Non-normality could greatly distort the meta-analytic results, according to the simulation in Sun and Cheung (2020) <doi: 10.3758/s13428-019-01334-x>. This package aims to detect non-normality for two independent groups with only limited descriptive statistics, including mean, standard deviation, minimum, and maximum.
Allows users to quickly and easily detect data containing
    Personally Identifiable Information (PII) through convenience functions.
Researchers carried out a series of experiments passing a number 
  of essays to different GPT detection models. Juxtaposing detector predictions 
  for papers written by native and non-native English writers, the authors argue 
  that GPT detectors disproportionately classify real writing from non-native 
  English writers as AI-generated.
Time series analysis of network connectivity. Detects and visualizes change points between networks.
    Methods included in the package are discussed in depth in Baek, C., Gates, K. M., Leinwand, B., Pipiras, V. (2021)
    "Two sample tests for high-dimensional auto-covariances" <doi:10.1016/j.csda.2020.107067>
    and Baek, C., Gampe, M., Leinwand B., Lindquist K., Hopfinger J. and Gates K. (2023)
    “Detecting functional connectivity changes in fMRI data” <doi:10.1007/s11336-023-09908-7>.
Detection of runs of homozygosity and of heterozygosity
    in diploid genomes using two methods: sliding windows (Purcell et al (2007)
    <doi:10.1086/519795>) and consecutive runs (Marras et al (2015)
    <doi:10.1111/age.12259>).
Provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, <https://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a>) using 'ROI' <https://cran.r-project.org/package=ROI> or 'lpSolveAPI' <https://cran.r-project.org/package=lpSolveAPI>. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' <https://cran.r-project.org/package=brglm2> package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, <https://www.jstor.org/stable/2345845>), divergence of those ratios indicates data separation.
Provides tools for detecting XOR-like patterns in variable pairs in 
    two-class data sets. Includes visualizations for pattern exploration and reporting
    capabilities with both text and HTML output formats.
Methods for valuation of life insurance premiums and reserves (including variable-benefit and fractional coverage) based on  "Actuarial Mathematics" by Bowers, H.U. Gerber, J.C. Hickman, D.A. Jones and C.J. Nesbitt (1997, ISBN: 978-0938959465), "Actuarial Mathematics for Life Contingent Risks" by Dickson, David C. M., Hardy, Mary R. and Waters, Howard R  (2009) <doi:10.1017/CBO9780511800146>  and "Life Contingencies" by Jordan, C. W (1952) <doi:10.1017/S002026810005410X>. It also contains functions for  equivalent interest and discount rate calculation, present and future values of annuities, and loan amortization schedule.
Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.
Provides 2D and 3D tour animations as HTML widgets. The user can interact with the widgets using orbit controls, tooltips, brushing, and timeline controls. Linked brushing is supported using 'crosstalk', and widgets can be embedded in Shiny apps or HTML documents.
Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.
A Graphical User Interface (GUI) to import, save, detrend and 
    perform standard tree-ring analyses. The interactive detrending allows the user to check how
    well the detrending curve fits each time-series and change it when needed.
Detrend fluorescence microscopy image series for fluorescence
    fluctuation and correlation spectroscopy ('FCS' and 'FFS') analysis.
    This package contains functionality published in a 2016 paper
    <doi:10.1093/bioinformatics/btx434> but it has been extended since
    then with the Robin Hood algorithm and thus contains unpublished work.
Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
Compare detrital zircon suites by uploading univariate,
      U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based
      user-interface. Outputs publication quality figures using 'ggplot2', and
      tables of statistics currently in use in the detrital zircon geochronology
      community.
A concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code.
Provides a method to detect values poorly explained by a Gaussian linear model. The procedure is based on the maximum of the absolute value of the studentized residuals, which is a parameter-free statistic. This approach generalizes several procedures used to detect abnormal values during longitudinal monitoring of biological markers. For methodological details, see: Berthelot G., Saulière G., Dedecker J. (2025). "DEViaN-LM An R Package for Detecting Abnormal Values in the Gaussian Linear Model". HAL Id: hal-05230549. <https://hal.science/hal-05230549>.
Provides a non-drawing graphic device for benchmarking purpose.
    In order to properly benchmark graphic drawing code it is necessary
    to factor out the device implementation itself so that results are not 
    related to the specific graphics device used during benchmarking. The 
    'devoid' package implements a graphic device that accepts all the required
    calls from R's graphic engine but performs no action. Apart from 
    benchmarking it is unlikely that this device has any practical use.
Data sets and sample analyses from Jay L. Devore (2008),
        "Probability and Statistics for Engineering and the Sciences
        (7th ed)", Thomson.
A set of functions to quantify the relationship between development
    rate and temperature and to build phenological models. The package comprises 
    a set of models and estimated parameters borrowed from a literature review 
    in ectotherms. The methods and literature review are described in Rebaudo 
    et al. (2018) <doi:10.1111/2041-210X.12935>, Rebaudo and Rabhi (2018) 
    <doi:10.1111/eea.12693>, and Regnier et al. (2021) <doi:10.1093/ee/nvab115>. 
    An example can be found in Rebaudo et al. (2017) 
    <doi:10.1007/s13355-017-0480-5>.
Collection of package development tools.
Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.
A software package for using 'DEXi' models. 'DEXi' models are
  hierarchical qualitative multi-criteria decision models developed according to the method
  DEX (Decision EXpert, <https://dex.ijs.si/documentation/DEX_Method/DEX_Method.html>),
  using the program 'DEXi' (<https://kt.ijs.si/MarkoBohanec/dexi.html>) or
  'DEXiWin' (<https://dex.ijs.si/dexisuite/dexiwin.html>).
  A typical workflow with 'DEXiR' consists of:
  (1) reading a '.dxi' file, previously made using the 'DEXi' software (function read_dexi()),
  (2) making a data frame containing input values of one or more decision alternatives,
  (3) evaluating those alternatives (function evaluate()),
  (4) analyzing alternatives (selective_explanation(), plus_minus(), compare_alternatives()),
  (5) drawing charts.
  'DEXiR' is restricted to using models produced externally by the 'DEXi' software and does not
  provide functionality for creating and/or editing 'DEXi' models directly in 'R'.
Provides a versatile toolkit for analyzing and visualizing 'DEXi' (Decision EXpert for education)
    decision trees, facilitating multi-criteria decision analysis directly within R. Users can
    read .dxi files, manipulate decision trees, and evaluate various scenarios. It supports sensitivity
    analysis through Monte Carlo simulations, one-at-a-time approaches, and variance-based
    methods, helping to discern the impact of input variations. Additionally, it includes functionalities
    for generating sampling plans and an array of visualization options for decision trees and
    analysis results. A distinctive feature is the synoptic table plot, aiding in the efficient
    comparison of scenarios. Whether for in-depth decision modeling or sensitivity analysis, this
    package stands as a comprehensive solution. Definition of sensitivity analyses available in
    Carpani, Bergez and Monod (2012) <doi:10.1016/j.envsoft.2011.10.002> and detailed description of the package soon available in 
    Alaphilippe et al. (2025) <doi:10.1016/j.simpa.2024.100729>.
A system for the management, assessment, and psychometric analysis of data from educational and psychological tests.
Classical Test and Item analysis, 
  Item Response analysis and data management for educational and psychological tests.
The 'df2yaml' aims to simplify the process of converting 'dataframe' to 'YAML' <https://yaml.org/>. 
    The 'dataframe' with multiple key columns and one value column will be converted to the multi-level hierarchy.
Containing the Detrended Fluctuation Analysis (DFA), Detrended Cross-Correlation Analysis (DCCA), Detrended Cross-Correlation Coefficient (rhoDCCA), Delta Amplitude Detrended Cross-Correlation Coefficient (DeltarhoDCCA), log amplitude Detrended Fluctuation Analysis (DeltalogDFA), and the Activity Balance Index, it also includes two DFA automatic methods for identifying crossover points and a Deltalog automatic method for identifying reference channels.
Produces SPSS- and SAS-like output for linear discriminant 
  function analysis and canonical correlation analysis. The methods are described in
  Manly & Alberto (2017, ISBN:9781498728966),
  Rencher (2002, ISBN:0-471-41889-7), and
  Tabachnik & Fidell (2019, ISBN:9780134790541).
Computes small-sample degrees of freedom adjustment for
    heteroskedasticity robust standard errors, and for clustered standard errors
    in linear regression. See Imbens and Kolesár (2016)
    <doi:10.1162/REST_a_00552> for a discussion of these adjustments.
A set of functions to perform distribution-free Bayesian analyses. 
             Included are Bayesian analogues to the frequentist Mann-Whitney U 
             test, the Wilcoxon Signed-Ranks test, Kendall's Tau Rank 
             Correlation Coefficient, Goodman and Kruskal's Gamma, McNemar's
             Test, the binomial test, the sign test, the median test, as well as 
             distribution-free methods for testing contrasts among condition and 
             for computing Bayes factors for hypotheses. The package also
             includes procedures to estimate the power of distribution-free
             Bayesian tests based on data simulations using various probability 
             models for the data. The set of functions provide data analysts 
             with a set of Bayesian procedures that avoids requiring parametric 
             assumptions about measurement error and is robust to problem of 
             extreme outlier scores.
Phase I/II adaptive dose-finding design for combination
   studies where toxicity rates are supposed to increase with both agents.
Compares two dataframes with a common key
            and returns the delta records. The package will return
            three dataframes that contain the added, changed,
            and deleted records.
Provides functions to run the CRM and
        TITE-CRM in phase I trials and calibration tools for trial
        planning purposes.
Get Drug information from given differential expression profile. The package search for the bioactive compounds from reference databases such as LINCS containing the genome-wide gene expression signature (GES) from tens of thousands of drug and genetic perturbations (Subramanian et al. (2017) <DOI:10.1016/j.cell.2017.10.049>).
Preferred methods for common analytical tasks that are
    undertaken across the Department, including number formatting, project
    templates and curated reference data.
Implements an algorithm to effortlessly split a column in an R data frame filled with multiple values separated by delimiters. This automates the process of creating separate columns for each unique value, transforming them into binary outcomes.
Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.
A set of functions to perform Raju, van der Linden and Fleer's
    (1995, <doi:10.1177/014662169501900405>) Differential Functioning of Items
    and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item
    Parameter Replication approach (Oshima, Raju, & Nanda, 2006, <doi:10.1111/j.1745-3984.2006.00001.x>)
    for obtaining the associated statistical significance
    tests cut-off points. They may also be used for a priori and post-hoc power
    calculations (Cervantes, 2017, <doi:10.18637/jss.v076.i05>).
The 'dfmirroR' package allows users to input a data frame, simulate some number of observations based on specified columns of that data frame, and then outputs a string that contains the code to re-create the simulation. The goal is to both provide workable test data sets and provide users with the information they need to set up reproducible examples with team members. This package was created out of a need to share examples in cases where data are private and where a full data frame is not needed for testing or coordinating.
Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
  or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. 
  Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and 
  Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>;
  iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) 
  <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, 
  allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses 
  the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports 
  interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases 
  following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, 
  following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
Phase I/II adaptive dose-finding design for single-agent
   Molecularly Targeted Agent (MTA), according to the paper "Phase
   I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
   Determination using Adaptive Randomization", Riviere Marie-Karelle et
   al. (2016) <doi:10.1177/0962280216631763>.
Tools to identify, quantify, analyze, and visualize growth
    suppression events in tree rings that are often produced by insect
    defoliation. Described in Guiterman et al. (2020) <doi:10.1016/j.dendro.2020.125750>.
Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.
Provides R-implementation of Decision forest algorithm, which combines the predictions of
    multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel 
    pattern-recognition method which can be used to analyze: (1) DNA microarray data; 
    (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry  (SELDI-TOF-MS) data; and 
    (3) Structure-Activity Relation (SAR) data.
    In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV.  
    run Dforest() to see more instructions.
    Weida Tong (2003) <doi:10.1021/ci020058s>.
A tool for manipulating data using the generic formula. A single formula allows to easily add, replace and remove variables before running the analysis.
Statistical methods for retrospectively detecting changes
             in location and/or dispersion of univariate and multivariate 
             variables. Data values are assumed to be independent, can be 
             individual (one observation at each instant of time) or subgrouped 
             (more than one observation at each instant of time). 
             Control limits are computed, often using a permutation approach, 
             so that a prescribed false alarm probability is guaranteed  
             without making any parametric assumptions on the stable 
             (in-control) distribution. See G. Capizzi and G. Masarotto (2018) 
             <doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.
Implementation of the Dual Feature Reduction (DFR) approach for the Sparse Group Lasso (SGL) and the Adaptive Sparse Group Lasso (aSGL) (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature reduction approach that applies strong screening to reduce the feature space before optimisation, leading to speed-up improvements for fitting SGL (Simon et al. (2013) <doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et al. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020) <doi:10.1007/s10463-018-0692-7>) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm, with linear and logistic SGL models supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported.
Implementing Function-on-Scalar Regression model in which the response function is dichotomized and observed sparsely. This package provides smooth estimations of functional regression coefficients and principal components for the dichotomized functional response regression (dfrr) model.
Secant acceleration applied to derivative-free Spectral
  Residual Methods for solving large-scale nonlinear systems of
  equations. The main reference follows: E. G. Birgin and
  J. M. Martinez (2022) <doi:10.1137/20M1388024>.
Decomposing value added growth into explanatory factors.
    A cost constrained value added function is defined to specify the 
    production frontier. Industry estimates can also be aggregated using 
    a weighted average approach.
    Details about the methodology and data can be found in Diewert and Fox (2018)
    <doi:10.1093/oxfordhb/9780190226718.013.19>
    and Zeng, Parsons, Diewert and Fox (2018)
    <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.
Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
Feature selection from high dimensional data using a diploid
    genetic algorithm with Incomplete Dominance for genotype to phenotype mapping
    and Random Assortment of chromosomes approach to recombination.
Analyses gene expression data derived from experiments to detect differentially expressed genes by employing the concept of majority voting with five different statistical models. It includes functions for differential expression analysis, significance testing, etc. It simplifies the process of uncovering meaningful patterns and trends within gene expression data, aiding researchers in downstream analysis. Boyer, R.S., Moore, J.S. (1991) <doi:10.1007/978-94-011-3488-0_5>.
Provides a flexible container to manage and annotate Differential Gene 
    Expression (DGE) analysis results (Smythe et. al (2015) <doi:10.1093/nar/gkv007>).  
    The DGEobj has data slots for row (gene), col (samples), assays (matrix n-rows 
    by m-samples dimensions) and metadata (not keyed to row, col, or assays).  
    A set of accessory functions to deposit, query and retrieve subsets of a data 
    workflow has been provided.  Attributes are used to capture metadata such as 
    species and gene model, including reproducibility information such that a 3rd 
    party can access a DGEobj history to see how each data object was created or 
    modified.  Since the DGEobj is customizable and extensible it is not limited 
    to RNA-seq analysis types of workflows -- it can accommodate nearly any data 
    analysis workflow that starts from a matrix of assays (rows) by samples (columns).
Provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE)
    analysis (Law (2015) <doi:10.12688/f1000research.9005.3>).  The tools include both analysis 
    work-flow and utility functions: mapping/unit conversion, count normalization, accounting for 
    unknown covariates, and more.  This is a complement/cohort to the 'DGEobj' package that 
    provides a flexible container to manage and annotate Differential Gene Expression analysis results.
Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.
Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.
Model fitting and evaluation tools for double generalized linear
    models (DGLMs). This class of models uses one generalized linear model (GLM)
    to fit the specified response and a second GLM to fit the deviance of the first
    model.
Model estimation, dispersion testing and diagnosis of hyper-Poisson
    Saez-Castillo, A.J. and Conde-Sanchez, A. (2013) 
    <doi:10.1016/j.csda.2012.12.009> and Conway-Maxwell-Poisson Huang, A. (2017)
    regression models.
A revision to the stats::ks.test() function and the associated
        ks.test.Rd help page. With one minor exception, it does not change the
        existing behavior of ks.test(), and it adds features necessary
        for doing one-sample tests with hypothesized discrete
        distributions. The package also contains cvm.test(), for doing
        one-sample Cramer-von Mises goodness-of-fit tests.
Functionalities for analyzing high-dimensional and longitudinal biomarker data to facilitate precision medicine, using a joint model of Bayesian sparse factor analysis and dependent Gaussian processes. This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) <doi:10.48550/arXiv.2307.02781>.
Interface to the 'python' package 'dgpsi' for Gaussian process, deep Gaussian process, 
             and linked deep Gaussian process emulations of computer models and networks using stochastic imputation (SI). 
             The implementations follow Ming & Guillas (2021) <doi:10.1137/20M1323771> and 
             Ming, Williamson, & Guillas (2023) <doi:10.1080/00401706.2022.2124311> and 
             Ming & Williamson (2023) <doi:10.48550/arXiv.2306.01212>. To get started with the package, 
             see <https://mingdeyu.github.io/dgpsi-R/>.
The 'DHARMa' package uses a simulation-based approach to create
    readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
    models. Currently supported are linear and generalized linear (mixed) models from 'lme4'
    (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive', and 'spaMM'; phylogenetic 
    linear models from 'phylolm' (classes 'phylolm' and 'phyloglm'); generalized additive 
    models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and
    'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations
    from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well.
