.
     On the other hand, string representations may be too inefficient and do not easily admit
     arithmetic operations. This package tries to make validation of financial years quicker while
     retaining clarity.
Create and maintain delayed-data packages (ddp's).  Data stored in
  a ddp are available on demand, but do not take up memory until requested.
  You attach a ddp with g.data.attach(), then read from it and assign to it in
  a manner similar to S-PLUS, except that you must run g.data.save() to
  actually commit to disk.
Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters.
 These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors.
 Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.
Full descriptive statistics, physical description of sediment,
            metric or phi sieves.
Interface for 'g3-lollipop' 'JavaScript' library.
    Visualize genetic mutation data using an interactive lollipop diagram in 'RStudio' or your web browser.
Flexible general-purpose toolbox implementing genetic 
  algorithms (GAs) for stochastic optimisation. Binary, real-valued, and
  permutation representations are available to optimize a fitness 
  function, i.e. a function provided by users depending on their 
  objective function. Several genetic operators are available and can be
  combined to explore the best settings for the current task. 
  Furthermore, users can define new genetic operators and easily 
  evaluate their performances. Local search using general-purpose 
  optimisation algorithms can be applied stochastically to exploit 
  interesting regions. GAs can be run sequentially or in parallel, using
  an explicit master-slave parallelisation or a coarse-grain islands 
  approach. For more details see Scrucca (2013) 
  <doi:10.18637/jss.v053.i04> and Scrucca (2017) 
  <doi:10.32614/RJ-2017-008>.
Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects.
  Inputs are : data frame, RDA (package 'vegan') and PCA (package 'FactoMineR') objects.
  Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters.
The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method.
  By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed.
  This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others.
  First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user.
  By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions.
  The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.
Analysis of complex ANOVA models with any combination of orthogonal/nested and fixed/random factors, as described by Underwood (1997). There are two restrictions: (i) data must be balanced; (ii) fixed nested factors are not allowed. Homogeneity of variances is checked using Cochran's C test and 'a posteriori' comparisons of means are done using Student-Newman-Keuls (SNK) procedure. For those terms with no denominator in the F-ratio calculation, pooled mean squares and quasi F-ratios are provided. Magnitute of effects are assessed by components of variation.
Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).
A statistical ecosystem modelling package, taking many features of
             the ecosystem into account. Gadget works by running an internal
             model based on many parameters, and then comparing the data from
             the output of this model to real data to get a goodness-of-fit
             likelihood score. These parameters can then be adjusted, and the
             model re-run, until an optimum is found, which corresponds to the
             model with the lowest likelihood score. Gadget allows the user to
             include a number of features into an ecosystem model: One or more
             species, each of which may be split into multiple stocks; multiple
             areas with migration between areas; predation between and within
             species; maturation; reproduction and recruitment; multiple
             commercial and survey fleets taking catches from the populations.
             For more details see <https://gadget-framework.github.io/gadget2/>.
             This is the C++ Gadget2 runtime, making it available for R.
A framework to assist creation of marine ecosystem models,
             generating either 'R' or 'C++' code which can then be optimised using
             the 'TMB' package and standard 'R' tools. Principally designed to
             reproduce gadget2 models in 'TMB', but can be extended beyond
             gadget2's capabilities.
             Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) <doi:10.18637/jss.v070.i05> "TMB: Automatic Differentiation and Laplace Approximation.".
             Begley, J., & Howell, D. (2004) <https://core.ac.uk/download/pdf/225936648.pdf> "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.".
A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).
Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm.  
 For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. 
 This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. 
 Further study is going on for the nonorthogonal cases and generalized linear models. 
 These works are in part supported by the National Natural Foundation of China (No.12171076).
Performs genetic algorithm (Scrucca, L (2013) <doi:10.18637/jss.v053.i04>) 
             assisted genomic best liner unbiased prediction for genomic selection.
             It also provides a binning method in natural population for genomic selection
             under the principle of linkage disequilibrium for dimensional reduction.
Tools for using genetic markers, stable isotope data, and habitat
    suitability data to calculate posterior probabilities of breeding origin of
    migrating birds.
Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators.
  H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.
Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.  The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11.
Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA.
The Global Biodiversity Information Facility 
    ('GBIF', <https://www.gbif.org>) sources data from an international network
    of data providers, known as 'nodes'. Several of these nodes - the "living 
    atlases" (<https://living-atlases.gbif.org>) - maintain their own web 
    services using software originally developed by the Atlas of Living 
    Australia ('ALA', <https://www.ala.org.au>). 'galah' enables the R community 
    to directly access data and resources hosted by 'GBIF' and its partner nodes.
Estimates generalized additive latent and
    mixed models using maximum marginal likelihood, 
    as defined in Sorensen et al. (2023) 
    <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and
    Skrondal (2004)'s unifying framework for multilevel latent variable 
    modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse 
    matrix methods, Laplace approximation, and automatic differentiation. The 
    framework includes generalized multilevel models with heteroscedastic 
    residuals, mixed response types, factor loadings, smoothing splines, 
    crossed random effects, and combinations thereof. Syntax for model 
    formulation is close to 'lme4' (Bates et al. (2015) 
    <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) 
    <doi:10.1080/00273171.2018.1516541>).
Collection of packages for work with API 'Google Ads' <https://developers.google.com/google-ads/api/docs/start>, 
    'Yandex Direct' <https://yandex.ru/dev/direct/>, 'Yandex Metrica' <https://yandex.ru/dev/metrika/>, 
    'MyTarget' <https://target.my.com/help/advertisers/api_arrangement/ru>, 'Vkontakte' <https://vk.com/dev/methods>, 
    'Facebook' <https://developers.facebook.com/docs/marketing-apis/> and 'AppsFlyer' <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>. 
    This packages allows you loading data from ads account and manage your ads materials.
Generates a variety of structured test matrices commonly used in numerical linear algebra and computational experiments. Includes well-known matrices for benchmarking and testing the performance, stability, and accuracy of linear algebra algorithms. Inspired by 'MATLAB' 'gallery' functions.
The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.
Includes the ga.lts() function that estimates
        LTS (Least Trimmed Squares) parameters using genetic algorithms
        and C-steps. ga.lts() constructs a genetic algorithm to form a
        basic subset and iterates C-steps as defined in Rousseeuw and
        van-Driessen (2006) to calculate the cost value of the LTS
        criterion. OLS (Ordinary Least Squares) regression is known to
        be sensitive to outliers. A single outlying observation can
        change the values of estimated parameters. LTS is a resistant
        estimator even the number of outliers is up to half of the
        data. This package is for estimating the LTS parameters with
        lower bias and variance in a reasonable time. Version >=1.3 
        includes the function medmad for fast outlier detection in
        linear regression.
An R interface to the Galvanize 'Highbond' API <https://docs-apis.highbond.com>.
Functions for fitting and working with generalized	additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).
Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of gam()in 'mgcv' package, applying the algorithm in this paper: Lai(2024) <doi:10.1016/j.pld.2024.06.002>.
Data sets and scripts used in the book 'Generalized Additive 
             Models: An Introduction with R', Wood (2006,2017) CRC.
Fits unimodal and multimodal gambin distributions to species-abundance distributions
    from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. 
    'gambin' is short for 'gamma-binomial'. The main function is fit_abundances(), which estimates 
    the 'alpha' parameter(s) of the gambin distribution using maximum likelihood. Functions are 
    also provided to generate the gambin distribution and for calculating likelihood statistics.
Simulates a gambling game under the gambler's ruin setup, after asking for the money you have and the money you want to win, along with your win probability in each round of the game.
Boosting models for fitting generalized additive models for
  location, shape and scale ('GAMLSS') to potentially high dimensional
  data.
Contains infrastructure for using mboost::gamboost() in order to estimate multistate models.
Functions and data are provided that support a course
        that emphasizes statistical issues of inference and generalizability.  
        The functions are designed to make it straightforward to illustrate
        the use of cross-validation, the training/test approach, simulation, 
        and model-based estimates of accuracy.  Methods considered are
        Generalized Additive Modeling, Linear and Quadratic Discriminant
        Analysis, Tree-based methods, and Random Forests.
Implementation of various inference and simulation tools to
    apply generalized additive models to bivariate dependence structures and
    non-simplified vine copulas.
Implements the GAMbag, GAMrsm and GAMens ensemble
    classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles
    implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601>
    , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.
Finds adaptive strategies for sequential symmetric 
    games using a genetic algorithm. Currently, any symmetric two by two matrix
    is allowed, and strategies can remember the history of an opponent's play
    from the previous three rounds of moves in iterated interactions between
    players. The genetic algorithm returns a list of adaptive strategies given
    payoffs, and the mean fitness of strategies in each generation.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', <arXiv:1308.5623>.
Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Interface for extra smooth functions including tensor products, 
             neural networks and decision trees.
This is an add-on package to GAMLSS. The purpose of this
        package is to allow users to fit interval response variables in
        GAMLSS models. The main function gen.cens() generates a
        censored version of an existing GAMLSS family distribution.
This is an add on package to 'GAMLSS'. The main purpose of this package is generating and fitting inflated distributions at any desired point (0, 1, 2, ...). The function gen.Kinf() generates K-inflated version of an existing discrete 'GAMLSS' family distribution.
Data used as examples in the current two books on Generalised Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>.
Demos for smoothing and gamlss.family distributions.
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.
Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.
Functions for plotting Generalized Additive Models for Location Scale and Shape from the 'gamlss' package, Stasinopoulos and Rigby (2007) <doi:10.18637/jss.v023.i07>, using the graphical methods from 'ggplot2'.
This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.
Interface for extra high-dimensional smooth functions for Generalized Additive Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge, elastic net and least angle regression.
The main purpose of this package is to allow fitting of
        mixture distributions with generalised additive models for location 
        scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.
It allows us to fit Gaussian Markov Random Field within the
    Generalized Additive Models for Location Scale and Shape algorithms.
This is an add on package to GAMLSS. The purpose of this
        package is to allow users to defined truncated distributions in
        GAMLSS models. The main function gen.trun() generates truncated
        version of an existing GAMLSS family distribution.
Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.
Fits generalized additive models for the location, scale and shape
    parameters of a generalized extreme value response distribution. The
    methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005),
    <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from
    the 'gamlss' package <doi:10.32614/CRAN.package.gamlss>.
Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', using 'lme4' for estimation.
Process in-situ Gamma-Ray Spectrometry for Luminescence
    Dating. This package allows to import, inspect and correct the energy
    shifts of gamma-ray spectra. It provides methods for estimating the
    gamma dose rate by the use of a calibration curve as described in
    Mercier and Falguères (2007). The package only supports Canberra CNF
    and TKA and Kromek SPE files.
Performs Gamma regression, where both mean and shape parameters follows lineal regression structures.
An interface for fitting generalized additive models (GAMs) and generalized additive mixed models (GAMMs) using the 'lme4' package as the computational engine, as described in Helwig (2024) <doi:10.3390/stats7010003>. Supports default and formula methods for model specification, additive and tensor product splines for capturing nonlinear effects, and automatic determination of spline type based on the class of each predictor. Includes an S3 plot method for visualizing the (nonlinear) model terms, an S3 predict method for forming predictions from a fit model, and an S3 summary method for conducting significance testing using the Bayesian interpretation of a smoothing spline.
Uses a slice sampling-based Markov chain Monte Carlo to
        conduct Bayesian fitting and inference for generalized additive
        mixed models.  Generalized linear mixed models and generalized 
        additive models are also handled as special cases of generalized
        additive mixed models. The methodology and software is described 
        in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand
        Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.
Robust regression via gamma-divergence with L1, elastic net and ridge.
To calculate the relative risk (RR) for the generalized additive model.
Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families. See  <doi:10.48550/arXiv.1506.03850> for more details.
Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) <doi:10.1007/s10182-023-00490-y>.
Read, analyze, modify, and write 'GAMS' (General Algebraic
    Modeling System) data. The main focus of 'gamstransfer' is the
    highly efficient transfer of data with 'GAMS' <https://www.gams.com/>,
    while keeping these operations as simple as possible for the user. The
    transfer of data usually takes place via an intermediate GDX (GAMS Data
    Exchange) file. Additionally, 'gamstransfer' provides utility
    functions to get an overview of 'GAMS' data and to check its validity.
Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' <https://cran.r-project.org/package=ganGenerativeData>.
Introduces a 'Copilot'-like completion experience, but it knows how
    to talk to the objects in your R environment. 'ellmer' chats are integrated 
    directly into your 'RStudio' and 'Positron' sessions, automatically 
    incorporating relevant context from surrounding lines of code and your
    global environment (like data frame columns and types). Open the package 
    dialog box with a keyboard shortcut, type your request, and the assistant 
    will stream its response directly into your documents.
Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.
A network-based gene weighting algorithm for pathway enrichment analysis, using either RNA-seq or microarray data. Zhaoyuan Fang, Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.
This is a dataset package for GANPA, which implements a
        network-based gene weighting approach to pathway analysis. This
        package includes data useful for GANPA, such as a functional
        association network, pathways, an expression dataset and
        multi-subunit proteins.
As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18.
        <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it contains functions for
        sample size calculations of both population-based and family-based designs, probability
        of familial disease aggregation, kinship calculation, statistics in linkage analysis,
        and association analysis involving genetic markers including haplotype analysis with or
        without environmental covariates. Over years, the package has been developed in-between
        many projects hence also in line with the name (gap).
Datasets associated with the 'gap' package. Currently,
             it includes an example data for regional association
             plot (CDKN), an example data for a genomewide association
             meta-analysis (OPG), data in studies of Parkinson's diease (PD),
             ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers
             and Schizophrenia (apoeapoc), cystic fibrosis (cf), a
             Olink/INF panel (inf1), Manhattan plots with (hr1420, mhtdata)
             and without (w4) gene annotations.
Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.
Provides functions to estimate the disparities across categories (e.g. Black and white) that persists if a treatment variable (e.g. college) is equalized. Makes estimates by treatment modeling, outcome modeling, and doubly-robust augmented inverse probability weighting estimation, with standard errors calculated by a nonparametric bootstrap. Cross-fitting is supported. Survey weights are supported for point estimation but not for standard error estimation; those applying this package with complex survey samples should consult the data distributor to select an appropriate approach for standard error construction, which may involve calling the functions repeatedly for many sets of replicate weights provided by the data distributor. The methods in this package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.
Tools to fill missing values in satellite data and to develop new
    gap-fill algorithms. The methods are tailored to data (images) observed
    at equally-spaced points in time. The package is illustrated with MODIS
    NDVI data.
The gap encodes the distance between clusters and improves
    interpretation of cluster heatmaps. The gaps can be of the same
    distance based on a height threshold to cut the dendrogram. Another
    option is to vary the size of gaps based on the distance between
    clusters.
An excerpt of the data available at Gapminder.org. For each
    of 142 countries, the package provides values for life expectancy, GDP
    per capita, and population, every five years, from 1952 to 2007.
Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.
Functions for estimating a GARCHSK model and GJRSK model based on a publication by Leon et,al (2005)<doi:10.1016/j.qref.2004.12.020> and Nakagawa and Uchiyama (2020)<doi:10.3390/math8111990>. These are a GARCH-type model allowing for time-varying volatility, skewness and kurtosis.
Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time, see <https://journal.r-project.org/archive/2021/RJ-2021-057/RJ-2021-057.pdf> for an overview of the package.
Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.
Provides utilities for working with Google APIs
    <https://developers.google.com/apis-explorer>.  This includes
    functions and classes for handling common credential types and for
    preparing, executing, and processing HTTP requests.
An event-Based framework for building 'Shiny' apps.
    Instead of relying on standard 'Shiny' reactive objects, this 
    package allow to relying on a lighter set of triggers, so that 
    reactive contexts can be invalidated with more control.
Methods for estimating univariate long memory-seasonal/cyclical
             Gegenbauer time series processes. See for example (2022) <doi:10.1007/s00362-022-01290-3>.
             Refer to the vignette for details of fitting these processes.
Organize a so-called ragged array as generalized arrays, which
	is simply an array with sub-dimensions denoting the subdivision of
	dimensions (grouping of members within dimensions).
	By the margins (names of dimensions and sub-dimensions) in generalized
	arrays, operators and utility functions provided in this package
	automatically match the margins,
	doing map-reduce style parallel computation
	along margins.  Generalized arrays are also cooperative to R's native
	functions that work on simple arrays.
Simulate, estimate and forecast using univariate and multivariate GAS models 
  as described in Ardia et al. (2019) <doi:10.18637/jss.v088.i06>.
The gasanalyzer R package offers methods for importing, preprocessing,
    and analyzing data related to photosynthetic characteristics (gas exchange,
    chlorophyll fluorescence and isotope ratios). It translates variable names
    into a standard format, and can recalculate derived, physiological 
    quantities using imported or predefined equations. The package also allows
    users to assess the sensitivity of their results to different assumptions
    used in the calculations. 
    See also Tholen (2024) <doi:10.1093/aobpla/plae035>.
Provides a genetic algorithm for finding variable
    subsets in high dimensional data with high prediction performance. The
    genetic algorithm can use ordinary least squares (OLS) regression models or
    partial least squares (PLS) regression models to evaluate the prediction
    power of variable subsets. By supporting different cross-validation
    schemes, the user can fine-tune the tradeoff between speed and quality of
    the solution.
Functions for greenhouse gas flux calculation from chamber
    measurements.
Estimation, forecasting, and simulation of generalized
    autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013)
    <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>.
    Model specification allows for various data types and distributions,
    different parametrizations, exogenous variables, joint and separate modeling
    of exogenous variables and dynamics, higher score and autoregressive orders,
    custom and unconditional initial values of time-varying parameters, fixed
    and bounded values of coefficients, and missing values. Model estimation is
    performed by the maximum likelihood method.
Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>.  Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.
Provides the standard operations for signal processing on graphs: 
    graph Fourier transform, spectral graph wavelet transform, 
    visualization tools. It also implements a data driven method
    for graph signal denoising/regression, for details see 
    De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. 
    The package also provides an interface to the SuiteSparse Matrix Collection, 
    <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix 
    benchmarks collected from a wide range of applications.
Fits gastric emptying time series from MRI or 'scintigraphic' measurements
   using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with 
   Stan; computes derived parameters such as t50 and AUC.
Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al <doi:10.1159/000488519>.
Allows user to choose/gate a region on the plot and returns points
    within it.
Estimates statistically significant marker combination values within
        which one immunologically distinctive group (i.e., disease case) is more associated than
        another group (i.e., healthy control), successively, using various combinations (i.e.,
        "gates") of markers to examine features of cells that may be different between
        groups. For a two-group comparison, the 'gateR' package uses the spatial relative risk
        function estimated using the 'sparr' package. Details about the 'sparr' package
        methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details
        about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.
        More information about relative risk functions using kernel density estimation can be
        found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Streamlines downloading and cleaning biodiversity data from Integrated Digitized Biocollections (iDigBio) and the Global Biodiversity Information Facility (GBIF).
Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel, and 'Rcpp'
 has been used to speed up calculations.
 For more info about Gaussian process software, see Erickson et al. (2018)
 <doi:10.1016/j.ejor.2017.10.002>.
A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. 'gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence' in the journal Ecology and Evolution.
A collection difference measures for multivariate Gaussian
        probability density functions, such as the Euclidea mean, the
        Mahalanobis distance, the Kullback-Leibler divergence, the
        J-Coefficient, the Minkowski L2-distance, the Chi-square
        divergence and the Hellinger Coefficient.
Display a random fact about Carl Friedrich Gauss
 based the on collection curated by Mike Cavers via the
 <http://gaussfacts.com> site.
Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.
Functions to fit two-dimensional Gaussian functions, predict values from
    fits, and produce plots of predicted data via either 'ggplot2' or base R 
    plotting.
A collection of functions to perform Gaussian quadrature
        with different weight functions corresponding to the orthogonal
        polynomials in package orthopolynom.  Examples verify the
        orthogonality and inner products of the polynomials.
It is well known that the distribution of a Gaussian ratio
    does not follow a Gaussian distribution.
    The lack of awareness among users of vegetation indices about this
    non-Gaussian nature could lead to incorrect statistical modeling
    and interpretation.
    This package provides tools to accurately handle and analyse such
    ratios: density function, parameter estimation, simulation.
    An example on the study of chlorophyll fluorescence can be found in
    A. El Ghaziri et al. (2023) <doi:10.3390/rs15020528>.
A statistical disclosure control tool to protect tables by suppression 
    using the Gaussian elimination secondary suppression algorithm 
    (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6>. A suggestion is 
    to start by working with functions SuppressSmallCounts() and 
    SuppressDominantCells(). These functions use primary suppression functions for 
    the minimum frequency rule and the dominance rule, respectively. Novel 
    functionality for suppression of disclosive cells is also included. General 
    primary suppression functions can be supplied as input to the general working 
    horse function, GaussSuppressionFromData(). Suppressed frequencies can be 
    replaced by synthetic decimal numbers as described in 
    Langsrud (2019) <doi:10.1007/s11222-018-9848-9>.
R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.
A collection of algorithms and functions 
  for fitting data to a generalized lambda distribution 
  via moment matching methods, and generalized 
  bootstrapping.
Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.
Estimation of the generalized beta distribution of the second 
    kind (GB2) and related models using grouped data in form of income shares. 
    The GB2 family is a general class of distributions that provides an accurate 
    fit to income data. 'GB2group' includes functions to estimate the GB2, the 
    Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 
    'GB2group' deploys two different econometric strategies to estimate these 
    parametric distributions, the equally weighted minimum distance (EWMD) estimator and the  
    optimally weighted minimum distance (OMD) estimator. Asymptotic standard errors are reported for the 
    OMD estimates. Standard errors of the EWMD estimates are obtained by Monte 
    Carlo simulation. See Jorda et al. (2018) <arXiv:1808.09831> for a detailed 
    description of the estimation procedure.
DNA methylation of 5-methylcytosine (5mC) is the result of a multi-step, enzyme-dependent process. Predicting these sites in-vitro is laborious, time consuming as well as costly. This ' Gb5mC-Pred ' package is an in-silico pipeline for predicting DNA sequences containing the 5mC sites. It uses a machine learning approach which uses Stochastic Gradient Boosting approach for prediction of the sequences with 5mC sites. This package has been developed by using the concept of Navarez and Roxas (2022) <doi:10.1109/TCBB.2021.3082184>.
Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) <doi:10.1214/11-BJPS139>, Hamza & Vallois (2016) <doi:10.1016/j.spl.2016.03.014>, Chen & Novick (1984) <doi:10.3102/10769986009002163>.
Supplies a set of functions to interface with bikeshare data
    following the General Bikeshare Feed Specification, allowing users to query
    and accumulate tidy datasets for specified cities/bikeshare programs.
A high performance interface to the Global Biodiversity
  Information Facility, 'GBIF'.  In contrast to 'rgbif', which can
  access small subsets of 'GBIF' data through web-based queries to
  a central server, 'gbifdb' provides enhanced performance for R users
  performing large-scale analyses on servers and cloud computing
  providers, providing full support for arbitrary 'SQL' or 'dplyr'
  operations on the complete 'GBIF' data tables (now over 1 billion
  records, and over a terabyte in size). 'gbifdb' accesses a copy
  of the 'GBIF' data in 'parquet' format, which is already readily
  available in commercial computing clouds such as the Amazon Open
  Data portal and the Microsoft Planetary Computer, or can be 
  accessed directly without downloading, or downloaded
  to any server with suitable bandwidth and storage space.
  The high-performance techniques for local and remote access 
  are described in <https://duckdb.org/why_duckdb>
  and <https://arrow.apache.org/docs/r/articles/fs.html> respectively.
Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
	association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ),
    Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence
    Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented 
    in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests.
    The GBJ has been shown to outperform other tests in genetic association studies when signals
    are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin
    (2017) <arXiv:1710.02469> for more details.
An implementation of extensions to Freund and Schapire's AdaBoost 
  algorithm and Friedman's gradient boosting machine. Includes regression 
  methods for least squares, absolute loss, t-distribution loss, quantile 
  regression, logistic, multinomial logistic, Poisson, Cox proportional hazards 
  partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and 
  Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.
  Newer version available at github.com/gbm-developers/gbm3.
Automates delta log-normal boosted regression tree abundance
    prediction. Loops through parameters provided (LR (learning rate), TC
    (tree complexity), BF (bag fraction)), chooses best, simplifies, &
    generates line, dot & bar plots, & outputs these & predictions & a
    report, makes predicted abundance maps, and Unrepresentativeness
    surfaces.  Package core built around 'gbm' (gradient boosting machine)
    functions in 'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020
    & ongoing), itself built around 'gbm' (Greenwell, Boehmke, Cunningham
    & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to
    Elith/Leathwick/Hastie 2008 'Working Guide'
    <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows Appendix S3.
    See <https://www.simondedman.com/> for published guides and papers
    using this package.
Writes SAS code to get predicted values from every tree of a gbm.object.
Estimation and analysis of group-based multivariate trajectory models (Nagin, 2018 <doi:10.1177/0962280216673085>; Magrini, 2022 <doi:10.1007/s10182-022-00437-9>). The package implements an Expectation-Maximization (EM) algorithm allowing unbalanced panel and missing values, and provides several functionalities for prediction and graphical representation.
Basic infrastructure and several algorithms for 1d-4d bin packing
    problem. This package provides a set of c-level classes and solvers for
    1d-4d bin packing problem, and an r-level solver for 4d bin packing problem,
    which is a wrapper over the c-level 4d bin packing problem solver.
    The 4d bin packing problem solver aims to solve bin packing problem, a.k.a
    container loading problem, with an additional constraint on weight.
    Given a set of rectangular-shaped items, and a set of rectangular-shaped bins
    with weight limit, the solver looks for an orthogonal packing solution
    such that minimizes the number of bins and maximize volume utilization.
    Each rectangular-shaped item i = 1, .. , n is characterized by length l_i,
    depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin
    j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j,
    and weight limit w_j.
    The item can be rotated into any orthogonal direction, and no further
    restrictions implied.
Provides utilities for processing Rd objects and files.
  Extract argument descriptions and other parts of the help pages of
  functions.
Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
An implementation of hyperparameter optimization for Gradient
    Boosted Trees on binary classification and regression problems. The current
    version provides two optimization methods: Bayesian optimization and random
    search. Instead of giving the single best model, the final output is an 
    ensemble of Gradient Boosted Trees constructed via the method of ensemble 
    selection.
Plot density and distribution functions with automatic selection of
       suitable regions. Numerically invert (compute quantiles) distribution
       functions. Simulate real and complex numbers from distributions of their
       magnitude and arguments. Optionally, the magnitudes and/or arguments may
       be fixed in almost arbitrary ways. Create polynomials from roots given in
       Cartesian or polar form. Small programming utilities: check if an object
       is identical to NA, count positional arguments in a call, set
       intersection of more than two sets, check if an argument is unnamed,
       compute the graph of S4 classes in packages.
Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.
Aligns peak based on peak retention times and matches homologous peaks
    across samples. The underlying alignment procedure comprises three sequential steps.
    (1) Full alignment of samples by linear transformation of retention times to 
    maximise similarity among homologous peaks (2) Partial alignment of peaks within 
    a user-defined retention time window to cluster homologous peaks (3) Merging rows
    that are likely representing homologous substances (i.e. no sample shows peaks in 
    both rows and the rows have similar retention time means). The algorithm is described in detail
    in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
'GPU'/CPU Benchmarking on Debian-package based systems
 This package benchmarks performance of a few standard linear algebra
 operations (such as a matrix product and QR, SVD and LU decompositions)
 across a number of different 'BLAS' libraries as well as a 'GPU' implementation.
 To do so, it takes advantage of the ability to 'plug and play' different
 'BLAS' implementations easily on a Debian and/or Ubuntu system.  The current
 version supports
  - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline
  - Atlas which are tuned but typically configure single-threaded
  - Atlas39 which are tuned and configured for multi-threaded mode
  - 'Goto Blas' which are accelerated and multi-threaded
  - 'Intel MKL' which is a commercial accelerated and multithreaded version.
 As for 'GPU' computing, we use the CRAN package
  - 'gputools'
 For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be
 used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10.
Provides methods for model selection, estimation, bootstrap inference, and simulation for the 
    multilevel factor model, based on the principal component estimation and generalised 
    canonical correlation approach. Details can be found in "Generalised Canonical Correlation 
    Estimation of the Multilevel Factor Model." Lin and Shin (2023) <doi:10.2139/ssrn.4295429>.
Contains the Global Charcoal database data. Data include charcoal
    series (age, depth, charcoal quantity, associated units and methods) and
    information on sedimentary sites (localisation, depositional environment, biome,
    etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.
Implements a generalized coordinate descent (GCD) algorithm
    for computing the solution paths of the hybrid Huberized support vector
    machine (HHSVM) and its generalizations. Supported models include the
    (adaptive) LASSO and elastic net penalized least squares, logistic
    regression, HHSVM, squared hinge loss SVM and expectile regression.
Generalized competing event model based on Cox PH model and Fine-Gray model.
    This function is designed to develop optimized risk-stratification methods for competing
    risks data, such as described in:
    1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F,
    and Mell LK (2014) <DOI:10.1016/j.ijrobp.2014.03.047>.
    2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose
    BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>.
    3. Lunn, Mary, and Don McNeil (1995) <DOI:10.2307/2532940>.
Scrapes Google Citation pages and creates data frames of 
    citations over time.
Provides a variety of functions to analyze and model
    geostatistical count data with Gaussian copulas, including
 1) data simulation and visualization; 
 2) correlation structure assessment (here also known as the Normal To Anything); 
 3) calculate multivariate normal rectangle probabilities; 
 4) likelihood inference and parallel prediction at predictive locations.
 Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.
Estimation of covariance matrices as solutions of 
             continuous time Lyapunov equations. 
             Sparse coefficient matrix and diagonal noise are estimated 
             with a proximal gradient 
             method for an l1-penalized loss minimization problem.
             Varando G, Hansen NR (2020) <arXiv:2005.10483>.
Orders panels in scatterplot matrices and parallel coordinate
 displays by some merit index. Package contains various indices of merit,
 ordering functions, and enhanced versions of pairs and parcoord which
 color panels according to their merit level.
Likelihood inference in Gaussian copula marginal
        regression models.
An implementation of the International Bureau of Weights and Measures (BIPM) generalized consensus estimators used to assign the reference value in a key comparison exercise. This can also be applied to any interlaboratory study. Given a set of different sources, primary laboratories or measurement methods this package provides an evaluation of the variance components according to the selected statistical method for consensus building. It also implements the comparison among different consensus builders and evaluates the participating method or sources against the consensus reference value. Based on a diverse set of references, DerSimonian-Laird (1986) <doi:10.1016/0197-2456(86)90046-2>, for a complete list of references look at the reference section in the package documentation.
Data sets used in the book "R Graphics Cookbook" by Winston
    Chang, published by O'Reilly Media.
Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) <doi:10.1002/sim.8855>.
Easy wrangling and model-free analysis of
    microbial growth curve data, as commonly output by plate readers.
    Tools for reshaping common plate reader outputs into 'tidy' formats and
    merging them with design information, making data easy to work with using 
    'gcplyr' and other packages. Also streamlines common growth curve
    processing steps, like smoothing and calculating derivatives, and
    facilitates model-free characterization and analysis of growth data.
    See methods at <https://mikeblazanin.github.io/gcplyr/>.
Analyze the default risk of credit portfolios. Commonly known models, 
		like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
		The portfolio loss distribution can be achieved either by simulation or analytically 
		in case of the classic CreditRisk+ model. Models are only implemented to respect losses
		caused by defaults, i.e. migration risk is not included. The package structure is kept
		flexible especially with respect to distributional assumptions in order to quantify the
		sensitivity of risk figures with respect to several assumptions. Therefore the package
		can be used to determine the credit risk of a given portfolio as well as to quantify
		model sensitivities.
Provides implementation of the generic composite similarity measure
    (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The
    implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations
    of the structural similarity (SSIM) and the composite similarity measure based
    on means, standard deviations, and correlation coefficient (CMSC), are included.
Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md <https://github.com/rikenbit/gcTensor>, for details of the method.
Provides complete detailed preprocessing of two-dimensional gas chromatogram (GCxGC) samples. Baseline correction, smoothing, peak detection, and peak alignment. Also provided are some analysis functions, such as finding extracted ion chromatograms, finding mass spectral data, targeted analysis, and nontargeted analysis with either the 'National Institute of Standards and Technology Mass Spectral Library' or with the mass data. There are also several visualization methods provided for each step of the preprocessing and analysis.
Geographical detectors for measuring spatial stratified heterogeneity,
             as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and 
             Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the
             optimal discretization of continuous data, four primary functions of 
             geographical detectors, comparison of size effects of spatial unit and 
             the visualizations of results. To use the package and to refer the 
             descriptions of the package, methods and case datasets, please cite 
             Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has 
             been applied in factor exploration of road performance and multi-scale 
             spatial segmentation for network data, as described in 
             Yongze Song (2018) <doi:10.3390/rs10111696> and 
             Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users
    define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and 
    resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, 
    applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, 
    exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features.  
    All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. 
    See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.
Interface to the Raster API of the 'Geospatial Data Abstraction
    Library' ('GDAL', <https://gdal.org>). Bindings are implemented in an
    exposed C++ class encapsulating a 'GDALDataset' and its raster band
    objects, along with several stand-alone functions. These support manual
    creation of uninitialized datasets, creation from existing raster as
    template, read/set dataset parameters, low level I/O, color tables, raster
    attribute tables, virtual raster (VRT), and 'gdalwarp' wrapper for
    reprojection and mosaicing. Includes 'GDAL' algorithms ('dem_proc()',
    'polygonize()', 'rasterize()', etc.), and functions for coordinate
    transformation and spatial reference systems. Calling signatures resemble
    the native C, C++ and Python APIs provided by the 'GDAL' project. Includes
    raster 'calc()' to evaluate a given R expression on a layer or stack of
    layers, with pixel x/y available as variables in the expression; and raster
    'combine()' to identify and count unique pixel combinations across multiple
    input layers, with optional output of the pixel-level combination IDs.
    Provides raster display using base 'graphics'. Bindings to a subset of the
    'OGR' API are also included for managing vector data sources. Bindings to a
    subset of the Virtual Systems Interface ('VSI') are also included to support
    operations on 'GDAL' virtual file systems. These are general utility
    functions that abstract file system operations on URLs, cloud storage
    services, 'Zip'/'GZip'/'7z'/'RAR' archives, and in-memory files.
    'gdalraster' may be useful in applications that need scalable, low-level
    I/O, or prefer a direct 'GDAL' API.
R's 'sf' package ships with self-contained 'GDAL'
    executables, including a bare bones interface to several
    'GDAL'-related utility programs collectively known as the 'GDAL
    utilities'. For each of those utilities, this package provides an
    R wrapper whose formal arguments closely mirror those of the
    'GDAL' command line interface. The utilities operate on data
    stored in files and typically write their output to other
    files. Therefore, to process data stored in any of R's more common
    spatial formats (i.e. those supported by the 'sf' and 'terra'
    packages), first write them to disk, then process them with the
    package's wrapper functions before reading the outputted results
    back into R. GDAL function arguments introduced in GDAL version
    3.5.2 or earlier are supported.
Various R programming tools for data manipulation, including
  medical unit conversions, combining objects, character vector operations,
  factor manipulation, obtaining information about R objects, generating
  fixed-width format files, extracting components of date & time objects,
  operations on columns of data frames, matrix operations, operations on
  vectors, operations on data frames, value of last evaluated expression, and a
  resample() wrapper for sample() that ensures consistent behavior for both
  scalar and vector arguments.
Many tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>), such as MCA variants (Specific Multiple Correspondence Analysis, Class Specific Analysis), many graphical and statistical aids to interpretation (structuring factors, concentration ellipses, inductive tests, bootstrap validation, etc.) and multiple-table analysis (Multiple Factor Analysis, between- and inter-class analysis, Principal Component Analysis and Correspondence Analysis with Instrumental Variables, etc.).
Functions to explore datasets from the Global Biodiversity Information Facility (GBIF - <https://www.gbif.org/>) using a Shiny interface.
The GDELT V1 Event data set is over 41 GB now and growing 250 MB 
    a month. The number of source articles has increased over time and unevenly 
    across countries. This package makes it easy to download a subset of that 
    data, then normalize that data to facilitate valid time series analysis.
Tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. GDI was first employed by Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999) <doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass estimation of (extinct) animals. The package 'gdi' aims to make this technique as convenient and versatile as possible. The core functions of 'gdi' provide utilities for automatically measuring diameters from digital silhouettes provided as image files and calculating volume via graphic double integration with simple elliptical, superelliptical (following Motani 2001 <doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex cross-sectional models. Additionally, the package provides functions for estimating the center of mass position (COM), the moment of inertia (I) for 3D shapes and the second moment of area (Ix, Iy, Iz) of 2D cross-sections, as well as for visualization of results.
Functions for performing graphical difference testing.     
             Differences are generated between raster images.
             Comparisons can be performed between different package
             versions and between different R versions.
Provides tools for simulating from spatial modeling of individual level  of infectious disease transmission when co-variates measured with error, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based model within Susceptible-Infectious-Removed (SIR) compartmental frameworks.
Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings.
Model and estimate the model parameters 
            for the spatial model of individual-level infectious disease 
            transmission in Susceptible-Infected-Recovered (SIR) framework.
Cross-validated eigenvalues are estimated by
    splitting a graph into two parts, the training and the test graph.
    The training graph is used to estimate eigenvectors, and
    the test graph is used to evaluate the correlation between the training
    eigenvectors and the eigenvectors of the test graph.
    The correlations follow a simple central limit theorem that can
    be used to estimate graph dimension via hypothesis testing, see
    Chen et al. (2021) <arXiv:2108.03336> for details.
A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) <DOI:10.18637/jss.v093.i14>, Ma and de la Torre (2019) <DOI:10.1111/emip.12262>, Ma (2019) <DOI:10.1007/978-3-030-05584-4_29> and de la Torre and Akbay (2019).
Provides classes and functions to calculate various 
             distance measures and routes in heterogeneous geographic 
             spaces represented as grids. The package implements measures
             to model dispersal histories first presented by van Etten and
             Hijmans (2010) <doi:10.1371/journal.pone.0012060>. Least-cost
             distances as well as more complex distances based on (constrained)
             random walks can be calculated. The distances implemented in 
             the package are used in geographical genetics, accessibility 
             indicators, and may also have applications in other fields of
             geospatial analysis.
Retrieve datasets from the 'Global Data Lab' website <https://globaldatalab.org>
    directly into R data frames. Functions are provided to reference available options
    (indicators, levels, countries, regions) as well.
A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) <doi:10.1111/geb.13459> Ferrier S, Manion G, Elith J, Richardson K (2007) <doi:10.1111/j.1472-4642.2007.00341.x>.
Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation
    includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02>
    for the identification of both the number of lags to be used
    in the generalized dynamic principal components as well as the number of components required
    for a given reconstruction accuracy.
Convert GDP time series data from one unit to
    another. All common GDP units are included, i.e. current and constant
    local currency units, US$ via market exchange rates and international
    dollars via purchasing power parities.
Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.
The method aims to identify important factors in screening experiments by aggregation over random models as studied in Singh and Stufken (2022) <doi:10.48550/arXiv.2205.13497>. This package provides functions to run the Gauss-Dantzig selector on screening experiments when interactions may be affecting the response. Currently, all functions require each factor to be at two levels coded as +1 and -1.
Tools are provided to compute metrics of formatted strings
    and to check the availability of a font.  Another set of functions is
    provided to support the collection of fonts from 'Google Fonts' in a
    cache. Their use is simple within 'R Markdown' documents and 'shiny'
    applications but also with graphic productions generated with the
    'ggiraph', 'ragg' and 'svglite' packages or with tabular productions
    from the 'flextable' package.
Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, while also taking into account local spatial dependencies, spatial interpretability, complex spatial interactions, and robust spatial stratification. Additionally, it supports the spatial stratified heterogeneity family established in academic literature.
Interfaces GAMS data (*.gdx) files with 'data.table's using the GAMS R package 'gdxrrw'. The 'gdxrrw' package is available on the GAMS wiki: <https://support.gams.com/doku.php?id=gdxrrw:interfacing_gams_and_r>.
Some tools for developing general equilibrium models and some general equilibrium models. These models can be used for teaching economic theory and are built by the methods of new structural economics (see LI Wu, 2019, ISBN: 9787521804225, General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press). The model form and mathematical methods can be traced back to J. von Neumann (1945, A Model of General Economic Equilibrium. The Review of Economic Studies, 13. pp. 1-9), J. G. Kemeny, O. Morgenstern and G. L. Thompson (1956, A Generalization of the von Neumann Model of an Expanding Economy, Econometrica, 24, pp. 115-135) et al. By the way, J. G. Kemeny is a co-inventor of the computer language BASIC.
Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).
Evaluate and validate the Geboes score for histological assessment
  of inflammation in ulcerative colitis.  The original Geboes score from Geboes,
  et al. (2000) <doi:10.1136/gut.47.3.404>, binary version from Li, et al.
  (2019) <doi:10.1093/ecco-jcc/jjz022>, and continuous version from Magro, et
  al. (2020) <doi:10.1093/ecco-jcc/jjz123> are all described and implemented.
Contains the framework of the estimation, sampling, and hypotheses testing for two special distributions (Exponentiated Exponential-Pareto and Exponentiated Inverse Gamma-Pareto) within the family of Generalized Exponentiated Composite distributions. The detailed explanation and the applications of these two distributions were introduced in Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.1080/03610926.2022.2050399>, Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/math10111895>, and Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/app13010645>.
Generalized Entropy Calibration produces calibration weights 
    using generalized entropy as the objective function for optimization. 
    This approach, as implemented in the 'GECal' package, is based on 
    Kwon, Kim, and Qiu (2024) <doi:10.48550/arXiv.2404.01076>. Unlike 
    traditional methods, 'GECal' incorporates design weights into the 
    constraints to maintain design consistency, rather than including 
    them in the objective function itself.
Includes a collection of geographical analysis functions aimed primarily at ecology and conservation science studies, allowing processing of both point and raster data. Now integrates SPECTRE (<https://biodiversityresearch.org/spectre/>), a dataset of global geospatial threat data, developed by the authors.
Spline Regression, Generalized Additive Models, and Component-wise Gradient
  Boosting, utilizing Geometrically Designed (GeD) Splines. GeDS regression is a
  non-parametric method inspired by geometric principles, for fitting spline regression
  models with variable knots in one or two independent variables. It efficiently estimates
  the number of knots and their positions, as well as the spline order, assuming the
  response variable follows a distribution from the exponential family. GeDS models
  integrate the broader category of Generalized (Non-)Linear Models, offering a flexible
  approach to modeling complex relationships. A description of the method can be found in
  Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023)
  <doi:10.1016/j.amc.2022.127493>. Further extending its capabilities, GeDS's implementation
  includes Generalized Additive Models (GAM) and Functional Gradient Boosting (FGB),
  enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of
  Dimitrova et al. (2024).
Provides adaptive association tests for SNP level, gene level and pathway level analyses.
Estimation of generalized linear models with
    correlated/clustered observations by use of generalized estimating
    equations (GEE). See e.g. Halekoh and Højsgaard, (2005,
    <doi:10.18637/jss.v015.i02>), for details. Several types of
    clustering are supported, including exchangeable variance
    structures, AR1 structures, M-dependent, user-specified variance
    structures and more. The model fitting computations are performed
    using modified code from the 'geeM' package, while the interface
    and output objects have been written to resemble the 'geepack'
    package. The package also contains additional tools for working
    with and inspecting results from the 'geepack' package, e.g. a
    'confint' method for 'geeglm' objects from 'geepack'.
Population-averaged models have been increasingly used in the design and analysis of 
             cluster randomized trials (CRTs). To facilitate the applications of population-averaged 
             models in CRTs, the package implements the generalized estimating equations (GEE) and 
             matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal 
             mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the 
             general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by 
             Li et al. (2022) <doi:10.1093/biostatistics/kxaa056>
             specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives 
             a simple and efficient estimating equations approach based on the cluster-period means to 
             estimate the intervention effects as well as correlation parameters. In addition, the package 
             also provides functions for generating correlated binary data with specific mean vector and 
             correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or 
             the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.
Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) <doi:10.1016/j.jmva.2013.09.003>.
GEE estimation of the parameters in mean structures with possible
    correlation between the outcomes. User-specified mean link and variance
    functions are allowed, along with observation weighting. The 'M' in the name
    'geeM' is meant to emphasize the use of the Matrix package, which allows for an
    implementation based fully in R.
Causal mediation analysis for a single exposure/treatment and a
    single mediator, both allowed to be either continuous or binary. The package
    implements the difference method and provides point and interval estimates as
    well as testing for the natural direct and indirect effects and the mediation
    proportion. Nevo, Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.
Generalized estimating equations solver for parameters in
    mean, scale, and correlation structures, through mean link,
    scale link, and correlation link. Can also handle clustered
    categorical responses. See e.g. Halekoh and Højsgaard, (2005,
    <doi:10.18637/jss.v015.i02>), for details.
Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.
Analyze small-sample clustered or longitudinal data with binary 
    outcome using modified generalized estimating equations (GEE) with 
    bias-adjusted covariance estimator. The package provides any combination of 
    three GEE methods and 12 covariance estimators.
To provide a comprehensive analysis of high dimensional longitudinal
    data,this package provides analysis for any combination of 1) simultaneous
    variable selection and estimation, 2) mean regression or quantile regression
    for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced
    or imbalanced data, 5) moderate, high or even ultra-high dimensional data,
    via computationally efficient implementations of penalized generalized
    estimating equations.
Provides a general, flexible framework for estimating parameters
    and empirical sandwich variance estimator from a set of unbiased estimating
    equations (i.e., M-estimation in the vein of Stefanski & Boos (2002)
    <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002)
    are published in the corresponding Journal of Statistical Software paper
    "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020)
    <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample 
    variance corrections.
Functions to compute generalized eigenvalues and eigenvectors,
             the generalized Schur decomposition and
             the generalized Singular Value Decomposition of a matrix pair,
             using Lapack routines.
Methods for fitting macroevolutionary models to phylogenetic trees
	Pennell (2014) <doi:10.1093/bioinformatics/btu181>.
Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions.
Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).
Represents generalized geometric ellipsoids with the "(U,D)" representation. It allows degenerate
	and/or unbounded ellipsoids, together with methods for linear and duality transformations, and for plotting.
	Thus ellipsoids are naturally extended to include lines, hyperplanes, points, cylinders, etc.
	This permits exploration of a variety to statistical issues that can be visualized using ellipsoids
	as discussed by Friendly, Fox & Monette (2013), Elliptical Insights: Understanding Statistical Methods 
	Through Elliptical Geometry <doi:10.1214/12-STS402>.
Implements several extensions of the elastic net regularization
    scheme. These extensions include individual feature penalties for the L1 term,
    feature-feature penalties for the L2 term, as well as translation coefficients
    for the latter.
Provides functions to calculate the best linear unbiased prediction of genotype-by-environment metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure, based on a multi-environment genomic prediction model.
Provides a comprehensive interface for Google Gemini API, 
            enabling users to access and utilize Gemini Large Language Model (LLM) functionalities directly from R. 
            This package facilitates seamless integration with Google Gemini, allowing for advanced language processing, 
            text generation, and other AI-driven capabilities within the R environment.
            For more information, please visit <https://ai.google.dev/docs/gemini_api_overview>.
Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package 'gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.
Simulate and analyze multistate models with general hazard
    functions. gems provides functionality for the preparation of hazard functions
    and parameters, simulation from a general multistate model and predicting future
    events. The multistate model is not required to be a Markov model and may take
    the history of previous events into account. In the basic version, it allows
    to simulate from transition-specific hazard function, whose parameters are
    multivariable normally distributed.
Network meta-analyses (mixed treatment comparisons) in the Bayesian
    framework using JAGS. Includes methods to assess heterogeneity and
    inconsistency, and a number of standard visualizations.
    van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>;
    van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.
One can find single-stage and two-stage designs for a phase II 
    single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>.
Contains an engine for spatially-explicit eco-evolutionary mechanistic models with a modular implementation and several support functions. It allows exploring the consequences of ecological and macroevolutionary processes across realistic or theoretical spatio-temporal landscapes on biodiversity patterns as a general term. Reference: Oskar Hagen, Benjamin Flueck, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago F. Rangel, Loic Pellissier (2021) "gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity" <doi:10.1371/journal.pbio.3001340>.
A collection of functions for processing 'Gen5' 2.06 exported data.
    'Gen5' is an essential data analysis software for BioTek plate readers <https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>. This package contains functions for data cleaning, 
    modeling and plotting using exported data from 'Gen5' version 2.06. It exports
    technically correct data defined in (Edwin de Jonge and Mark van der Loo 
    (2013) <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>) for customized analysis. It 
    contains Boltzmann fitting for general kinetic analysis.  
    See <https://www.github.com/yanxianUCSB/gen5helper> for more information,
    documentation and examples.
Implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.archives-ouvertes.fr/hal-00764996>.
Utilizing Generative Artificial Intelligence models like 'GPT-4' and 'Gemini Pro' as coding and writing assistants for 'R' users. Through these models, 'GenAI' offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid 'R' users in streamlining laborious coding and language processing tasks.
R based genetic algorithm for binary and floating point
        chromosomes.
Defines classes and methods that can be used
  to implement genetic algorithms for feature selection.  The idea is
  that we want to select a fixed number of features to combine into a
  linear classifier that can predict a binary outcome, and can use a
  genetic algorithm heuristically to select an optimal set of features.
Provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.
Allows users to generate a gendered language score according to the gendered language dictionary in Roberts and Utych (2019) <doi:10.1177/1065912919874883>.
Set of functions to create datasets using a correlation matrix.
Infers state-recorded gender categories from first names and dates of birth using historical
    datasets. By using these datasets instead of lists of male and female names,
    this package is able to more accurately infer the gender of a name, and it
    is able to report the probability that a name was male or female. GUIDELINES:
    This method must be used cautiously and responsibly. Please be sure to see the
    guidelines and warnings about usage in the 'README' or the package documentation.
    See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.
A method to predict and report gender from Brazilian first names
    using the Brazilian Institute of Geography and Statistics' Census data (<https://censo2010.ibge.gov.br/nomes/>).
Implementation of functions, which combines binomial calculation 
    and data visualisation, to analyse the differences in publishing authorship
    by gender described in Day et al. (2020) <doi:10.1039/C9SC04090K>.
    It should only be used when self-reported gender is unavailable.
Provides tools for quantitative analysis in gender studies, including functions to calculate various gender inequality metrics such as the Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI), and Gender Empowerment Measure (GEM). Also includes extracted secondary example datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center and the World Bank Gender Data Portal by the author the dataset is available on <doi:10.34740/kaggle/dsv/6359326>. References: Miller, Kevin; Vagins, Deborah J. (2021) <https://eric.ed.gov/?id=ED596219>. Jacques Charmes & Saskia Wieringa (2003) <doi:10.1080/1464988032000125773>. Gaëlle Ferrant (2010) <https://shs.hal.science/halshs-00462463/>.
Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
Segmentation and classification procedures for data from the 'Activinsights GENEActiv' <https://activinsights.com/technology/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing.
    Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.
Functions and analytics for GENEA-compatible accelerometer data into R objects. 
             See topic 'GENEAread' for an introduction to the package. 
             See <https://activinsights.com/technology/geneactiv/> for more details on the GENEActiv device.
The GeneCycle package implements the approaches of Wichert
        et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki 
        et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al.
        (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically 
        expressed genes from gene expression time series data.
A function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293,  GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>. More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, <doi:10.1007/978-1-0716-1839-4_12>.
Implementation of several generalized F-statistics. The
    current version includes a generalized F-statistic based on the
    flexible isotonic/monotonic regression or order restricted hypothesis
    testing. Based on: Y. Lai (2011) <doi:10.1371/journal.pone.0019754>.
Import genome variants data and perform gene haplotype Statistics, 
  visualization and phenotype association with 'R'.
A pipeline with high specificity and sensitivity in extracting 
  proteins from the RefSeq database (National Center for Biotechnology 
  Information). Manual identification of gene families is highly 
  time-consuming and laborious, requiring an iterative process of manual and 
  computational analysis to identify members of a given family. The pipelines 
  implements an automatic approach for the identification of gene families 
  based on the conserved domains that specifically define that family. See 
  Die et al. (2018) <doi:10.1101/436659> for more information and examples.
Provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.
Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.
Analyzes gene expression
  (time series) data with focus on the inference of gene networks.
  In particular, GeneNet implements the methods of Schaefer and 
  Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
  for learning large-scale gene association networks (including
  assignment of putative directions).
A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures.
Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies.
Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Function gmcmtx0() computes a more reliable (general) 
    correlation matrix. Since causal paths from data are important for all sciences, the
    package provides many sophisticated functions. causeSummBlk() and causeSum2Blk()
    give easy-to-interpret causal paths.  Let Z denote control variables and compare 
    two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 
    says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent"
    than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These
    inequalities between many absolute values are quantified by four orders of 
    stochastic dominance. Our third criterion Cr3, for the causal path X to Y,
    requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|.
    The function parcorVec() reports generalized partials between the first
    variable and all others.  The package provides several R functions including
    get0outliers() for outlier detection, bigfp() for numerical integration by the
    trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
    canonRho() for generalized canonical correlations, depMeas() measures nonlinear
    dependence, and causeSummary(mtx) reports summary of causal paths among matrix 
    columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx()
    can rank several stocks. Functions whose names begin with 'boot' provide bootstrap
    statistical inference, including a new bootGcRsq() test for "Granger-causality" 
    allowing nonlinear relations. A new tool for evaluation of out-of-sample
    portfolio performance is outOFsamp(). Panel data implementation is now included.