    The resulting residuals are standardized to values between 0 and 1 and can be interpreted
    as intuitively as residuals from a linear regression. The package also provides a number of
    plot and test functions for typical model misspecification problems, such as
    over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.
Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.
Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.
Implements double hierarchical generalized linear models in which the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), 
  and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, 
  childhood mortality probabilities and rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), 
  Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-five Mortality Rate (U5MR), and adult mortality indicators 
  such as the Age Specific Mortality Rate (ASMR), Age Adjusted Mortality Rate (AAMR), Age Specific Maternal Mortality Rate (ASMMR),
  Age Adjusted Maternal Mortality Rate (AAMMR), Age Specific Pregnancy Related Mortality Rate (ASPRMR), 
  Age Adjusted Pregnancy Related Mortality Rate (AAPRMR), Maternal Mortality Ratio (MMR) and Pregnancy Related Mortality Ratio (PRMR).  
  In addition to the indicators, the 'DHS.rates' package estimates sampling errors indicators such as Standard Error (SE), 
  Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). 
  The package is developed according to the DHS methodology of calculating the fertility indicators and 
  the childhood mortality rates outlined in the 
  "Guide to DHS Statistics" (Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. 2018, <https://dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm>) 
  and the DHS methodology of estimating the sampling errors indicators outlined in 
  the "DHS Sampling and Household Listing Manual" (ICF International 2012, <https://dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf>).
We provide 70 data sets of females of reproductive age from 19 Asian countries, ranging in age from 15 to 49. The data sets are extracted from demographic and health surveys that were conducted over an extended period of time. Moreover, the functions also provide Whipple’s index as well as age reporting quality such as very rough, rough, approximate, accurate, and highly accurate.
Contains an implementation of the
	     d-variable Hilbert Schmidt independence criterion
	     and several hypothesis tests based on it, as described
	     in Pfister et al. (2017) <doi:10.1111/rssb.12235>.
Mapping, spatial analysis, and statistical modeling of microdata from sources such as the Demographic and Health Surveys <https://www.dhsprogram.com/> and Integrated Public Use Microdata Series <https://www.ipums.org/>. It can also be extended to other datasets. The package supports spatial correlation index construction and visualization, along with empirical Bayes approximation of regression coefficients in a multistage setup.  The main functionality is repeated regression — for example, if we have to run regression for n groups, the group ID should be vertically composed into the variable for the parameter `location_var`. It can perform various kinds of regression, such as Generalized Regression Models, logit, probit, and more. Additionally, it can incorporate interaction effects. The key benefit of the package is its ability to store the regression results performed repeatedly on a dataset by the group ID, along with respective p-values and map those estimates.
A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. 
             The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood 
             and represents a proxy measure of aging and also can be served as
             a sensitive predictor of survival. For more information, see 
             (i)"Accumulation of Deficits as a Proxy Measure of Aging" 
             by Arnold B. Mitnitski et al. (2001), 
             The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>;
             (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" 
             by Arnold B Mitnitski et al. (2001), 
             BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.
Implements a system of linear equations to recover
    unreported diagnostic test accuracy cell counts from commonly
    reported measures such as sensitivity, specificity, predictive
    values, prevalence, and sample size. The package is intended for
    applied researchers who require complete 2x2 table counts for
    downstream analyses.
Fast functions for effective sample size, weighted multivariate 
    mean, variance, and quantile computation, and weight diagnostic plot for 
    generic importance sampling type or other probability weighted samples.
Diagnostics for linear L1 regression (also known as LAD - Least Absolute Deviations), including: estimation, confidence intervals, tests of hypotheses, measures of leverage, methods of diagnostics for L1 regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005)  <doi:10.1080/0094965042000223680>, Elian et al. (2000) <doi:10.1080/03610920008832518> and Dodge (1997) <doi:10.1006/jmva.1997.1666>. This package builds on the 'quantreg' package, which is a well-established package for tuning quantile regression models. There are also tests to verify if the errors have a Laplace distribution based on the work of Puig and Stephens (2000) <doi:10.2307/1270952>.
Provides methods by Steinhauser et al. (2016) <DOI:10.1186/s12874-016-0196-1> for meta-analysis of diagnostic accuracy studies with several cutpoints.
Several tools for handling block-matrix diagonals and similar
    constructs are implemented. Block-diagonal matrices can be extracted or removed
    using two small functions implemented here. In addition, non-square matrices
    are supported. Block diagonal matrices occur when two dimensions of a data set
    are combined along one edge of a matrix. For example, trade-flow data in the
    'decompr' and 'gvc' packages have each country-industry combination occur along
    both edges of the matrix.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, 
     visualising webs, electrical networks, etc.
     Support for the book "A practical guide to ecological modelling -
     using R as a simulation platform"
     by Karline Soetaert and Peter M.J. Herman (2009), Springer.
     and the book "Solving Differential Equations in R"
     by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer.
     Includes demo(flowchart), demo(plotmat), demo(plotweb).
Build graph/network structures using functions for stepwise
    addition and deletion of nodes and edges. Work with data available in
    tables for bulk addition of nodes, edges, and associated metadata. Use
    graph selections and traversals to apply changes to specific nodes or
    edges. A wide selection of graph algorithms allow for the analysis of
    graphs. Visualize the graphs and take advantage of any aesthetic
    properties assigned to nodes and edges.
Allows for export of DiagrammeR Graphviz objects to SVG.
Performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different Methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two Models (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach.
Parse, format, and validate international phone
    numbers using Google's 'libphonenumber' java library,
    <https://github.com/google/libphonenumber>.
Collects 'libphonenumber' jars required for the
    'dialr' package.
Many models contain tuning parameters (i.e. parameters that
    cannot be directly estimated from the data). These tools can be used
    to define objects for creating, simulating, or validating values for
    such parameters.
Visualize one-factor data frame. 
  Beads plot consists of diamonds of each factor of each data series. 
  A diamond indicates average and range. 
  Look over a data frame with many numeric columns and a factor column.
Provides a set of functions for securely storing 'API' tokens and interacting with the <https://diariodeobras.net> system. Includes convenient wrappers around the 'httr2' package to perform authenticated requests, retrieve project details, tasks, reports, and more.
Calculate multiple biotic indices using diatoms from environmental samples. Diatom species are recognized by their species' name using a heuristic search, and their ecological data is retrieved from multiple sources.
    It includes number/shape of chloroplasts diversity indices, size classes, ecological guilds, and multiple biotic indices.
    It outputs both a dataframe with all the results and plots of all the obtained data in a defined output folder.
    - Sample data was taken from Nicolosi Gelis, Cochero & Gómez (2020, <doi:10.1016/j.ecolind.2019.105951>).
    - The package uses the 'Diat.Barcode' database to calculate morphological and ecological information by Rimet & Couchez (2012, <doi:10.1051/kmae/2012018>),and the combined classification of guilds and size classes established by B-Béres et al. (2017, <doi:10.1016/j.ecolind.2017.07.007>).
    - Current diatom-based biotic indices include the DES index by Descy (1979)
    - EPID index by Dell'Uomo (1996, ISBN: 3950009002)
    - IDAP index by Prygiel & Coste (1993, <doi:10.1007/BF00028033>)
    - ID-CH index by Hürlimann & Niederhauser (2007)
    - IDP index by Gómez & Licursi (2001, <doi:10.1023/A:1011415209445>)
    - ILM index by Leclercq & Maquet (1987)
    - IPS index by Coste (1982)
    - LOBO index by Lobo, Callegaro, & Bender (2002, ISBN:9788585869908)
    - SLA by Sládeček (1986, <doi:10.1002/aheh.19860140519>)
    - TDI index by Kelly, & Whitton (1995, <doi:10.1007/BF00003802>)
    - SPEAR(herbicide) index by Wood, Mitrovic, Lim, Warne, Dunlop, & Kefford (2019, <doi:10.1016/j.ecolind.2018.12.035>)
    - PBIDW index by Castro-Roa & Pinilla-Agudelo (2014)
    - DISP index by Stenger-Kovács et al. (2018, <doi:10.1016/j.ecolind.2018.07.026>)
    - EDI index by Chamorro et al. (2024, <doi:10.1021/acsestwater.4c00126>)
    - DDI index by Álvarez-Blanco et al. (2013, <doi: 10.1007/s10661-012-2607-z>)
    - PDISE index by Kahlert et al. (2023, <doi:10.1007/s10661-023-11378-4>).
Provides a 'dibble' that implements data cubes (derived from 
    'dimensional tibble'), and allows broadcasting by dimensional names.
This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.
Space-Filling Designs and space-filling criteria (distance-based and uniformity-based), with emphasis to computer experiments; <doi:10.18637/jss.v065.i11>.
Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
A Natural Language Processing Model trained to detect directness and intensity during conflict. See <https://www.mikeyeomans.info>.
Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" <doi:10.18637/jss.v051.i01> and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") <doi:10.1016/j.csda.2013.03.018>, parallel infill, and problems with constraints.
Easy visualization for datasets with more than two categorical variables and additional continuous variables. The package is particularly useful for exploring complex categorical data in the context of pathway analysis across multiple conditions. This package is now in maintenance-only mode and kept for legacy compatibility; for new projects and active development, please use the successor package 'ggdiceplot' (see <https://github.com/maflot/ggdiceplot> and <https://dice-and-domino-plot.readthedocs.io/en/latest/>).
Performs cluster analysis using an ensemble clustering
    framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>.
    Results from a diverse set of algorithms are pooled together using
    methods such as majority voting, K-Modes, LinkCluE, and CSPA. There
    are options to compare cluster assignments across algorithms using
    internal and external indices, visualizations such as heatmaps, and
    significance testing for the existence of clusters.
View 2D/3D sections, contour plots, mesh of excursion sets for computer experiments designs, surrogates or test functions.
Collapse red-green or green-blue distinctions to simulate
        the effects of different types of color-blindness.
This function provides an interface between 'Matlab' and 'R' in facilitating fast processing for reading and saving DICOM images.
Given  an initial set of points, this package minimizes the number of elements to discard from this set such that there exists at least one monotonic and convex mapping within pre-specified upper and lower bounds.
A key-value dictionary data structure based on R6 class which is designed to be similar usages with other languages dictionary (e.g. 'Python') with reference semantics and extendabilities by R6.
Efficient object-oriented R6 dictionary capable of holding objects of any class, including R6. Typed and untyped dictionaries are provided as well as the 'usual' dictionary methods that are available in other OOP languages, for example listing keys, items, values, and methods to get/set these.
An R interface to the 'Free Dictionary API' 
    <https://dictionaryapi.dev/>, 
    <https://github.com/meetDeveloper/freeDictionaryAPI>. Retrieve
    dictionary definitions for English words, as well 
    as additional information including phonetics, part of speech, origins, 
    audio pronunciation, example usage, synonyms and antonyms, returned in 
    'tidy' format for ease of use.
The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group.  Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time.  This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>.  The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time.  These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these.  There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.
Estimates Two-way Fixed Effects difference-in-differences/event-study models using the approach proposed by Gardner (2021) <doi:10.48550/arXiv.2207.05943>. To avoid the problems caused by OLS estimation of the Two-way Fixed Effects model, this function first estimates the fixed effects and covariates using untreated observations and then in a second stage, estimates the treatment effects.
A basic, clear implementation of tree-based gradient boosting
    designed to illustrate the core operation of boosting models. Tuning
    parameters (such as stochastic subsampling, modified learning rate, or
    regularization) are not implemented. The only adjustable parameter is the
    number of training rounds. If you are looking for a high performance boosting
    implementation with tuning parameters, consider the 'xgboost' package.
Directed Dependence Coefficient (didec) is a measure of directed dependence. Multivariate Feature Ordering by Conditional Independence (MFOCI) is a variable selection algorithm based on didec. Hierarchical Variable Clustering (VarClustPartition) is a variable clustering method based on didec. For more information, see the paper by 
    Ansari and Fuchs (2024, <doi:10.48550/arXiv.2212.01621>), and the paper by 
    Fuchs and Wang (2024, <doi:10.1016/j.ijar.2024.109185>).
Provides a big-data-friendly and memory-efficient difference-in-differences estimator for staggered (and non-staggered) treatment contexts.
Estimation of Difference-in-Differences (DiD) estimators from de Chaisemartin et al. (2025) <doi:10.48550/arXiv.2405.04465> in Heterogeneous Adoption Designs with Quasi Untreated Groups.
Estimates Two-way Fixed Effects difference-in-differences/event-study models using the imputation-based approach proposed by Borusyak, Jaravel, and Spiess (2021).
Estimators of Difference-in-Differences based on de Chaisemartin and D'Haultfoeuille.
Estimation of heterogeneity-robust difference-in-differences estimators, with a binary, discrete, or continuous treatment, in designs where past treatments may affect the current outcome.
This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
             customer transaction data. The function consumes a data frame with Transaction Number,
             Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
             The function returns a data frame with RFM data for the sales information.
Allows to visualize high-density electroencephalography (HD-EEG) data through interactive plots and animations, enabling exploratory and communicative analysis of temporal-spatial brain signals. Funder: Masaryk University (Grant No. MUNI/A/1457/2023).
Implements a likelihood-based method for genome polarization, identifying
    which alleles of SNV markers belong to either side of a barrier to gene flow. The
    approach co-estimates individual assignment, barrier strength, and divergence between
    sides, with direct application to studies of hybridization. Includes VCF-to-diem
    conversion and input checks, support for mixed ploidy and parallelization, and tools
    for visualization and diagnostic outputs. Based on diagnostic index expectation
    maximization as described in Baird et al. (2023) <doi:10.1111/2041-210X.14010>.
Easily perform a Monte Carlo simulation to evaluate the cost and carbon, ecological, and water footprints of a set of ideal diets. Pre-processing tools are also available to quickly treat the data, along with basic statistical features to analyze the simulation results — including the ability to establish confidence intervals for selected parameters, such as nutrients and price/emissions. A 'standard version' of the datasets employed is included as well, allowing users easy access to customization. This package brings to R the 'Python' software initially developed by Vandevijvere, Young, Mackay, Swinburn and Gahegan (2018) <doi:10.1186/s12966-018-0648-6>.
Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Borstein (2020) <doi:10.1007/s10750-020-04417-5>.
A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.
Performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) <doi:10.1111/bmsp.12060>.
Estimation of DIFferential COexpressed NETworks using diverse and user metrics.
			 This package is basically used for three functions related to the estimation
			 of differential coexpression. 
			 First, to estimate differential coexpression where
			 the coexpression is estimated, by default, by Spearman correlation. For this,
			 a metric to compare two correlation distributions is needed. The package includes
			 6 metrics. Some of them needs a threshold. A new metric can also be specified as
			 a user function with specific parameters (see difconet.run). The significance is
			 be estimated by permutations.
			 Second, to generate datasets with controlled differential correlation data. This 
			 is done by either adding noise, or adding specific correlation structure.
			 Third, to show the results of differential correlation analyses. Please see
			 <http://bioinformatica.mty.itesm.mx/difconet> for further information.
Computations of Fisher's z-tests concerning different kinds of correlation differences. The 'diffpwr' family entails approaches to estimating statistical power via Monte Carlo simulations. Important to note, the Pearson correlation coefficient is sensitive to linear association, but also to a host of statistical issues such as univariate and bivariate outliers, range restrictions, and heteroscedasticity (e.g., Duncan & Layard, 1973 <doi:10.1093/BIOMET/60.3.551>; Wilcox, 2013 <doi:10.1016/C2010-0-67044-1>). Thus, every power analysis requires that specific statistical prerequisites are fulfilled and can be invalid if the prerequisites do not hold. To this end, the 'bootcor' family provides bootstrapping confidence intervals for the incorporated correlation difference tests.
A method for identifying pattern changes between 2
    experimental conditions in correlation networks (e.g., gene
    co-expression networks), which builds on a commonly used association
    measure, such as Pearson's correlation coefficient. This package
    includes functions to calculate correlation matrices for
    high-dimensional dataset and to test differential correlation, which
    means the changes in the correlation relationship among variables
    (e.g., genes and metabolites) between 2 experimental conditions.
Functions for comparing two data.frames against 
    each other. The core functionality is to provide a detailed breakdown of any differences 
    between two data.frames as well as providing utility functions to help narrow down the 
    source of problems and differences.
Shows you which rows have changed between two data frames with the same column structure. Useful for diffing slowly mutating data.
This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) <doi: 10.1371/journal.pone.0198139>. Given a list of gene symbols, 'diffEnrich'  will
  perform differential enrichment analysis using the Kyoto Encyclopedia of Genes
  and Genomes (KEGG) REST API. This package provides a number of functions that are 
  intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will
  download and clean species specific ENTREZ gene IDs and map them to their respective
  KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly
  enriched pathways from two gene sets, and finally, the user will identify 
  pathways that are differentially enriched between the two gene sets. In addition to 
  the analysis pipeline, this package also provides a plotting function.
An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> from the R programming language.
  It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE),
  delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality,
  including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods.
  Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs).
  'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information,
  see Rackauckas and Nie (2017) <doi:10.5334/jors.151>.
Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.
Computes the ATM (Attractor Transition Matrix) structure and
    the tree-like structure describing the cell differentiation process
    (based on the Threshold Ergodic Set concept introduced by Serra and
    Villani), starting from the Boolean networks with synchronous updating
    scheme of the 'BoolNet' R package. TESs (Threshold Ergodic Sets) are
    the mathematical abstractions that represent the different cell types
    arising during ontogenesis. TESs and the powerful model of biological
    differentiation based on Boolean networks to which it belongs have
    been firstly described in "A Dynamical Model of Genetic Networks for
    Cell Differentiation" Villani M, Barbieri A, Serra R (2011) A
    Dynamical Model of Genetic Networks for Cell Differentiation. PLOS ONE
    6(3): e17703.
Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit using marginal maximum 
	likelihood. Parameter restrictions (fixed value and equality 
	constraints) are possible. In addition, factor scores (person drift 
	rate and person boundary separation) can be estimated. Model fit 
	assessment tools are also available. The traditional diffusion model 
	can be estimated as well.
A wrapper for Google's 'diff-match-patch' library. It provides basic tools
    for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.
This R function implements the nonstationary Kriging model 
    proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> 
    for analyzing multi-fidelity computer outputs. This function computes 
    the maximum likelihood estimates for the model parameters as well 
    as the predictive means and variances of the exact solution.
Designed for network analysis, leveraging the personalized PageRank algorithm to calculate node scores in a given graph. This innovative approach allows users to uncover the importance of nodes based on a customized perspective, making it particularly useful in fields like bioinformatics, social network analysis, and more.
Generate a colorized diff of two R objects for an intuitive
    visualization of their differences.
An implementation of major general-purpose mechanisms for privatizing
    statistics, models, and machine learners, within the framework of differential
    privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms
    include the Laplace mechanism for releasing numeric aggregates, and the 
    exponential mechanism for releasing set elements. A sensitivity sampler 
    (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target 
    non-private function sensitivity; combined with the generic mechanisms, it 
    permits turn-key privatization of arbitrary programs.
An R interface to the 'codediff' JavaScript library (a copy of which is included in the package,
  see <https://github.com/danvk/codediff.js> for information).
  Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.
Various diffusion models to forecast new product growth. Currently
    the package contains Bass, Gompertz, Gamma/Shifted Gompertz and Weibull curves. See
    Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.
Implements diffusion map method of data
    parametrization, including creation and visualization of
    diffusion map, clustering with diffusion K-means and
	  regression using adaptive regression model.
	  Richards (2009) <doi:10.1088/0004-637X/691/1/32>.
Find, visualize and explore patterns of differential taxa in
    vegetation data (namely in a phytosociological table), using the
    Differential Value (DiffVal). Patterns are searched through
    mathematical optimization algorithms. Ultimately, Total Differential
    Value (TDV) optimization aims at obtaining classifications of
    vegetation data based on differential taxa, as in the traditional
    geobotanical approach (Monteiro-Henriques 2025,
    <doi:10.3897/VCS.140466>). The Gurobi optimizer, as well
    as the R package 'gurobi', can be installed from
    <https://www.gurobi.com/products/gurobi-optimizer/>.  The useful
    vignette Gurobi Installation Guide, from package 'prioritizr', can be
    found here:
    <https://prioritizr.net/articles/gurobi_installation_guide.html>.
A HTML widget that shows differences between files (text,
    images, and data frames).
Statistical hypothesis testing of pattern heterogeneity
    via differences in underlying distributions across multiple
    contingency tables. Five tests are included: the comparative
    chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086>
    (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song
    test (Sharma et al. 2021) <doi:10.1093/bioinformatics/btab240>, 
    the heterogeneity test, the marginal-change test (Sharma et al.
    2020) <doi:10.1145/3388440.3412485>, and the strength test
    (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. Under the
    null hypothesis that row and column variables are statistically
    independent and joint distributions are equal, their test
    statistics all follow an asymptotically chi-squared distribution.
    A comprehensive type analysis categorizes the relation among the
    contingency tables into type null, 0, 1, and 2 (Sharma et al.
    2020) <doi:10.1145/3388440.3412485>. They can identify
    heterogeneous patterns that differ in either the first order
    (marginal) or the second order (differential departure from
    independence). Second-order differences reveal more
    fundamental changes than first-order differences across
    heterogeneous patterns.
Performs DIFlasso as proposed by Tutz and Schauberger (2015) <doi:10.1007/s11336-013-9377-6>, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.
Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.
Detection of differential item functioning (DIF) among dichotomously scored items and differential distractor functioning (DDF) among unscored items with non-linear regression  procedures based on generalized logistic regression models (Hladka & Martinkova, 2020, <doi:10.32614/RJ-2020-014>).
Clustered or multilevel data structures are common in the assessment of differential item functioning (DIF), particularly in the context of large-scale assessment programs. This package allows users to implement extensions of the Mantel-Haenszel DIF detection procedures in the presence of multilevel data based on the work of Begg (1999) <doi:10.1111/j.0006-341X.1999.00302.x>, Begg & Paykin (2001) <doi:10.1080/00949650108812115>, 
	and French & Finch (2013) <doi:10.1177/0013164412472341>.
Methods to detect differential item functioning (DIF) in dichotomous, polytomous, 
    and continuous items, using both classical and modern approaches. These include
    Mantel-Haenszel procedures, logistic regression (including ordinal models), and
    regularization-based methods such as LASSO. Uniform and non-uniform DIF effects
    can be detected, and some methods support multiple focal groups. The package
    also provides tools for anchor purification, rest score matching, effect size
    estimation, and DIF simulation. See Magis, Beland, Tuerlinckx, and De Boeck
    (2010, Behavior Research Methods, 42, 847–862, <doi:10.3758/BRM.42.3.847>) for
    a general overview.
Differential Item Functioning (DIF) Analysis with shiny application interfaces. You can run the functions in this package without any arguments and perform your DIF analysis using user-friendly interfaces.
Item focussed recursive partitioning for simultaneous selection of items and variables that induce Differential Item Functioning (DIF) in dichotomous or polytomous items.
Implementation of a function 'digest()' for the creation of hash
 digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32',
 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128'
 algorithms) permitting easy comparison of R language objects, as well as functions
 such as 'hmac()' to create hash-based message authentication code. Please note that
 this package is not meant to be deployed for cryptographic purposes for which more
 comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.
Provides an extensive and curated collection of datasets related to the digestive system, stomach, intestines, liver, pancreas, and associated diseases. 
    This package includes clinical trials, observational studies, experimental datasets, cohort data, and case series involving gastrointestinal disorders such as gastritis, ulcers, pancreatitis, liver cirrhosis, colon cancer, colorectal conditions, Helicobacter pylori infection, irritable bowel syndrome, intestinal infections, and post-surgical outcomes.
    The datasets support educational, clinical, and research applications in gastroenterology, public health, epidemiology, and biomedical sciences. 
    Designed for researchers, clinicians, data scientists, students, and educators interested in digestive diseases, the package facilitates reproducible analysis, modeling, and hypothesis testing using real-world and historical data.
Analyze and visualize the rhythmic behavior of animals using the 
  degree of functional coupling (See Scheibe (1999) <doi:10.1076/brhm.30.2.216.1420>),
  compute and visualize harmonic power, actograms, average activity and diurnality 
  index.
The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
Provides statistical tests and support functions for detecting irregular digit patterns in numerical data. The package includes tools for extracting digits at various locations in a number, tests for repeated values, and (Bayesian) tests of digit distributions.
Simulation tool to estimate the rate of success that surveys possessing user-specific characteristics have in identifying archaeological sites (or any groups of clouds of objects), given specific parameters of survey area, survey methods, and site properties. The survey approach used is largely based on the work of Kintigh (1988) <doi:10.2307/281113>.
Use leaf physiognomic methods to reconstruct mean annual
    temperature (MAT), mean annual precipitation (MAP), and leaf dry mass
    per area (Ma), along with other useful quantitative leaf traits. Methods
    in this package described in Lowe et al. (in review).
A robust identification of differential binding sites method for analyzing ChIP-seq (Chromatin Immunoprecipitation Sequencing) 
    comparing two samples that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) 
    allowing for flexible modeling of data. Methods for Differential Identification using Mixture Ensemble (DIME) is described in: 
    Taslim et al., (2011) <doi:10.1093/bioinformatics/btr165>.
Simple Principal Components Analysis (PCA) and (Multiple)
    Correspondence Analysis (CA) based on the Singular Value Decomposition
    (SVD). This package provides S4 classes and methods to compute,
    extract, summarize and visualize results of multivariate data
    analysis. It also includes methods for partial bootstrap validation
    described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et
    al. (2006, ISBN: 978-2-10-049616-7).
A set of tools to extract bibliographic content from 'Digital Science Dimensions' using 'DSL' API <https://www.dimensions.ai/dimensions-apis/>.
The 'DImodels' package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) <doi:10.1890/08-1684.1>. Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function).
An add-on package to 'DImodels' for the fitting of biodiversity and ecosystem function relationship study data with multiple ecosystem function responses and/or time points. This package uses the multivariate and repeated measures Diversity-Interactions (DI) methods developed by Kirwan et al. (2009) <doi:10.1890/08-1684.1>, Finn et al. (2013) <doi:10.1111/1365-2664.12041>, and Dooley et al. (2015) <doi:10.1111/ele.12504>.
Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object.
The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition.
A collection of dimensionality reduction
    techniques from R packages and a common
    interface for calling the methods.
Estimate the Deterministic Input, Noisy "And" Gate (DINA)
    cognitive diagnostic model parameters using the Gibbs sampler described
    by Culpepper (2015) <doi:10.3102/1076998615595403>.
In tumor tissue, underlying genomic instability can lead to DNA copy number alterations,
	e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the
	genome, whereas recurrent alterations are observed in the same genomic region across multiple
	independent samples, perhaps because they provide a selective growth advantage.
	This package implements the DiNAMIC procedure for assessing the statistical significance of 
	recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
In tumor tissue, underlying genomic instability can lead to DNA copy number alterations,
	e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the
	genome, whereas recurrent alterations are observed in the same genomic region across multiple
	independent samples, perhaps because they provide a selective growth advantage. Here we use
	cyclic shift permutations to identify recurrent copy number alterations in a single cohort or
	recurrent copy number differences in two cohorts based on a common set of genomic markers.
	Additional functionality is provided to perform downstream analyses, including the creation of
	summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al.
	(2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al.
	(2015) <doi:10.1093/biomet/asv046>. An article describing DiNAMIC.Duo by Walter et al. (2022)
	can be found at <doi: 10.1093/bioinformatics/btac542>.
Decomposition of (income) inequality by population sub groups. 
    For a decomposition on a single variable the mean log deviation can be used
      (see Mookherjee Shorrocks (1982) <DOI:10.2307/2232673>).
    For a decomposition on multiple variables a regression based technique can be 
      used (see Fields (2003) <DOI:10.1016/s0147-9121(03)22001-x>).
    Recentered influence function regression for marginal effects of the (income 
      or wealth) distribution  (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>).
    Some extensions to inequality functions to handle weights and/or missings.
An efficient and convenient set of functions to perform differential network estimation through the use of alternating direction method of multipliers optimization with a variety of loss functions.
S3 classes and methods to create and work
    with year-quarter, year-month and year-isoweek vectors. Basic
    arithmetic operations (such as adding and subtracting) are supported,
    as well as formatting and converting to and from standard R date
    types.
Individual gene expression patterns are encoded into a series of eigenvector patterns ('WGCNA' package). Using the framework of linear model-based differential expression comparisons ('limma' package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see: Greenham K, Sartor RC, Zorich S, Lou P, Mockler TC and McClung CR. eLife. 2020 Sep 30;9(4). <doi:10.7554/eLife.58993>.
Implements the algorithm described in Jun Li and Alicia T. Lamere, 
    "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes"  (Unpublished). 
    Detects not only genes that show different 
    average expressions ("differential expression", DE), but also genes that show different 
    diversities of expressions in different groups ("differentially dispersed", DD). DD genes 
    can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the 
    quasi-likelihood function, and thus has superior robustness against outliers and other noise.
    Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi.
    (2) Added a function for estimating the proportion of outliers in the data.
    (3) Modified the input parameter names for clarity, and modified the output format for the main function.
An implementation by Chen, Li, and Zhang (2022) <doi: 10.1093/bioadv/vbac041> of the Depth Importance in Precision Medicine (DIPM) method 
             in Chen and Zhang (2022) <doi:10.1093/biostatistics/kxaa021> and Chen and 
             Zhang (2020) <doi:10.1007/978-3-030-46161-4_16>. The DIPM method is a classification 
             tree that searches for subgroups with especially poor or strong performance in a given treatment group.
Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization.
    Yu and Rosenbaum (2019) <doi:10.1111/biom.13098>.
Works as an "add-on" to packages like 'shiny', 'future', as well as 
    'rlang', and provides utility functions. Just like dipping sauce adding 
    flavors to potato chips or pita bread, 'dipsaus' for data analysis and 
    visualizations adds handy functions and enhancements to popular packages. 
    The goal is to provide simple solutions that are frequently asked for 
    online, such as how to synchronize 'shiny' inputs without freezing the app,
    or how to get memory size on 'Linux' or 'MacOS' system. The enhancements 
    roughly fall into these four categories: 1. 'shiny' input widgets; 2. 
    high-performance computing using the 'future' package; 3. 
    modify R calls and convert among numbers, strings, and other objects. 4. 
    utility functions to get system information such like CPU chip-set, memory 
    limit, etc.
Compute Hartigan's dip test statistic for unimodality /
 multimodality and provide a test with simulation based p-values,  where
 the original public code has been corrected.
Estimation of the average treatment effect when controlling for
    high-dimensional confounders using debiased inverse propensity score
    weighting (DIPW). DIPW relies on the propensity score following a sparse
    logistic regression model, but the regression curves are not required to be
    estimable. Despite this, our package also allows the users to estimate 
    the regression curves and take the estimated curves as input to our
    methods. Details of the methodology can be found in Yuhao Wang and
    Rajen D. Shah (2020) "Debiased Inverse Propensity Score Weighting for
    Estimation of Average Treatment Effects with High-Dimensional Confounders"
    <arXiv:2011.08661>. The package relies on the optimisation
    software 'MOSEK' <https://www.mosek.com/> which must be installed separately;
    see the documentation for 'Rmosek'.
Convert a directory structure into a JSON format. This package lets you recursively traverse a directory and convert its contents into a JSON object, making it easier to import code base from file systems into large language models.
Fit latent variable linear models, estimating score distributions for groups of people, following Cohen and Jiang (1999) <doi:10.2307/2669917>. In this model, a latent distribution is conditional on students item response, item characteristics, and conditioning variables the user includes. This latent trait is then integrated out. This software is intended to fit the same models as the existing software 'AM' <https://am.air.org/>. As of version 2, also allows the user to draw plausible values.
A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data.  It estimates the number of clusters directly from the data using a Dirichlet-process prior.  See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.
Provides tools to compute directly age-standardised rates 
    using the 2013 European Standard Population. Includes variance 
    estimation and 95% confidence intervals for population health 
    applications. Functions are flexible to handle any grouping variable 
    and age bands, allowing reproducible and automated analyses.
Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. 
    It allows to compute clustering coefficients that are not present in 'igraph' package. 
    A description of clustering coefficients can be found in "Directed clustering in weighted networks: a new perspective", Clemente, G.P., Grassi, R. (2017),  	
    <doi:10.1016/j.chaos.2017.12.007>.
A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216> and the telescope matching estimator described in Blackwell and Strezhnev (2020) <doi:10.1111/rssa.12759>.
A collection of functions for directional data (including massive data, with millions of observations) analysis. 
             Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. 
			 The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). 
			 Other references include:
			 a) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2018). "An elliptically symmetric angular Gaussian distribution". Statistics and Computing 28(3): 689-697. <doi:10.1007/s11222-017-9756-4>. 
			 b) Tsagris M. and Alenazi A. (2019). "Comparison of discriminant analysis methods on the sphere". Communications in Statistics: Case Studies, Data Analysis and Applications 5(4):467--491. <doi:10.1080/23737484.2019.1684854>. 
			 c) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2020). "Spherical regression models with general covariates and anisotropic errors". Statistics and Computing 30(1): 153--165. <doi:10.1007/s11222-019-09872-2>. 
			 d) Tsagris M. and Alenazi A. (2024). "An investigation of hypothesis testing procedures for circular and spherical mean vectors". Communications in Statistics-Simulation and Computation, 53(3): 1387--1408. <doi:10.1080/03610918.2022.2045499>. 
			 e) Yu Z. and Huang X. (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statistics, 18(1): 301--334. <doi:10.1214/23-EJS2210>. 
			 f) Tsagris M. and Alzeley O. (2025). "Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling". Australian & New Zealand Journal of Statistics, 67(1): 77--103. <doi:10.1111/anzs.12434>. 
			 g) Tsagris M., Papastamoulis P. and Kato S. (2025). "Directional data analysis: spherical Cauchy or Poisson kernel-based distribution". Statistics and Computing, 35:51. <doi:10.1007/s11222-025-10583-0>.