    See eight vignettes of the package for theory, examples, and
    usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.
Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal).
A statistical hypothesis test for conditional independence. It performs nonlinear regressions on the conditioning variable and then tests for a vanishing covariance between the resulting residuals. It can be applied to both univariate random variables and multivariate random vectors. Details of the method can be found in Rajen D. Shah and Jonas Peters: The Hardness of Conditional Independence Testing and the Generalised Covariance Measure, Annals of Statistics 48(3), 1514--1538, 2020.
Functions for the hyperbolic and related distributions.
  Density, distribution and quantile functions and random number generation
  are provided for the hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, normal inverse
	Gaussian distribution and generalized inverse Gaussian distribution,
	including fitting of these distributions to data. Linear models with
        hyperbolic errors may be fitted using hyperblmFit.
Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.
A fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation.
  Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) <url:<https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2F17-AOS1652 >>.
  Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) <arXiv:2008.02904>.
  Reinhard Furrer and Roman Flury and Florian Gerber (2022) <url:<https://CRAN.R-project.org/package=spam >>.
Perform the Blinder-Oaxaca decomposition for generalized linear 
   model with bootstrapped standard errors. The twofold and threefold 
   decomposition are given, even the generalized linear model output in each group.
Ranked Set Sampling (RSS) is a stratified sampling method known for its efficiency compared to Simple Random Sampling (SRS). When sample allocation is equal across strata, it is referred to as balanced RSS (BRSS) whereas unequal allocation is called unbalanced RSS (URSS), which is particularly effective for asymmetric or skewed distributions. This package offers practical statistical tools and sampling methods for both BRSS and URSS, emphasizing flexible sampling designs and inference for population means, medians, proportions, and Area Under the Curve (AUC). It incorporates parametric and nonparametric tests, including empirical likelihood ratio (LR) methods. The package provides ranked set sampling methods from a given population, including sampling with imperfect ranking using auxiliary variables. Furthermore, it provides tools for efficient sample allocation in URSS, ensuring greater efficiency than SRS and BRSS. For more details, refer e.g. to Chen et al. (2003) <doi:10.1007/978-0-387-21664-5>, Ahn et al. (2022) <doi:10.1007/978-3-031-14525-4_3>, and Ahn et al. (2024) <doi:10.1111/insr.12589>.
Allows users to quickly and easily generate fake data containing
    Personally Identifiable Information (PII) through convenience functions.
Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802>. This package's workhorse is the 'mlr3' framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802> for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported.
In order to reduce potential package dependencies and
    conflicts, generics provides a number of commonly used S3 generics.
Estimate gender from names in Spanish and Portuguese. 
    Works with vectors and dataframes. The estimation works not only
    for first names but also full names. The package relies on a
    compilation of common names with it's most frequent associated 
    gender in both languages which are used as look up tables for gender
    inference.
Simulating single cell RNA-seq data with complicated structure. This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) <doi:10.1186/s13059-017-1305-0>). 'GeneScape' incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes.
Provides two methods for automatic calculation of gene scores from gene count tables: the z-score method, which requires a table of samples being scored and a count table with control samples, and the geometric mean method, which does not rely on control samples. The mathematical methods implemented are described by Kim et al. (2018) <doi:10.1089/jir.2017.0127>.
The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (<https://scikit-learn.org/stable/index.html>) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) <doi:10.1002/pro.4218>, using 'clusterProfiler' (Wu et al., 2021) <doi:10.1016/j.xinn.2021.100141> and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) <doi:10.1016/j.gpb.2022.04.008>. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.
Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying 
    gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), 
    'KEGG' (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), 
    'WikiPathway' (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), 
    'MsigDb' (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), 
    'Reactome' (David et al. (2011) <doi:10.1093/nar/gkq1018>),
    'MeSH' (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), 
    'DisGeNET' (Janet et al. (2017) <doi:10.1093/nar/gkw943>),
    'Disease Ontology' (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), 
    'Network of Cancer Genes' (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and 
    'COVID-19' (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>).
    Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. 
    The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of 
    terms ID and term description.
Genome-wide association study (GWAS) performed with SLOPE,
    short for Sorted L-One Penalized Estimation, a
    method for estimating the vector of coefficients in a linear model.
    In the first step of GWAS, single nucleotide polymorphisms (SNPs) are 
    clumped according to their correlations and distances. 
    Then, SLOPE is performed on the data where each clump has
    one representative.
    Malgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su 
    and Emmanuel Candes (2014) 
    "SLOPE - Adaptive Variable Selection via Convex Optimization" 
    <arXiv:1407.3824>.
Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>).
  Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.
Genetic algorithm are a class of optimization
    algorithms inspired by the process of natural selection and genetics.
    This package is for learning purposes and allows users to optimize
    various functions or parameters by mimicking biological evolution
    processes such as selection, crossover, and mutation. Ideal for tasks
    like machine learning parameter tuning, mathematical function
    optimization, and solving an optimization problem that involves finding 
    the best solution in a discrete space.
Data from multi environment agronomic trials, which are often 
  carried out by plant breeders, can be analyzed with the tools offered by this 
  package such as the Additive Main effects and Multiplicative Interaction model 
  or 'AMMI' ('Gauch' 1992, ISBN:9780444892409) 
  and the Site Regression model or 'SREG' ('Cornelius' 1996, 
  <doi:10.1201/9780367802226>). Since these methods present a poor performance 
  under the presence of outliers and missing values, this package includes 
  robust versions of the 'AMMI' model ('Rodrigues' 2016, 
  <doi:10.1093/bioinformatics/btv533>), and also imputation techniques 
  specifically developed for this kind of data ('Arciniegas-Alarcón' 2014, 
  <doi:10.2478/bile-2014-0006>).
Classes and methods for handling genetic data. Includes
        classes to represent genotypes and haplotypes at single markers
        up to multiple markers on multiple chromosomes. Function
        include allele frequencies, flagging homo/heterozygotes,
        flagging carriers of certain alleles, estimating and testing
        for Hardy-Weinberg disequilibrium, estimating and testing for
        linkage disequilibrium, ...
Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
Implementation of spatial graph-theoretic genetic gravity models.
    The model framework is applicable for other types of spatial flow questions.
    Includes functions for constructing spatial graphs, sampling and summarizing
    associated raster variables and building unconstrained and singly constrained
    gravity models.
Provides tools for plotting gene clusters and transcripts by 
  importing data from GenBank, FASTA, and GFF files. It performs BLASTP and 
  MUMmer alignments [Altschul et al. (1990) <doi:10.1016/S0022-2836(05)80360-2>;
  Delcher et al. (1999) <doi:10.1093/nar/27.11.2369>] and displays results on 
  gene arrow maps. Extensive customization options are available, including 
  legends, labels, annotations, scales, colors, tooltips, and more.
Triangular and trapezoidal fuzzy numbers are used to study fuzzy logic,
    fuzzy reasoning and approximating, fuzzy regression models, etc.
    This package builds the generating function for triangular and trapezoidal fuzzy numbers based on Souliotis et al. (2022)<doi:10.3390/math10183350>. 
    They proposed a method for the construction of fuzzy numbers via a cumulative distribution function based on the possibility theory.
Includes the reference implementation of Genie - a hierarchical
    clustering algorithm that links two point groups in such a way that
    an inequity measure (namely, the Gini index) of the cluster sizes
    does not significantly increase above a given threshold.
    This method most often outperforms many other data segmentation approaches
    in terms of clustering quality as tested on a wide range of benchmark
    datasets. At the same time, Genie retains the high speed of the single
    linkage approach, therefore it is also suitable for analysing larger data sets.
    For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>).
    For an even faster and more feature-rich implementation, including,
    amongst others, noise point detection, see the 'genieclust' package.
The American Association Research (AACR) Project 
    Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma 
    Collaborative represents a multi-year, multi-institution effort
    to build a pan-cancer repository of linked clinico-genomic data. 
    The genomic and clinical data are provided in multiple releases (separate
    releases for each cancer cohort with updates following data corrections), 
    which are stored on the data sharing platform 
    'Synapse' <https://www.synapse.org/>. 
    The 'genieBPC' package provides a seamless way to obtain the
    data corresponding to each release from 'Synapse' and to prepare
    datasets for analysis.
A retake on the Genie algorithm
    (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) - a robust
    hierarchical clustering method
    (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).
    Now faster and more memory efficient; determining the whole hierarchy
    for datasets of 10M points in low dimensional Euclidean spaces or
    100K points in high-dimensional ones takes only 1-2 minutes.
    Allows clustering with respect to mutual reachability distances
    so that it can act as a noise point detector or a robustified version of
    'HDBSCAN*' (that is able to detect a predefined number of
    clusters and hence it does not dependent on the somewhat
    fragile 'eps' parameter).
    The package also features an implementation of inequality indices
    (the Gini, Bonferroni index), external cluster validity measures
    (e.g., the normalised clustering accuracy and partition similarity scores
    such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information,
    and the pair sets index),
    and internal cluster validity indices (e.g., the Calinski-Harabasz,
    Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices).
    See also the 'Python' version of 'genieclust' available on 'PyPI', which
    supports sparse data, more metrics, and even larger datasets.
Implements readers and writers for file formats associated with genetics data.  Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations.  Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files).  There are also make functions for FAM and BIM tables with default values to go with simulated genotype data.
Provides tools to interact nicely with the 'Genius' API 
    <https://docs.genius.com/>. 
    Search hosted content, extract associated metadata and retrieve lyrics with ease.
Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations. See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) <doi:10.1186/s12859-015-0581-5>.
Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive 'RStudio' plot for better guessing dynamically of initial values for ease of included optimization and simulating.
Provides routines to estimate the Mixture Transition Distribution Model based on Raftery (1985) <http://www.jstor.org/stable/2345788> and Nicolau (2014) <doi:10.1111/sjos.12087> specifications, for multivariate data. Additionally, provides a  function for the estimation of a new model for multivariate non-homogeneous Markov chains. This new specification, Generalized Multivariate Markov Chains (GMMC) was proposed by Carolina Vasconcelos and Bruno Damasio and considers (continuous or discrete) covariates exogenous to the Markov chain.
Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC) algorithms on highly multivariate or unordered spaces. Methods outlined in a forthcoming paper.
Generalized meta-analysis is a technique for estimating parameters associated with a multiple regression model through meta-analysis of studies which may have information only on partial sets of the regressors. It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) <doi:10.1093/biomet/asz030>.The current version (0.2.0) is updated to address some of the stability issues in the previous version (0.1).
Using the DNA sequence and gene annotation files provided in 
            'ENSEMBL' <https://www.ensembl.org/index.html>, 
            the functions implemented in the package try to find the DNA sequences and 
            protein sequences of any given genomic loci, and to find the genomic coordinates 
            and protein sequences of any given protein locations, which are the frequent
            tasks in the analysis of genomic and proteomic data.
Calculates Agresti's generalized odds ratios.
  For a randomly selected pair of observations
  from two groups, calculates the odds that
  the second group will have a higher scoring outcome
  than that of the first group.
  Package provides hypothesis testing for if this odds
  ratio is significantly different to 1 (equal chance).
Evaluates likelihood ratio tests for alleged ancestry. Implements the methods of Tvedebrink et al (2018) <doi:10.1016/j.tpb.2017.12.004>.
Individual-based simulations forward in time,
    simulating how patterns in ancestry along the genome change after
    admixture. Full description can be found in Janzen (2021)
    <doi:10.1111/2041-210X.13612>.
Local structure in genomic data often induces dependence between observations taken at different genomic locations.  Ignoring this dependence leads to underestimation of the standard error of parameter estimates.  This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.
Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure(Cabrera et al (2012) <doi:10.1534/g3.112.002618>). All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user.
Genomic signatures represent unique features within a species' DNA, enabling the differentiation of species and offering broad applications across various fields. This package provides essential tools for calculating these specific signatures, streamlining the process for researchers and offering a comprehensive and time-saving solution for genomic analysis.The amino acid contents are identified based on the work published by Sandberg et al. (2003) <doi:10.1016/s0378-1119(03)00581-x> and Xiao et al. (2015) <doi:10.1093/bioinformatics/btv042>. The Average Mutual Information Profiles (AMIP) values are calculated based on the work of Bauer et al. (2008) <doi:10.1186/1471-2105-9-48>. The Chaos Game Representation (CGR) plot visualization was done based on the work of Deschavanne et al. (1999) <doi:10.1093/oxfordjournals.molbev.a026048> and Jeffrey et al. (1990) <doi:10.1093/nar/18.8.2163>. The GC content is calculated based on the work published by Nakabachi et al. (2006) <doi:10.1126/science.1134196> and Barbu et al. (1956) <https://pubmed.ncbi.nlm.nih.gov/13363015>. The Oligonucleotide Frequency Derived Error Gradient (OFDEG) values are computed based on the work published by Saeed et al. (2009) <doi:10.1186/1471-2164-10-S3-S10>. The Relative Synonymous Codon Usage (RSCU) values are calculated based on the work published by Elek (2018) <https://urn.nsk.hr/urn:nbn:hr:217:686131>.
A collection of I/O tools for handling the most commonly used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.
Draws gene or genome maps and comparisons between these, in a 
   publication-grade manner. Starting from simple, common files, it will 
   draw postscript or PDF files that can be sent as such to journals.
Tools for efficient processing of large, whole genome genotype data sets in variant call format (VCF). It includes several functions to calculate commonly used population genomic metrics and a method for reference panel free genotype imputation, which is described in the preprint Gurke & Mayer (2024) <doi:10.22541/au.172515591.10119928/v1>.
A gaussian copula based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.
Functions for whole-genome sequencing studies, including genome-wide scan, candidate region scan and single window test.
Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids.
	Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and
	Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny().
It provides an interesting solution for handling a high number 
	of segmentation variables in partial least squares structural equation 
	modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, 
	and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient 
	test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) 
	to detect the path coefficients responsible for the identified differences). 
	The package also allows running the hybrid multi-group approach (Lamberti (2021) 
	<doi:10.1007/s11135-021-01096-9>).
Power and sample size calculations for genetic association studies allowing 
    for misspecification of the model of genetic susceptibility.
    "Hum Hered. 2019;84(6):256-271.<doi:10.1159/000508558>. Epub 2020 Jul 28."
    Power and/or sample size can be calculated for logistic (case/control study design) 
    and linear (continuous phenotype) regression models, using additive, dominant, 
    recessive or degree of freedom coding of the genetic covariate while assuming 
    a true dominant, recessive or additive genetic effect. In addition, power and 
    sample size calculations can be performed for gene by environment interactions.  
    These methods are extensions of Gauderman (2002) 
    <doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973>
    and are described in: 
    Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations 
    for Genetic Association Studies in the Presence of Genetic Model Misspecification. 
    American Society of Human Genetics. 
    October 2018, San Diego.
The genridge package introduces generalizations of the standard univariate
  ridge trace plot used in ridge regression and related methods.  These graphical methods
  show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
  coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
  both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
  predictors.
Performs search for global minimum of a very complex non-linear
    objective function with a very large number of optima.
Implementation of the Generalized Score Matching estimator in Yu et al. (2019) <https://jmlr.org/papers/v20/18-278.html> for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) <doi:10.1214/16-EJS1126>, with the addition of a diagonal multiplier.
Performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.
For educational purposes to demonstrate the importance of multiple regression. The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results.
Define and compute with generalized spherical distributions - multivariate probability
 laws that are specified by a star shaped contour (directional behavior) and a radial component.
 The methods are described in Nolan (2016) <doi:10.1186/s40488-016-0053-0>.
Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated.
Generation of survival data with one (binary)
  time-dependent covariate.  Generation of survival data arising
  from a progressive illness-death model.
The GenSVM classifier is a generalized multiclass support vector
	machine (SVM). This classifier aims to find decision boundaries that
	separate the classes with as wide a margin as possible. In GenSVM, the
	loss function is very flexible in the way that misclassifications are
	penalized.  This allows the user to tune the classifier to the dataset
	at hand and potentially obtain higher classification accuracy than
	alternative multiclass SVMs.  Moreover, this flexibility means that
	GenSVM has a number of other multiclass SVMs as special cases. One of
	the other advantages of GenSVM is that it is trained in the primal
	space, allowing the use of warm starts during optimization.  This
	means that for common tasks such as cross validation or repeated model
	fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den
	Burg and P.J.F. Groenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.
Implement a coherent and flexible protocol for animal color tagging. 'GenTag' provides a simple computational routine with low CPU usage to create color sequences for animal tag. First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications.
Make it easy to create simplified trial summary (TS) domain
  based on FDA FDA guide
  <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.
Two arms clinical trials required sample size is calculated in the comprehensive parametric context. The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) <doi:10.1201/9781315183084>, Wittes, J (2002) <doi:10.1093/epirev/24.1.39>, Sato, T (2000) <doi:10.1002/1097-0258(20001015)19:19%3C2689::aid-sim555%3E3.0.co;2-0>, Lachin J M, Foulkes, M A (1986) <doi:10.2307/2531201>, Whitehead J(1993) <doi:10.1002/sim.4780122404>, Julious SA (2023) <doi:10.1201/9780429503658>.
Defines window or bin boundaries for the analysis of genomic data.
    Boundaries are based on the inflection points of a cubic smoothing spline
    fitted to the raw data. Along with defining boundaries, a technique to
    evaluate results obtained from unequally-sized windows is provided.
    Applications are particularly pertinent for, though not limited to, genome
    scans for selection based on variability between populations (e.g. using
    Wright's fixations index, Fst, which measures variability in subpopulations
    relative to the total population).
The purpose is to account for the random displacements 
 (jittering) of true survey household cluster center coordinates in geostatistical 
 analyses of Demographic and Health Surveys program (DHS) data. Adjustment for 
 jittering can be implemented either in the spatial random effect, or in the 
 raster/distance based covariates, or in both. Detailed information about the methods 
 behind the package functionality can be found in two preprints.
 Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2202.11035v2>. 
 Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2211.07442v1>.
Collection of tools that facilitates data access and workflow for spatial analysis of Argentina. Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English.
Provides extension types and conversions to between R-native
  object types and 'Arrow' columnar types. This includes integration among
  the 'arrow', 'nanoarrow', 'sf', and 'wk' packages such that spatial
  metadata is preserved wherever possible. Extension type implementations
  ensure first-class geometry data type support in the 'arrow' and 'nanoarrow'
  packages.
Functions to fit geostatistical data. The data can be
        continuous, binary or count data and the models implemented are
        flexible. Conjugate priors are assumed on some parameters while
        inference on the other parameters can be done through a full
        Bayesian analysis of by empirical Bayes methods.
Easy access to official spatial data sets of Brazil as 'sf' objects 
             in R. The package includes a wide range of geospatial data available
             at various geographic scales and for various years with harmonized
             attributes, projection and fixed topology.
Tools for solving common geocaching puzzle types, and other
    Geocaching-related tasks.
Spatio-temporal causal inference based on point process data. 
    You provide the raw data of locations and timings of treatment and 
    outcome events, specify counterfactual scenarios, and the package 
    estimates causal effects over specified spatial and temporal windows.
    See Papadogeorgou, et  al. (2022) <doi:10.1111/rssb.12548> and
    Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.
Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed.
    The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).
Método simples e eficiente de geolocalizar dados no Brasil. O 
    pacote é baseado em conjuntos de dados espaciais abertos de endereços 
    brasileiros, utilizando principalmente o Cadastro Nacional de Endereços para 
    Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de 
    Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia do 
    Brasil. (A simple and efficient method for geolocating data in Brazil. The 
    package is based on open spatial datasets of Brazilian addresses, primarily 
    using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), 
    published by the Instituto Brasileiro de Geografia e Estatística (IBGE), 
    Brazil's official statistics and geography agency.)
The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.
Stores small spatial datasets used to teach basic spatial analysis
    concepts. Datasets are based off of the 'GeoDa' software workbook and data
    site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin
    and team at the University of Chicago. Datasets are stored as 'sf' objects.
Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.
Spatial stratified heterogeneity (SSH), referring to the within strata are more similar than the between strata, a model with global parameters would be confounded if input data is SSH. Note that the "spatial" here can be either geospatial or the space in mathematical meaning. Geographical detector is a novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2) test the power of determinant X of a dependent variable Y according to the consistency between their spatial distributions; and (3) investigate the interaction between two explanatory variables X1 and X2 to a dependent variable Y (Wang et al 2014 <doi:10.1080/13658810802443457>, Wang, Zhang, and Fu 2016 <doi:10.1016/j.ecolind.2016.02.052>).
The geographic dimension plays a fundamental role in
    multidimensional systems. To define a geographic dimension in a star
    schema, we need a table with attributes corresponding to the levels of
    the dimension. Additionally, we will also need one or more geographic
    layers to represent the data using this dimension. The goal of this
    package is to support the definition of geographic dimensions from
    layers of geographic information related to each other. It makes it
    easy to define relationships between layers and obtain the necessary
    data from them.
Dependency-free, ultra fast calculation of geodesic
    distances.  Includes the reference nanometre-accuracy geodesic
    distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by
    the 'sf' package, as well as Haversine and Vincenty distances. Default
    distance measure is the "Mapbox cheap ruler" which is generally more
    accurate than Haversine or Vincenty for distances out to a few hundred
    kilometres, and is considerably faster. The main function accepts one
    or two inputs in almost any generic rectangular form, and returns
    either matrices of pairwise distances, or vectors of sequential
    distances.
Methods for calculating gradient surface metrics for
    continuous analysis of landscape features.
Provides tools for semantic segmentation of geospatial data using convolutional neural 
  network-based deep learning. Utility functions allow for creating masks, image chips, data frames listing image 
  chips in a directory, and DataSets for use within DataLoaders. Additional functions are provided to serve as checks 
  during the data preparation and training process. A UNet architecture can be defined with 4 blocks in the encoder, a 
  bottleneck block, and 4 blocks in the decoder. The UNet can accept a variable number of input channels, and the user 
  can define the number of feature maps produced in each encoder and decoder block and the bottleneck. Users can also 
  choose to (1) replace all rectified linear unit (ReLU) activation functions with leaky ReLU or swish, (2) implement attention gates along the 
  skip connections, (3) implement squeeze and excitation modules within the encoder blocks, (4) add residual connections 
  within all blocks, (5) replace the bottleneck with a modified atrous spatial pyramid pooling (ASPP) module, and/or 
  (6) implement deep supervision using predictions generated at each stage in the decoder. A unified focal loss framework is implemented after
  Yeung et al. (2022) <doi:10.1016/j.compmedimag.2021.102026>. We have also implemented 
  assessment metrics using the 'luz' package including F1-score, recall, and precision. Trained models can be used to predict to spatial 
  data without the need to generate chips from larger spatial extents. Functions are available for performing accuracy assessment. The package 
  relies on 'torch' for implementing deep learning, which does not require the installation of a 'Python' environment. Raster geospatial 
  data are handled with 'terra'. Models can be trained using a Compute Unified Device Architecture (CUDA)-enabled graphics processing unit (GPU); 
  however, multi-GPU training is not supported by 'torch' in 'R'.
Draw geospatial objects by clicks on the map.
    This packages can help data analyst who want to check
    their own geospatial hypothesis but has no ready-made geospatial objects.
Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y>.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Provides geographical faceting functionality for 'ggplot2'.
    Geographical faceting arranges a sequence of plots of data for different
    geographical entities into a grid that preserves some of the geographical
    orientation.
Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.
Methods for processing spatial data for decision-making. 
  This package is an R implementation of methods provided by the open source software GeoFIS <https://www.geofis.org> (Leroux et al. 2018) <doi:10.3390/agriculture8060073>. 
  The main functionalities are the management zone delineation (Pedroso et al. 2010) <doi:10.1016/j.compag.2009.10.007> and data aggregation (Mora-Herrera et al. 2020) <doi:10.1016/j.compag.2020.105624>.
A Kriging method for functional datasets with spatial dependency.
    This functional Kriging method avoids the need to estimate the
    trace-variogram, and the curve is estimated by minimizing a quadratic
    form. The curves in the functional dataset are smoothed using Fourier
    series. The functional Kriging of this package is a modification of the
    method proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>.
Implementation of the ordinary functional kriging method 
    proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>. This
    implements an alternative method to estimate the trace-variogram using
    Fourier Smoothing and Gaussian Quadrature.
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.
The American Community Survey (ACS)
    <https://www.census.gov/programs-surveys/acs> offers geodatabases with
    geographic information and associated data of interest to researchers
    in the area. The goal of this package is to generate objects that
    allow us to access and consult the information available in various
    formats, such as in 'GeoPackage' format or in multidimensional 'ROLAP'
    (Relational On-Line Analytical Processing) star format.
Turn irregular polygons (such as geographical regions) into regular or hexagonal grids.
    This package enables the generation of regular (square) and hexagonal grids through the package 
    'sp' and then assigns the content of the existing polygons to the new grid using 
    the Hungarian algorithm, Kuhn (1955) (<doi:10.1007/978-3-540-68279-0_2>). 
    This prevents the need for manual generation of hexagonal grids or regular grids 
    that are supposed to reflect existing geography.
Tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries.
The functionality provided by this package is an expansion of the code 
    of the 'statebins' package, created by B. Rudis (2022), <doi:10.32614/CRAN.package.statebins>. It allows for the creation of square choropleths 
    for the entire world, provided an appropriate specified grid is supplied.
Classes for 'GeoJSON' to make working with 'GeoJSON' easier.
    Includes S3 classes for 'GeoJSON' classes with brief summary output,
    and a few methods such as extracting and adding bounding boxes,
    properties, and coordinate reference systems; working with 
    newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' 
    format.
Convert data to 'GeoJSON' or 'TopoJSON' from various R
    classes, including vectors, lists, data frames, shape files, and
    spatial classes.  'geojsonio' does not aim to replace packages like
    'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to
    simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'.
Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://datatracker.ietf.org/doc/html/rfc7946>. The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' <https://github.com/dropbox/json11>. Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages.
Converts Between GeoJSON and simple feature objects.
A compilation of tools to complete common tasks for studying gerrymandering. 
    This focuses on the geographic tool side of common problems, such as linking 
    different levels of spatial units or estimating how to break up units. Functions 
    exist for creating redistricting-focused data for the US.
Set of routines for making map projections (forward and inverse), topographic maps, perspective plots, geological maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map.
Performs Geometrical Archetypal Analysis after creating Grid Archetypes 
    which are the Cartesian Product of all minimum, maximum variable values. Since the archetypes 
    are fixed now, we have the ability to compute the convex composition coefficients for all 
    our available data points much faster by using the half part of Principal Convex Hull Archetypal method. 
    Additionally we can decide to keep as archetypes the closer to the Grid Archetypes ones. Finally 
    the number of archetypes is always 2 to the power of the dimension of our data points 
    if we consider them as a vector space. 