An extensible framework
 for automatically placing direct labels onto multicolor 'lattice' or
 'ggplot2' plots.
 Label positions are described using Positioning Methods
 which can be re-used across several different plots.
 There are heuristics for examining "trellis" and "ggplot" objects
 and inferring an appropriate Positioning Method.
Represents the content of a directory as an interactive collapsible tree. Offers
    the possibility to assign a text (e.g., a 'Readme.txt') to each folder
    (represented as a clickable node), so that when the user hovers the pointer
    over a node, the corresponding text is displayed as a tooltip.
Direction analysis is a set of tools designed to identify
    combinatorial effects of multiple treatments/conditions on pathways
    and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics
    data. See Yang P et al (2014) <doi:10.1093/bioinformatics/btt616>; and Yang P et al. (2016) <doi:10.1002/pmic.201600068>.
Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.
Perform nonparametric Bayesian analysis using Dirichlet 
    processes without the need to program the inference algorithms. 
    Utilise included pre-built models or specify custom 
    models and allow the 'dirichletprocess' package to handle the 
    Markov chain Monte Carlo sampling. 
    Our Dirichlet process objects can act as building blocks for a variety 
    of statistical models including and not limited to: density estimation, 
    clustering and prior distributions in hierarchical models.
    See Teh, Y. W. (2011) 
    <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, 
    among many other sources.
By adding over-relaxation factor to PXEM (Parameter Expanded Expectation Maximization) method, the MOPXEM (Monotonically Overrelaxed Parameter Expanded Expectation Maximization) method is obtained. Compare it with the existing EM (Expectation-Maximization)-like methods. Then, distribute and process five methods and compare them, achieving good performance in convergence speed and result quality.The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.
Estimate parameters in Dirichlet-Multinomial and compute log-likelihoods.
Nonparametric kernel density estimation, bandwidth selection,
    and other utilities for analyzing directional data. Implements the estimator
    in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the
    cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987)
    <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in
    García-Portugués (2013) <doi:10.1214/13-ejs821>.
Contains the support functions for the Time Series Analysis book.
	     We present a function to calculate MSE and MAE for inputs
	     of actual and forecast values.  We also have the code for
	     disaggregation as found in Wei and Stram
	     (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>),
	     and Hodgess and Wei (1996, "Temporal Disaggregation of Time
	     Series").
The twoStepsBenchmark() and threeRuleSmooth() functions allow you to 
    disaggregate a low-frequency time series with higher frequency time series, 
    using the French National Accounts methodology. The aggregated sum of the 
    resulting time series is strictly equal to the low-frequency time series within the 
    benchmarking window. Typically, the low-frequency time series is an annual one, 
    unknown for the last year, and the high frequency one is either quarterly or 
    monthly. See "Methodology of quarterly national accounts", Insee Méthodes 
    N°126, by Insee (2012, ISBN:978-2-11-068613-8, <https://www.insee.fr/en/information/2579410>).
Provides tools for temporal disaggregation, including:
    (1) High-dimensional and low-dimensional series generation for simulation studies;
    (2) A toolkit for temporal disaggregation and benchmarking using low-dimensional indicator series 
        as proposed by Dagum and Cholette (2006, ISBN:978-0-387-35439-2);
    (3) Novel techniques by Mosley, Gibberd, and Eckley (2022, <doi:10.1111/rssa.12952>)
        for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.
Fits disaggregation regression models using 'TMB' ('Template Model
    Builder'). When the response data are aggregated to polygon level but
    the predictor variables are at a higher resolution, these models can be
    useful. Regression models with spatial random fields. The package is 
    described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.
We have the code for
	     disaggregation as found in Wei and Stram
	     (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>),
	     and Hodgess and Wei (1996, "Temporal Disaggregation of Time
	     Series" in Statistical Science I, Nova Publishing).  The disaggregation models have different orders
	     of the moving average component.  These are based on ARIMA
	     models rather than differencing or using similar time series.
By systematically aggregating and processing textual reports from earthquakes, floods, storms,
  wildfires, and other natural disasters, the framework enables a holistic assessment of crisis narratives.  
  Intelligent cleaning and normalization techniques transform raw commentary into structured data, ensuring
  precise extraction of disaster-specific insights. Collective sentiments of affected communities are  
  quantitatively scored and qualitatively categorized, providing a multifaceted view of societal responses  
  under duress. Interactive geographic maps and temporal charts illustrate the evolution and spatial dispersion
  of emotional reactions and impact indicators.
Access and manage the application programming interface (API) of the United Nations Office for the Coordination of Humanitarian Affairs' (OCHA) ReliefWeb disaster events at <https://reliefweb.int/disasters>. The package requires a minimal number of dependencies. It offers functionality to retrieve a user-defined sample of disaster events from ReliefWeb, providing an easy alternative to scraping the ReliefWeb website. It enables a seamless integration of regular data updates into the research work flow.
Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence.  The methods are described in Jackson et al. (2023) <doi:10.1093/jrsssa/qnac015>.
Area under the curve (AUC; Myerson et al., 2001) <doi:10.1901/jeab.2001.76-235> 
  is a popular measure used in discounting research. Although the calculation of
  AUC is standardized, there are differences in AUC based on some assumptions. 
  For example, Myerson et al. (2001) <doi:10.1901/jeab.2001.76-235> 
  assumed that (with delay discounting data) a researcher would impute an
  indifference point at zero delay equal to the value of the larger, later outcome.
  However, this practice is not clearly followed. This imputed zero-delay indifference
  point plays an important role in log and ordinal versions of AUC.
  Ordinal and log versions of AUC are described by Borges et al. (2016)<doi:10.1002/jeab.219>. 
  The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)],
  impute indifference points when x = 0, calculate ordinal AUC in the case of Halton
  sampling of x-values, and account for probability discounting AUC.
An open, multi-algorithmic pipeline for easy, fast and efficient
  analysis of cellular sub-populations and the molecular signatures that
  characterize them. The pipeline consists of four successive steps: data
  pre-processing, cellular clustering with pseudo-temporal ordering, defining
  differential expressed genes and biomarker identification. More details on
  Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements
  extensions of the work published by Ghannoum et. al. (2019)
  <doi:10.1101/700989>.
Discrete factor analysis for dependent Poisson and negative binomial models with truncation, zero inflation, and zero inflated truncation.
Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) <doi:10.1093/biostatistics/kxy071>. There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function.
Computes discrete fast Fourier transform of river discharge data and the derived metrics.
  The methods are described in J. L. Sabo, D. M. Post (2008) <doi:10.1890/06-1340.1> and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) <doi:10.1126/science.aao1053>.
The discrete Laplace exponential family for use in fitting generalized linear models.
Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
Fit a mixture of Discrete Laplace distributions using plain numerical optimisation. This package has similar applications as the 'disclapmix' package that uses an EM algorithm.
Transform 'newswire' and earnings call transcripts as PDF obtained from 'Nexis Uni' to R data frames. Various 'newswires' and 'FairDisclosure' earnings call formats are supported. Further, users can apply several pre-defined dictionaries on the data based on Graffin et al. (2016)<doi:10.5465/amj.2013.0288> and Gamache et al. (2015)<doi:10.5465/amj.2013.0377>.
Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution.
             The test is described by Foldnes and Grønneberg (2019) <doi:10.1080/10705511.2019.1673168>. 
             In addition, an adjusted polychoric correlation estimator is provided that takes marginal knowledge into account,
             as described by Grønneberg and Foldnes (2022) <doi:10.1037/met0000495>.
Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 <doi:10.1016/j.intell.2016.08.008>], [Sims, Trattner, and Garrison, 2024 <doi:10.3389/fpsyg.2024.1430978>] for empirical examples, and [Garrison and colleagues for theoretical work <doi:10.1101/2025.08.25.25334395>].
Analyzes group patterns using discourse analysis data with
    graph theory mathematics. Takes the order of which individuals talk
    and converts it to a network edge and weight list. Returns the
    density, centrality, centralization, and subgroup information for each
    group. Based on the analytical framework laid out in Chai et al.
    (2019) <doi:10.1187/cbe.18-11-0222>.
Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Interactive 'R' tutorials and datasets for the textbook Field
    (2026), "Discovering Statistics Using R and RStudio",
    <https://www.discovr.rocks/>.  Interactive tutorials cover general
    workflow in 'R' and 'RStudio', summarizing data, visualizing data,
    fitting models and bias, correlation, the general linear model (GLM),
    moderation, mediation, missing values, comparing means using the GLM
    (analysis of variance), comparing adjusted means (analysis of covariance), factorial
    designs, repeated measures designs, exploratory factor analysis (EFA).
    There are no functions, only datasets and interactive tutorials.
Provides several data sets for use with discrete statistical tests
  and discrete multiple testing procedures. Some of them are also available as a
  four-column version, so that each row represents a 2x2 table.
Implementation of new discrete statistical distributions. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters within the 'gamlss' framework.
Tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) <doi:10.48550/arXiv.2403.03646>.
Implementations of the multiple testing procedures for discrete
  tests described in the paper Döhler, Durand and Roquain (2018) "New FDR
  bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The
  main procedures of the paper (HSU and HSD), their adaptive counterparts
  (AHSU and AHSD), and the HBR variant are available and are coded to 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 a wrapper allowing to apply discrete procedures directly to data.
Implements fast Monte Carlo simulations for 
    goodness-of-fit (GOF) tests for discrete distributions. This 
    includes tests based on the Chi-squared statistic, the 
    log-likelihood-ratio (G^2) statistic, the Freeman-Tukey 
    (Hellinger-distance) statistic, the Kolmogorov-Smirnov 
    statistic, the Cramer-von Mises statistic as described in 
    Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>, 
    and the root-mean-square statistic, see Perkins, 
    Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.
Implementations of several multiple testing procedures that control
    the family-wise error rate (FWER) designed specifically for discrete tests.
    Included are discrete adaptations of the Bonferroni, Holm, Hochberg and
    Šidák procedures as described in the papers Döhler (2010) "Validation of
    credit default probabilities using multiple-testing procedures"
    <doi:10.21314/JRMV.2010.062> and Zhu & Guo (2019) "Family-Wise Error Rate
    Controlling Procedures for Discrete Data" 
    <doi:10.1080/19466315.2019.1654912>. The main procedures of this package
    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 apply discrete procedures directly to data is also
    provided.
The gap statistic approach is extended to estimate the number of clusters for categorical response format data. This approach and accompanying software is designed to be used with the output of any clustering algorithm and with distances specifically designed for categorical (i.e. multiple choice) or ordinal survey response data.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.
Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.
We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), <doi:10.1073/pnas.1530509100>. The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values.
Provides vectorised functions for computing p-values of various
  common discrete statistical tests, as described e.g. in Agresti (2002)
  <doi:10.1002/0471249688>, including their distributions. Exact and
  approximate computation methods are provided. For exact p-values, several
  procedures of determining two-sided p-values are included, which are
  outlined in more detail in Hirji (2006) <doi:10.1201/9781420036190>.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.
A collection of supervised discretization
        algorithms. It can also be grouped in terms of top-down or
        bottom-up, implementing the discretization algorithms.
Bindings for additional classification models for use with
    the 'parsnip' package. Models include flavors of discriminant
    analysis, such as linear (Fisher (1936)
    <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized (Friedman
    (1989) <doi:10.1080/01621459.1989.10478752>), and flexible (Hastie,
    Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), as
    well as naive Bayes classifiers (Hand and Yu (2007)
    <doi:10.1111/j.1751-5823.2001.tb00465.x>).
Templates and data files to support "Discrete Choice Analysis with R",
    Páez, A. and Boisjoly, G. (2023) <doi:10.1007/978-3-031-20719-8>.
Provides data transformations, estimation utilities,
    predictive evaluation measures and simulation functions for discrete time
    survival analysis.
A simple approach to measure political sophistication based on open-ended survey responses. Discursive sophistication captures the complexity of individual attitude expression by quantifying its relative size, range, and constraint. For more information on the measurement approach see: Kraft, Patrick W. 2023. "Women Also Know Stuff: Challenging the Gender Gap in Political Sophistication." American Political Science Review (forthcoming).
Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.
Formatting of population and case data, calculation of Standardized
    Incidence Ratios, and fitting the BYM model using 'INLA'. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.
Simple feature stores and tools for creating personalised feature stores.
  'diseasystore' powers feature stores which can automatically link and aggregate features to a given stratification
  level. These feature stores are automatically time-versioned (powered by the 'SCDB' package) and allows you to easily
  and dynamically compute features as part of your continuous integration.
Model cell type heterogeneity of bulk renal cell carcinoma. The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions.
Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.
Implements methods for calculating disproportionate impact: the percentage point gap, proportionality index, and the 80% index.
 California Community Colleges Chancellor's Office (2017).  Percentage Point Gap Method. <https://www.cccco.edu/-/media/CCCCO-Website/About-Us/Divisions/Digital-Innovation-and-Infrastructure/Research/Files/PercentagePointGapMethod2017.ashx>.
 California Community Colleges Chancellor's Office (2014).  Guidelines for Measuring Disproportionate Impact in Equity Plans. <https://www.cccco.edu/-/media/CCCCO-Website/Files/DII/guidelines-for-measuring-disproportionate-impact-in-equity-plans-tfa-ada.pdf>.
Methods for species distribution modeling, that is, predicting the environmental similarity of any site to that of the locations of known occurrences of a species.
Functionality for manipulating values of associative
  maps.  The package is a dependency for mvp-type
  packages that use the STL map class: it traps
  plausible idiom that is ill-defined (implementation-specific) and
  returns an informative error, rather than returning a possibly
  incorrect result.  To cite the package in publications please use
  Hankin (2022) <doi:10.48550/ARXIV.2210.03856>.
The disparity filter algorithm is a network reduction technique to
    identify the 'backbone' structure of a weighted network without destroying
    its multi-scale nature. The algorithm is documented in M. Angeles Serrano,
    Marian Boguna and Alessandro Vespignani in "Extracting the multiscale
    backbone of complex weighted networks", Proceedings of the National Academy
    of Sciences 106 (16), 2009. This implementation of the algorithm supports
    both directed and undirected networks.
Simulates demic diffusion building on models previously developed
	for the expansion of Neolithic and other food-producing economies during
	the Holocene (Fort et al. (2012) <doi:10.7183/0002-7316.77.2.203>, Souza et al.
	(2021) <doi:10.1098/rsif.2021.0499>). Growth and emigration are modelled as
	density-dependent processes using logistic growth and an asymptotic threshold
	model. Environmental and terrain layers, which can change over time, affect
	carrying capacity, growth and mobility. Multiple centres of origin with
	their respective starting times can be specified.
Provides methods for analyzing the dispersion of tabular datasets
    with batched and ordered samples. Based on convex hull or integrated covariance
    Mahalanobis, several indicators are implemented for inter and intra batch dispersion
    analysis. It is designed to facilitate robust statistical assessment of data
    variability, supporting applications in exploratory data analysis and quality
    control, for such datasets as the one found in metabololomics studies.
    For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and
    Salanon (2025) <doi:10.1101/2025.08.01.668073>.
When visualising changes between two values over time, a strict
 linear interpolation can look jarring and unnatural.  By applying a non-linear
 easing to the transition, the motion between values can appear smoother and 
 more natural.  This package includes functions for applying such non-linear
 easings to colors and numeric values, and is useful where smooth animated movement
 and transitions are desired.
Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 <doi:10.2307/2347792>), overdispersed binomial logit models (Williams, 1987 <doi:10.2307/2347977>), and overdispersed Poisson log-linear models (Breslow, 1984 <doi:10.2307/2347661>), using a quasi-likelihood approach.
Create disposable R packages for testing.
    You can create, install and load multiple R packages with a single
    function call, and then unload, uninstall and destroy them with another
    function call. This is handy when testing how some R code or an R package
    behaves with respect to other packages.
Evaluate the presence of disposition effect and others irrational
    investor's behaviors based solely on investor's transactions and financial
    market data. Experimental data can also be used to perform the analysis.  
    Four different methodologies are implemented to account for the different
    nature of human behaviors on financial markets.  
    Novel analyses such as portfolio driven and time series disposition effect
    are also allowed.
A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.
Similarity of dissolution profiles is assessed using the
    similarity factor f2 according to the EMA guideline (European
    Medicines Agency 2010) "On the investigation of bioequivalence".
    Dissolution profiles are regarded as similar if the f2 value is
    between 50 and 100. For the applicability of the similarity factor f2,
    the variability between profiles needs to be within certain limits.
    Often, this constraint is violated. One possibility in this situation
    is to resample the measured profiles in order to obtain a bootstrap
    estimate of f2 (Shah et al. (1998) <doi:10.1023/A:1011976615750>).
    Other alternatives are the model-independent non-parametric
    multivariate confidence region (MCR) procedure (Tsong et al. (1996)
    <doi:10.1177/009286159603000427>) or the T2-test for equivalence
    procedure (Hoffelder (2016)
    <https://www.ecv.de/suse_item.php?suseId=Z|pi|8430>). Functions for
    estimation of f1, f2, bootstrap f2, MCR / T2-test for equivalence
    procedure are implemented.