    Cutler, A., Breiman, L. (1994)      <doi:10.1080/00401706.1994.10485840>.
    Morup, M., Hansen, LK. (2012) <doi:10.1016/j.neucom.2011.06.033>.
    Christopoulos, DT. (2024) <doi:10.13140/RG.2.2.14030.88642>.
Tools to easily visualize geographic data of Morocco.
    This package interacts with data available through the
    'geomarocdata' package, which is available in a 'drat' 
    repository.   The size of the 'geomarocdata' package is
    approximately 12 MB.
Provides eigenvector-based (geometric) forecast
    combination methods; also includes simple approaches (simple average,
    median, trimmed and winsorized mean, inverse rank method) and regression-based
    combination. Tools for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity).
Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution.
Provides facilities to read, write and validate geographic metadata 
 defined with ISO TC211 / OGC ISO geographic information metadata standards, and 
 encoded using the ISO 19139 and ISO 19115-3 (XML) standard technical specifications. 
 This includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119 (service metadata) 
 and 19136 (GML). Other interoperable schemas from the OGC are progressively supported 
 as well, such as the Sensor Web Enablement (SWE) Common Data Model, the OGC GML 
 Coverage Implementation Schema (GMLCOV), or the OGC GML Referenceable Grid (GMLRGRID).
Geometry shapes in 'R' are typically represented by matrices (points, lines), with more complex 
  shapes being lists of matrices (polygons). 'Geometries' will convert various 'R' objects into these shapes. 
  Conversion functions are available at both the 'R' level, and through 'Rcpp'.
Makes the 'Qhull' library <http://www.qhull.org>
    available in R, in a similar manner as in Octave and MATLAB. Qhull
    computes convex hulls, Delaunay triangulations, halfspace
    intersections about a point, Voronoi diagrams, furthest-site
    Delaunay triangulations, and furthest-site Voronoi diagrams. It
    runs in 2D, 3D, 4D, and higher dimensions. It implements the
    Quickhull algorithm for computing the convex hull. Qhull does not
    support constrained Delaunay triangulations, or mesh generation of
    non-convex objects, but the package does include some R functions
    that allow for this.
Simulates from discrete and continuous target distributions 
        using geometric Metropolis-Hastings (MH) 
        algorithms. Users specify the target distribution 
        by an R function that evaluates the log un-normalized pdf or pmf. The package 
        also contains a function implementing a specific geometric MH algorithm for 
        performing high dimensional Bayesian variable selection.
The 'geomod' does spatial prediction of the Geotechnical soil properties. 
    It predicts the spatial distribution of Geotechnical properties of soil e.g. shear strength, 
    permeability, plasticity index, Standard Penetration Test (SPT) counts, etc. The output of the prediction takes the form of a 
    map or a series of maps. It uses the interpolation technique where a single or statistically “best” 
    estimate of spatial occurrence soil property is determined. The interpolation is based on both the 
    sampled data and a variogram model for the spatial correlation of the sampled data. 
    The single estimate is produced by a Kriging technique.
Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields,  b) inference  for random fields using standard likelihood and a likelihood approximation  method called  weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial  and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.
Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://github.com/mongodb/mongo-python-driver> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.
Read, manipulate, and digitize landmark data, generate shape
    variables via Procrustes analysis for points, curves and surfaces, perform
    shape analyses, and provide graphical depictions of shapes and patterns of
    shape variation.
A 'ggplot2' extension that allows text to follow curved paths.
    Curved text makes it easier to directly label paths or neatly annotate in 
    polar co-ordinates.
Multidimensional systems allow complex queries to be carried
    out in an easy way. The geographical dimension, together with the
    temporal dimension, plays a fundamental role in multidimensional
    systems. Through this package, vector geographic data layers can be
    associated to the attributes of geographic dimensions, so that the
    results of multidimensional queries can be obtained directly as vector
    layers.  The multidimensional structures on which we can define the
    queries can be created from a flat table or imported directly using
    functions from this package.
The web service at <https://www.geonames.org/> provides a number of spatial data queries, including 
    administrative area hierarchies, city locations and some country postal code queries. A (free) username
    is required and rate limits exist.
Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and expose it to OGC CSW.
Provides classes and methods for handling networks or 
  graphs whose nodes are geographical (i.e. locations in the globe).
  The functionality includes the creation of objects of class geonetwork
  as a graph with node coordinates, the computation of network measures,
  the support of spatial operations (projection to different Coordinate
  Reference Systems, handling of bounding boxes, etc.) and the plotting of
  the geonetwork object combined with supplementary cartography for spatial 
  representation.
Provides an interface to the 'GeoNode' API, allowing to upload and publish metadata and data in 'GeoNode'. 
  For more information about the 'GeoNode' API, see <https://geonode.org/>.
Provides convenient access to the official spatial datasets of Peru as 'sf' objects in R. This package includes a wide range of geospatial data covering various aspects of Peruvian geography, such as: administrative divisions (Source: INEI <https://ide.inei.gob.pe/>), protected natural areas  (Source: GEO ANP - SERNANP <https://geo.sernanp.gob.pe/visorsernanp/>). All datasets are harmonized in terms of attributes, projection, and topology, ensuring consistency and ease of use for spatial analysis and visualization.
Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.
Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.
Reference datasets commonly used in the geosciences. These include 
  standard atomic weights of the elements, a periodic table, a list of minerals 
  including their abbreviations and chemistry, geochemical data of reservoirs 
  (primitive mantle, continental crust, mantle, basalts, etc.), decay constants 
  and isotopic ratios frequently used in geochronology, color codes of the 
  chronostratigraphic chart. In addition, the package provides functions for 
  basic queries of atomic weights, the list of minerals, and chronostratigraphic
  chart colors. All datasets are fully referenced, and a BibTeX file containing 
  the references is included.
Provides functions for efficiently fitting linear models with spatially correlated errors by robust (Kuensch et al. (2011) <doi:10.3929/ethz-a-009900710>) and Gaussian (Harville (1977) <doi:10.1080/01621459.1977.10480998>) (Restricted) Maximum Likelihood and for computing robust and customary point and block external-drift Kriging predictions (Cressie (1993) <doi:10.1002/9781119115151>), along with utility functions for variogram modelling in ad hoc geostatistical analyses, model building, model evaluation by cross-validation, (conditional) simulation of Gaussian processes (Davies and Bryant (2013) <doi:10.18637/jss.v055.i09>), unbiased back-transformation of Kriging predictions of log-transformed data (Cressie (2006) <doi:10.1007/s11004-005-9022-8>).
Provides an R API to the Open Source Geometry Engine
  ('GEOS') library (<https://libgeos.org/>) and a vector format 
  with which to efficiently store 'GEOS' geometries. High-performance functions 
  to extract information from, calculate relationships between, and
  transform geometries are provided. Finally, facilities to import 
  and export geometry vectors to other spatial formats are provided.
This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016)<doi:10.1002/9781118814963.ch13>, and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018)<doi:10.21082/jpptp.v2n2.2018.p101-110>.
Provides an R interface to the GeoServer REST API, allowing to upload 
 and publish data in a GeoServer web-application and expose data to OGC Web-Services. 
 The package currently supports all CRUD (Create,Read,Update,Delete) operations
 on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes,
 layers, styles, as well as vector data upload operations. For more information about 
 the GeoServer REST API, see <https://docs.geoserver.org/stable/en/user/rest/>.
Functionality for adding the geological timescale to bivariate plots.
Find the smallest circle that contains all longitude and latitude input points. From the generated center and radius, variable side polygons can be created, navigation based on bearing and distance can be applied, and more. Based on a modified version of Welzl's algorithm for smallest circle. Distance calculations are based on the haversine formula. Calculations for distance, midpoint, bearing and more are derived from <https://www.movable-type.co.uk>.
Understanding spatial association is essential for spatial 
             statistical inference, including factor exploration and spatial prediction. 
             Geographically optimal similarity (GOS) model is an effective method 
             for spatial prediction, as described in Yongze Song (2022) 
             <doi:10.1007/s11004-022-10036-8>. GOS was developed based on 
             the geographical similarity principle, as described in Axing Zhu (2018) 
             <doi:10.1080/19475683.2018.1534890>. GOS has advantages in 
             more accurate spatial prediction using fewer samples and 
             critically reduced prediction uncertainty.
R binds 'GeoSpark' <http://geospark.datasyslab.org/> extending 'sparklyr' 
    <https://spark.rstudio.com/> R package to make distributed 'geocomputing' easier. Sf is a
    package that provides [simple features] <https://en.wikipedia.org/wiki/Simple_Features> access
    for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring 
    the same simple features access like sf but running on Spark distributed system.
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis 
        functions (optimization, prediction and cross-validation), summary
        statistics from cross-validation, pocket plot, and design of
        optimal sampling networks through sequential and simultaneous
        points methods.
Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
A collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as 'compositions', 'robCompositions', 'sp', 'gstat' and 'RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at <https://tinyurl.com/UCLgeostats/>.
Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector'
    objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian
    geostatistical models use Maximum Likelihood Estimation.  For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The 'RandomFields' package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.
Provides efficient geospatial thinning algorithms to reduce
    the density of coordinate data while maintaining spatial
    relationships. Implements K-D Tree and brute-force distance-based
    thinning, as well as grid-based and precision-based thinning methods.
    For more information on the methods, see Elseberg et al. (2012)
    <https://hdl.handle.net/10446/86202>.
The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.
It analyzes raster maps and other information as input/output
    files from the Hydrological Distributed Model GEOtop. It contains functions
    and methods to import maps and other keywords from geotop.inpts file. Some
    examples with simulation cases of GEOtop 2.x/3.x are presented in the package.
    Any information about the GEOtop Distributed Hydrological Model source code
    is available on www.geotop.org. Technical details about the model are
    available in Endrizzi et al (2014) <https://gmd.copernicus.org/articles/7/2831/2014/gmd-7-2831-2014.html>.
Connecting spatiotemporal exposure to individual and
    population-level risk via source-to-outcome continuum modeling. The package,
    methods, and case-studies are described in Messier, Reif, and Marvel (2024)
    <doi:10.1101/2024.09.23.24314096> and Eccles et al. (2023)
    <doi:10.1016/j.scitotenv.2022.158905>.
Provides functions and methods for: splitting large raster objects
             into smaller chunks, transferring images from a binary format into raster 
             layers, transferring raster layers into an 'RData' file, calculating the 
             maximum gap (amount of consecutive missing values) of a numeric vector, 
             and fitting harmonic regression models to periodic time series. The homoscedastic
             harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) <doi:10.1080/014311600209814>.
The toolbox have functions to load and process geographic
    information for Uruguay.  And extra-function to get address
    coordinates and orthophotos through the uruguayan 'IDE' API
    <https://www.gub.uy/infraestructura-datos-espaciales/tramites-y-servicios/servicios/sistema-unico-direcciones-geograficas>.
Simulation, estimation and inference for the geopolitical volatility (GEOVOL) model of Engle and Campos-Martins (2020) <doi:10.2139/ssrn.3685213>, where GEOVOL is modelled as a latent multiplicative volatility factor with heterogeneous factor loadings. Estimation is carried out as a maximization-maximization procedure, where GEOVOL and the GEOVOL loadings are estimated iteratively until convergence.
Contains the development of a tool that provides a web-based
    graphical user interface (GUI) to perform Techniques from a subset of
    spatial statistics known as geographically weighted (GW) models.
    Contains methods described by Brunsdon et al., 1996
    <doi:10.1111/j.1538-4632.1996.tb00936.x>, Brunsdon et al., 2002
    <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011
    <doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007
    <doi:10.1111/j.1538-4632.2007.00709.x>.
Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.
Encode and decode the Google Encoded Polyline Algorithm Format.
    See <https://developers.google.com/maps/documentation/utilities/polylinealgorithm>
    for more information.
Implements key features of 'Gephi' for network visualization, including 'ForceAtlas2' (with LinLog mode), network scaling, and network rotations. It also includes easy network visualization tools such as edge and node color assignment for recreating 'Gephi'-style graphs in R. The package references layout algorithms developed by Jacomy, M., Venturini T., Heymann S., and Bastian M. (2014) <doi:10.1371/journal.pone.0098679> and Noack, A. (2009) <doi:10.48550/arXiv.0807.4052>.
Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. <doi:10.1080/02664763.2016.1268571>. Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. <doi:10.1080/03610918.2018.1491988>. Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. <doi:10.1016/j.spl.2009.09.003>.
Implements a new multiple imputation method that draws
    imputations from a latent joint multivariate normal model which
    underpins generally structured data. This model is constructed using a
    sequence of flexible conditional linear models that enables the
    resulting procedure to be efficiently implemented on high dimensional
    datasets in practice. See Robbins (2021) <arXiv:2008.02243>.
Provides tools to download comprehensive datasets of local, 
    state, and federal election results in Germany from 1990 to 2021. The package 
    facilitates access to data on turnout, vote shares for major parties, and 
    demographic information across different levels of government (municipal, state, 
    and federal). It offers access to geographically harmonized datasets 
    that account for changes in municipal boundaries over time and incorporate 
    mail-in voting districts. Users can easily retrieve, clean, and standardize 
    German electoral data, making it ready for analysis. Data is sourced from 
    <http://www.german-elections.com>.
Generates a file, containing the main scientific references, prepared to be automatically inserted into an academic paper. 
    The articles present in the list are chosen from the main references generated, by function principal_lister(), of the package 'bibliorefer'. 
    The generated file contains the list of metadata of the principal references in 'BibTex' format. 
    Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>.
    Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>.
    Hamid Derviş. (2019) <doi:10.5530/jscires.8.3.32>.
A collection of different indices and visualization techniques for evaluate the seed germination process in ecophysiological studies (Lozano-Isla et al. 2019) <doi:10.1111/1440-1703.1275>.
Provides functions to compute various germination indices
    such as germinability, median germination time, mean germination time,
    mean germination rate, speed of germination, Timson's index,
    germination value, coefficient of uniformity of germination,
    uncertainty of germination process, synchrony of germination etc. from
    germination count data. Includes functions for fitting cumulative seed
    germination curves using four-parameter hill function and computation
    of associated parameters. See the vignette for more, including full
    list of citations for the methods implemented.
Simulating, visualizing and comparing tumor clonal data by using simple commands. This aims at providing a tool to help researchers to easily simulate tumor data and analyze the results of their approaches for studying the composition and the evolutionary history of tumors.
Simple git client for R based on 'libgit2' <https://libgit2.org> with
    support for SSH and HTTPS remotes. All functions in 'gert' use basic R data 
    types (such as vectors and data-frames) for their arguments and return values.
    User credentials are shared with command line 'git' through the git-credential
    store and ssh keys stored on disk or ssh-agent.
Implementing generalized structured component analysis (GSCA) and
    its basic extensions, including constrained single and multiple group
    analysis, and second order latent variable modeling. For a comprehensive
    overview of GSCA, see Hwang & Takane (2014, ISBN: 9780367738754).
Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.
Reproducible, programmatic retrieval of datasets from the
    GESIS Data Archive.  The GESIS Data Archive <https://search.gesis.org>  
    makes available thousands of invaluable datasets, but researchers using
    these datasets are caught in a bind.  The archive's terms and conditions
    bar dissemination of downloaded datasets to third parties, but to ensure 
    that one's work can be reproduced, assessed, and built upon by others, one
    must provide access to the raw data one has employed.  The 'gesisdata'
    package cuts this knot by providing registered users with programmatic,
    reproducible access to GESIS datasets from within 'R'.
Promote access to the GESLA
    <https://gesla787883612.wordpress.com> (Global Extreme Sea Level
    Analysis) dataset, a higher-frequency sea-level record data from all
    over the world. It provides functions to download it entirely, or
    query subsets directly into R, without the need of downloading the
    full dataset. Also, it provides a built-in web-application, so that
    users can apply basic filters to select the data of interest,
    generating informative plots, and showing the selected sites.
The method focuses on a single environmental exposure and induces 
    a main-effect-before-interaction hierarchical structure for the joint selection of interaction terms 
    in a regularized regression model. For details see Zemlianskaia et al. (2021) <arxiv:2103.13510>.
Provides a suite of function-building tools centered around a
  (forward) composition operator, %>>>%, which extends the semantics of the
  'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables
  you to construct composite functions that can be inspected and transformed as
  list-like objects. In conjunction with %>>>%, a compact function constructor,
  fn(), and a partial-application constructor, partial(), are also provided;
  both support quasiquotation.
Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).
Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) <doi:10.1515/em-2015-0005> and Dukes and Vansteelandt <doi:10.1093/aje/kwx347>. The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories. The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable.
Implementation of global envelopes for a set of general d-dimensional vectors T
    in various applications. A 100(1-alpha)% global envelope is a band bounded by two
    vectors such that the probability that T falls outside this envelope in any of the d
    points is equal to alpha. Global means that the probability is controlled simultaneously
    for all the d elements of the vectors. The global envelopes can be used for graphical
    Monte Carlo and permutation tests where the test statistic is a multivariate vector or
    function (e.g. goodness-of-fit testing for point patterns and random sets, functional
    analysis of variance, functional general linear model, n-sample test of correspondence
    of distribution functions), for central regions of functional or multivariate data (e.g.
    outlier detection, functional boxplot) and for global confidence and prediction bands
    (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See
    Myllymäki and Mrkvička (2024) <doi:10.18637/jss.v111.i03>,
    Myllymäki et al. (2017) <doi:10.1111/rssb.12172>,
    Mrkvička and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>,
    Mrkvička et al. (2016) <doi:10.1016/j.spasta.2016.04.005>,
    Mrkvička et al. (2017) <doi:10.1007/s11222-016-9683-9>,
    Mrkvička et al. (2020) <doi:10.14736/kyb-2020-3-0432>,
    Mrkvička et al. (2021) <doi:10.1007/s11009-019-09756-y>,
    Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>,
    Mrkvička et al. (2022) <doi:10.1002/sim.9236>,
    Dai et al. (2022) <doi:10.5772/intechopen.100124>,
    Dvořák and Mrkvička (2022) <doi:10.1007/s00180-021-01134-y>,
    Mrkvička et al. (2023) <doi:10.48550/arXiv.2309.04746>, and
    Konstantinou et al. (2024) <doi: 10.1007/s00180-024-01569-z>.
Dynamically retrieve data from the web to render HTML tables
    on inspection in R Markdown HTML documents.
Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the 'memoise' package and
    , multicore/multisession with 'furrr' and format of output data (long/wide).
Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.
Reads annual financial reports including assets, liabilities, dividends history, stockholder composition and much more from Bovespa's DFP, FRE and FCA systems <http://www.b3.com.br/pt_br/produtos-e-servicos/negociacao/renda-variavel/empresas-listadas.htm>.
 These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing.
Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange 
            <https://www.b3.com.br/>. 
            All data is downloaded and imported from CVM's public ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/>.
The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design.
    The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.
Reads corporate data such as board composition and compensation for companies traded at B3, 
             the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from the ftp site <http://dados.cvm.gov.br/dados/CIA_ABERTA/DOC/FRE/>.
A micro-package for getting your 'IP' address, either the 
    local/internal or the public/external one. Currently only 'IPv4' addresses
    are supported.
Tool for import and process data from 'Lattes' curriculum platform (<http://lattes.cnpq.br/>). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in 'Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. 'getLattes' package allows work with 'Lattes' data exported to XML format.
A simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.
Quantifying systematic heterogeneity in meta-analysis using R.
    The M statistic aggregates heterogeneity information across multiple
    variants to, identify systematic heterogeneity patterns and their direction
    of effect in meta-analysis. It's primary use is to identify outlier studies,
    which either show "null" effects or consistently show stronger or weaker
    genetic effects than average across, the panel of variants examined in a
    GWAS meta-analysis. In contrast to conventional heterogeneity metrics
    (Q-statistic, I-squared and tau-squared) which measure random heterogeneity
    at individual variants, M measures systematic (non-random)
    heterogeneity across multiple independently associated variants. Systematic
    heterogeneity can arise in a meta-analysis due to differences in the study
    characteristics of participating studies. Some of the differences may
    include: ancestry, allele frequencies, phenotype definition, age-of-disease
    onset, family-history, gender, linkage disequilibrium and quality control
    thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical
    statistical theory, documentation and examples.
Package designed to be used with Rscript to write
    '#!' shebang scripts that accept short and long flags/options.
    Many users will prefer using instead the packages optparse or argparse
    which add extra features like automatically generated help option and usage,
    support for default values, positional argument support, etc.
This is a command-line argument parser which wraps the 
    powerful Perl module Getopt::Long and with some adaptations for easier use
	in R. It also provides a simple way for variable interpolation in R.
A micro-package for reading "passwords", i.e.  reading
    user input with masking, so that the input is not displayed as it 
    is typed.  Currently we have support for 'RStudio', the command line
    (every OS), and any platform where 'tcltk' is present.
Allows get address and port 
	of the free proxy server, from one of two services
	<http://gimmeproxy.com/> or <https://getproxylist.com/>. 
	And it's easy to redirect your Internet connection through
	a proxy server.
Imports time series data from the 'Quandl' database <https://data.nasdaq.com/>. The package uses the  'json api' at <https://data.nasdaq.com/search>, local caching ('memoise' package) and the tidy format by default. 
   Also allows queries of databases, allowing the user to see which time series are available for each database id. In short, it is an alternative to package 'Quandl', with faster data importation in the tidy/long format.
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.
Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the 'gets' package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.
An implementation of SPRE (standardised predicted random-effects)
    statistics in R to explore heterogeneity in genetic association meta-
    analyses, as described by Magosi et al. (2019) 
    <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision 
    weighted residuals that indicate the direction and extent with which 
    individual study-effects in a meta-analysis deviate from the average 
    genetic effect. Overly influential positive outliers have the potential 
    to inflate average genetic effects in a meta-analysis whilst negative 
    outliers might lower or change the direction of effect. See the 'getspres' 
    website for documentation and examples 
    <https://magosil86.github.io/getspres/>.
Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <https://www.tesourodireto.com.br/>.
A function to retrieve the system timezone on Unix systems
 which has been found to find an answer when 'Sys.timezone()' has failed.
 It is based on an answer by Duane McCully posted on 'StackOverflow', and
 adapted to be callable from R. The package also builds on Windows, but
 just returns NULL.
A simple wrapper for 'Wikipedia' data. Specifically, this 
    package looks to fill a gap in retrieving text data in a tidy format that can be used for Natural Language Processing.
A novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.
Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.
Generates experiments - simulating structured or experimental data as: 
             completely randomized design, randomized block design, latin square design, 
             factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; 
             Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; 
             Montgomery, 2001, ISBN:0471316490).
Download geyser eruption and observation data from the GeyserTimes
  site (<https://geysertimes.org>) and optionally store it locally. The vignette
  shows a simple analysis of downloading, accessing, and summarizing the data.
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.
Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL '<https://oops.uni-oldenburg.de/id/eprint/4290>').
Supports analyses using the Global Forest Change dataset released
    by Hansen et al. gfcanalysis was originally written for the Tropical Ecology 
    Assessment and Monitoring (TEAM) Network. For additional details on the 
    Global Forest Change dataset, see: Hansen, M. et al. 2013. "High-Resolution 
    Global Maps of 21st-Century Forest Cover Change." Science 342 (15 
    November): 850-53. The forest change data and more information on the 
    product is available at <http://earthenginepartners.appspot.com>.
Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
    Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.
Performs test procedures for general hypothesis testing problems for four multivariate coefficients of variation (Ditzhaus and Smaga, 2023 <arXiv:2301.12009>). We can verify the global hypothesis about equality as well as the particular hypotheses defined by contrasts, e.g., we can conduct post hoc tests. We also provide the simultaneous confidence intervals for contrasts.
We implemented multiple tests based on the restricted mean survival time (RMST) for general factorial designs as described in Munko et al. (2024) <doi:10.1002/sim.10017>. Therefore, an asymptotic test, a groupwise bootstrap test, and a permutation test are incorporated with a Wald-type test statistic. The asymptotic and groupwise bootstrap test take the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMST contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.
We implemented multiple tests based on the restricted mean time lost (RMTL) for general factorial designs as described in Munko et al. (2024) <doi:10.48550/arXiv.2409.07917>. Therefore, an asymptotic test and a permutation test are incorporated with a Wald-type test statistic. The asymptotic test takes the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMTL contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.
Implemented are three Wald-type statistic and respective
  permuted versions for null hypotheses formulated in terms of cumulative hazard rate functions, medians and the concordance measure, respectively, in the general framework of survival factorial designs with possibly heterogeneous survival and/or censoring distributions, for crossed designs with an arbitrary number of factors and nested designs with up to three factors.
	Ditzhaus, Dobler and Pauly (2020) <doi:10.1177/0962280220980784> 
	Ditzhaus, Janssen, Pauly (2020) <arXiv: 2004.10818v2>
	Dobler and Pauly (2019) <doi:10.1177/0962280219831316>.
The philosophy in the package is described in Stasny (1988) <doi:10.2307/1391558> and Gutierrez, A., Trujillo, L. & Silva, N. (2014), <ISSN:1492-0921> to estimate the gross flows under complex surveys using a Markov chain approach with non response.
Focuses on data collecting, analyzing and visualization in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites such as MEP (Ministry of Environmental Protection of China, <https://www.mee.gov.cn>), water 
  related projects identification and environmental data visualization.
Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.
Fiducial framework to perform inference on the quantiles for a generalized Pareto distribution model and on the parameters of the Pareto exceedance distribution, assuming the exceedance threshold is a known or unknown parameter. Reference: Damian V. Wandler & Jan Hannig (2012) <doi:10.1007/s10687-011-0127-9>.
Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.
Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.
Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables.
    Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model,
    respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. 
    This model can be employed in social and behavioral sciences, economy and finance, and  genomics, 
    to extract interpretable nonlinear factors. More details can be referred to 
    Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2021) <doi:10.1080/01621459.2021.1999818>.
Download 'Google' fonts and generate 'CSS' to use in 'rmarkdown' documents and 
  'shiny' applications. Some popular fonts are included and ready to use.
Implements the non-iterative conditional expectation (NICE) 
    algorithm of the g-formula algorithm (Robins (1986) 
    <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). 
    The g-formula can estimate an outcome's counterfactual mean or risk under 
    hypothetical treatment strategies (interventions) when there is sufficient 
    information on time-varying treatments and confounders. 
    This package can be used for discrete or continuous time-varying treatments 
    and for failure time outcomes or continuous/binary end of follow-up 
    outcomes. The package can handle a random measurement/visit process and a 
    priori knowledge of the data structure, as well as censoring (e.g., by loss 
    to follow-up) and two options for handling competing events for failure time
    outcomes. Interventions can be flexibly specified, both as interventions on 
    a single treatment or as joint interventions on multiple treatments.
    See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on 
    how to use the package.
Implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (Wen, Young, Robins, and Hernán (2020) <doi: 10.1111/biom.13321>). 
    Both singly robust and doubly robust ICE estimators based on parametric models are available. 
    The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring, and competing events.
    The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.
Implements the G-Formula method for causal inference with time-varying treatments and
  confounders using Bayesian multiple imputation methods, as described by
  Bartlett, Olarte Parra and Daniel (2023) <arXiv:2301.12026>. It creates multiple synthetic imputed
  datasets under treatment regimes of interest using the 'mice' package. These can then be analysed
  using rules developed for analysing multiple synthetic datasets.
Modified versions of the lag() and summary() functions: glag() and gsummary(). The prefix 'g' is a reminder of who to blame if things do not work as they should.
It is not very easy to define segments for y-axis in a 'ggplot2' plot. gg.gap() function in this package can carry it out.
Streamlines exploratory data analysis by providing a turnkey approach 
  to visualising n-dimensional data which graphically reveals correlative or associative relationships 
  between 2 or more features. Represents all dataset features as distinct, vertically 
  aligned bar or tile plots, with plot types auto-selected based on whether variables are categorical or numeric.
A 'ggplot2' extension offers various tools for organizing and arranging plots. 
    It is designed to consistently align a specific axis across multiple 'ggplot' objects, 
    making it especially useful for plots requiring data order manipulation. A typical use 
    case includes organizing combinations like a dendrogram and a heatmap.
Providing publication-ready graphs for Multiple sequence alignment. Moreover, it provides a unique solution for visualizing the multiple sequence alignment without the need to do the alignment in each run which is a big limitation in other available packages.
'D&D' alignment charts show 9 boxes with values for good
    through evil and values for chaotic through lawful. This package
    easily creates these alignment charts from user-provided image paths
    and alignment values.
Extra geoms and scales for 'ggplot2', including geom_cloud(),
  a Normal density cloud replacement for errorbars;
  transforms ssqrt_trans and pseudolog10_trans, which are loglike but 
  appropriate for negative data; interp_trans() and warp_trans() which
  provide scale transforms based on interpolation;
  and an infix compose operator for scale transforms.
Alluvial plots use variable-width ribbons and stacked bar plots to
    represent multi-dimensional or repeated-measures data with categorical or
    ordinal variables; see Riehmann, Hanfler, and Froehlich (2005)
    <doi:10.1109/INFVIS.2005.1532152> and Rosvall and Bergstrom (2010)
    <doi:10.1371/journal.pone.0008694>.
    Alluvial plots are statistical graphics in the sense of Wilkinson (2006)
    <doi:10.1007/0-387-28695-0>; they share elements with Sankey diagrams and
    parallel sets plots but are uniquely determined from the data and a small
    set of parameters. This package extends Wickham's (2010)
    <doi:10.1198/jcgs.2009.07098> layered grammar of graphics to generate
    alluvial plots from tidy data.
The R package 'ggplot2' is a plotting system based on the grammar of graphics.
    'GGally' extends 'ggplot2' by adding several functions
    to reduce the complexity of combining geometric objects with transformed data.
    Some of these functions include a pairwise plot matrix, a two group pairwise plot
    matrix, a parallel coordinates plot, a survival plot, and several functions to
    plot networks.
A compendium of new geometries, coordinate systems, statistical 
    transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, 
    univariate average shifted histograms, a new map coordinate system based on the 
    'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of 
    geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly'
    compatibility and the 'StateFace' open source font 'ProPublica'. Further new 
    functionality includes lollipop charts, dumbbell charts, the ability to encircle
    points and coordinate-system-based text annotations.
Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.
The grammar of graphics as implemented in the 'ggplot2'
    package has been successful in providing a powerful API for creating
    static visualisation. In order to extend the API for animated graphics
    this package provides a completely new set of grammar, fully
    compatible with 'ggplot2' for specifying transitions and animations in
    a flexible and extensible way.
Geoms for placing arrowheads at multiple points along a segment, not just at the end; position function to shift starts and ends of arrows to avoid exactly intersecting points.
A 'ggplot2' extension that adds specialised arrow geometry layers. 
    It offers more arrow options than the standard 'grid' arrows that 
    are built-in many line-based geom layers.
Mapping tools that convert place names to coordinates on the fly.
    These 'ggplot2' extensions make maps from a data frame where one of the
    columns contains place names, without having to directly work with the
    underlying geospatial data and tools. The corresponding map data must be
    registered with 'cartographer' either by the user or by another package.
Provides two methods of plotting categorical scatter plots such
    that the arrangement of points within a category reflects the density of
    data at that region, and avoids over-plotting.
A 'ggplot2' based implementation of biplots, giving a representation of a dataset in
    a two dimensional space accounting for the greatest variance, together with variable vectors
    showing how the data variables relate to this space. It provides a 
    replacement for stats::biplot(), but with many enhancements to control the analysis and
    graphical display. It implements 
    biplot and scree plot methods which can be used with the results of prcomp(), princomp(),
    FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using 'ggplot2' techniques.
Simplify 'ggplot2' visualisation with 'ggblanket' wrapper
    functions.
Algebra of operations for blending, copying, adjusting, and 
    compositing layers in 'ggplot2'. Supports copying and adjusting the 
    aesthetics or parameters of an existing layer, partitioning a layer 
    into multiple pieces for re-composition, applying affine transformations
    to layers, and combining layers (or partitions of layers) using blend modes
    (including commutative blend modes, like multiply and darken). Blend 
    mode support is particularly useful for creating plots with overlapping 
    groups where the layer drawing order does not change the output; 
    see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.
A set of geometries to make line plots a little bit nicer. Use 
    along with 'ggplot2' to:
    - Improve the clarity of line plots with many overlapping lines
    - Draw more realistic worms.
Provides curly braces in 'ggplot2' plus matching text.
    stat_brace() plots braces partially in the confines of data so that the brace is set apart from it.
    stat_bracetext() plots corresponding text, fitting to the braces from stat_brace().
A 'ggplot2'-consistent approach to generating 2D displays of  volumetric brain imaging data.
  Display data from multiple NIfTI images using standard 'ggplot2' conventions such scales, limits, and
  themes to control the appearance of displays. The resulting plots are returned as 'patchwork' objects,
  inheriting from 'ggplot', allowing for any standard modifications of display aesthetics supported by 'ggplot2'.
An implementation of scale functions for setting axis breaks of a 'gg' plot (S Xu (2021) <doi:10.3389/fgene.2021.774846>).
A new take on the bar chart. Similar to a waffle style chart but
  instead of squares the layout resembles a brick wall.
When comparing discrete data mini bubble plots allow displaying 
   more information than traditional bubble plots via colour, shape or labels. 
   Exact overlapping coordinates will be transformed so they surround the
   original point circularly without overlapping. This is implemented as a 
   position_surround() function for 'ggplot2'.
Saves a 'ggplot' object into multiple files, each with a layer 
    added incrementally. Generally to be used in presentation slides.
    Flexible enough to allow different file types for the final complete plot,
    and intermediate builds.
A geom for ggplot to create bump plots. Can be good to use for showing rank over time.
R provides fantastic tools for changepoint
    analysis, but plots generated by the tools do 
    not have the 'ggplot2' style. This tool, however, 
    combines 'changepoint', 'changepoint.np' and 'ecp'
    together, and uses 'ggplot2' to visualize changepoints.
Streamline the creation of common charts by taking care of a lot of
    data preprocessing and plot customization for the user. Provides a
    high-level interface to create plots using 'ggplot2'.
Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data
    to human-like faces. Inspired by Chernoff (1973) <doi:10.1080/01621459.1973.10482434>.
Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.
William S. Cleveland's book 'Visualizing Data' is a classic piece 
	of literature on Exploratory Data Analysis. Although it was written 
	several decades ago, its content is still relevant as it proposes several 
	tools which are useful to discover patterns and relationships among the data 
	under study, and also to assess the goodness of fit o a model.  This package 
	provides functions to produce the 'ggplot2' versions of the visualization tools 
	described in this book and is thought to be used in the context of courses on 
	Exploratory Data Analysis.
Add mean comparison annotations to a 'ggplot'. 
             This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. 
             Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. 
             For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).
The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for
    reordering the correlation matrix and displays the significance level
    on the plot. It also includes a function for computing a matrix of
    correlation p-values.
Corset plots are a visualization technique used strictly to visualize repeat measures 
  at 2 time points (such as pre- and post- data). The distribution of measurements are visualized at 
  each time point, whilst the trajectories of individual change are visualized by connecting the pre- 
  and post- values linearly. These lines can be coloured to represent the magnitude of change, or 
  other user-defined value. This method of visualization is ideal for showing the heterogeneity of 
  data, including differences by sub-groups. The package relies on 'ggplot2' allowing for easy 
  integration so that users can customize their visualizations as required. Users can create corset 
  plots using data in either wide or long format using the functions gg_corset() or gg_corset_elongated(), respectively.
Tidy, analyze, and plot directed acyclic graphs (DAGs).
    'ggdag' is built on top of 'dagitty', an R package that uses the
    'DAGitty' web tool (<https://dagitty.net/>) for creating and analyzing
    DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using
    'ggplot2' and 'ggraph', as well as common analytic and graphical
    functions, such as determining adjustment sets and node relationships.
Activate dark mode on your favorite 'ggplot2' theme 
             with dark_mode() or use the dark versions of 
             'ggplot2' themes, including dark_theme_gray(),
             dark_theme_minimal(), and others. When a dark theme 
             is applied, all geom color and geom fill defaults 
             are changed to make them visible against a dark 
             background. To restore the defaults to their original 
             values, use invert_geom_defaults().
It provides a custom 'ggplot2' geom to add day/night patterns to plots.
    It visually distinguishes daytime and nighttime periods. It is useful for 
    visualizing data that spans multiple days and for highlighting diurnal patterns.
Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment 
    made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>),
    the seasonal adjustment software officially recommended to the members of the European Statistical System and
    the European System of Central Banks.
This is a set of tools for dendrograms and
    tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
    clearly separate data from the presentation.
    Unfortunately the plot method for dendrograms plots
    directly to a plot device without exposing the data.
    The 'ggdendro' package resolves this by making available
    functions that extract the dendrogram plot data. The package
    provides implementations for 'tree', 'rpart', as well as diana and agnes
    (from 'cluster') diagrams.
The 'ggplot2' package provides simple functions for visualizing contours
  of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators 
  as well as more interpretable visualizations based on highest density regions instead of
  the traditional height of the estimated density surface.
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for
    visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as 
    frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as 
    bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include
    but are not limited to: points with multiple uncertainty intervals, 
    eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>,
    density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>,
    quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>,
    complementary cumulative distribution function 
    barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>,
    and fit curves with multiple uncertainty ribbons.
Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.
Generate commonly used plots in the field of design of experiments using 'ggplot2'.
  'ggDoE' currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot,
  regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs,
  contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.
A data visualization design that provides comparison between 
    two (Double) data sources (usually on a par with each other) on one 
    reformed heatmap, while inheriting 'ggplot2' features.
Create biplots for GGE (genotype plus
    genotype-by-environment) and GGB (genotype plus
    genotype-by-block-of-environments) models.  See Laffont et al. (2013)
    <doi:10.2135/cropsci2013.03.0178>.
Provides a series of aliases to commonly used but difficult 
    to remember 'ggplot2' sequences.
Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within the archived package 'GGEBiplotGUI'.
Interactively edit 'ggplot2' layer and theme aesthetics definitions.
Compute marginal effects and adjusted predictions from statistical
    models and returns the result as tidy data frames. These data frames are
    ready to use with the 'ggplot2'-package. Effects and predictions can be
    calculated for many different models. Interaction terms, splines and
    polynomial terms are also supported. The main functions are ggpredict(),
    ggemmeans() and ggeffect(). There is a generic plot()-method to plot the
    results using 'ggplot2'.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.
Implements an extension of 'ggplot2' and visualizes the symbolic data with multiple plot which can be adjusted by more general and flexible input arguments. It also provides a function to transform the classical data to symbolic data by both clustering algorithm and customized method.
Extension of 'ggplot2' providing layers, scales and preprocessing functions
    useful to represent behavioural variables that are recorded over multiple animals and days.
    This package is part of the 'rethomics' framework <https://rethomics.github.io/>.
Collection of functions and layers to enhance 'ggplot2'. The 
    flagship function is 'ggMarginal()', which can be used to add marginal
    histograms/boxplots/density plots to 'ggplot2' scatterplots.
Readable, complete and pretty graphs for correspondence analysis made 
    with 'FactoMineR'. They can be rendered as interactive 'HTML' plots, showing useful
    informations at mouse hover. The interest is not mainly visual but statistical:
    it helps the reader to keep in mind the data contained in the cross-table or Burt
    table while reading the correspondence analysis, thus preventing over-interpretation.
    Most graphs are made with 'ggplot2', which means that you can use the + syntax to 
    manually add as many graphical pieces you want, or change theme elements. 3D 
    graphs are made with 'plotly'.
Add vector field layers to ggplots. Ideal for visualising
    wind speeds, water currents, electric/magnetic fields, etc.
    Accepts data.frames, simple features (sf), and spatiotemporal arrays (stars)
    objects as input. Vector fields are depicted as arrows starting at specified
    locations, and with specified angles and radii.
When you prepare a presentation or a report, you often need to manage a large number of 'ggplot' figures. You need to change the figure size, modify the title, label, themes, etc. It is inconvenient to go back to the original code to make these changes. This package provides a simple way to manage 'ggplot' figures. You can easily add the figure to the database and update them later using CLI (command line interface) or GUI (graphical user interface).
Contains functions to create life history parameter plots from raw data. 
	The plots are created using 'ggplot2', and calculations done using the 'tidyverse'
	collection of packages. The package contains references to FishBase 
	(Froese R., Pauly D., 2023) <https://www.fishbase.se/>.
A 'ggplot2' extension to fit text into a box by growing, shrinking or wrapping the text.
Provides 'ggplot2' equivalents of fixest::coefplot() and fixest::iplot(),
    for producing nice coefficient plots and interaction plots. Enables some
    additional functionality and convenience features, including grouped
    multi-'fixest' object faceting and programmatic updates to existing plots
    (e.g., themes and aesthetics).
Flowcharts can be a useful way to visualise complex processes. This package 
    uses the layered grammar of graphics of 'ggplot2' to create simple flowcharts.
A 'ggplot2' extension that provides tools for automatically
    creating scales to focus on subgroups of the data plotted 
    without losing other information.
Scrapes shots data from 'Understat' <https://understat.com/> and visualizes it using interactive plots:
    - A detailed shot map displaying the location, type, and xG value of shots taken by both teams.
    - An xG timeline chart showing the cumulative xG for each team over time, annotated with the details of scored goals.
The aim of 'ggplot2' is to aid in visual data investigations. This
    focus has led to a lack of facilities for composing specialised plots.
    'ggforce' aims to be a collection of mainly new stats and geoms that fills
    this gap. All additional functionality is aimed to come through the official
    extension system so using 'ggforce' should be a stable experience.
Provides a formula interface to 'ggplot2' graphics.
Unified plotting tools for statistics commonly used, such as GLM,
    time series, PCA families, clustering and survival analysis. The package offers
    a single plotting interface for these analysis results and plots in a unified
    style using 'ggplot2'.
A 'ggplot2' extension that supports arbitrary 
    hand-crafted colourable & fillable shapes. New shapes may be feature 
    requested via a Github issue.
Useful functions and utilities for 'ggplot' object (e.g., geometric layers, themes, and utilities to edit the object).
Provides a range of filters that can be applied to layers from the 
    'ggplot2' package and its extensions, along with other graphic elements such 
    as guides and theme elements. The filters are applied at render time and 
    thus uses the exact pixel dimensions needed.
The function gggap() streamlines the creation of segments on
    the y-axis of 'ggplot2' plots which is otherwise not a trivial task to
    accomplish.
A 'ggplot2' extension for drawing gene arrow maps.
An extension of 'ggplot2' for creating complex genomic
 maps. It builds on the power of 'ggplot2' and 'tidyverse' adding new 'ggplot2'-style
 geoms & positions and 'dplyr'-style verbs to manipulate the underlying data. It
 implements a layout concept inspired by 'ggraph' and introduces tracks to bring
 tidiness to the mess that is genomics data.
Creates a reproducible 'ggplot2' object by storing the data and calls.
Moon charts are like pie charts except that the proportions are
    shown as crescent or gibbous portions of a circle, like the lit and unlit
    portions of the moon. As such, they work best with only one or two groups.
    'gggibbous' extends 'ggplot2' to allow for plotting multiple moon charts in
    a single panel and does not require a square coordinate system.
An extension of 'ggplot2' that makes it easy to add
             raw 'grid' output, such as customised annotations, to a 
             'ggplot2' plot.
A 'ggplot2' extension that does a variety of little
    helpful things.  The package extends 'ggplot2' facets through
    customisation, by setting individual scales per panel, resizing panels
    and providing nested facets.  Also allows multiple colour and fill
    scales per plot. Also hosts a smaller collection of stats, geoms and axis 
    guides.
Reproduce the halfnorm() function found in the 'faraway' package 
    using the 'ggplot2' API.
A 'ggplot2' extension for easy plotting of half-half geom combinations. Think half boxplot and half jitterplot, or half violinplot and half dotplot.
A Humanitarian Data Exchange (HDX) theme, color palettes, and
    scales for 'ggplot2' to allow users to easily follow the HDX visual design
    guide, including convenience functions for for loading and using the
    Source Sans 3 font.
The flexibility and excellence of 'ggplot2' is unquestionable, 
    so many drawing tools basically need 'ggplot2' as the operating object. 
    In order to develop a heatmap drawing system based on ggplot2, we developed 
    this tool, mainly to solve the heatmap puzzle problem and the flexible 
    connection between the heatmap and the 'ggplot2' object.
    The advantages of this tool are as follows: 
    1. More flexible label settings; 
    2. Realize the linkage of heatmap and 'ggplot2' drawing system, 
       which is helpful for operations such as puzzles; 
    3. Simple and easy to operate; 
    4. Optimization of clustering tree visualization.
Make it easier to explore data with highlights.
A set of functions that help to create plots based on Hilbert curves. 
    Hilbert curves are used to map one dimensional data into the 2D plane.
    The package provides a function that generate a 2D coordinate from an 
    integer position. As a specific use case the package provides a function 
    that allows mapping a character column in a data frame into 2D space 
    using 'ggplot2'. This allows visually comparing long lists of URLs, words,
    genes or other data that has a fixed order and position.
Wrapper around geom_histogram() of 'ggplot2' to plot the histogram of a numeric vector. This is especially useful, since qplot() was deprecated in 'ggplot2' 3.4.0.
A user-friendly, highly customizable R package for building
    horizon plots in the 'ggplot2' environment.
Supports image files and graphic objects to be visualized in
    'ggplot2' graphic system.
Provides two new layer types for displaying image data as layers
  within the Grammar of Graphics framework. Displays images using either a
  rectangle interface, with a fixed bounding box, or a point interface using a
  central point and general size parameter. Images can be given as local
  JPEG or PNG files, external resources, or as a list column containing
  raster image data.
Visualise the results of F test to compare two variances, Student's t-test, test of equal or given proportions, Pearson's chi-squared test for count data and test for association/correlation between paired samples.
Extensions to 'ggplot2' providing low-level debug tools: statistics
    and geometries echoing their data argument. Layer manipulation: deletion,
    insertion, extraction and reordering of layers. Deletion of unused variables
    from the data object embedded in "ggplot" objects.
A 'ggplot2' extension that enables visualization of IP
    (Internet Protocol) addresses and networks. The address space is
    mapped onto the Cartesian coordinate system using a space-filling
    curve. Offers full support for both IPv4 and IPv6 (Internet Protocol
    versions 4 and 6) address spaces.
A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://activinsights.com/>, binary (.gt3x) and .csv-export data from  'Actigraph' <https://theactigraph.com> devices, and binary (.cwa) and .csv-export data from 'Axivity' <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format. Also the package allows for external function embedding.
Create interactive 'ggplot2' graphics using 'htmlwidgets'.
Collection of functions to enhance 'ggplot2' and 'ggiraph'. Provides functions for exploratory plots.
    All plot can be a 'static' plot or an 'interactive' plot using 'ggiraph'.
Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' <https://activinsights.com/>, .bin-format from GENEA devices (not for sale), and .cwa-format from 'Axivity' <https://axivity.com>. Further, it has functions for reading text files with epoch level aggregates from 'Actical', 'Fitbit', 'Actiwatch', 'ActiGraph', and 'PhilipsHealthBand'. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.
Provides stat_isotonic() to add weighted univariate isotonic regression curves.
Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
Provides multiple palettes based on pride flags with tailored themes.
Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics.  
  Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.
Provides a way to log 'ggplot' component calls, which can be
    useful for debugging and understanding how 'ggplot' objects are created.
    The logged calls can be printed, saved, and re-executed to reproduce the
    original 'ggplot' object.
Provides statistical transformations for plotting empirical 
    ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and 
    generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.
Provides functions for defining 
    mixed graphs containing three types of edges, directed, 
    undirected and bi-directed, with possibly multiple edges.
    These graphs are useful because they capture fundamental
    independence structures in multivariate distributions
    and in the induced distributions after marginalization 
    and conditioning.
    The package is especially concerned with Gaussian graphical
    models for
    (i) ML estimation for directed acyclic graphs, undirected and 
    bi-directed graphs and ancestral graph models
    (ii) testing several conditional independencies
    (iii) checking global identification of DAG Gaussian models
    with one latent variable
    (iv) testing Markov equivalences and generating Markov 
    equivalent graphs of specific types.
A collection of functions to visualize spatial data and models
    on top of static maps from various online sources (e.g Google Maps and Stamen
    Maps). It includes tools common to those tasks, including functions for
    geolocation and routing.
A 'ggplot2' extension for visualizing China’s map, offering customizable projections, boundary styles, and buffer zones for thematic maps. 
    Suitable for spatial data analysis and enhancing map visualization with flexible styling options.
Helper to add insets based on geom_sf() from 'ggplot2'.
    This package gives you a drop-in replacement for geom_sf() that supports
    adding a zoomed inset map without having to create and embed a separate plot.
A quick and easy way of plotting the columns of two matrices or 
    data frames against each other using 'ggplot2'. Although 'ggmatplot' doesn't 
    provide the same flexibility as 'ggplot2', it can be used as a workaround for 
    having to wrangle wide format data into long format for plotting with
    'ggplot2'.
Tools for assessing and diagnosing convergence of
    Markov Chain Monte Carlo simulations, as well as for graphically display
    results from full MCMC analysis. The package also facilitates the graphical
    interpretation of models by providing flexible functions to plot the
    results against observed variables, and functions to work with
    hierarchical/multilevel batches of parameters
    (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Enhance a 'mice' imputation workflow with visualizations for
    incomplete and/or imputed data. The plotting functions produce
    'ggplot' objects which may be easily manipulated or extended. Use
    'ggmice' to inspect missing data, develop imputation models, evaluate
    algorithmic convergence, or compare observed versus imputed data.
Fit penalized multivariable linear mixed models with a single 
    random effect to control for population structure in genetic association 
    studies. The goal is to simultaneously fit many genetic variants at the 
    same time, in order to select markers that are independently associated 
    with the response. Can also handle prior annotation information, 
    for example, rare variants, in the form of variable weights. For more 
    information, see the website below and the accompanying paper: 
    Bhatnagar et al., "Simultaneous SNP selection and adjustment for 
    population structure in high dimensional prediction models", 2020, 
    <DOI:10.1371/journal.pgen.1008766>.
Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>, 
  including the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>, 
  seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>,
  exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>, 
  smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>,
  logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>,
  Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>,
  and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions
  for computing variable inclusion probabilities, multiple regression coefficients, and 
  statistical inference <doi:10.1214/15-EJS1031>.
Estimate non-regularized Gaussian graphical models, Ising models, 
  and mixed graphical models. The current methods consist of multiple regression, 
  a non-parametric bootstrap  <doi:10.1080/00273171.2019.1575716>, and Fisher z 
  transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, 
  predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.
Mosaic plots in the 'ggplot2' framework. Mosaic
    plot functionality is provided in a single 'ggplot2' layer by calling
    the geom 'mosaic'.
Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.
Estimation of partial correlation matrix using ridge penalty 
    followed by thresholding and reestimation. Under multivariate Gaussian 
    assumption, the matrix constitutes an Gaussian graphical model (GGM).
Graph estimation in Gaussian Graphical Models, following the method
  developed by C. Giraud, S. Huet and N. Verzelen (2012) <doi:10.1515/1544-6115.1625>.
  The main functions return the adjacency matrix of an undirected graph
  estimated from a data matrix.
A 'grammar of graphics' approach for visualizing summary statistics from multiple Genome-wide Association Studies (GWAS). It offers geneticists, bioinformaticians, and researchers a powerful yet flexible tool for illustrating complex genetic associations using data from various GWAS datasets. The visualizations can be extensively customized, facilitating detailed comparative analysis across different genetic studies. Reference: Uffelmann, E. et al. (2021) <doi:10.1038/s43586-021-00056-9>.
Create plots that combine a phylogeny and frequency dynamics.
    Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo".
    Inspired by similar plots in publications of the labs of RE Lenski and JE
    Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose
    code this package exploits).
It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.
Geometries to plot network objects with 'ggplot2'.
Use multiple fill and colour scales in 'ggplot2'.
Uses 'ggplot2' to create normally distributed
    violin plots with specified means and standard deviations. This
    function can be useful in showing hypothetically normal distributions
    and confidence intervals.
Provides a theme, a discrete color palette, and continuous scales
    to make 'ggplot2' look like 'gnuplot'. This may be helpful if you use both
    'ggplot2' and 'gnuplot' in one project.
Allows plotting data on bathymetric maps using 'ggplot2'. Plotting
  oceanographic spatial data is made as simple as feasible, but also flexible
  for custom modifications. Data that contain geographic information from 
  anywhere around the globe can be plotted on maps generated by the basemap()
  or qmap() functions using 'ggplot2' layers separated by the '+' operator. The 
  package uses spatial shape- ('sf') and raster ('stars') files, geospatial 
  packages for R to manipulate, and the 'ggplot2' package to plot these 
  files. The package ships with low-resolution spatial data files and 
  higher resolution files for detailed maps are stored in the 
  'ggOceanMapsLargeData' repository on GitHub and downloaded automatically 
  when needed.
Discrete scales for the colorblind-friendly 'Okabe-Ito'
    palette, including 'color', 'fill', and 'edge_colour'. 'ggokabeito'
    provides 'ggplot2' and 'ggraph' scales to easily use the 'Okabe-Ito'
    palette in your data visualizations.
The 'vegan' package includes several functions for adding features
  to ordination plots: ordiarrows(),  ordiellipse(), ordihull(),
  ordispider() and ordisurf(). This package adds these same   features to 
  ordination plots made with 'ggplot2'. In addition, gg_ordibubble() sizes
  points relative to the value of an environmental variable.
Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.
Create groups of 'ggplot2' layers that can be easily migrated from one plot
    to another, reducing redundant code and improving the ability to format many
    plots that draw from the same source 'ggpacket' layers.