Calculate multiple or pairwise dissimilarity for orders q = 0-N 
    (CqN; Chao et al. 2008 <doi:10/fcvn63>) for a set of species assemblages or 
    interaction networks.
Spatial downscaling of coarse grid mapping to fine grid
    mapping using predictive covariates and a model fitted using the 'caret'
    package. The original dissever algorithm was published by Malone et al. 
    (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al.
    (2017) <doi:10.1016/j.compag.2017.08.021>.
Efficiently creates, manipulates, and subsets "dist" objects, commonly used in cluster analysis. Designed to minimise unnecessary conversions and computational overhead while enabling seamless interaction with distance matrices.
Tools to fit sample selection models in case of discrete response 
  variables, through a parametric formulation which represents a natural 
  extension of the well-known Heckman selection model are provided in the 
  package. The response variable can be of Bernoulli, Poisson or Negative 
  Binomial type. The sample selection mechanism allows to choose among a 
  Normal, Logistic or Gumbel distribution.
A simple way of fitting detection functions to distance sampling
    data for both line and point transects. Adjustment term selection, left and
    right truncation as well as monotonicity constraints and binning are
    supported. Abundance and density estimates can also be calculated (via a
    Horvitz-Thompson-like estimator) if survey area information is provided. See
    Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on
    methods and <https://distancesampling.org/resources/vignettes.html> for example analyses.
We provide three distance metrics for measuring the separation between two clusters in high-dimensional spaces. The first metric is the centroid distance, which calculates the Euclidean distance between the centers of the two groups. The second is a ridge Mahalanobis distance, which incorporates a ridge correction constant, alpha, to ensure that the covariance matrix is invertible. The third metric is the maximal data piling distance, which computes the orthogonal distance between the affine spaces spanned by each class. These three distances are asymptotically interconnected and are applicable in tasks such as discrimination, clustering, and outlier detection in high-dimensional settings.
Provides tools for constructing, manipulating and using distance metrics.
Calculates distances from point locations to features.
    The usual approach for eg. resource selection function analyses is to
    generate a complete distance to features surface then sample it with your 
    observed and random points. Since these raster based approaches can be
    pretty costly with large areas, and often lead to memory issues in R, 
    the distanceto package opts to compute these distances using
    efficient, vector based approaches. As a helper, there's a decidedly 
    low-res raster based approach for visually inspecting your region's 
    distance surface. But the workhorse is distance_to.
Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.
Implement DiSTATIS and CovSTATIS  (three-way multidimensional scaling).  DiSTATIS and CovSTATIS are used to analyze multiple distance/covariance matrices collected on the same set of observations. These methods are based on Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012) <doi:10.1002/wics.198>.
Implementing algorithms and fitting models when sites (possibly remote) share
  computation summaries rather than actual data over HTTP with a master R process (using
  'opencpu', for example). A stratified Cox model and a singular value decomposition are
  provided. The former makes direct use of code from the R 'survival' package. (That is,
  the underlying Cox model code is derived from that in the R 'survival' package.)
  Sites may provide data via several means: CSV files, Redcap API, etc. An extensible
  design allows for new methods to be added in the future and includes facilities
  for local prototyping and testing. Web applications are provided (via 'shiny') for
  the implemented methods to help in designing and deploying the computations.
Creates discretised versions of continuous 
      distribution functions by mapping continuous values 
      to an underlying discrete grid, based on a (uniform) 
      frequency of discretisation, a valid discretisation 
      point, and an integration range. For a review of 
      discretisation methods, see 
      Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>.
Contains a range of functions covering the present
  development of the distributional method for the dichotomisation of continuous outcomes. 
  The method provides estimates with standard error of a comparison of proportions 
  (difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means. 
  See the URL below or <arXiv:1809.03279> for more information.
Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.
Implements the distribution-free goodness-of-fit regression test
  for the mean structure of parametric models introduced in Khmaladze (2021)
  <doi:10.1007/s10463-021-00786-3>. The test is implemented for general
  functions with minimal distributional assumptions as well as common models
  (e.g., lm, glm) with the usual assumptions.
Given a set of predictive quantiles from a distribution,
    estimate the distribution and create `d`, `p`, `q`, and `r` functions
    to evaluate its density function, distribution function, and quantile
    function, and generate random samples. On the interior of the provided
    quantiles, an interpolation method such as a monotonic cubic spline is
    used; the tails are approximated by a location-scale family.
Scientific and technical article format for the web.
    'Distill' articles feature attractive, reader-friendly typography,
    flexible layout options for visualizations, and full support for
    footnotes and citations.
Some very simple method functions for confidence interval calculation, bootstrap resampling aimed at atmospheric science applications, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.  To reference this package and for a tutorial on the bootstrap functions, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0069.1> and Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
Calculates Distinctiveness Centrality in social networks. 
	For formulas and descriptions, see Fronzetti Colladon and Naldi (2020) <doi:10.1371/journal.pone.0233276>.
Create and evaluate probability distribution objects from a
    variety of families or define custom distributions. Automatically compute
    distributional properties, even when they have not been specified.
    This package supports statistical modeling and simulations, and forms
    the core of the probaverse suite of R packages.
Provides a high level API to interface over sources storing distance, dissimilarity, similarity matrices with matrix style extraction, replacement and other utilities. Currently, in-memory dist object backend is supported.
It provides the subset operator for dist objects and a function to
    compute medoid(s) that are fully parallelized leveraging the 'RcppParallel'
    package. It also provides functions for package developers to easily
    implement their own parallelized dist() function using a custom 'C++'-based
    distance function.
Geodesic distance between phylogenetic trees and
  associated functions. The theoretical background of 'distory'
  is published in Billera et al. (2001) "Geometry of the space of
  phylogenetic trees." <doi:10.1006/aama.2001.0759>.
Package including an interactive Shiny application for plotting 
    common univariate distributions.
Go beyond standard probability distributions such as the Normal
    or Exponential by combining, shifting, maximizing, and otherwise
    transforming distributions with simple, verb-based functions. Provides
    easy access to a broader space of distributions more representative of 
    real-world systems such as river flows or insurance claims. 
    Part of the probaverse framework of packages to support advanced
    statistical modeling and simulations with an intuitive workflow.
S4-classes and methods for distributions.
Provides documentation in form of a common vignette to packages 'distr',
            'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.
Distribution (S4-)classes for elliptically contoured distributions (based on
            package 'distr').
Extends package 'distr' by functionals, distances, and conditional distributions.
A different way for calculating pdf/pmf, cdf, quantile and random data such that the user is able to consider the name of related distribution as an argument and so easily can changed by a changing argument by user. It must be mentioned that the core and computation base of package 'DISTRIB' is package 'stats'. Although similar functions are introduced previously in package 'stats', but the package 'DISTRIB' has some special applications in some special computational programs.
Calculates expected values, variance, different moments (kth 
    moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR)
    and Tail Value-at-Risk (TVaR) as well as some density and cumulative 
    (survival) functions of continuous, discrete and compound distributions. 
    This package also includes a visual 'Shiny' component to enable students 
    to visualize distributions and understand the impact of their parameters.
    This package is intended to expand the 'stats' package so as
    to enable students to develop an intuition for probability.
Vectorised distribution objects with tools for manipulating, 
    visualising, and using probability distributions. Designed to allow model
    prediction outputs to return distributions rather than their parameters, 
    allowing users to directly interact with predictive distributions in a
    data-oriented workflow. In addition to providing generic replacements for
    p/d/q/r functions, other useful statistics can be computed including means,
    variances, intervals, and highest density regions.
Distributional instrumental variable (DIV) model
    for estimation of the interventional distribution of the outcome Y under a
    do intervention on the treatment X. Instruments, predictors and targets can
    be univariate or multivariate. Functionality includes estimation of the (conditional)
    interventional mean and quantiles, as well as sampling from the fitted (conditional)
    interventional distribution.
Fits Gaussian Mixtures by applying evolution. As fitness function a mixture of the chi square test for distributions and a novel measure for approximating the common area under curves between multiple Gaussians is used. The package presents an alternative to the commonly used Likelihood Maximization as is used in Expectation Maximization. The algorithm and applications of this package are published under: Lerch, F., Ultsch, A., Lotsch, J. (2020) <doi:10.1038/s41598-020-57432-w>. The evolution is based on the 'GA' package: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04> while the Gaussian Mixture Logic stems from 'AdaptGauss': Ultsch, A, et al. (2015) <doi:10.3390/ijms161025897>.
Tools to create and manipulate probability distributions
    using S3.  Generics pdf(), cdf(), quantile(), and random() provide
    replacements for base R's d/p/q/r style functions.  Functions and
    arguments have been named carefully to minimize confusion for students
    in intro stats courses. The documentation for each distribution
    contains detailed mathematical notes.
A library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.
Provides new types of omnibus tests which are generally much more powerful than traditional tests (including the Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling tests),see Zhang (2002) <doi:10.1111/1467-9868.00337>.
Utilities are provided which are of use in the
    packages I have developed for dealing with
    distributions. Currently these packages are GeneralizedHyperbolic,
    VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these
    packages requires DistributionUtils. Functionality includes sample
    skewness and kurtosis, log-histogram, tail plots, moments by
    integration, changing the point about which a moment is
    calculated, functions for testing distributions using inversion
    tests and the Massart inequality. Also includes an implementation
    of the incomplete Bessel K function.
Implements S4 classes for probability models based on packages 'distr' and
            'distrEx'.
In order to provide unified access to Linux distribution details
    in R, this package wraps the various files and commands that may exist
    on a system. It is similar in spirit to the 'lsb_release' command and the
    'Python' package of the same name.
Fast distributed/parallel estimation for multinomial logistic regression via Poisson factorization and the 'gamlr' package.  For details see: Taddy (2015, AoAS), Distributed Multinomial Regression, <doi:10.48550/arXiv.1311.6139>.
Tools to estimate and manage empirical distributions,
    which should work with survey data. One of the main features is the 
    possibility to create data cubes of estimated statistics, that include
    all the combinations of the variables of interest (see for example functions
    dcc5() and dcc6()).
S4-distribution classes based on package distr for distributions from packages
            'fBasics' and 'fGarch'.
S4-classes for setting up a coherent framework for simulation within the distr
            family of packages.
Provides flexible examples of LLN and CLT for teaching purposes in secondary
            school.
Evaluation (S4-)classes based on package distr for evaluating procedures
            (estimators/tests) at data/simulation in a unified way.
A full definition for Weibull tails and Full-Tails Gamma and tools for fitting these distributions to empirical tails. This package build upon the paper by del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012) <doi:10.1017/asb.2017.9>.
Provides convenient methods for accessing the data in 'dist' objects with minimal memory and computational overhead. 'disttools' can be used to extract the distance between any pair or combination of points encoded by a 'dist' object using only the indices of those points. This is an improvement over existing functionality, which requires either coercing a 'dist' object into a matrix or calculating the one dimensional index corresponding to a pair of observations. Coercion to a matrix is undesirable because doing so doubles the amount of memory required for storage. In contrast, there is no inherent downside to the latter solution. However, in part due to several edge cases, correctly and efficiently implementing such a solution can be challenging. 'disttools' abstracts away these challenges and provides a simple interface to access the data in a 'dist' object using the latter approach.
Testing and documenting code that communicates with remote
  databases can be painful. Although the interaction with R is usually relatively 
  simple (e.g. data(frames) passed to and from a database), because they rely on 
  a separate service and the data there, testing them can be difficult to set up,
  unsustainable in a continuous integration environment, or impossible without 
  replicating an entire production cluster. This package addresses that by 
  allowing you to make recordings from your database interactions and then play 
  them back while testing (or in other contexts) all without needing to spin up 
  or have access to the database your code would typically connect to.
A comprehensive visualization toolkit built with coders of all
    skill levels and color-vision impaired audiences in mind. It allows creation
    of finely-tuned, publication-quality figures from single function calls.
    Visualizations include scatter plots, compositional bar plots, violin, box,
    and ridge plots, and more. Customization ranges from size and title
    adjustments to discrete-group circling and labeling, hidden data overlay
    upon cursor hovering via ggplotly() conversion, and many more, all with
    simple, discrete inputs. Color blindness friendliness is powered by legend
    adjustments (enlarged keys), and by allowing the use of shapes or
    letter-overlay in addition to the carefully selected dittoColors().
The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to 
 support storm data management, monitoring,  and early warning applications in Sri Lanka. 
 The publicly available data were converted to tidy data format for easy analysis.
 The package processes weather data, flood data and situation report data (families affected, etc.).
 The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics.
Provides curated early warning data on landslides in Sri Lanka during the Ditwah storm. 
             It includes structured, machine-readable tidy dataset. This is developed for education and research purposes.
Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.
Contains functions for the 'DivE' estimator <doi:10.1371/journal.pcbi.1003646>. The 'DivE' estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.
Utilities to represent, visualize, filter, analyse, and summarize
	     time-depth recorder (TDR) data.  Miscellaneous functions for
	     handling location data are also provided.
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>.
  'divent' provides functions to estimate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
A suite of 'loon' related packages providing data analytic tools for 
    Direct Interactive Visual Exploration in R ('diveR').
    These tools work with and complement those of the 'tidyverse' suite, 
    extending the grammar of 'ggplot2' to become a grammar of interactive
    graphics.
    The suite provides many visual tools designed for moderately (100s of variables)
    high dimensional data analysis, through 'zenplots' and novel tools in 'loon', and
    extends the 'ggplot2' grammar to provide parallel coordinates, Andrews plots, and arbitrary 
    glyphs through 'ggmulti'.
    The  'diveR' package gathers together and installs all these related packages
    in a single step.
Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework.
Diversification is one of the most important concepts in portfolio management. This framework offers scholars, practitioners and policymakers a useful toolbox to measure diversification. Specifically, this framework provides recent diversification measures from the recent literature. These diversification measures are based on the works of Rudin and Morgan (2006) <doi:10.3905/jpm.2006.611807>, Choueifaty and Coignard (2008) <doi:10.3905/JPM.2008.35.1.40>, Vermorken et al. (2012) <doi:10.3905/jpm.2012.39.1.067>, Flores et al. (2017) <doi:10.3905/jpm.2017.43.4.112>, Calvet et al. (2007) <doi:10.1086/524204>, and Candelon, Fuerst and Hasse (2020).
Contains a number of comparative 'phylogenetic' methods,
  mostly focusing on analysing diversification and character
  evolution.  Contains implementations of 'BiSSE' (Binary State
  'Speciation' and Extinction) and its unresolved tree extensions,
  'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE',
  'GeoSSE', and 'BiSSE-ness' Other included methods include Markov
  models of discrete and continuous trait evolution and constant rate
  'speciation' and extinction.
Companion package of Arnaud Barat, Andreu Sansó, Maite Arilla-Osuna, 
    Ruth Blasco, Iñaki Pérez-Fernández, Gabriel Cifuentes-Alcobenda, 
    Rubén Llorente, Daniel Vivar-Ríos, Ella Assaf, Ran Barkai, Avi Gopher, &
    Jordi Rosell-Ardèvol (2025), "Quantifying Diversity through Entropy 
    Decomposition. Insights into Hominin Occupation and Carcass Processing 
    at Qesem cave".
Implementation of three methods based on the diversity forest (DF) algorithm 
  (Hornung, 2022, <doi:10.1007/s42979-021-00920-1>), a split-finding approach that 
  enables complex split procedures in random forests.
  The package includes:
    1. Interaction forests (IFs) (Hornung & Boulesteix, 2022, <doi:10.1016/j.csda.2022.107460>): 
    Model quantitative and qualitative interaction effects using bivariable splitting. 
    Come with the Effect Importance Measure (EIM), which can be used to identify variable 
    pairs that have well-interpretable quantitative and qualitative interaction effects 
    with high predictive relevance.
	2. Two random forest-based variable importance measures (VIMs) for multi-class outcomes: 
	the class-focused VIM, which ranks covariates by their ability to distinguish individual 
	outcome classes from the others, and the discriminatory VIM, which measures overall 
	covariate influence irrespective of class-specific relevance.
    3. The basic form of diversity forests that uses conventional univariable, binary 
    splitting (Hornung, 2022).
  Except for the multi-class VIMs, all methods support categorical, metric, and survival 
  outcomes. The package includes visualization tools for interpreting the identified 
  covariate effects.
  Built as a fork of the 'ranger' R package (main author: Marvin N. Wright), which 
  implements random forests using an efficient C++ implementation.
Provides tools to sort DICOM-format medical image files, and
    convert them to NIfTI-1 format.
Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, 'DIVINE' makes the process more efficient, transparent, and reproducible.
A set of tools for empirical analysis of diversity (a number 
    and frequency of different types in a population) and similarity 
    (a number and frequency of shared types in two populations) in 
    biological or ecological systems.
Alpha and beta diversity for taxonomic (TD), functional (FD),
    and phylogenetic (PD) dimensions based on rasters. Spatial and
    temporal beta diversity can be partitioned into replacement and
    richness difference components. It also calculates standardized effect
    size for FD and PD alpha diversity and the average individual traits
    across multilayer rasters. The layers of the raster represent species,
    while the cells represent communities. Methods details can be found at
    Cardoso et al. 2022 <https://CRAN.R-project.org/package=BAT> and
    Heming et al. 2023 <https://CRAN.R-project.org/package=SESraster>.
Implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a 'tidyselect' based workflow.
Divide taxonomic occurrence data into geographic regions of fair
    comparison, with three customisable methods to standardise area and extent.
    Calculate common biodiversity and range-size metrics on subsampled data.
    Background theory and practical considerations for the methods are described
    in Antell and others (2024) <doi:10.1017/pab.2023.36>.
Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some 'Fortran' code has been included to the original dixon2002() function of the 'ecespa' package to improve speed.
For outlier detection in small and normally distributed 
    samples the ratio test of Dixon (Q-test) can be used. Density,
    distribution function, quantile function and random generation 
    for Dixon's ratio statistics are provided as wrapper functions.
    The core applies McBane's Fortran functions <doi:10.18637/jss.v016.i03> 
    that use Gaussian quadrature for a numerical solution.
An R package for iterative and batched record linkage, 
    and applying epidemiological case definitions.
    'diyar' can be used for deterministic and probabilistic record linkage, 
    or multistage record linkage combining both approaches.
    It features the implementation of nested match criteria, and mechanisms to 
    address missing data and conflicting matches during stepwise record linkage.
    Case definitions are implemented by assigning records to groups based on 
    match criteria such as person or place, and overlapping time or duration of 
    events e.g. sample collection dates or periods of hospital stays.
    Matching records are assigned a unique group ID. Index and duplicate records 
    are removed or further analyses as required.
Lightweight utility functions used for the R package
    development infrastructure inside the data integration centers ('DIZ')
    to standardize and facilitate repetitive tasks such as setting up a
    database connection or issuing notification messages and to avoid
    redundancy.
Utility functions used for the R package development
    infrastructure inside the data integration centers ('DIZ') to
    standardize and facilitate repetitive tasks such as setting up a
    database connection or issuing notification messages and to avoid
    redundancy.
Implements various decision support tools related to the Econometrics & Technometrics.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.
Provides functions to facilitate access to the 'DKAN' API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the 'DKAN' REST API (metadata), and the 'DKAN' datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the 'DKAN' datastore.
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.
An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.
The Dirichlet Laplace shrinkage prior in Bayesian linear regression and variable selection, featuring:
    utility functions in implementing Dirichlet-Laplace priors such as visualization; 
    scalability in Bayesian linear regression; 
    penalized credible regions for variable selection.
The load estimation method is based on a general factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.
Distributed estimation method is based on a Laplace factor model
    to solve the estimates of load and specific variance. The philosophy of
    the package is described in Guangbao Guo. (2022).
    <doi:10.1007/s00180-022-01270-z>.
Interface for 'Rcpp' users to 'dlib' <http://dlib.net> which is a
    'C++' toolkit containing machine learning algorithms and computer vision tools.
    It is used in a wide range of domains including robotics, embedded devices,
    mobile phones, and large high performance computing environments. This package
    allows R users to use 'dlib' through 'Rcpp'.
Collection of functions for fitting and interpreting distributed lag interaction models (DLIM). A DLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a continuous variable. Includes a dlim() function for fitting, predict() function for inference, and plotting functions for visualization. Details on methodology are described in Demateis et al. (2024) <doi:10.1002/env.2843>.
Implementation of the Decorrelated Local Linear estimator proposed 
    in <arxiv:1907.12732>. It constructs the confidence interval for the derivative 
    of the function of interest under the high-dimensional sparse additive model.
Provides routines for Maximum likelihood,
    Kalman filtering and smoothing, and Bayesian
    analysis of Normal linear State Space models, also known as 
    Dynamic Linear Models.
As a distributed imputation strategy, the Distributed full
        information Multiple Imputation method is developed to impute
        missing response variables in distributed linear regression.
        The philosophy of the package is described in 'Guo' (2025)
        <doi:10.1038/s41598-025-93333-6>.
A collection of data-limited management procedures that can be evaluated 
    with management strategy evaluation with the 'MSEtool' package, or applied to 
    fishery data to provide management recommendations.
Estimation of distributed lag models (DLMs) based on a Bayesian additive regression trees framework. Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) <doi:10.1111/biom.13568>; treed distributed lag nonlinear models (Mork and Wilson, 2022) <doi:10.1093/biostatistics/kxaa051>; heterogeneous DLMs (Mork, et. al., 2024) <doi:10.1080/01621459.2023.2258595>; monotone DLMs (Mork and Wilson, 2024) <doi:10.1214/23-BA1412>. The package also includes visualization tools and a 'shiny' interface to check model convergence and to help interpret results.
Implement dynamic linear models outlined in Shumway and Stoffer (2025) <doi:10.1007/978-3-031-70584-7>. Two model structures for data smoothing and forecasting are considered. The specific models proposed will be added once the manuscript is published.
Collection of functions for distributed lag linear and non-linear models.
A collection of tools that support data diagnosis, exploration, and transformation. 
    Data diagnostics provides information and visualization of missing values, outliers, and unique 
    and negative values to help you understand the distribution and quality of your data. 
    Data exploration provides information and visualization of the descriptive statistics of 
    univariate variables, normality tests and outliers, correlation of two variables, 
    and the relationship between the target variable and predictor. Data transformation supports binning 
    for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. 
    And it creates automated reports that support these three tasks.
Algorithm to handle with optimal subset selection for distributed local principal component analysis. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
The goal of dlr is to provide a friendly wrapper around the common 
    pattern of downloading a file if that file does not already exist locally.
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018 <doi:10.2478/bile-2018-0012>; Magrini et al., 2019 <doi:10.1007/s11135-019-00855-z>). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic, quadratic decreasing, linearly decreasing and gamma lag shapes are available.
Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <https://cranlogs.r-pkg.org:443>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.
Provides tools for working with multiple related
    tables, stored as data frames or in a relational database.  Multiple
    tables (data and metadata) are stored in a compound object, which can
    then be manipulated with a pipe-friendly syntax.
Dynamic model averaging for binary and continuous
        outcomes.
Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (<DOI:10.1016/0304-4076(80)90070-6>).
Functions for fitting a Bayesian model for grouping binary
    dissimilarity matrices in homogeneous clusters. Currently, it includes
    methods only for binary data (<doi:10.18637/jss.v100.i16>).
DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015).
  Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.
  Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>.
  Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs),
  a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008).
  Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common
  features from within standard diffusion models with the addition of superimposed controlled and automatic activation.
  The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural
  conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality
  to fit the model to observed data. Further details are provided in the following paper: 
  Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction 
  time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>.
Computational tools for meta-analysis of diagnostic accuracy test. Bootstrap-based computational methods of the confidence interval for AUC of summary ROC curve and some related AUC-based inference methods are available (Noma et al. (2021) <doi:10.1080/23737484.2021.1894408>).
The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Mixed model analysis for quantitative genetics
  with multi-trait responses and pedigree-based partitioning 
  of individual variation into a range of environmental and 
  genetic variance components for individual and 
  maternal effects. Method documented in dmmOverview.pdf; dmm is an 
  implementation of dispersion mean model described by
  Searle et al. (1992) "Variance Components", Wiley, NY.
  Dmm() can do 'MINQUE', 'bias-corrected-ML', and 'REML'  variance and 
  covariance component estimates.
Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.
The framework provides functions to generate ODEs of reaction
    networks, parameter transformations, observation functions, residual functions,
    etc. The framework follows the paradigm that derivative information should be
    used for optimization whenever possible. Therefore, all major functions produce
    and can handle expressions for symbolic derivatives. The methods used in dMod
    were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.
Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and Agnieszka Sołtys, 2023. Improving Group Lasso for High-Dimensional Categorical Data. In: Computational Science – ICCS 2023. Lecture Notes in Computer Science, vol 14074, p. 455-470. Springer, Cham. <doi:10.1007/978-3-031-36021-3_47>. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. <https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. <doi:10.1111/sjos.12546>.
Provides statistical tools for 
	analyzing net and relative survival, with a key feature 
	of relaxing the assumption of independent censoring and 
	incorporating the effect of dependent competing risks. 
	It employs a copula-based methodology, specifically the 
	Archimedean copula, to simulate data, conduct survival 
	analysis, and offer comparisons with other methods. 
	This approach is detailed in the work of 
	Adatorwovor et al. (2022) <doi:10.1515/ijb-2021-0016>.
Several tests for differential methylation in methylation array data, including one-sided differential mean and variance test. Methods used in the package refer to Dai, J, Wang, X, Chen, H and others (2021) "Incorporating increased variability in discovering cancer methylation markers", Biostatistics, submitted.
Implementation of a transfer learning framework employing distribution mapping based domain transfer. Uses the renowned concept of histogram matching (see Gonzalez and Fittes (1977) <doi:10.1016/0094-114X(77)90062-3>, Gonzalez and Woods (2008) <isbn:9780131687288>) and extends it to include distribution measures like kernel density estimates (KDE; see Wand and Jones (1995) <isbn:978-0-412-55270-0>, Jones et al. (1996) <doi:10.2307/2291420). In the typical application scenario, one can use the underlying sample distributions (histogram or KDE) to generate a map between two distinct but related domains to transfer the target data to the source domain and utilize the available source data for better predictive modeling design. Suitable for the case where a one-to-one sample matching is not possible, thus one needs to transform the underlying data distribution to utilize the more available data for modeling.
For checking the dataset from EDC(Electronic Data Capture) in clinical trials.
             'dmtools' reshape your dataset in a tidy view and check events.
             You can reshape the dataset and choose your target to check, for example, the laboratory reference range.
Work within the 'dplyr' workflow to add random variates to your data frame. 
  Variates can be added at any level of an existing column.  Also, bounds can be specified 
  for simulated variates.
The function takes a DNA sequence, a start point, an end point in the sequence, dot size and dot color and draws a fractal image of the sequence. The fractal starts in the center of the canvas. The image is drawn by moving base by base along the sequence and dropping a midpoint between the actual point and the corner designated by the actual base. For more details see Jeffrey (1990) <doi:10.1093/nar/18.8.2163>, Hill, Schisler, and Singh (1992) <doi:10.1007/BF00178602>, and Löchel and Heider (2021) <doi:10.1016/j.csbj.2021.11.008>.
Performs Diffusion Non-Additive (DNA) model proposed by Heo, Boutelet, and Sung (2025+) <doi:10.48550/arXiv.2506.08328> for multi-fidelity computer experiments with tuning parameters. The DNA model captures nonlinear dependencies across fidelity levels using Gaussian process priors and is particularly effective when simulations at different fidelity levels are nonlinearly correlated. The DNA model targets not only interpolation across given fidelity levels but also extrapolation to smaller tuning parameters including the exact solution corresponding to a zero-valued tuning parameter, leveraging a nonseparable covariance kernel structure that models interactions between the tuning parameter and input variables. Closed-form expressions for the predictive mean and variance enable efficient inference and uncertainty quantification. Hyperparameters in the model are estimated via maximum likelihood estimation.
Statistical methods for DNA mixture analysis. This package is a lite-version of the 'DNAmixtures' package to allow users without a 'HUGIN' software license to experiment with the statistical methodology. While the lite-version aims to provide the full functionality it is noticeably less efficient than the original 'DNAmixtures' package. For details on implementation and methodology see <https://dnamixtures.r-forge.r-project.org/>.
Motifs within biological sequences show a significant role. This package utilizes a user-defined threshold value (window size and similarity) to create consensus segments or motifs through local alignment of dynamic  programming with gap and it calculates the frequency of each identified motif, offering a detailed view of their prevalence within the dataset. It allows for thorough exploration and understanding of sequence patterns and their biological importance.
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.
Computationally efficient tools for comparing all pairs of profiles
    in a DNA database. The expectation and covariance of the summary statistic
    is implemented for fast computing. Routines for estimating proportions of
    close related individuals are available. The use of wildcards (also called F-
    designation) is implemented. Dedicated functions ease plotting the results. 
    See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. 
    Compute the distribution of the numbers of alleles in DNA mixtures. 
    See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.
The goal of 'dndR' is to provide a suite of Dungeons & Dragons related functions.
    This package is meant to be useful both to players and Dungeon Masters (DMs).
    Some functions apply to many tabletop role-playing games (e.g., dice rolling), but others are focused on Fifth Edition (a.k.a. "5e") and where possible both the 2014 and 2024 versions are supported.
Provides a modified hierarchical test (Liu (2017) <doi:10.1214/17-AOS1539>) for detecting the structural difference between two Semiparametric Gaussian graphical models. The multiple testing procedure asymptotically controls the false discovery rate (FDR) at a user-specified level. To construct the test statistic, a truncated estimator is used to approximate the transformation functions and two R functions including lassoGGM() and lassoNPN() are provided to compute the lasso estimates of the regression coefficients.
Provides density, distribution function, quantile function and random
             generation for the split normal and split-t distributions, and computes their
             mean, variance, skewness and kurtosis for the two distributions (Li, F,
             Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. It includes several practical applications, such as the (deepAFT) model, which utilizes a deep neural network approach to enhance the accelerated failure time (AFT) model for survival data. Another example is the (deepGLM) model that applies deep neural network to the generalized linear model (glm), accommodating data types with continuous, categorical and Poisson distributions.
Provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.
Functions are provided to fit temporal lag models to dynamic
    networks. The models are build on top of exponential random graph models (ERGM) framework. There are
    functions for simulating or forecasting networks for future time points.
    Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, <DOI: 10.1080/10618600.2019.1594834>.
Flexibly convert data between long and wide format using just two
    functions: reshape_toLong() and reshape_toWide().
Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. Provides methods to implement alternative models of trait evolution including Brownian motion (BM), Ornstein-Uhlenbeck (OU), Early Burst (EB), and Pagel's lambda. Also provides flexible biplot functions.
A dimension reduction technique for outlier detection. DOBIN: a Distance 
    based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier 
    detection. This is not an outlier detection method; rather it is a pre-processing 
    method for outlier detection. It brings outliers to the fore-front using fewer basis 
    vectors (Kandanaarachchi, Hyndman 2020) <doi:10.1080/10618600.2020.1807353>.
Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.
Utility package containing: Main categories: Working with grouped data: 'do' something to data when  stratified 'by' some variables.  General linear estimates. Data handling utilities. Functional programming, in particular restrict functions to a smaller domain. Miscellaneous functions for data handling. Model stability in connection with model selection. Miscellaneous other tools.
Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.
Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms,
    namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. 
    The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <doi:10.48550/arXiv.1405.4053>.
    The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. 
    Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering.
    It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps
    these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm 
    and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense
    areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the 
    document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can 
    be found which are representative of the topic.
    More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at <doi:10.48550/arXiv.2008.09470>.
Build a Dockerfile straight from your R session.
    'dockerfiler' allows you to create step by step a Dockerfile, and
    provide convenient tools to wrap R code inside this Dockerfile.
This is the core package that provides both the user API and developer API to deploy 
    the parallel cluster on the cloud using the container service. The user can call 
    clusterPreset() to define the cloud service provider and container and makeDockerCluster()
    to create the cluster. The developer should see "developer's cookbook" on how to define
    the cloud provider and container.
Populate data from an R environment into '.doc' and '.docx' templates. Create a template document in a program such as 'Word', and add strings encased in guillemet characters to create flags («example»). Use getDictionary() to create a dictionary of flags and replacement values, then call docket() to generate a populated document.
Gives you the ability to use arbitrary Docker images
    (including custom ones) to process Rmarkdown code chunks.
Provides R bindings to the 'dockview' 'JavaScript' library <https://dockview.dev/>.
    Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or 'Quarto' or 
    in 'shiny' apps <https://shiny.posit.co/>. In 'shiny' mode, modify docks by dynamically adding, removing or moving panels or 
    groups of panels from the server function. Choose among 8 stunning themes (dark and light),
    serialise the state of a dock to restore it later.
It provides the ability to generate images from documents
 of different types. Three main features are provided: functions for
 generating document thumbnails, functions for performing visual tests
 of documents and a function for updating fields and table of contents
 of a 'Microsoft Word' or 'RTF' document. In order to work, 'LibreOffice' must be
 installed on the machine and or 'Microsoft Word'. If the latter is
 available, it can be used to produce PDF documents or images
 identical to the originals; otherwise, 'LibreOffice' is used and
 the rendering can be sometimes different from the original
 documents.
Define a command-line interface by just giving it
    a description in the specific format.
A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.
A framework for creating production outputs. Users can frame a table, listing, or figure with headers and footers and save to an output file. Stores an intermediate 'docorator' object for reproducibility and rendering to multiple output types.
Basic routines used in scientific coding, such as timing routines, vector/array handing functions and I/O support routines.
Distributed Online Covariance Matrix Tests 'Docovt' is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Docovt' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of 'Docovt' is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.
Provides the ability to display something analogous to
    Python's docstrings within R.  By allowing the user to document
    their functions as comments at the beginning of their function
    without requiring putting the function into a package we allow
    more users to easily provide documentation for their functions.
    The documentation can be viewed just like any other help files
    for functions provided by packages as well.
Creates 'testthat' tests from 'roxygen' examples using simple tags.
Have you ever been tempted to create 'roxygen2'-style documentation
    comments for one of your functions that was not part of one of your
    packages (yet)?
    This is exactly what this package is about: running 'roxygen2' on
    (chunks of) a single code file.