Facilitates the creation of page layout
    visualizations in which words are represented as rectangles with sizes
    relating to the length of the words. Which then is divided in lines
    and pages for easy overview of up to quite large texts.
Create hammock plots, parallel sets, and common angle plots
    with 'ggplot2'.
Extends 'ggplot2' functionality to the 'partykit' package. 'ggparty' provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class 'party'.
A 'ggplot2' extension that enables robust image grobs in
    panels and theme elements.
Provides 'ggplot2' geoms filled with various patterns.  Includes a patterned version of every 'ggplot2' geom that has a region that can be filled with a pattern.  Provides a suite of 'ggplot2' aesthetics and scales for controlling pattern appearances.  Supports over a dozen builtin patterns (every pattern implemented by 'gridpattern') as well as allowing custom user-defined patterns.
Provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). This package helps visualize high-dimensional data with options for custom labels, density plots, and faceting, using the 'ggplot2' framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.
Modern Parallel Coordinate Plots have been introduced in the 1980s as 
    a way to visualize arbitrarily many numeric variables. This Grammar of Graphics implementation
    also incorporates categorical variables into the plots in a principled manner. 
    By separating the data managing part from the visual rendering, we give full access
    to the users while keeping the number of parameters manageably low.
Implements methods to plot periodic data in any arbitrary range on the fly.
Create pie, donut and rose pie plot with 'ggplot2'.
Enables users to create simple plots of biological culture plates as well as microplates. Both continuous and discrete values can be plotted onto the plate layout.
Provides the ggplot binning layer stat_summaries_hex(),
    which functions similar to its singular form,
    but allows the use of multiple statistics per bin.
    Those statistics can be mapped to multiple bin aesthetics.
A system for 'declaratively' creating graphics, based on "The
    Grammar of Graphics". You provide the data, tell 'ggplot2' how to map
    variables to aesthetics, what graphical primitives to use, and it
    takes care of the details.
Selected utilities, in particular 'geoms' and 'stats'
    functions, extending the 'ggplot2' package. This package imports
    functions from 'EnvStats' <doi:10.1007/978-1-4614-8456-1> by Millard
    (2013), 'ggpp' <https://CRAN.R-project.org/package=ggpp> by Aphalo et
    al. (2023) and 'ggstats' <doi:10.5281/zenodo.10183964> by Larmarange
    (2023), and then exports them. This package also contains modified
    code from 'ggquickeda' <https://CRAN.R-project.org/package=ggquickeda>
    by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions
    to plots.  All functions are tested to make sure that they work
    reliably.
A dataset about movies. This was previously contained in ggplot2,
   but has been moved its own package to reduce the download size of ggplot2.
An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package.
    You can learn each steps of making plot by clicking your mouse without coding.
    You can get resultant code for the plot.
Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.
Convert plot function call (using expression or formula) to 'grob' or 'ggplot' object that compatible to the 'grid' and 'ggplot2' ecosystem. With this package, we are able to e.g. using 'cowplot' to align plots produced by 'base' graphics, 'ComplexHeatmap', 'eulerr', 'grid', 'lattice', 'magick', 'pheatmap', 'vcd' etc. by converting them to 'ggplot' objects.
Convenience functions for smooth conversion from 'ggplot' to 'plotly' where the conversion using ggplotly() usually gives an unexpected labels. The package ease the process of making a 'plotly' figures generated from 'ggplot2' object more aesthetic in terms of labels and customizability.
Extensions to 'ggplot2' respecting the grammar of graphics
    paradigm. Statistics: locate and tag peaks and valleys; label plot with the
    equation of a fitted polynomial or other types of models; labels
    with P-value, R^2 or adjusted R^2 or information criteria for fitted models;
    label with ANOVA table for fitted models; label with summary for fitted
    models. Model fit classes for which suitable methods are provided by package
    'broom' and 'broom.mixed' are supported. Scales and stats to build volcano
    and quadrant plots based on outcomes, fold changes, p-values and false 
    discovery rates.
At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling
  activities in order to reduce the overall effort required for generating such plots.
  For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox,
  called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient
  for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP
  <doi:10.1002/psp4.12161>. While not required, you can get/install the 'R' 'lixoftConnectors'
    package in the 'Monolix' installation, as described at the following url
    <https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/>.
    When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required
    Chart Data instead of exporting it from the 'Monolix' gui.
A cross between a 2D density plot and a scatter plot,
    implemented as a 'ggplot2' geom. Points in the scatter plot are 
    colored by the number of neighboring points. This is useful to
    visualize the 2D-distribution of points in case of overplotting.
An (aspirational) collection of additional geometries and 
    statistics for 'ggplot2'.
A 'ggplot2' extension for implementing parliament charts and several other useful visualizations.
Provides basic graphing functions to fully demonstrate
    point-to-point connections in a polar coordinate space.
Tools for working with polygons with holes in 'ggplot2', with a
    new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding'
    rules.
Extensions to 'ggplot2' respecting the grammar of graphics 
    paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to 
    plots using native data coordinates, while geom_table_npc(), geom_plot_npc()
    and geom_grob_npc() do the same using "npc" coordinates through new 
    aesthetics "npcx" and "npcy". Statistics: select observations based on 2D 
    density. Positions: radial nudging away from a center point and nudging away
    from a line or curve; combined stacking and nudging; combined dodging and
    nudging.
Provides various themes, palettes, and other functions
    that are used to customise ggplots to look like they were made in 'GraphPad 
    Prism'. The 'Prism'-look is achieved with theme_prism() and 
    scale_fill|colour_prism(), axes can be changed with custom guides like 
    guide_prism_minor(), and significance indicators added with add_pvalue().
The 'ggplot2' package is excellent and flexible for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.
Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, 
    and adds the resulting p-values to the plot with an annotation bar.
    Visualizing group differences are frequently performed by boxplots, bar plots, etc.
    Statistical test results are often needed to be annotated on these plots. 
    This package provides a convenient function that works on 'ggplot2' objects, 
    performs the desired statistical test between groups of interest and annotates the test results on the plot.
Plot single and faceted type quality control charts
  for 'ggplot'.
A quantile-quantile plot can be used to compare a sample of p-values 
  to the uniform distribution.  But when the dataset is big (i.e. > 1e4 p-values),
  plotting the quantile-quantile plot can be slow.  geom_QQ uses all the data to
  calculate the quantiles, but thins it out in a way that focuses on points near zero 
  before plotting to speed up plotting and decrease file size, when vector graphics are stored.
Quickly and easily perform exploratory data analysis by uploading your
     data as a 'csv' file. Start generating insights using 'ggplot2' plots and
     'table1' tables with descriptive stats, all using an easy-to-use point and click 
     'Shiny' interface.
An extension of 'ggplot2' to provide quiver plots to visualise vector fields. 
    This functionality is implemented using a geom to produce a new graphical layer, which
    allows aesthetic options. This layer can be overlaid on a map to improve visualisation
    of mapped data.
Extend 'ggplot2' facets to panel layouts arranged in a grid
    with ragged edges. facet_ragged_rows() groups panels into rows that
    can vary in length, facet_ragged_cols() does the same but for columns.
    These can be useful, for example, to represent nested or partially
    crossed relationships between faceting variables.
The 'geom_rain()' function adds different geoms together using 'ggplot2' to create raincloud plots.
Graphic elements for exploring Random Forests using the 'randomForest' or
    'randomForestSRC' package for survival, regression and classification forests and
    'ggplot2' package plotting.
The grammar of graphics as implemented in ggplot2 is a poor fit for
    graph and network visualizations due to its reliance on tabular data input.
    ggraph is an extension of the ggplot2 API tailored to graph visualizations
    and provides the same flexible approach to building up plots layer by layer.
Intended for both technical and non-technical users to create
    interactive data visualizations through a web browser GUI without writing any
    code.
Given a group of genomes and their relationship with each other, the package clusters the genomes and selects the most representative members of each cluster. Additional data can be provided to the prioritize certain genomes. The results can be printed out as a list or a new phylogeny with graphs of the trees and distance distributions also available. For detailed introduction see: Thomas H Clarke, Lauren M Brinkac, Granger Sutton, and Derrick E Fouts (2018), GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models, Bioinformatics, bty300, <doi:10.1093/bioinformatics/bty300>.
Rasterize only specific layers of a 'ggplot2' plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without loosing vector properties of the scale-sensitive information.
You can use this function to easily draw a combined histogram and restricted cubic spline. 
    The function draws the graph through 'ggplot2'. RCS fitting requires the use of the rcs() function of the 'rms' package. 
    Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.
Provides 'ggplot2' extensions for political map making. Implements
    new geometries for groups of simple feature geometries. Adds palettes and scales 
    for red to blue color mapping and for discrete maps. Implements tools for easy 
    label generation and placement, automatic map coloring, and themes.
Provides text and label geoms for 'ggplot2' that help to avoid
    overlapping text labels. Labels repel away from each other and away from the
    data points.
An R package for creating panels of diagnostic plots for residuals from a model 
    using ggplot2 and plotly to analyze residuals and model assumptions from a variety of 
    viewpoints. It also allows for the creation of interactive diagnostic plots.
Provides functions that make it easy to reveal 'ggplot2' graphs incrementally. The functions take a plot produced with 'ggplot2' and return a list of plots showing data incrementally by panels, layers, groups, the values in an axis or any arbitrary aesthetic.
The Graphical Group Ridge 'GGRidge' package package classifies ridge regression predictors in disjoint groups of conditionally correlated variables and derives different penalties (shrinkage parameters) for these groups of predictors. It combines the ridge regression method with the graphical model for high-dimensional data (i.e. the number of predictors exceeds the number of cases) or ill-conditioned data (e.g. in the presence of multicollinearity among predictors). The package reduces the mean square errors and the extent of over-shrinking of predictors as compared to the ridge method.Aldahmani, S. and Zoubeidi, T. (2020) <DOI:10.1080/00949655.2020.1803320>.
Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
The risk plot may be one of the most commonly used figures in 
    tumor genetic data analysis. We can conclude the following two points: 
    Comparing the prediction results of the model with the real survival situation 
    to see whether the survival rate of the high-risk group is lower than that of the 
    low-level group, and whether the survival time of the high-risk group is 
    shorter than that of the low-risk group. The other is to compare the heat 
    map and scatter plot to see the correlation between the predictors and the 
    outcome.
Creates bar plots with rounded corners using 'ggplot2'.
    The code in this package was adapted from a solution provided by 
    Stack Overflow user 'sthoch' in the following post
    <https://stackoverflow.com/questions/62176038/r-ggplot2-bar-chart-with-round-corners-on-top-of-bar>.
Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Works with 'ggplot2' to add a Van Gogh color palette to the user’s repertoire.
 It also has a function that work alongside 'ggplot2' to create more interesting data visualizations and add contextual information to the user’s plots.
Sankey and alluvial diagrams visualise flows of quantities across
    stages in stacked bars. This package makes it easy to create such
    diagrams using 'ggplot2'.
The function combines a scatter plot with ridgelines to better visualise the distribution between sample groups. The plot is created with 'ggplot2'.
A collection of 'ggplot2' color palettes inspired by
    plots in scientific journals, data visualization libraries,
    science fiction movies, and TV shows.
Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The 'ggscidca' package adds coloured bars of discriminant relevance to the traditional decision curve. Improved practicality and aesthetics. This method was described by Balachandran VP (2015) <doi:10.1016/S1470-2045(14)71116-7>.
Provides 'ggplot2' 'stats' that estimate seasonally adjusted series 
    and rolling summaries such as rolling average on the fly for time series.
Some useful functions that can use 'grid' and 'ggplot2' to plot sectors and interact with 'Seurat' to plot gene expression percentages. Also, there are some examples of how to draw sectors in 'ComplexHeatmap'.
Contains 'ggplot2' geom for plotting brain atlases using 
    simple features. The largest component of the package is the data 
    for the two built-in atlases. Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.
Mainly contains a plotting function ggseg3d(), 
    and data of two standard brain atlases (Desikan-Killiany and aseg). 
    By far, the largest bit of the package is the data for each of the atlases.
    The functions and data enable users to plot tri-surface mesh plots of
    brain atlases, and customise these by projecting colours onto the brain
    segments based on values in their own data sets. Functions are wrappers
    for 'plotly'. Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.
It is an R package and web-based application, allowing users to perform interactive and reproducible visualizations of path diagrams for structural equation modeling (SEM) and networks using the 'ggplot2' engine. Its app (built with 'shiny') provides an interface that allows extensive customization, and creates CSV outputs, which can then be used to recreate the figures either using the web app or script-based workflow.
The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.
A set of wrapper functions that mainly re-produces most of the 
  sequence plots rendered with TraMineR::seqplot(). Whereas 'TraMineR' uses base 
  R to produce the plots this library draws on 'ggplot2'. 
  The plots are produced on the basis of a sequence object defined  
  with TraMineR::seqdef(). The package automates the reshaping and plotting 
  of sequence data. Resulting plots are of class 'ggplot', i.e. components 
  can be added and tweaked using '+' and regular 'ggplot2' functions.
A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), 
    geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more 
    aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a 
    neon glow around lines to get a steampunk style.
The grammar of graphics as shown in 'ggplot2' has provided
  an expressive API for users to build plots. 'ggside' extends 'ggplot2' 
  by allowing users to add graphical information about one of the main panel's
  axis using a familiar 'ggplot2' style API with tidy data. This package is
  particularly useful for visualizing metadata on a discrete axis, or summary
  graphics on a continuous axis such as a boxplot or a density distribution.
Enrich your 'ggplots' with group-wise comparisons.
    This package provides an easy way to indicate if two groups are
    significantly different. Commonly this is shown by a bracket on top
    connecting the groups of interest which itself is annotated with the
    level of significance (NS, *, **, ***).  The package provides a single
    layer (geom_signif()) that takes the groups for comparison and the
    test (t.test(), wilcox.text() etc.) as arguments and adds the
    annotation to the plot.
Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions.
The 'ggplot2' package provides a powerful set of tools
  for visualising and investigating data. The 'ggsoccer' package provides a
  set of functions for elegantly displaying and exploring soccer event data
  with 'ggplot2'. Providing extensible layers and themes, it is designed to
  work smoothly with a variety of popular sports data providers.
An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. 
 Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014.
 <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AL%3A2015%3A012%3ATOC>. 
 Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided.
 The structure files can be adopted for localization or for insurance companies who use Internal Models (IM).
 Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts.
 With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment).
The aim of this package is to offer more variability of graphics based on the self-organizing maps.
Functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in 'ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.
Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of spatial objects.
Additional annotations, stats, geoms and scales for plotting 
    "light" spectra with 'ggplot2', together with specializations of ggplot()
    and autoplot() methods for spectral data and waveband definitions
    stored in objects of classes defined in package 'photobiology'. Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Easily create overlapping grammar of graphics plots for scientific data visualization. 
  This style of plotting is particularly common in climatology and oceanography research communities.
A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'. This package is now superseded by 'ggplot2' itself
    which now has full native support for horizontal layouts. It
    remains available for backward compatibility.
To create the multiple polygonal point layer for easily discernible shapes, 
             we developed the package, it is like the 'geom_point' of 'ggplot2'.
             It can be used to draw the scatter plot.
Provides new statistics, new geometries and new positions for 
    'ggplot2' and a suite of functions to facilitate the creation of 
    statistical plots.
Extension of 'ggplot2', 'ggstatsplot' creates graphics with
    details from statistical tests included in the plots themselves. It
    provides an easier syntax to generate information-rich plots for
    statistical analysis of continuous (violin plots, scatterplots,
    histograms, dot plots, dot-and-whisker plots) or categorical (pie and
    bar charts) data. Currently, it supports the most common types of
    statistical approaches and tests: parametric, nonparametric, robust,
    and Bayesian versions of t-test/ANOVA, correlation analyses,
    contingency table analysis, meta-analysis, and regression analyses.
    References: Patil (2021) <doi:10.21105/joss.03236>.
Make smoothed stacked area charts in 'ggplot2'. Stream plots are useful to show magnitude trends over time.
Provides an extension to 'ggplot2' (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two 
    types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean.
    These plots contain multiple user-defined confidence areas with varying colours, 
    defined by the underlying t-distribution used to compute standard confidence intervals for 
    the mean of the normal distribution when the variance is unknown.
    Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the 
    shape (horizontal width) is defined by the probability density function of the t-distribution.
Create epicurves or epigantt charts in 'ggplot2'. Prepare
    data for visualisation or other reporting for infectious disease
    surveillance and outbreak investigation. Includes tidy functions to solve
    date based transformations for common reporting tasks, like 
    (A) seasonal date alignment for respiratory disease surveillance, 
    (B) date-based case binning based on specified time intervals like
    isoweek, epiweek, month and more, (C) automated detection and marking
    of the new year based on the date/datetime axis of the 'ggplot2'. 
    An introduction on how to use epicurves can be found on the US CDC website 
    (2012, <https://www.cdc.gov/training/quicklearns/epimode/index.html>).
Functions for survey data including svydesign objects from the 'survey' package that call 'ggplot2' to make bar charts, histograms, boxplots, and hexplots of survey data.
Ease the creation of time-to-event (i.e. survival) endpoint
    figures. The modular functions create figures ready for publication.
    Each of the functions that add to or modify the figure are written as
    proper 'ggplot2' geoms or stat methods, allowing the functions from
    this package to be combined with any function or customization from
    'ggplot2' and other 'ggplot2' extension packages.
Offers various swiss maps as data frames and 'ggplot2' objects and gives the
    possibility to add layers of data on the maps. Data are publicly available
    from the swiss federal statistical office.
Extends the 'ggplot2' plotting system to support network visualization. Inspired by the 'Method 1' in 'ggtree' (G Yu (2018) <doi:10.1093/molbev/msy194>), 'ggtangle' is designed to work with network associated data.
Provides a comprehensive suite of functions for processing and visualizing taxonomic data. It includes functionality to clean and transform taxonomic data, categorize it into hierarchical ranks (such as Phylum, Class, Order, Family, and Genus), and calculate the relative abundance of each category. The package also generates a color palette for visual representation of the taxonomic data, allowing users to easily identify and differentiate between various taxonomic groups. Additionally, it features a river plot visualization to effectively display the distribution of individuals across different taxonomic ranks, facilitating insights into taxonomic visualization.
A collection of palettes and themes for 'ggplot2', offering a light, pastel aesthetic. Syntax follows the 'viridis' package.
Extends the functionality of 'ggplot2', providing the capability
    to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally,
    'ggtern' has implemented several NEW geometries which are unavailable to the
    standard 'ggplot2' release. For further examples and documentation, please
    proceed to the 'ggtern' website.
A 'ggplot2' extension that enables the rendering of
    complex formatted plot labels (titles, subtitles, facet labels,
    axis labels, etc.). Text boxes with automatic word wrap are also
    supported.
Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
Some extra themes, geoms, and scales for 'ggplot2'.
    Provides 'ggplot2' themes and scales that replicate the look of plots
    by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata',
    'Excel', and 'The Wall Street Journal', among others.
    Provides 'geoms' for Tufte's box plot and range frame.
Designed to customize 'ggplot' graphics according to the institutional identity of the University of Ljubljana.
Create tibbles and lists of 'ggplot' figures that can be modified as easily as
  regular 'ggplot' figures.  Typical use cases are for creating reports or web
  pages where many figures are needed with different data and similar
  formatting.
Annotation of 'ggplot2' plots with arbitrary 'TikZ' code, using absolute data or relative plot coordinates.
Provides additional display mediums for time series visualisations.
Provides 'ggplot2' geoms that allow groups of data points to be
    outlined or highlighted for emphasis. This is particularly useful when
    working with dense datasets that are prone to overplotting.
Add trendline and confidence interval of linear or nonlinear regression model and
    show equation to 'ggplot' as simple as possible. For a general overview of the methods used in
	  this package, see Ritz and Streibig (2008) <doi:10.1007/978-0-387-09616-2> and 
	  Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.
A collection of several geoms to create graphics, using
    'ggplot2' and the Cartesian coordinate system. You use the familiar
    mapping 'Grammar of Graphics' without the need to do another
    transformation into polar coordinates.
An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and 'GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available.
Replace the standard x-axis in 'ggplots' with a combination matrix
  to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize
  the overlap of sets as an alternative to Venn diagrams. 
  This package provides a simple way to produce such plots using 'ggplot2'. 
  In addition it can convert any categorical axis into a combination
  matrix axis.
An easy-to-use way to draw pretty venn diagram by 'ggplot2'.
Easy-to-use functions to generate 2-7 sets Venn or upset plot in publication quality. 
  'ggVennDiagram' plot Venn or upset using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets 
  Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which 
  are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to 
  integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections 
  of them, then separately plot Venn in four components, set edges/labels, and region edges/labels.
  From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar.
  From version 1.4.4, it supports unlimited number of sets, as it can draw a plain upset plot automatically when
  number of sets is more than 7.
A collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.
Preview what a 'ggplot2' plot would look like if you save it to a file. 
    Attach picture dimensions as a canvas() element and get an instant preview. 
    These dimensions will then be used when you save the plot.
An implementation of an interactive grammar of graphics,
    taking the best parts of 'ggplot2', combining them with the reactive
    framework of 'shiny' and drawing web graphics using 'vega'.
Provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the 'ggplot2' framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.
Provides a word cloud text geom for 'ggplot2'. Texts
    are placed so that they do not overlap as in 'ggrepel'.  The algorithm
    used is a variation around the one of 'wordcloud2.js'.
The 'ggplot2' package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid 'ggplot2' commands.
Minimal client to access the 'GitHub' 'API'.
Haplotype calling from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. The package was first described in Bioinformatics by Utsunomiya et al. (2016, <doi:10.1093/bioinformatics/btw356>). Since the v2 release, the package provides functions for unsupervised and supervised detection of ancestry tracks. The methods implemented in these functions were described in an article published in Methods in Ecology and Evolution by Utsunomiya et al. (2020, <doi:10.1111/2041-210X.13467>). The source code for v3 was modified for improved performance and inclusion of new functionality, including analysis of unphased data, runs of homozygosity, sampling methods for virtual gamete mating, mixed model fitting and GWAS.
'GitHub' apps provide a powerful way to manage fine grained
    programmatic access to specific 'git' repositories, without having to
    create dummy users, and which are safer than a personal access token
    for automated tasks. This package extends the 'gh' package to let you
    authenticate and interact with 'GitHub' <https://docs.github.com/en/rest/overview>
    in 'R' as an app.
Functions are provided for quantifying evolution and selection on complex traits. 
              The package implements effective handling and analysis algorithms scaled for 
              genome-wide data and calculates a composite statistic, denoted Ghat, which is used 
              to test for selection on a trait. The package provides a number of simple examples 
              for handling and analysing the genome data and visualising the output and results. 
              Beissinger et al., (2018) <doi:10.1534/genetics.118.300857>.
Interface for the GitHub API that enables efficient 
    management of courses on GitHub. It has a functionality for 
    managing organizations, teams, repositories, and users on GitHub 
    and helps automate most of the tedious and repetitive tasks 
    around creating and distributing assignments.
A statistical hypothesis test for conditional independence.
    Given residuals from a sufficiently powerful regression, it tests whether 
    the covariance of the residuals is vanishing. It can be applied to both
    discretely-observed functional data and multivariate data. 
    Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas
    Peters (2022) <doi:10.1111/rssb.12544>.
The goal of 'GHCNr' is to provide a fast and friendly interface with the Global Historical Climatology Network daily (GHCNd) database, which contains daily summaries of weather station data worldwide (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>). GHCNd is accessed through the web API <https://www.ncei.noaa.gov/access/services/data/v1>. 'GHCNr' main functionalities consist of downloading data from GHCNd, filter it, and to aggregate it at monthly and annual scales.
Colour palettes inspired by Studio Ghibli <https://en.wikipedia.org/wiki/Studio_Ghibli> 
    films, ported to R for your enjoyment.
Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.
Functions for multiple knockoff inference using summary statistics, e.g. Z-scores. The knockoff inference is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. This package provides a procedure which performs knockoff inference without ever constructing individual knockoffs (GhostKnockoff). It additionally supports multiple knockoff inference for improved stability and reproducibility. Moreover, it supports meta-analysis of multiple overlapping studies.
A 'GraphQL' client, with an R6 interface for initializing
    a connection to a 'GraphQL' instance, and methods for constructing
    queries, including fragments and parameterized queries. Queries
    are checked with the 'libgraphqlparser' C++ parser via the
    'gaphql' package.
Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.
Detailed functionality for working
        with the univariate and multivariate Generalized Hyperbolic
        distribution and its special cases (Hyperbolic (hyp), Normal
        Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
        and Gaussian distribution). Especially, it contains fitting
        procedures, an AIC-based model selection routine, and functions
        for the computation of density, quantile, probability, random
        variates, expected shortfall and some portfolio optimization
        and plotting routines as well as the likelihood ratio test. In
        addition, it contains the Generalized Inverse Gaussian
        distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. 
        Quantitative risk management: Concepts, techniques and tools. 
        Princeton University Press, Princeton (2005).
Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG).
    To learn how to use it, check the vignettes for a quick tutorial.
    Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300>
    together with those relevant references from the one listed below.
    The package is based on the research developed at the Chair of Systems Design, ETH Zurich.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <arXiv:1607.02441>.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>.
    Casiraghi, G., (2017) <arXiv:1702.02048>
    Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>
    Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>.
    Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>.
    Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.
'Giac'
    <https://www-fourier.ujf-grenoble.fr/~parisse/giac/doc/en/cascmd_en/cascmd_en.html>
    is a general purpose symbolic algebra software. It powers the
    graphical interface 'Xcas'. This package allows to execute 'Giac'
    commands in 'R'.
Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets, Schmid et al. (2016) <doi:10.1093/bioinformatics/btw030>.
A plain 'Rcpp' wrapper for 'MeCab' that can segment Chinese,
    Japanese, and Korean text into tokens. The main goal of this package
    is to provide an alternative to 'tidytext' using morphological
    analysis.
Build a map of path-based geometry, this is a simple description of the number
 of parts in an object and their basic structure. Translation and restructuring operations for 
 planar shapes and other hierarchical types require a data model with a record of the underlying
 relationships between elements. The gibble() function creates a geometry map, a simple record of 
 the underlying structure in path-based hierarchical types. There are methods for the planar shape 
 types in the 'sf' and 'sp' packages and for types in the 'trip' and 'silicate' packages.
An iterative algorithm that improves the proximity matrix (PM) from a random forest (RF) and the resulting clusters as measured by the silhouette score.
Providing access to the API for Gas Infrastructure Europe's
    natural gas transparency platforms <https://agsi.gie.eu/> and <https://alsi.gie.eu/>. Lets the user
    easily download metadata on companies and gas storage units covered by
    the API as well as the respective data on regional, country, company
    or facility level.
Provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.
Implements categorical principal component analysis ('PRINCALS'), multiple correspondence analysis ('HOMALS'), monotone regression analysis ('MORALS'). It replaces the 'homals' package.
Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. 
    Converts images to GIF animations using pngquant's efficient cross-frame 
    palettes and temporal dithering with thousands of colors per frame.
Retrieving regional plant checklists, species traits and
  distributions, and environmental data from the Global Inventory of Floras and
  Traits (GIFT). More information about the GIFT database can be found at
  <https://gift.uni-goettingen.de/about> and the map of available floras can be
  visualized at <https://gift.uni-goettingen.de/map>. The API and associated
  queries can be accessed according the following scheme:
  <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.
Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.
A framework and functions to create 'MOODLE' quizzes. 'GIFTr' takes dataframe of questions of
    four types: multiple choices, numerical, true or false and short answer questions, and exports a text 
    file formatted in 'MOODLE' GIFT format. You can prepare a spreadsheet in any software and import 
    it into R to generate any number of questions with 'HTML', 'markdown' and 'LaTeX' support.
A Gibbs sampler corresponding to a Group 
    Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. 
    Hyperparameters in the GIGG prior specification can either be fixed by the 
    user or can be estimated via Marginal Maximum Likelihood Estimation.
    Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar 
    Mukherjee (2021) <arXiv:2102.10670>.
Generator and density function for the
  Generalized Inverse Gaussian (GIG) distribution.
Provides a simple to use, intuitive, and
  extensible interface to several stochastic simulation
  algorithms for generating simulated trajectories of finite
  population continuous-time model. Currently it implements
  Gillespie's exact stochastic simulation algorithm (Direct
  method) and several approximate methods (Explicit tau-leap,
  Binomial tau-leap, and Optimized tau-leap). The package also
  contains a library of template models that can be run as demo
  models and can easily be customized and extended. Currently the
  following models are included, 'Decaying-Dimerization' reaction
  set, linear chain system, logistic growth model, 'Lotka'
  predator-prey model, Rosenzweig-MacArthur predator-prey model,
  'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model.
  Pineda-Krch et al. (2008) <doi:10.18637/jss.v025.i12>.
A fast, scalable, and versatile framework for
    simulating large systems with Gillespie's Stochastic Simulation
    Algorithm ('SSA').  This package is the spiritual successor to the
    'GillespieSSA' package originally written by Mario Pineda-Krch.
    Benefits of this package include major speed improvements (>100x),
    easier to understand documentation, and many unit tests that try to
    ensure the package works as intended. Cannoodt and Saelens et al. (2021) 
    <doi:10.1038/s41467-021-24152-2>.
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.
Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the 'gimap' package is genetic interaction scores which are  the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) <doi:10.1016/j.celrep.2021.109597>.
Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the 'graphicalVAR' approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
Simplifies the process of creating essential visualizations
    in R, offering a range of plotting functions for common chart types
    like violin plots, pie charts, and histograms.  With an intuitive
    interface, users can effortlessly customize colors, labels, and
    styles, making it an ideal tool for both beginners and experienced
    data analysts. Whether exploring datasets or producing quick visual
    summaries, this package provides a streamlined solution for
    fundamental graphics in R.
Supplemental tools for the 'gimme' R package. It contains an interactive graphical user interface, allowing for the flexible specification of a variety of both basic and advanced options. It will expand to include a variety of tools for navigating output.
This is a set of functions to retrieve information about GIMMS
    NDVI3g files currently available online; download (and re-arrange, in the 
    case of NDVI3g.v0) the half-monthly data sets; import downloaded files from 
    ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based 
    on the widespread 'raster' package; conduct quality control; and generate 
    monthly composites (e.g., maximum values) from the half-monthly input data. 
    As a special gimmick, a method is included to conveniently apply the 
    Mann-Kendall trend test upon 'Raster*' images, optionally featuring 
    trend-free pre-whitening to account for lag-1 autocorrelation.
Providing various equations to calculate Gini coefficients.
    The methods used in this package can be referenced from Brown MC (1994) <doi: 10.1016/0277-9536(94)90189-9>.
An implementation of Gini-based weighting approaches in constructing composite indicators, providing functionalities for normalization, aggregation, and ranking comparison.
Estimation of the effect of each income source on income inequalities based on the decomposition of Lerman and Yitzhaki (1985) <doi:10.2307/1928447>.
An implementation of a new Gini covariance and correlation to measure dependence between a categorical and numerical variables. Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) <arXiv:1809.09793>.
Estimates the Gini index and computes variances and confidence intervals for finite and infinite populations, using different methods; also computes Gini index for continuous probability distributions, draws samples from continuous probability distributions with Gini indices set by the user; uses 'Rcpp'.
    References: 
    Muñoz et al. (2023) <doi:10.1177/00491241231176847>.
    Álvarez et al. (2021) <doi:10.3390/math9243252>.
    Giorgi and Gigliarano (2017) <doi:10.1111/joes.12185>.
    Langel and Tillé (2013) <doi:10.1111/j.1467-985X.2012.01048.x>.
Density function and generation of random variables from the Generalized Inverse Normal (GIN) distribution from Robert (1991) <doi:10.1016/0167-7152(91)90174-P>. Also provides density functions and generation from the GIN distribution truncated to positive or negative reals. Theoretical guarantees supporting the sampling algorithms and an application to Bayesian estimation of network formation models can be found in the working paper Ding, Estrada and Montoya-Blandón (2023) <https://www.smontoyablandon.com/publication/networks/network_externalities.pdf>.
A workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework.
    References: 
            Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.
Maximum likelihood estimation under relational models, with or without the overall effect.
The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean
    and the standard deviation of a quantitative variable. It also flags 
    potential outliers (together with their frequencies) that are c standard deviations away from the mean.
Find the permutation symmetry group such that the covariance
    matrix of the given data is approximately invariant under it.
    Discovering such a permutation decreases the number of observations
    needed to fit a Gaussian model, which is of great use when it is
    smaller than the number of variables. Even if that is not the case,
    the covariance matrix found with 'gips' approximates the actual
    covariance with less statistical error. The methods implemented in
    this package are described in Graczyk et al. (2022)
    <doi:10.1214/22-AOS2174>.
Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general 
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices. 
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the 
 likelihood using MCMC samplers for large lattices.
Tools to download data from the GISCO (Geographic Information
    System of the Commission) Eurostat database
    <https://ec.europa.eu/eurostat/web/gisco>. Global and European map
    data available.  This package is in no way officially related to or
    endorsed by Eurostat.
Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases
  <https://www.sciencedirect.com/topics/computer-science/gis-database>,
  the R package 'GISINTEGRATION' offers a robust solution for efficiently preparing  GIS data for advanced 
  spatial analyses. This package excels in simplifying intrica  procedures like data cleaning, normalization, 
  and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.
A collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.
Mapping and spatial data manipulation tools - in particular
    drawing thematic maps with nice looking legends,  and aggregation of point
    data to polygons.
Work with 'GitHub' 'gists' from 'R' (e.g., 
    <https://en.wikipedia.org/wiki/GitHub#Gist>, 
    <https://docs.github.com/en/github/writing-on-github/creating-gists/>). A 'gist'
    is simply one or more files with code/text/images/etc. This package allows
    the user to create new 'gists', update 'gists' with new files, rename files,
    delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists',
    open a 'gist' in your default browser, get embed code for a 'gist', list
    'gist' 'commits', and get rate limit information when 'authenticated'. Some
    requests require authentication and some do not. 'Gists' website: 
    <https://gist.github.com/>.
Interface to the 'libgit2' library, which is a pure C
    implementation of the 'Git' core methods. Provides access to 'Git'
    repositories to extract data and running some basic 'Git'
    commands.
The git2rdata package is an R package for writing and reading
    dataframes as plain text files.  A metadata file stores important
    information.  1) Storing metadata allows to maintain the classes of
    variables.  By default, git2rdata optimizes the data for file storage.
    The optimization is most effective on data containing factors.  The
    optimization makes the data less human readable.  The user can turn
    this off when they prefer a human readable format over smaller files.
    Details on the implementation are available in vignette("plain_text",
    package = "git2rdata").  2) Storing metadata also allows smaller row
    based diffs between two consecutive commits.  This is a useful feature
    when storing data as plain text files under version control.  Details
    on this part of the implementation are available in
    vignette("version_control", package = "git2rdata").  Although we
    envisioned git2rdata with a git workflow in mind, you can use it in
    combination with other version control systems like subversion or
    mercurial.  3) git2rdata is a useful tool in a reproducible and
    traceable workflow.  vignette("workflow", package = "git2rdata") gives
    a toy example.  4) vignette("efficiency", package = "git2rdata")
    provides some insight into the efficiency of file storage, git
    repository size and speed for writing and reading.
An interactive git user interface from the R command line. Intuitive
         tools to make commits, branches, remotes, and diffs an integrated part
         of R coding. Built on git2r, a system installation of git is not required
         and has default on-premises remote option.
Scan multiple 'Git' repositories, pull specified files content and process it with large language models. You can summarize the content in specific way, extract information and data, or find answers to your questions about the repositories. The output can be stored in vector database and used for semantic search or as a part of a RAG (Retrieval Augmented Generation) prompt.
Query, set, delete credentials from the 'git' credential
    store. Manage 'GitHub' tokens and other 'git' credentials. This
    package is to be used by other packages that need to authenticate to
    'GitHub' and/or other 'git' repositories.
Read all commit messages of your local git repository and
    sort them according to tags or specific text pattern into chapters of
    a HTML book using 'bookdown'. The git history book presentation helps
    organisms required to testify for every changes in their source code,
    in relation to features requests.
'Gitea' is a community managed, lightweight code hosting solution 
  were projects and their respective git repositories can be managed 
  <https://gitea.io>. This package gives an interface to the 'Gitea' API to 
  access and manage repositories, issues and organizations directly in R.
An 'Rstudio' addin for version control that allows users to clone
    repositories, create and delete branches, and sync forks on GitHub, GitLab, etc.
    Furthermore, the addin uses the GitLab API to allow instructors to create
    forks and merge requests for all students/teams with one click of a button.
Automates the process of adding,
    committing, and pushing changes to a 'git' repository
    using commit messages generated by passing the git diff output 
    to the 'OpenAI'
    'GPT-3.5 Turbo' model (<https://platform.openai.com/docs/models/gpt-3>).
Provides an helpful way to install packages hosted on GitHub.
This is a 'GitHub' API wrapper for R. <https://docs.github.com/en/rest> It uses the 'gh' package but has things wrapped up for convenient use cases.
Simple interface to query gitignore.io to fetch
    gitignore templates that can be included in the .gitignore file. More
    than 450 templates are currently available.
Provides R functions to access the API of the project and
    repository management web application 'GitLab'. For many common tasks
    (repository file access, issue assignment and status, commenting)
    convenience wrappers are provided, and in addition the full API can be
    used by specifying request locations. 'GitLab' is open-source software
    and can be self-hosted or used on <https://about.gitlab.com>.
Provides helpers to add 'Git' links to 'shiny'
    applications, 'rmarkdown' documents, and other 'HTML' based resources.
    This is most commonly used for 'GitHub' ribbons.
A light-weight, dependency-free, application programming interface
    (API) to access system-level 'Git' commands from within 'R'. Contains wrappers
    and defaults for common data science workflows as well as
    'Zsh' plugin aliases. A generalized API syntax is also available.
    A system installation of 'Git' <https://git-scm.com/downloads> is required.
Obtain standardized data from multiple 'Git' services, including 'GitHub' and 'GitLab'. 
    Designed to be 'Git' service-agnostic, this package assists teams with activities spread across various 
    'Git' platforms by providing a unified way to access repository data.
In computationally demanding data analysis pipelines,
  the 'targets' R package (2021, <doi:10.21105/joss.02959>) maintains
  an up-to-date set of results while skipping tasks that do not need to rerun.
  This process increases speed and increases trust in the final end product.
  However, it also overwrites old output with new output, and past
  results disappear by default. To preserve historical output, the 'gittargets'
  package captures version-controlled snapshots of the data store,
  and each snapshot links to the underlying commit of the source code.
  That way, when the user rolls back the code to a previous branch or commit,
  'gittargets' can recover the data contemporaneous with that commit so that
  all targets remain up to date.
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.  Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.
An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Functions for the g-and-k and generalised g-and-h distributions.
Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
A simple way to interact with and extract data from
  the official 'Google Knowledge Graph' API <https://developers.google.com/knowledge-graph/>.
Imports the 'gkmSVM' v2.0 functionalities into R <https://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
    Users should note that the suggested packages 'rtracklayer', 'GenomicRanges', 'BSgenome', 'BiocGenerics', 
    'Biostrings', 'GenomeInfoDb', 'IRanges', and 'S4Vectors' are all BioConductor packages <https://bioconductor.org>.
Kernel regularized least squares, also known as kernel ridge regression, 
    is a flexible machine learning method. This package implements this method by 
    providing a smooth term for use with 'mgcv' and uses random sketching to 
    facilitate scalable estimation on large datasets. It provides additional 
    functions for calculating marginal effects after estimation and for use with 
    ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), 
    and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024)
    <doi:10.1017/pan.2023.27> provide further details.
Contains methods for fitting Generalized Linear Models (GLMs) 
  and Generalized Additive Models (GAMs). Generalized regression models are
  common methods for handling data for which assuming Gaussian-distributed
  errors is not appropriate. For instance, if the response of interest is
  binary, count, or proportion data, one can instead model the expectation of
  the response based on an appropriate data-generating distribution.
  This package provides methods for fitting GLMs and GAMs under
  Beta regression, Poisson regression, Gamma regression, and Binomial regression
  (currently GLM only) settings. Models are fit using local scoring algorithms
  described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.
Efficient design matrix free  lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a  component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs.
Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
Efficiently implements the Graphical Lasso algorithm,
            utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm 
            introduces an L1 penalty to derive sparse inverse covariance matrices from 
            observations of multivariate normal distributions. Features include the 
            generation of random and structured sparse covariance matrices, beneficial 
            for simulations, statistical method testing, and educational purposes in 
            graphical modeling. A unique function for regularization parameter selection 
            based on predefined sparsity levels is also offered, catering to users with
            specific sparsity requirements in their models. The methodology for sparse 
            inverse covariance estimation implemented in this package is based on the 
            work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.
Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arXiv:2007.08623v1>.
Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to
    search job statistics, employer statistics, and job progression,
    where 'Glassdoor' provides a breakdown of other jobs a person did
    after their current one.
Estimation of a sparse inverse covariance matrix using a lasso (L1)
             penalty. Facilities are provided for estimates along a path of values
	     for the regularization parameter.
A fast and improved implementation of the graphical LASSO.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Fits multiple-group latent class analysis (LCA) for exploring 
    differences between populations in the data with a multilevel structure. 
    There are two approaches to reflect group differences in glca: 
    fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>;
    Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA 
    (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).
Enables calculation of image textures (Haralick 1973) 
    <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices 
    (GLCMs). Supports processing images that cannot fit in memory.
Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.
The generalised lambda distribution, or Tukey lambda distribution, 
  provides a wide variety of shapes with one functional form.   
  This package provides random numbers, quantiles, probabilities, 
  densities and density quantiles for four different types of the distribution,
  the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden
  and Loots 2009) and FM5 - see documentation for details.
  It provides the density function, distribution function, and Quantile-Quantile 
  plots.  
  It implements a variety of estimation methods for the distribution, 
  including diagnostic plots. 
  Estimation methods include the starship (all 4 types), 
  method of L-Moments for the GPD and FKML types, and a 
  number of methods for only the FKML type.  
  These include maximum likelihood, maximum product of spacings, 
  Titterington's method, Moments, Trimmed L-Moments and 
  Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" <doi:10.1016/j.csda.2007.06.021>, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" <doi:10.1111/1467-842X.00089>, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" <doi:10.22237/jmasm/1130803560>, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" <doi:10.1016/j.csda.2006.06.008>, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" <doi:10.18637/jss.v021.i09>, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" <doi:10.1016/j.csda.2009.02.014>, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" <doi:10.1201/b10159>, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" <doi:10.1201/b10159>, Su (2015)   "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.
Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of  the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.  References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.
Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
    Huang (2014) <doi:10.1080/01621459.2013.824892>.
    Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
    Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.
Routines for log-linear models of incomplete contingency tables,
             including some latent class models, via EM and Fisher scoring
             approaches. Allows bootstrapping. See Espeland and Hui (1987)
             <doi:10.2307/2531553> for general approach.
Analysis of multivariate data using generalized linear latent variable models (gllvm). 
      Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).
Provides two functions that generate source code implementing the predict 
  function of fitted glm objects. In this version, code can be generated for either 'C' 
  or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm 
  predictive models into production. The source code generated by this package implements 
  two function/methods. One of such functions implements the equivalent to 
  predict(type="response"), while the second implements predict(type="link"). Source code 
  is written to disk as a .c or .java file in the specified path. In the case of c, an .h 
  file is also generated.
Functions to calculate predicted values and the difference between
    the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model],
	polr() [ordinal logistic model], vglm() [generalized ordinal logistic model],	multinom() [multinomial model], tobit() [tobit model],
	svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive  sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
Find all hierarchical models of specified generalized linear
    model with information criterion (AIC, BIC, or AICc) within specified
    cutoff of minimum value.  Alternatively, find all such graphical models.
    Use branch and bound algorithm so we do not have to fit all models.
Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.
In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix.
Provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference.
Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 
    'partykit'. The fitting algorithm is described in more detail in Fokkema,
    Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).
    For detecting and modeling subgroups in growth curves with GLMM trees see
    Fokkema & Zeileis (2024; <DOI:10.3758/s13428-024-02389-1>).
Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple
   and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values,
   less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint().
   See Wilks (1938) <doi:10.1214/aoms/1177732360> for the LRT chi-square approximation.
   See Rao (1948) <doi:10.1017/S0305004100023987> for Rao's score test.
   See Wald (1943) <doi:10.2307/1990256> for Wald's test.
Efficient algorithms for fitting regularization paths for
        linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.
Fits generalized linear mixed models for a single grouping factor under
    maximum likelihood approximating the integrals over the random effects with an 
    adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) 
    <doi:10.1080/10618600.1995.10474663>.
Allows users to fit a cosinor model using the 'glmmTMB' framework. 
    This extends on existing cosinor modeling packages, including 'cosinor' 
    and 'circacompare', by including a wide range of available link functions 
    and the capability to fit mixed models. The cosinor model is described by 
    Cornelissen (2014) <doi:10.1186/1742-4682-11-16>.
Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.
Implements Bayesian spatial and spatiotemporal
    models that optionally allow for extreme spatial deviations through
    time. 'glmmfields' uses a predictive process approach with random
    fields implemented through a multivariate-t distribution instead of
    the usual multivariate normal.  Sampling is conducted with 'Stan'.
    References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.
Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) <https://variancejournal.org/article/73151-maximum-likelihood-approaches-to-misrepresentation-models-in-glm-ratemaking-model-comparisons>.
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, 
	see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>.
	See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.
Binomial and Poisson regression for clustered data, fixed
        and random effects with bootstrapping.
Fits high dimensional penalized generalized linear 
    mixed models using 
    the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. 
    The purpose of the package is to perform variable selection on both the fixed and 
    random effects simultaneously for generalized linear mixed models.
    The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and
    supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm
    is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>.
    The techniques used in the minimization portion of the procedure (the M-step) are
    derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) 
    <doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015)
    <doi:10.1007/s11222-013-9424-2>), with
    appropriate modifications to account for the estimation and penalization of
    the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, 
    and LASSO penalties.
Specification, analysis, simulation, and fitting of generalised linear mixed models. 
  Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models, 
  non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily,
  robust and bias-corrected standard error estimation, power calculation, data simulation, and more. 
  See <https://samuel-watson.github.io/glmmr-web/> for a detailed manual.
Optimal design analysis algorithms for any study design that can be represented or
  modelled as a generalised linear mixed model including cluster randomised trials,
  cohort studies, spatial and temporal epidemiological studies, and split-plot designs.
  See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a
  detailed manual on model specification. A detailed discussion of the methods in this
  package can be found in Watson, Hemming, and Girling (2023) <doi:10.1177/09622802231202379>.
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. 
    Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.
A Bayesian model selection approach for generalized linear mixed models.
    Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. 
    Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix.
    'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics.
    'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.
Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.
Fit linear and generalized linear mixed models with various
    extensions, including zero-inflation. The models are fitted using maximum
    likelihood estimation via 'TMB' (Template Model Builder). Random effects are
    assumed to be Gaussian on the scale of the linear predictor and are integrated
    out using the Laplace approximation. Gradients are calculated using automatic
    differentiation.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model,  multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead.  The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.
Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal
 response dataset for fitting continuation ratio models for datasets  where the number of covariates exceeds the sample size or when
 there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
Cross validation informed Relaxed LASSO, Artificial Neural Network (ANN), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Oblique Random Forest ('aorsf'), Recursive Partitioning ('RPART') or step wise regression models are fit.  Cross validation leave out samples (leading to nested cross validation) or bootstrap out-of-bag samples are used to evaluate and compare performances between these models with results presented in tabular or graphical means.  Calibration plots can also be generated, again based upon (outer nested) cross validation or bootstrap leave out (out of bag) samples. 
    For some datasets, for example when the design matrix is not of full rank, 'glmnet' may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet().  This may be remedied by using the 'path=TRUE' option when calling glmnet() and cv.glmnet().  Within the glmnetr package the approach of path=TRUE is taken by default. 
    When fitting not a relaxed lasso model but an elastic-net model, then the R-packages 'nestedcv' <https://cran.r-project.org/package=nestedcv>, 'glmnetSE' <https://cran.r-project.org/package=glmnetSE> or others may provide greater functionality when performing a nested CV. 
    Use of the 'glmnetr' has many similarities to the 'glmnet' package and it is recommended that the user of 'glmnetr' also become familiar with the 'glmnet' package <https://cran.r-project.org/package=glmnet>, with the "An Introduction to 'glmnet'" and "The Relaxed Lasso" being especially useful in this regard.
Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or
    'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value)
    for selected coefficients with no shrinkage applied for them. Model
    performance can be evaluated on test data and an automated alpha
    selection is implemented for Elastic Net. Parallelized computation is
    used to speed up the process. The methods are described in Friedman et
    al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011)
    <doi:10.18637/jss.v039.i05>.
Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.
Contains all the data and functions used in Generalized
    Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create
    all models, tables, and plots are included for each data set.
A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.
Provides a function for fitting a penalized constrained  continuation ratio model using the glmpath algorithm and methods for  extracting coefficient estimates, predicted class, class probabilities, and plots as described by Archer and Williams (2012) <doi:10.1002/sim.4484>.
Implements a generalized version of principal components analysis
    (GLM-PCA) for dimension reduction of non-normally distributed data such as
    counts or binary matrices.
    Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.
    Townes FW (2019) <arXiv:1907.02647>.
In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.
Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
Creates ideal data for all distributions in the generalized
    linear model framework.
Extremely efficient procedures for fitting regularization path with l0, l1, and truncated lasso penalty for linear regression and logistic regression models. This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized.
Set of tools for the statistical analysis of data using: (1) normal linear models; (2) generalized linear models; (3) negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion; (4) beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) Zero-inflated and zero-altered regression models to deal with zero-excess in count data; (6) generalized nonlinear models; (7) generalized estimating equations for cluster correlated data.
We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The relevant paper
    is available on arXiv: <arXiv:2105.14328>.
A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user's criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://github.com/adimajo/manuscrit_these/releases/>.
Automated model selection and model-averaging. Provides a
        wrapper for glm and other functions, automatically generating
        all possible models (under constraints set by the user) with
        the specified response and explanatory variables, and finding
        the best models in terms of some Information Criterion (AIC,
        AICc or BIC). Can handle very large numbers of candidate
        models. Features a Genetic Algorithm to find the best models
        when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
             including parametric links and heteroscedastic latent variables.
Provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by 'VGAM' package), beta regression model (estimated by 'betareg' package) and negative binomial regression model (estimated by 'MASS' package). Since most of functions implemented in 'glmxdiag' already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.
Second-order summary statistics K- and pair-correlation functions describe
    interactions in point pattern data. This package provides computations to estimate those
    statistics on inhomogeneous point processes, using the methods of
    in T Shaw, J Møller, R Waagepetersen, 2020 <doi:10.48550/arXiv.2004.00527>.
It provides more configurations on the option values such as validation
    and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown" or "free") objects in R expressions
    by code inspection using various strategies (ordered, liberal, or
    conservative).  The objective of this package is to make it as simple as
    possible to identify global objects for the purpose of exporting them in
    parallel, distributed compute environments.
Google offers public access to global search volumes from its
    search engine through the Google Trends portal. The package downloads
    these search volumes provided by Google Trends and uses them to
    measure and analyze the distribution of search scores across countries
    or within countries. The package allows researchers and analysts to
    use these search scores to investigate global trends based on patterns
    within these scores. This offers insights such as degree of
    internationalization of firms and organizations or dissemination of
    political, social, or technological trends across the globe or within
    single countries.  An outline of the package's methodological
    foundations and potential applications is available as a working
    paper: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3969013>.
Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.
Tools for the generalized logistic distribution (Type I,
             also known as skew-logistic distribution), encompassing
	     basic distribution functions (p, q, d, r, score), maximum
	     likelihood estimation, and structural change methods.
A user-friendly 'shiny' application for Bayesian machine
    learning analysis of marine species distributions. GLOSSA (Global
    Species Spatiotemporal Analysis) uses Bayesian Additive Regression
    Trees (BART; Chipman, George, and McCulloch (2010)
    <doi:10.1214/09-AOAS285>) to model species distributions with
    intuitive workflows for data upload, processing, model fitting, and
    result visualization. It supports presence-absence and presence-only
    data (with pseudo-absence generation), spatial thinning,
    cross-validation, and scenario-based projections. GLOSSA is designed
    to facilitate ecological research by providing easy-to-use tools for
    analyzing and visualizing marine species distributions across
    different spatial and temporal scales.
Add glossaries to markdown and quarto documents by tagging individual words. Definitions can be provided inline or in a separate file.
Read examples with interlinear glosses from files
    or from text and print them in a way compatible with both
    Latex and HTML outputs.
Provides a framework for creating plots with glowing points.
R Interface to C API of GLPK, depends on GLPK Version >= 4.42.
Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An implementation of interpreted string literals, inspired by
    Python's Literal String Interpolation
    <https://www.python.org/dev/peps/pep-0498/> and Docstrings
    <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted
    String Literals
    <https://docs.julialang.org/en/v1.3/manual/strings/#Triple-Quoted-String-Literals-1>.
Ease the transition between R vectors and markdown text. With
    'gluedown' and 'rmarkdown', users can create traditional vectors in R,
    glue those strings together with the markdown syntax, and print those
    formatted vectors directly to the document. This package primarily
    uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous
    CommonMark specification by John MacFarlane (2019)
    <https://spec.commonmark.org/>.
Provides residual global fit indices for generalized latent variable models.
Provides a simple and intuitive high-level language for music
    representation. Generates and embeds music scores and audio files in
    'RStudio', 'R Markdown' documents, and R 'Jupyter Notebooks'.
    Internally, uses 'MusicXML' <https://github.com/w3c/musicxml> to represent
    music, and 'MuseScore' <https://musescore.org/> to convert 'MusicXML'.
Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.
Performs genomic mediation
    analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale
    mediation analysis and adaptively selects potential confounding variables to
    adjust for each mediation test from a pool of candidate confounders. The package
    is tailored for but not limited to genomic mediation analysis (e.g., cis-gene
    mediating trans-gene regulation pattern where an eQTL, its cis-linking gene
    transcript, and its trans-gene transcript play the roles as treatment, mediator
    and the outcome, respectively), restricting to scenarios with the presence of
    cis-association (i.e., treatment-mediator association) and random eQTL (i.e.,
    treatment).
An interface to the 'Gmail' 'RESTful' API.  Allows access to
    your 'Gmail' messages, threads, drafts and labels.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described
    multiple test procedures.
A lightweight fork of 'gMCP' with functions for graphical
    described multiple test procedures introduced in
    Bretz et al. (2009) <doi:10.1002/sim.3495> and
    Bretz et al. (2011) <doi:10.1002/bimj.201000239>.
    Implements a flexible function using 'ggplot2' to create
    multiplicity graph visualizations.
    Contains instructions of multiplicity graph and graphical testing for
    group sequential design, described in
    Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>,
    with necessary unit testing using 'testthat'.
A template for a geometallurgical database and a fast and easy
    interface for accessing it is provided in this package.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response (Dag et al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).
Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.
    Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms
    that performs gradually complicated polynomial models and selecting the best solution by an external criterion.
    In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and
    selecting an optimal structure of model or network.
    The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.
Fast algorithms for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
Supply implementation to model generalized multivariate functional
 data using Bayesian additive mixed models of R package 'bamlss' via a latent
 Gaussian process (see Umlauf, Klein, Zeileis (2018) 
 <doi:10.1080/10618600.2017.1407325>).
Some methods for the inference and clustering of univariate and 
             multivariate functional data, using a generalization of Mahalanobis
             distance, along with some functions useful for the analysis of functional data.
             For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
Support for geostatistical analysis of multivariate data, 
         in particular data with restrictions, e.g. positive amounts, 
         compositions, distributional data, microstructural data, etc. 
         It includes descriptive analysis and modelling for such data, both 
         from a two-point Gaussian perspective and multipoint perspective.
         The methods mainly follow Tolosana-Delgado, Mueller and van den
         Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
Gaussian mixture graphical models include Bayesian networks and
    dynamic Bayesian networks (their temporal extension) whose local probability
    distributions are described by Gaussian mixture models. They are powerful
    tools for graphically and quantitatively representing nonlinear dependencies
    between continuous variables. This package provides a complete framework to
    create, manipulate, learn the structure and the parameters, and perform
    inference in these models. Most of the algorithms are described in the PhD
    thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.
Tools for making the descriptive "Table 1" used in medical
    articles, a transition plot for showing changes between categories
    (also known as a Sankey diagram), flow charts by extending the grid package,
    a method for variable selection based on the SVD, Bézier lines with arrows
    complementing the ones in the 'grid' package, and more.
High performance trainers for parameterizing and clustering weighted data. The Gaussian mixture (GM) module includes the conventional EM (expectation maximization) trainer, the component-wise EM trainer, the minimum-message-length EM trainer by Figueiredo and Jain (2002) <doi:10.1109/34.990138>. These trainers accept additional constraints on mixture weights, covariance eigen ratios and on which mixture components are subject to update. The K-means (KM) module offers clustering with the options of (i) deterministic and stochastic K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii) Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse representation of data input. The package improved the typical implementations of GM and KM algorithms in various aspects. It is carefully crafted in multithreaded C++ for modeling large data for industry use.
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (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>).
Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Likelihood-based boosting approaches for generalized mixed models are provided.
The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.
An implementation of maximum simulated likelihood method for the
    estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>.
    Specifically, it allows estimating models with continuous heterogeneity
    such as the mixed multinomial logit and the generalized multinomial logit.
    It also allows estimating models with discrete heterogeneity such as the
    latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Make 2D and 3D plots of linear programming (LP), 
    integer linear programming (ILP), or mixed integer linear programming (MILP) models 
    with up to three objectives. Plots of both the solution and criterion space are possible.
    For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models 
    (see vignettes for an overview). The package also contains an function for checking if a point
    is inside the convex hull.
Datasets analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".
Efficiently manage and process data from 'oTree' experiments.
    Import 'oTree' data and clean them by using functions that handle
    messy data, dropouts, and other problematic cases.  Create IDs,
    calculate the time, transfer variables between app data frames, and
    delete sensitive information.  Review your experimental data prior to
    running the experiment and automatically generate a detailed summary
    of the variables used in your 'oTree' code.  Information on 'oTree' is
    found in Chen, D. L., Schonger, M., & Wickens, C. (2016)
    <doi:10.1016/j.jbef.2015.12.001>.
Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).
Symbolic calculation (addition or multiplication) and evaluation of multivariate polynomials with rational coefficients.
Functions for graph matching via nodes' degree profiles are provided in this package. The models we can handle include Erdos-Renyi random graphs and stochastic block models(SBM). More details are in the reference paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <arXiv:2006.03284>.
A system for fitting Gompertz Curve in a Time Series Data.
Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a precondition matrix B: BAx=Bb (dim(B)=(n,m)). Implemented method is based on GMRES (Saad, Youcef; Schultz, Martin H. (1986). "GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems" <doi:10.1137/0907058>) with callback functions, i.e. no explicit A, B or b are required.
A collection of tools to create, use and maintain modularized model code written in the modeling 
    language 'GAMS' (<https://www.gams.com/>). Out-of-the-box 'GAMS' does not come with support for modularized
    model code. This package provides the tools necessary to convert a standard 'GAMS' model to a modularized one
    by introducing a modularized code structure together with a naming convention which emulates local
    environments. In addition, this package provides tools to monitor the compliance of the model code with
    modular coding guidelines.
Integrates game theory and ecological theory to construct 
    social-ecological models that simulate the management of populations and 
    stakeholder actions. These models build off of a previously developed 
    management strategy evaluation (MSE) framework to simulate all aspects of 
    management: population dynamics, manager observation of populations, manager
    decision making, and stakeholder responses to management decisions. The 
    newly developed generalised management strategy evaluation (GMSE) 
    framework uses genetic algorithms to mimic the decision-making process of 
    managers and stakeholders under conditions of change, uncertainty, and 
    conflict. Simulations can be run using gmse(), gmse_apply(), and
    gmse_gui() functions.
Two-Step Lasso (TS-Lasso) and compound minimum methods to recover 
             the abundance of missing peaks in mass spectrum analysis. TS-Lasso is an 
             imputation method that handles various types of missing peaks simultaneously. This package 
             provides the procedure to generate missing peaks (or data) for simulation study, 
             as well as a tool to estimate and visualize the proportion of missing at random.
Interface between the GMT map-making software and R, enabling the
  user to manipulate geographic data within R and call GMT commands to draw and
  annotate maps in postscript format. The gmt package is about interactive data
  analysis, rapidly visualizing subsets and summaries of geographic data, while
  performing statistical analysis in the R console.
The multiple contrast tests for univariate were proposed by Munko, Ditzhaus, Pauly, Smaga, and Zhang (2023) <doi:10.48550/arXiv.2306.15259>. Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/arXiv.2406.01242>. 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.
Unconstrained and constrained maximum likelihood estimation of structural and reduced form 
    Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t
    mixture vector autoregressive models, quantile residual tests, graphical diagnostics,
    simulations, forecasting, and estimation of generalized impulse response function and generalized 
    forecast error variance decomposition.
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>,
    Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>,
    Savi Virolainen (2022) <doi:10.48550/arXiv.2109.13648>.
Fit joint models of survival and multivariate longitudinal data. The longitudinal
    data is specified by generalised linear mixed models. The joint models are fit via maximum
    likelihood using an approximate expectation maximisation algorithm. 
    Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.
Implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>.
             The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals. 
             The 'gmwmx' package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.
Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots, for the underlying manuscript see Fischer, Oja (2015) <doi:10.18637/jss.v065.i09>.
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables.  Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>.  Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.
Compute standard and generalized Nash Equilibria of non-cooperative games. 
 Optimization methods available are nonsmooth reformulation, fixed-point formulation, 
 minimization problem and constrained-equation reformulation. 
 See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.
Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures.
A variety of functions to fit linear and nonlinear
    regression with a large selection of distributions.
Functions to specify and fit generalized nonlinear models,
    including models with multiplicative interaction terms such as the
    UNIDIFF model from sociology and the AMMI model from crop science, and
    many others.  Over-parameterized representations of models are used
    throughout; functions are provided for inference on estimable
    parameter combinations, as well as standard methods for diagnostics
    etc.
Tools to set up, train, store, load, investigate and analyze
             generative neural networks. In particular, functionality for
	     generative moment matching networks is provided.
Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>.
    It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.
The goal of 'gnonadd' is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.
Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.
Estimation of gross output production functions and productivity in the presence of numerous fixed (nonflexible) and a single flexible input using the nonparametric identification strategy specified in Gandhi, Navarro, and Rivers (2020) <doi:10.1086/707736>. Monte Carlo evidence from the paper demonstrates high performance in estimating production function elasticities.
Provides tools for interacting with the 'geographic name resolution service' ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The 'GNRS' is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The 'GNRS' resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the 'GNRS' attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The 'GNRS' works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the 'GNRS' does not attempt to match state or county.
Homogenize GNSS (Global Navigation Satellite System) time-series. The general model is a segmentation in the mean model including a periodic function and considering monthly variances,  see Quarello (2020) <arXiv:2005.04683>.
Read data files readable by 'gnumeric' into 'R'. Can read
        whole sheet or a range, from several file formats, including
        the native format of 'gnumeric'. Reading is done by using
        'ssconvert' (a file converter utility included in the 'gnumeric'
        distribution <http://www.gnumeric.org>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.
This function converts mfpr, numeric, or character strings representing numbers to bigq format without loss of precision.
Solves goal programming problems of the weighted and  
    lexicographic type, as well as combinations of the two, as described 
    by Ignizio (1983) <doi:10.1016/0305-0548(83)90003-5>. Allows for 
    a simple human-readable input describing the problem as a series 
    of equations. Relies on the 'lpSolve' package to solve the underlying 
    linear optimisation problem.
Perform gene set enrichment analyses using the Gene set Ordinal
    Association Test (GOAT) algorithm and visualize your
    results. Koopmans, F. (2024) <doi:10.1038/s42003-024-06454-5>.
Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.
A collection of functions useful in (vegetation) community analyses and ordinations. Includes automatic species selection for ordination diagrams, NMDS stress/scree plots, species response curves, merging of taxa as well as calculation and sorting of synoptic tables.
Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.
A post-estimation method for categorical response models (CRM). 
    Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), 
    vglm() and glm() are currently supported. Available tests include the 
    Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic 
    regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal 
    models. The proportional odds, adjacent-category, and constrained continuation-ratio 
    models are particularly supported at ordinal level. Tests for the proportional 
    odds assumptions in ordinal models are also possible with the Brant and the 
    Likelihood-Ratio tests. Moreover, several summary measures of predictive strength 
    (Pseudo R-squared), and some useful error metrics, including, the brier 
    score, misclassification rate and logloss are also available for the 
    binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) 
    <http://www.statmod.org/workshops_archive_proceedings_2018.html>.
Graphical tools and goodness-of-fit tests for complete and right-censored data:
             1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests,
                which utilize the empirical distribution function for complete data 
                and are extended to handle right-censored data.
             2. Generalized chi-squared-type test, which is based on the squared 
                differences between observed and expected counts using random 
                cells with right-censored data.
             3. Graphical tools, such as probability and cumulative hazard plots, 
                to help guide decisions about the most appropriate parametric model for the data.
Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.
Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test a sample against Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.
Implementation of several goodness-of-fit tests for functional
    data. Currently, mostly related with the functional linear model with
    functional/scalar response and functional/scalar predictor. The package
    allows for the replication of the data applications considered in
    García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga
    (2021) <doi:10.1111/sjos.12486>.
We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.
We implement various tests for the composite hypothesis of
    testing the fit to the family of inverse Gaussian distributions. 
    Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) <doi:10.48550/arXiv.1910.14119>, 
    as well as two tests from Henze and Klar (2002) <doi:10.1023/A:1022442506681>.
    Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) <doi:10.1016/j.jmva.2015.05.013>. 
    For each test a parametric bootstrap procedure is implemented.
Integrates with your 'RMarkdown' documents to automatically publish
  figures to the <https://GoFigr.io> service. Supports both 'knitr' and interactive 
  execution within 'RStudio'.
Tests of goodness-of-fit based on a kernel smoothing of the data.
    References:
       Pavía (2015) <doi:10.18637/jss.v066.c01>.
Consider a goodness-of-fit (GOF) problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze martingale transformation method - which was proposed by Khmaladze (1981) <DOI:10.1137/1126027> - provides asymptotic distribution free test for the GOF problem. This package contains one function: KhmaladzeTrans(). In this version, KhmaladzeTrans() provides test statistic and critical value of GOF test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) <DOI:10.1007/s00180-020-00971-7> and tests for other distributions will be available at the later version.
Provides statistical methods to check if a parametric family of
  conditional density functions fits to some given dataset of covariates and 
  response variables. Different test statistics can be used to determine the 
  goodness-of-fit of the assumed model, see Andrews (1997) 
  <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>,
  Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta 
  (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the 
  corresponding p-values are approximated using a parametric bootstrap method.
An interactive document on  the topic of goodness of fit analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.
Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, 'Weibull', 'Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
	     for continuous univariate distributions, using
	     efficient algorithms.
Provision of classes and methods for estimating generalized
   orthogonal GARCH models. This is an alternative approach to CC-GARCH models
   in the context of multivariate volatility modeling.
Tools for fitting statistical network models to dynamic network data.
   Can be used for fitting both dynamic network actor models ('DyNAMs') and
   relational event models ('REMs').
   Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>,
   Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>,
   Stadtfeld and Block (2017) <doi:10.15195/v4.a14>,
   Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.
Implements the Goldilocks adaptive trial design for a time to event
    outcome using a piecewise exponential model and conjugate Gamma prior
    distributions. The method closely follows the article by Broglio and
    colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore
    the operating characteristics of different trial designs.
A collection of gold price data in various currencies in the form of USD, EUR, JPY, GBP, CAD, CHF, INR, CNY, TRY, SAR, IDR, AED, THB, VND, EGP, KRW, RUB, ZAR, and AUD. This data comes from the World Gold Council. In addition, the data is in the form of daily, weekly, monthly (average and the end of period), quarterly (average and the end of period), and yearly (average and the end of period).
An opinionated framework for building a production-ready
    'Shiny' application. This package contains a series of tools for
    building a robust 'Shiny' application from start to finish.
A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.
Allows the user to animate text within 'rmarkdown' documents and 'shiny' applications. 
    The animations are activated using the 'Animate.css' library. See <https://animate.style/> for more information.
The gamma-Orthogonal Matching Pursuit (gamma-OMP) is a recently suggested modification of the OMP feature selection algorithm for a wide range of response variables. The package offers many alternative regression models, such linear, robust, survival, multivariate etc., including k-fold cross-validation. References: Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. <doi:10.1101/431734>. Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214--1224. <doi:10.1109/TCBB.2020.3029952>.
Estimates hazard ratios and mortality differentials for
    doubly-truncated data without population denominators. This method is
    described in Goldstein et al. (2023) <doi:10.1007/s11113-023-09785-z>.
Contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms
             Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.
Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length and curvature).
Performing goodness-of-fit tests for stochastic block models used to fit network data. Among the three variants discussed in Karwa et al. (2023) <doi:10.1093/jrsssb/qkad084>, goodness-of-fit test has been performed for the Erdos-Renyi (ER) and Beta versions.
Association analysis between categorical
    variables using the Goodman and Kruskal tau measure. This asymmetric association
    measure allows the detection of asymmetric relations between categorical
    variables (e.g., one variable obtained by re-grouping another).
Give advice about good practices when building R packages.
    Advice includes functions and syntax to avoid, package structure, code
    complexity, code formatting, etc.
A comprehensive toolkit for scraping and analyzing book data from <https://www.goodreads.com/>. This package provides functions to search for books, scrape book details and reviews, perform sentiment analysis on reviews, and conduct topic modeling. It's designed for researchers, data analysts, and book enthusiasts who want to gain insights from 'Goodreads' data.
Collect  marketing data from 'Google Ads' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Interact with the Google Analytics 
  APIs <https://developers.google.com/analytics/>, including 
  the Core Reporting API (v3 and v4), Management API, User Activity API
  GA4's Data API and Admin API and Multi-Channel Funnel API.
Create R functions that interact with OAuth2 Google APIs 
    <https://developers.google.com/apis-explorer/> easily,
    with auto-refresh and Shiny compatibility.
Interact with Google Cloud Storage <https://cloud.google.com/storage/> 
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> project.
Interact with the 'Google Cloud Vision' <https://cloud.google.com/vision/>
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> project.
Interact with the 'Google Compute Engine' API in R. Lets you create, 
  start and stop instances in the 'Google Cloud'.  Support for preconfigured instances, 
  with templates for common R needs.
Send error reports to the Google Error Reporting service <https://cloud.google.com/error-reporting/> and view errors and assign error status in the Google Error Reporting user interface.
Images are provided as an array dataset of 2D image thumbnails from Google Image Search <https://www.google.com/search>.
  This array data may be suitable for a training data of machine learning or deep learning as a first trial.
Allows you to retrieve information from the 'Google Knowledge Graph' API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The 'Knowledge Graph Search' API lets you find entities in the 'Google Knowledge Graph'. The API uses standard 'schema.org' types and is compliant with the 'JSON-LD' specification.
Call 'Google Cloud' machine learning APIs for text and speech tasks.
  Call the 'Cloud Translation' API <https://cloud.google.com/translate/> for detection 
  and translation of text, the 'Natural Language' API <https://cloud.google.com/natural-language/> to 
  analyse text for sentiment, entities or syntax, the 'Cloud Speech' API 
  <https://cloud.google.com/speech/> to transcribe sound files to text and 
  the 'Cloud Text-to-Speech' API <https://cloud.google.com/text-to-speech/> to turn text 
  into sound files.
Interact with Google's Cloud Natural Language API
    <https://cloud.google.com/natural-language/> (v1) via R. The API has
    four main features, all of which are available through this
    R package: syntax analysis and part-of-speech tagging, entity
    analysis, sentiment analysis, and language identification.
Encodes simple feature ('sf') objects and coordinates, and decodes polylines 
    using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Provides a collection of functions to set up 'Google Public Data Explorer'
  <https://www.google.com/publicdata/> data visualization tool with your own data,
  building automatically the corresponding DataSet Publishing Language file, or
  DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to
  be published in 'Public Data Explorer'.
Provides an easy to use interface to the 'Google
	Pub/Sub' REST API <https://cloud.google.com/pubsub/docs/reference/rest>.
This is a wrapper for the command line tool 'googler', which can be
    found at the following URL: <https://github.com/jarun/googler>.
Interact with Google Sheets through the Sheets API v4
    <https://developers.google.com/sheets/api>. "API" is an acronym for
    "application programming interface"; the Sheets API allows users to
    interact with Google Sheets programmatically, instead of via a web
    browser. The "v4" refers to the fact that the Sheets API is currently
    at version 4. This package can read and write both the metadata and
    the cell data in a Sheet.
Interact with the 'Google Tag Manager' API <https://developers.google.com/tag-platform/tag-manager/api/v2>, enabling scripted deployments and updates across multiple tags, triggers, variables and containers.
Create geographically referenced traffic data from the Google Maps JavaScript API <https://developers.google.com/maps/documentation/javascript/examples/layer-traffic>.
R interface to Google's chart tools, allowing users
    to create interactive charts based on data frames. Charts
    are displayed locally via the R HTTP help server. A modern
    browser with an Internet connection is required. The data 
    remains local and is not uploaded to Google.
Provides a mechanism to plot a 'Google Map' from 'R' and overlay
    it with shapes and markers. Also provides access to 'Google Maps' APIs,
    including places, directions, roads, distances, geocoding, elevation and
    timezone.
Implementation of multilayered visualizations for enhanced
    graphical representation of functional analysis data. It combines and integrates
    omics data derived from expression and functional annotation enrichment
    analyses. Its plotting functions have been developed with an hierarchical
    structure in mind: starting from a general overview to identify the most
    enriched categories (modified bar plot, bubble plot) to a more detailed one
    displaying different types of relevant information for the molecules in a given
    set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).
Informal implementation of some algorithms from Graph
  Theory and Combinatorial Optimization which arise in the subject
  "Graphs and Network Optimization" from first course of the EUPLA
  (Escuela Universitaria Politecnica de La Almunia) degree of Data
  Engineering in Industrial Processes.
  References used are:
  Cook et al (1998, ISBN:0-471-55894-X),
  Korte, Vygen (2018) <doi:10.1007/978-3-662-56039-6>,
  Hromkovic (2004) <doi:10.1007/978-3-662-05269-3>,
  Hartmann, Weigt (2005, ISBN:978-3-527-40473-5).
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models.
Implements the generalized order-restricted information criterion
    approximation (GORICA), an AIC-like information criterion that can be
    utilized to evaluate informative hypotheses specifying directional
    relationships between model parameters in terms of (in)equality
    constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper,
    2021), <doi:10.31234/osf.io/t3c8g>. The GORICA is applicable not only to
    normal linear models, but also to generalized linear models (GLMs), 
    generalized linear mixed models (GLMMs), structural equation models
    (SEMs), and contingency tables. For contingency tables, restrictions on cell
    probabilities can be non-linear.
Methods to analyse experimental agriculture data, 
    from data synthesis to model selection and visualisation. 
    The package is named after W.S. Gosset aka ‘Student’, a pioneer 
    of modern statistics in small sample experimental design and analysis.
Add a scroll back to top 'Font Awesome' icon 
    <https://fontawesome.com/> in 'rmarkdown' documents and 'shiny'
    apps thanks to 'jQuery GoTop' <https://scottdorman.blog/jquery-gotop/>.
A suite of custom R Markdown formats and templates
    for authoring web pages styled with the GOV.UK Design System.
It can be necessary to limit the rate of execution of a loop or 
 repeated function call e.g. to show or gather data only at particular intervals.
 This package includes two methods for limiting this execution rate; speed governors
 and timers.  A speed governor will insert pauses during execution to meet
 a user-specified loop time.  Timers are alarm clocks which will indicate 
 whether a certain time has passed.  These mechanisms are implemented in 'C'
 to minimize processing overhead.
Access data provided by the United States Government Publishing Office (GPO) 'GovInfo' API (<https://github.com/usgpo/api>).
This package purposes to deal with public survey data of
        Japanese government via their Application Programming Interface
        (http://statdb.nstac.go.jp/)
Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.
Provides tools for applying the Bayesian Gower agreement methodology (presented in the package vignette) to nominal or ordinal data. The framework can accommodate any number of units, any number of coders, and missingness; and can handle both one-way and two-way random study designs. Influential units and/or coders can be identified easily using leave-one-out statistics.
It provides an effective, efficient, and fast way to explore the Gene Ontology (GO). 
             Given a set of genes, the package contains functions to assess the GO and obtain the 
             terms associated with the genes and the levels of the GO terms. The package provides 
             functions for the three different GO ontology. We discussed the methods explicitly in 
             the following article <doi:10.1038/s41598-020-73326-3>.
A collection of tools which extract a model documentation from 'GAMS' code and comments. 
             In order to use the package you need to install 'pandoc' and 'pandoc-citeproc' 
             first (<https://pandoc.org/>).
Functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.
Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.
Gaussian process regression models, a.k.a. Kriging models, are
    applied to global multi-objective optimization of black-box functions.
    Multi-objective Expected Improvement and Step-wise Uncertainty Reduction
    sequential infill criteria are available. A quantification of uncertainty
    on Pareto fronts is provided using conditional simulations.
Derivative Free Gradient Projection Algorithms for Factor Rotation.
    For more details see ?GPArotateDF. Theory for these functions can be found in
    the following publications:
    Jennrich (2004) <doi:10.1007/BF02295647>.
    Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.
Gradient Projection Algorithms for Factor Rotation. 
    For details see ?GPArotation. When using this package, please cite:
    Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.
    "Gradient Projection Algorithms and Software 
    for Arbitrary Rotation Criteria in Factor Analysis".
Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'.
An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.
Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/> and Diallel analysis as per Griffing, B. (1956) <https://www.publish.csiro.au/bi/pdf/BI9560463>.
Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint <doi:10.48550/arXiv.2105.03454>.
Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
This function performs genomic prediction of cross performance using genotype and phenotype data.
  It processes data in several steps including loading necessary software, converting genotype data, processing phenotype data,
  fitting mixed models, and predicting cross performance based on weighted marker effects.
  For more information, see Labroo et al. (2023) <doi:10.1007/s00122-023-04377-z>.
Parameter estimation and prediction of Gaussian Process Classifier models as described in Bachoc et al. (2020) <doi:10.1007/S10898-020-00920-0>. Important functions : gpcm(), predict.gpcm(), update.gpcm().
Provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (<https://www.researchsquare.com/article/rs-2363586/v1>). An interactive web application is also available at [GPEMR Web App](<https://gpem-r.shinyapps.io/GPEM-R/>).
Functionalities for modelling functional data with 
    multidimensional inputs, multivariate functional data, and non-separable 
    and/or non-stationary covariance structure of function-valued processes. 
    In addition, there are functionalities for functional regression models where 
    the mean function depends on scalar and/or functional covariates and 
    the covariance structure depends on functional covariates. 
    The development version of the package can be found on 
    <https://github.com/gpfda/GPFDA-dev>.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic 
    methods. Includes utilities for public key encryption, creating and verifying
    digital signatures, and managing your local keyring. Some functionality 
    depends on the version of GnuPG that is installed on the system. On Windows
    this package can be used together with 'GPG4Win' which provides a GUI for 
    managing keys and entering passphrases.
Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) <doi:10.1007/s10898-018-0688-0>. M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) <arXiv:1902.06565v2>.
Functions for fitting and doing predictions with
    Gaussian process models using Vecchia's (1988) approximation. 
    Package also includes functions for reordering input locations, 
    finding ordered nearest neighbors (with help from 'FNN' package), 
    grouping operations, and conditional simulations.
    Covariance functions for spatial and spatial-temporal data
    on Euclidean domains and spheres are provided. The original 
    approximation is due to Vecchia (1988) 
    <http://www.jstor.org/stable/2345768>, and the reordering and
    grouping methods are from Guinness (2018) 
    <doi:10.1080/00401706.2018.1437476>.
    Model fitting employs a Fisher scoring algorithm described
    in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.