It is sometimes necessary to create documentation for all files in a directory. Doing so by hand can be very tedious. This task is made fast and reproducible using the functionality of 'documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion.
Connect to the 'DocuSign' Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, 
  which supports embedded signing, and sending of documents.
'Microsoft Word' 'docx' files provide an 'XML' structure that is fairly
    straightforward to navigate, especially when it applies to 'Word' tables and
    comments. Tools are provided to determine table count/structure, comment count
    and also to extract/clean tables and comments from 'Microsoft Word' 'docx' documents.
    There is also nascent support for '.doc' and '.pptx' files.
Distances on dual-weighted directed graphs using
    priority-queue shortest paths (Padgham (2019) <doi:10.32866/6945>).
    Weighted directed graphs have weights from A to B which may differ
    from those from B to A.  Dual-weighted directed graphs have two sets
    of such weights. A canonical example is a street network to be used
    for routing in which routes are calculated by weighting distances
    according to the type of way and mode of transport, yet lengths of
    routes must be calculated from direct distances.
Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.
'CRAN' packages 'DoE.base' and 'Rmosek' and non-'CRAN' package 'gurobi' are enhanced with functionality for the creation of optimized arrays for experimentation, where optimization is in terms of generalized minimum aberration. It is also possible to optimally extend existing arrays to larger run size. The package writes 'MPS' (Mathematical Programming System) files for use with any mixed integer optimization software that can process such files. If at least one of the commercial products 'Gurobi' or 'Mosek' (free academic licenses available for both) is available, the package also creates arrays by optimization. For installing 'Gurobi' and its R package 'gurobi', follow instructions at <https://support.gurobi.com/hc/en-us/articles/14462206790033-How-do-I-install-Gurobi-for-R>. For installing 'Mosek' and its R package 'Rmosek', follow instructions at <https://www.mosek.com/downloads/> and <https://docs.mosek.com/8.1/rmosek/install-interface.html>, or use the functionality in the stump CRAN R package 'Rmosek'.
Various kinds of designs for
 (industrial) experiments can be created. The package uses, and sometimes enhances,
        design generation routines from other packages. 
        So far, response surface designs from package 'rsm', Latin hypercube
        samples from packages 'lhs' and 'DiceDesign', and 
        D-optimal designs from package 'AlgDesign' have been implemented.
Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples.
The distributed online expectation maximization algorithms are used to solve parameters of Poisson mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.
Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017)  <doi:10.1371/journal.pone.0179364>.
The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends <doi:10.32614/RJ-2021-048>. The 'foreach' package provides a powerful API for iterating over an R expression in parallel. The 'doFuture' package brings the best of the two together. There are two alternative ways to use this package. The recommended approach is to use 'y <- foreach(...) %dofuture% { ... }', which does not require using 'registerDoFuture()' and has many advantages over '%dopar%'. The alternative is the traditional 'foreach' approach by registering the 'foreach' adapter 'registerDoFuture()' and so that 'y <- foreach(...) %dopar% { ... }' runs in parallelizes with the 'future' framework.
Work with data on Venetian doges and dogaresse and the noble families of the Republic of Venice, and use it for social network analysis, as used in Merelo (2022) <doi:10.48550/arXiv.2209.07334>.
Distributed Online Goodness-of-Fit Test can process the distributed  datasets. The philosophy of the package is described in Guo G.(2024) <doi:10.1016/j.apm.2024.115709>.
Read 'Word' documents containing bibliographic references, search for corresponding DOIs using the 'Crossref' API, and append the retrieved DOIs directly to the references. Supports parallel processing for faster retrieval and produces a new 'Word' document with numbered references including DOIs.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
Distributed Online Mean Tests  is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Domean' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for mean  testing. The philosophy of 'Domean' is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.
Dominance analysis is a method that allows to compare the
             relative importance of predictors in multiple regression models:
             ordinary least squares, generalized linear models, 
             hierarchical linear models, beta regression and dynamic linear models. 
             The main principles and methods of 
             dominance analysis are described in
             Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and  
             Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>
             for ordinary least squares regression. Subsequently, the extensions 
             for multivariate regression, logistic regression and
             hierarchical linear models were described in 
             Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>,
             Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and
             Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>,
             respectively.
A wrapper on top of the 'Domino Command-Line Client'. It lets you
    run 'Domino' commands (e.g., "run", "upload", "download") directly from your
    R environment. Under the hood, it uses R's system function to run the 'Domino'
    executable, which must be installed as a prerequisite. 'Domino' is a service
    that makes it easy to run your code on scalable hardware, with integrated
    version control and collaboration features designed for analytical workflows
    (see <http://www.dominodatalab.com> for more information).
A wrapper on top of the 'Domino Data Python SDK' library. It lets 
    you query and access 'Domino Data Sources' directly from your R environment.
    Under the hood, 'Domino Data R SDK' leverages the API provided by the 
    'Domino Data Python SDK', which must be installed as a prerequisite.
    'Domino' is a platform that makes it easy to run your code on scalable 
    hardware, with integrated version control and collaboration features 
    designed for analytical workflows. See 
    <https://docs.dominodatalab.com/en/latest/api_guide/140b48/domino-data-api>
    for more information.
Methods to apply decomposition-based relative importance 
  analysis for R functions. This package supports the application of 
  decomposition methods by providing 'lapply'- or 'Map'-like meta-functions that 
  compute dominance analysis (Azen, R., & Budescu, D. V. (2003) 
  <doi:10.1037/1082-989X.8.2.129>; Grömping, U. (2007) 
  <doi:10.1198/000313007X188252>) an extension of Shapley value regression 
  (Lipovetsky, S., & Conklin, M. (2001) <doi:10.1002/asmb.446>)
  based on the values returned from other functions.
Provides a parallel backend for the %dopar% function using
        the Rmpi package.
Finds the k nearest neighbours in a dataset of specified points, 
  adding the option to wrap certain variables on a torus.  The user chooses
  the algorithm to use to find the nearest neighbours. Two such algorithms,
  provided by the packages 'RANN' <https://cran.r-project.org/package=RANN>, 
  and 'nabor' <https://cran.r-project.org/package=nabor>, are suggested.
Build donut/pie charts with 'ggplot2' layer by layer, exploiting the advantages of polar symmetry. Leverage layouts to distribute labels effectively. Connect labels to donut segments using pins. Streamline annotation and highlighting.
A facility to generate efficient designs for order-of-additions experiments under pair-wise-order model, see Dennis K. J. Lin  and Jiayu Peng (2019)."Order-of-addition 
    experiments: A review and some new thoughts".  Quality Engineering, 31:1,  49-59, <doi:10.1080/08982112.2018.1548021>. It also provides a facility to generate component 
	orthogonal arrays under component position model, see Jian-Feng Yang, Fasheng Sun & Hongquan Xu (2020): "A Component Position Model, Analysis and 
	Design for Order-of-Addition Experiments". Technometrics, <doi:10.1080/00401706.2020.1764394>.
Tools for exploring the topography of 3d triangle meshes.
    The functions were developed with dental surfaces in mind, but could be 
    applied to any triangle mesh of class 'mesh3d'. More specifically, 'doolkit'
    allows to isolate the border of a mesh, or a subpart of the mesh using the 
    polygon networks method; crop a mesh; compute basic descriptors (elevation, 
    orientation, footprint area); compute slope, angularity and relief index
    (Ungar and Williamson (2000) <https://palaeo-electronica.org/2000_1/gorilla/issue1_00.htm>;
    Boyer (2008) <doi:10.1016/j.jhevol.2008.08.002>), inclination and occlusal
    relief index or gamma (Guy et al. (2013) <doi:10.1371/journal.pone.0066142>),
    OPC (Evans et al. (2007) <doi:10.1038/nature05433>), OPCR (Wilson et al. 
    (2012) <doi:10.1038/nature10880>), DNE (Bunn et al. (2011) <doi:10.1002/ajpa.21489>;
    Pampush et al. (2016) <doi:10.1007/s10914-016-9326-0>), form factor (Horton 
    (1932) <doi:10.1029/TR013i001p00350>), basin elongation (Schum (1956) 
    <doi:10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2>), lemniscate ratio 
    (Chorley et al; (1957) <doi:10.2475/ajs.255.2.138>), enamel-dentine distance
    (Guy et al. (2015) <doi:10.1371/journal.pone.0138802>; Thiery et al. (2017) 
    <doi:10.3389/fphys.2017.00524>), absolute crown strength (Schwartz et al. 
    (2020) <doi:10.1098/rsbl.2019.0671>), relief rate (Thiery et al. (2019)
    <doi:10.1002/ajpa.23916>) and area-relative curvature; draw cumulative
    profiles of a topographic variable; and map a variable over a 3d triangle 
    mesh.
Statistical methods and related graphical representations for the Desirability of Outcome Ranking (DOOR) methodology. The DOOR is a paradigm for the design, analysis, interpretation of clinical trials and other research studies based on the patient centric benefit risk evaluation. The package provides functions for generating summary statistics from individual level/summary level datasets, conduct DOOR probability-based inference, and visualization of the results. For more details of DOOR methodology, see Hamasaki and Evans (2025) <doi:10.1201/9781003390855>. For more explanation of the statistical methods and the graphics, see the technical document and user manual of the DOOR 'Shiny' apps at <https://methods.bsc.gwu.edu>.
Provides a 'foreach' parallel adapter for 'parabar' backends. This
    package offers a minimal implementation of the '%dopar%' operator, enabling
    users to run 'foreach' loops in parallel, leveraging the parallel and
    progress-tracking capabilities of the 'parabar' package. Learn more about
    'parabar' and 'doParabar' at <https://parabar.mihaiconstantin.com>.
Provides a parallel backend for the %dopar% function using
        the parallel package.
Provides information on drug names (brand, 
    generic and street) for drugs tracked by the DEA. There are functions that 
    will search synonyms and return the drug names and types. The vignettes 
    have extensive information on the work done to create the data for the package.
Create and manage fault-tolerant task queues for the 'foreach' package using the 'Redis' key/value database.
Provides models to fit the dynamics of a regulated system experiencing exogenous inputs. 
    The underlying models use differential equations and linear mixed-effects regressions to estimate the 
    coefficients of the equation. With them, the functions can provide an estimated signal.
    The package provides simulation and analysis functions and also print, summary, plot and predict methods,
    adapted to the function outputs, for easy implementation and presentation of results.
Provides functions to perform
    reproducible parallel foreach loops, using independent
    random streams as generated by L'Ecuyer's combined
    multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>].
    It enables to easily convert standard '%dopar%' loops into
    fully reproducible loops, independently of the number
    of workers, the task scheduling strategy, or the chosen
    parallel environment and associated foreach backend.
Contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.
Contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010)  <doi:10.1007/978-1-4419-1213-8>.
Identification of causal effects from arbitrary observational and 
    experimental probability distributions via do-calculus and standard 
    probability manipulations using a search-based algorithm by 
    Tikka, Hyttinen and Karvanen (2021) <doi:10.18637/jss.v099.i05>. 
    Allows for the presence of  mechanisms related to selection bias 
    (Bareinboim and Tian, 2015) <doi:10.1609/aaai.v29i1.9679>, 
    transportability (Bareinboim and Pearl, 2014) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, 
    missing data (Mohan, Pearl, and Tian, 2013) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations 
    of these. Also supports identification in the presence of context-specific 
    independence (CSI) relations through labeled directed acyclic graphs 
    (LDAG). For details on CSIs see (Corander et al., 2019) 
    <doi:10.1016/j.apal.2019.04.004>.
Provides the user with an interactive application which can be used to facilitate the planning of dose finding studies by applying the theory of optimal experimental design.
The DoseFinding package provides functions for the design and analysis
	     of dose-finding experiments (with focus on pharmaceutical Phase
	     II clinical trials). It provides functions for: multiple contrast
	     tests, fitting non-linear dose-response models (using Bayesian and
	     non-Bayesian estimation), calculating optimal designs and an
	     implementation of the MCPMod methodology (Pinheiro et al. (2014)
	     <doi:10.1002/sim.6052>).
Performs sensitivity analysis for the sharp null, attributable effects, and weak nulls in matched studies with continuous exposures and binary or continuous outcomes as described in Zhang, Small, Heng (2024) <doi:10.48550/arXiv.2401.06909> and Zhang, Heng (2024) <doi:10.48550/arXiv.2409.12848>. Two of the functions require installation of the 'Gurobi' optimizer. Please see <https://docs.gurobi.com/current/#refman/ins_the_r_package.html> for guidance.
Provides a parallel backend for the %dopar% function using
        the snow package of Tierney, Rossini, Li, and Sevcikova.
Constructs dynamic optimal shrinkage estimators for the weights of the global 
  minimum variance portfolio which are reconstructed at given reallocation 
  points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>).
  Two dynamic shrinkage estimators are available in this package. One using 
  overlapping samples while the other use nonoverlapping samples.
Estimates dose-response relations from summarized dose-response
    data and to combines them according to principles of (multivariate)
    random-effects models.
A small package containing helper utilities for creating functions
    for computing statistics.
Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.
Provides .C64(), which is an enhanced version of .C()
    and .Fortran() from the foreign function interface. .C64() supports long
    vectors, arguments of type 64-bit integer, and provides a mechanism to
    avoid unnecessary copies of read-only and write-only arguments. This
    makes it a convenient and fast interface to C/C++ and Fortran code.
Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.
Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.
A toolbox to create and manage metadata files and
    configuration profiles: files used to configure the parameters and
    initial settings for some computer programs.
Generate point data for representing people within spatial data. This
  collects a suite of tools for creating simple dot density maps. Several functions 
  from different spatial packages are standardized to take the same arguments so
  that they can be easily substituted for each other.
Modifies dot plots to have different sizes of dots mimicking violin plots and identifies
  modes or peaks for them based on frequency and kernel density estimates 
  (Rosenblatt, 1956) <doi:10.1214/aoms/1177728190> (Parzen, 1962) <doi:10.1214/aoms/1177704472>.
Provides a `.` object which can be used for unpacking
  assignments. For example, `.[rows, columns] <- dim(cars)` could be used to
  pull the number of rows and number of columns from `dim(cars)` into
  individual variables `rows` and `columns` in a single step.
Create quick and easy dot-and-whisker plots of regression results. It takes as input either (1) a coefficient table in standard form or (2) one (or a list of) fitted model objects (of any type that has methods implemented in the 'parameters' package). It returns 'ggplot' objects that can be further customized using tools from the 'ggplot2' package. The package also includes helper functions for tasks such as rescaling coefficients or relabeling predictor variables. See more methodological discussion of the visualization and data management methods used in this package in Kastellec and Leoni (2007) <doi:10.1017/S1537592707072209> and Gelman (2008) <doi:10.1002/sim.3107>.
Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola  & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].
Likelihood-based inference methods with doubly-truncated data are developed under various models.
 Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and
 Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>.
 Parametric models from the special exponential family (SEF) are based on
 Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and
 Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>.
 The parametric location-scale models are based on Dorre et al. (2021) <doi:10.1007/s00180-020-01027-6>.
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
Implementation of the double/debiased machine learning framework of
    Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear
    regression models, partially linear instrumental variable regression models,
    interactive regression models and interactive instrumental variable
    regression models. 'DoubleML' allows estimation of the nuisance parts in
    these models by machine learning methods and computation of the Neyman
    orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the
    'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on
    the 'R6' package is very flexible. More information available in the
    publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.
Visualize contact tracing data using a 'shiny' app and estimate the incubation or latency time of an infectious disease respecting the following characteristics in the analysis; (i) doubly interval censoring with (partly) overlapping or distinct windows; (ii) an infection risk corresponding to exponential growth; (iii) right truncation allowing for individual truncation times; (iv) different choices concerning the family of the distribution. For our earlier work, we refer to Arntzen et al. (2023) <doi:10.1002/sim.9726>. A paper describing our approach in detail will follow.
Overload utils::'?' to build unary and binary operators from existing functions, 
    piping operators of different precedence, and flexible syntaxes.
Double constrained correspondence analysis (dc-CA) analyzes 
   (multi-)trait (multi-)environment ecological data by using the 'vegan' 
   package and native R code. Throughout the two step algorithm of ter Braak 
   et al. (2018) is used. This algorithm combines and extends community- 
   (sample-) and species-level analyses, i.e. the usual community weighted 
   means (CWM)-based regression analysis and the species-level analysis of 
   species-niche centroids (SNC)-based regression analysis. The two steps use 
   canonical correspondence analysis to regress the abundance data on to the 
   traits and (weighted) redundancy analysis to regress the CWM of the 
   orthonormalized traits on to the environmental predictors. The function 
   dc_CA() has an option to divide the abundance data of a site by the site 
   total, giving equal site weights. This division has the advantage that the 
   multivariate analysis corresponds with an unweighted (multi-trait) 
   community-level analysis, instead of being weighted. The first step of 
   the algorithm uses vegan::cca(). The second step uses wrda() but 
   vegan::rda() if the site weights are equal. This version has a predict() 
   function. For details see ter Braak et al. 2018 
   <doi:10.1007/s10651-017-0395-x>.
   and ter Braak & van Rossum 2025 <doi:10.1016/j.ecoinf.2025.103143>.
Creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate, bivariate analysis and parse it into HTML.
    It also allows you to visualize all your data with graphic representation.
Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.
Implements maximum likelihood methods for 
    evaluating the durability of vaccine efficacy in a randomized, 
    placebo-controlled clinical trial with staggered enrollment of participants 
    and potential crossover of placebo recipients before the end of the trial. 
    Lin, D. Y., Zeng, D., and Gilbert, P. B. (2021) 
    <doi:10.1093/cid/ciab226>
    and 
    Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) 
    <doi:10.1093/cid/ciab630>.
'Kevin Dowd's' book Measuring Market Risk is a widely read book 
          in the area of risk measurement by students and 
          practitioners alike. As he claims, 'MATLAB' indeed might have been the most 
          suitable language when he originally wrote the functions, but,
          with growing popularity of R it is not entirely 
	  valid. As 'Dowd's' code was not intended to be error free and were mainly 
	  for reference, some functions in this package have inherited those 
	  errors. An attempt will be made in future releases to identify and correct 
	  them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. 
          It should be noted that 'Dowd' offers both
          'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more 
          recent version of 'MMR1' toolbox and they both have mostly similar 
          function. The toolbox mainly contains different parametric and non 
	  parametric methods for measurement of market risk as well as 
	  backtesting risk measurement methods.
Syntax highlighting of R code, specifically designed for the
    needs of 'RMarkdown' packages like 'pkgdown', 'hugodown', and
    'bookdown'. It includes linking of function calls to their
    documentation on the web, and automatic translation of ANSI escapes in
    output to the equivalent HTML.
Provides a wrapper for the download.file function,
    making it possible to download files over HTTPS on Windows, Mac OS X, and
    other Unix-like platforms. The 'RCurl' package provides this functionality
    (and much more) but can be difficult to install because it must be compiled
    with external dependencies. This package has no external dependencies, so
    it is much easier to install.
Implement download buttons in HTML output from 'rmarkdown' without the need for 'runtime:shiny'.
Uses species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) <doi:10.18637/jss.v086.c03>.
Toggles the test and production versions of a large
  data analysis project.
Provides a set of functions for inferring, visualizing, and analyzing B cell phylogenetic trees.
    Provides methods to 1) reconstruct unmutated ancestral sequences,
    2) build B cell phylogenetic trees using multiple methods,
    3) visualize trees with metadata at the tips,
    4) reconstruct intermediate sequences,
    5) detect biased ancestor-descendant relationships among metadata types
    Workflow examples available at documentation site (see URL).
    Citations:
    Hoehn et al (2022) <doi:10.1371/journal.pcbi.1009885>,
    Hoehn et al (2021) <doi:10.1101/2021.01.06.425648>.
Access diverse 'ggplot2'-compatible color palettes for
    simplified data visualization.
A Scannerless GLR parser/parser generator.  Note that GLR standing for "generalized LR", where L stands for "left-to-right" and
   R stands for "rightmost (derivation)".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita
   (1987) algorithm. (Paper can be found at <https://aclanthology.org/P84-1073.pdf>).
   The original 'dparser' package documentation can be found at <https://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like
   rxode2's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate
   them to R code.   To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use
   #include <dparser.h>.  This also provides a R-based port of the make_dparser <https://dparser.sourceforge.net/d/make_dparser.cat> command called
   mkdparser().  Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar
   testing.  The fastest parsing, of course, occurs at the C level, and is suggested.
Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) <doi:10.1007/s10985-006-9004-2>. The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates.
Use dynamic programming method to solve l1 convex clustering with identical weights.
A Bayesian hierarchical model for clustering dissimilarity data using the Dirichlet process. The latent configuration of objects and the number of clusters are automatically inferred during the fitting process. The package supports multiple models which are available to detect clusters of various shapes and sizes using different covariance structures. Additional functions are included to ensure adequate model fits through prior and posterior predictive checks.
Differential partial correlation identification with the ridge and the fusion penalties.
The automated clustering and quantification of the digital PCR
            data is based on the combination of 'DBSCAN' (Hahsler et al. (2019)
            <doi:10.18637/jss.v091.i01>) and 'c-means' (Bezdek et al. (1981) 
            <doi:10.1007/978-1-4757-0450-1>) algorithms.
            The analysis is independent of multiplexing geometry, dPCR system, 
            and input amount.
            The details about input data and parameters are available in the
            vignette.
Analysis, visualisation and simulation of digital polymerase chain
    reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). 
    Supports data formats of commercial systems (Bio-Rad QX100 and
    QX200; Fluidigm BioMark) and other systems.
Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting.
The Directed Prediction Index ('DPI') is
    a quasi-causal inference (causal discovery) method for observational data
    designed to quantify the relative endogeneity (relative dependence)
    of outcome (Y) versus predictor (X) variables in regression models.
    By comparing the proportion of variance explained (R-squared)
    between the Y-as-outcome model and the X-as-outcome model
    while controlling for a sufficient number of possible confounders,
    it can suggest a plausible (admissible) direction of influence
    from a less endogenous variable (X) to a more endogenous variable (Y).
    Methodological details are provided at
    <https://psychbruce.github.io/DPI/>.
    This package also includes functions for data simulation and network
    analysis (correlation, partial correlation, and Bayesian networks).
Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.
Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a 'parquet' file or released to a 'GitHub' repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.
Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.
A fast, consistent tool for working with data frame like
    objects, both in memory and out of memory.
An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
    You can learn each steps of data manipulation by clicking your mouse without coding.
    You can get resultant data (as a 'tibble') and the code for data manipulation.
This MCMC method takes a data numeric vector (Y) and assigns the elements of Y
  to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred.
  Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
An implementation of common statistical analysis and models with
    differential privacy (Dwork et al., 2006a) <doi:10.1007/11681878_14>
    guarantees. The package contains, for example, functions providing
    differentially private computations of mean, variance, median, histograms,
    and contingency tables. It also implements some statistical models and
    machine learning algorithms such as linear regression (Kifer et al., 2012)
    <https://proceedings.mlr.press/v23/kifer12.html>
    and SVM (Chaudhuri et al., 2011)
    <https://jmlr.org/papers/v12/chaudhuri11a.html>. In addition, it implements
    some popular randomization mechanisms, including
    the Laplace mechanism (Dwork et al., 2006a)
    <doi:10.1007/11681878_14>, Gaussian mechanism (Dwork et al., 2006b)
    <doi:10.1007/11761679_29>, analytic Gaussian mechanism (Balle & Wang, 2018)
    <https://proceedings.mlr.press/v80/balle18a.html>, and exponential mechanism
    (McSherry & Talwar, 2007) <doi:10.1109/FOCS.2007.66>.
Multivariate Gaussian mixture model with a determinant point 
  process prior to promote the discovery of parsimonious components from 
  observed data. See Xu, Mueller, Telesca (2016) <doi:10.1111/biom.12482>.
Computations for approximations and alternatives for the 'DPQ'
  (Density (pdf), Probability (cdf) and Quantile) functions for probability
  distributions in R.
  Primary focus is on (central and non-central) beta, gamma and related
  distributions such as the chi-squared, F, and t.
  --
  For several distribution functions, provide functions implementing formulas from
  Johnson, Kotz, and Kemp (1992) <doi:10.1002/bimj.4710360207>  and
  Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous
  distributions respectively.
  This is for the use of researchers in these numerical approximation
  implementations, notably for my own use in order to improve standard
  R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.
An extension to the 'DPQ' package with computations for 'DPQ'
  (Density (pdf), Probability (cdf) and Quantile) functions, where
  the functions here partly use the 'Rmpfr' package and hence the
  underlying 'MPFR' and 'GMP' C libraries.
Generally, most of the packages specify the probability density function, cumulative distribution function, quantile function, and random numbers generation of the probability distributions. The present package allows to compute some important distributional properties, including the first four ordinary and central moments, Pearson's coefficient of skewness and kurtosis, the mean and variance, coefficient of variation, median, and quartile deviation at some parametric values of several well-known and extensively used probability distributions.
Piecewise linear segmentation of ordered data by a
        dynamic programming algorithm. The algorithm was developed for time
	series data, e.g. growth curves, and for genome-wide read-count data
        from next generation sequencing, but is broadly applicable.
        Generic implementations of dynamic programming routines allow
        to scan for optimal segmentation parameters and test custom
        segmentation criteria ("scoring functions").
Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) <doi:10.1080/07474938.2019.1624401>, and dynamic panel linear model suggested by Hsiao et al. (2002) <doi:10.1016/S0304-4076(01)00143-9>, where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed.
Contains functions to perform copula estimation 
	by the non-parametric Bayesian method, 
	Dirichlet-based Polya Tree. See Ning (2018) <doi:10.1080/00949655.2017.1421194>.
In the context of data quality assessment, this package provides a number of functions for evaluating data quality across various dimensions, including completeness, plausibility, concordance, conformance, currency, timeliness, and correctness. It has been developed based on two well-known frameworks—Michael G. Kahn (2016) <doi: 10.13063/2327-9214.1244> and Nicole G. Weiskopf (2017) <doi: 10.5334/egems.218>—for data quality assessment. Using this package, users can evaluate the quality of their datasets, provided that corresponding metadata are available.
A graphical user interface (GUI) to the functions implemented
    in the R package 'DQAstats'. Publication: Mang et al. (2021)
    <doi:10.1186/s12911-022-01961-z>.
Perform data quality assessment ('DQA') of electronic health
    records ('EHR'). Publication: Kapsner et al. (2021)
    <doi:10.1055/s-0041-1733847>.
Several fast random number generators are provided as C++
  header only libraries: The PCG family by O'Neill (2014
  <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>) as well as
  the Xoroshiro / Xoshiro family by Blackman and Vigna (2021
  <doi:10.1145/3460772>). In addition fast functions for generating random
  numbers according to a uniform, normal and exponential distribution
  are included. The latter two use the Ziggurat algorithm originally
  proposed by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>).
  The fast sampling methods support unweighted sampling both with and without
  replacement. These functions are exported to R and as a C++ interface and are
  enabled for use with the default 64 bit generator from the PCG family,
  Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the
  20 rounds Threefry engine (Salmon et al., 2011, <doi:10.1145/2063384.2063405>)
  as provided by the package 'sitmo'.
The new (dQTG.seq1 and dQTG.seq2) and existing (SmoothLOD, G', deltaSNP and ED) bulked segregant analysis methods are used to identify various types of quantitative trait loci for complex traits via extreme phenotype individuals in bi-parental segregation populations (F2, backcross, doubled haploid and recombinant inbred line). The numbers of marker alleles in extreme low and high pools are used in existing methods to identify trait-related genes, while the numbers of marker alleles and genotypes in extreme low and high pools are used in the new methods to construct a new statistic Gw for identifying trait-related genes. dQTG-seq2 is feasible to identify extremely over-dominant and small-effect genes in F2. Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM (2022, <doi: 10.1016/j.xplc.2022.100319>).
Functions, methods, and datasets for fitting dimension
 reduction regression, using slicing (methods SAVE and SIR), Principal
 Hessian Directions (phd, using residuals and the response), and an
 iterative IRE.  Partial methods, that condition on categorical
 predictors are also available.  A variety of tests, and stepwise
 deletion of predictors, is also included.  Also included is
 code for computing permutation tests of dimension.  Adding additional
 methods of estimating dimension is straightforward.
 For documentation, see the vignette in the package.   With version 3.0.4,
 the arguments for dr.step have been modified.
Joint dimension reduction and spatial clustering is conducted for
    Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to 
    Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.
Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems.  Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.
Decodes meshes and point cloud data encoded by the Draco mesh
    compression library from Google. Note that this is only designed for basic
    decoding and not intended as a full scale wrapping of the Draco library.
Statistical tests and test statistics to identify events in a
    dataset that are dragon kings (DKs). The statistical methods in this
    package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>.
These are data sets for the hit TV show, RuPaul's Drag Race. 
    Data right now include episode-level data, contestant-level data, and 
    episode-contestant-level data. This is a work in progress, and a love letter
    of a kind to RuPaul's Drag Race and the performers that have appeared on the show.
    This may not be the most productive use of my time, but I have tenure and what 
    are you going to do about it? I think there is at least some value in this package
    if it allows the show's fandom to learn more about the R programming language 
    around its contents.
Move elements between containers in 'Shiny' without explicitly using 'JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables.
A general-purpose computational engine for data
  analysis, drake rebuilds intermediate data objects when their
  dependencies change, and it skips work when the results are already up
  to date.  Not every execution starts from scratch, there is native
  support for parallel and distributed computing, and completed projects
  have tangible evidence that they are reproducible.  Extensive
  documentation, from beginner-friendly tutorials to practical examples
  and more, is available at the reference website
  <https://docs.ropensci.org/drake/> and the online manual
  <https://books.ropensci.org/drake/>.
Analysis of preprocessed dramatic texts, with respect to literary research. 
  The package provides functions to analyze and visualize information about characters,
  stage directions, the dramatic structure and the text itself.
  The dramatic texts are expected to be in CSV format, which can be installed from within 
  the package, sample texts are provided. The package and the reasoning behind it are described in 
  Reiter et al. (2017) <doi:10.18420/in2017_119>.
Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) <doi:10.48550/arXiv.2308.09207>.
Creation and use of R Repositories via helper functions 
 to insert packages into a repository, and to add repository information 
 to the current R session. Two primary types of repositories are support:
 gh-pages at GitHub, as well as local repositories on either the same machine
 or a local network. Drat is a recursive acronym: Drat R Archive Template.
A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings.
  Compatible with and based on the R 'grid' package.
An interactive image editing tool that can be added as part of the HTML in Shiny,
    R markdown or any type of HTML document. Often times, plots, photos are embedded
    in the web application/file. 'drawer' can take screenshots of these image-like elements, or 
    any part of the HTML document and send to an image editing space called 'canvas' to allow users 
    immediately edit the screenshot(s) within the same document. Users can quickly 
    combine, compare different screenshots, upload their own images 
    and maybe make a scientific figure.
We present DRaWR, a network-based method for ranking genes or
    properties related to a given gene set. Such related genes or properties are
    identified from among the nodes of a large, heterogeneous network of biological
    information. Our method involves a random walk with restarts, performed on
    an initial network with multiple node and edge types, preserving more of the
    original, specific property information than current methods that operate
    on homogeneous networks. In this first stage of our algorithm, we find the
    properties that are the most relevant to the given gene set and extract a
    subnetwork of the original network, comprising only the relevant properties. We
    then rerank genes by their similarity to the given gene set, based on a second
    random walk with restarts, performed on the above subnetwork.
A tool to sample data with the desired properties.Samples can be 
    drawn by purposive sampling with determining distributional 
    conditions, such as deviation from normality (skewness and kurtosis), 
    and sample size in quantitative research studies.
    For purposive sampling, a researcher has something in mind and participants that 
    fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) 
    <doi:10.11648/j.ajtas.20160501.11>.Purposive sampling can be useful for answering
    many research questions (Klar & Leeper, 2019) 
    <doi:10.1002/9781119083771.ch21>.
We offer an implementation of the series representation put 
  forth in "A series representation for multidimensional 
  Rayleigh distributions" by Wiegand and Nadarajah <DOI: 
  10.1002/dac.3510>. Furthermore we have implemented an 
  integration approach proposed by Beaulieu et al. for 3 and 
  4-dimensional Rayleigh densities (Beaulieu, Zhang,  "New 
  simplest exact forms for the 3D and 4D multivariate Rayleigh 
  PDFs with applications to antenna array geometrics", <DOI: 
  10.1109/TCOMM.2017.2709307>).
Feed longitudinal data into a Bayesian Latent Factor Model to obtain 
  a low-rank representation. Parameters are estimated using a Hamiltonian 
  Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and 
  S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", 
  Actes des JdS 2016.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). 
    In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. 
    Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022)  <doi:10.48550/arXiv.2201.13004>.
Consider ambiguity in probabilistic descriptions by replacing a parametric probabilistic description of uncertainty by a non-parametric set of probability distributions in the form of a Density Ratio Class. This is of particular interest in Bayesian inference.
    The Density Ratio Class is particularly suited for this purpose as it is invariant under Bayesian inference, marginalization, and propagation through a deterministic model. Here, invariant means that the result of the operation applied to a Density Ratio Class is again a Density Ratio Class. In particular the invariance under Bayesian inference thus enables iterative learning within the same framework of Density Ratio Classes.
    The use of imprecise probabilities in general, and Density Ratio Classes in particular, lead to intervals of characteristics of probability distributions, such as cumulative distribution functions, quantiles, and means.
    The package is based on a sample of the distribution proportional to the upper bound of the class. Typically this will be a sample from the posterior in Bayesian inference. Based on such a sample, the package provides functions to calculate lower and upper class boundaries and lower and upper bounds of cumulative distribution functions, and quantiles.
    Rinderknecht, S.L., Albert, C., Borsuk, M.E., Schuwirth, N., Kuensch, H.R. and Reichert, P. (2014) "The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction." Environmental Modelling & Software. 62, 300-315, 2014. <doi:10.1016/j.envsoft.2014.08.020>.
    Sriwastava, A. and Reichert, P. "Robust Bayesian Estimation of Value Function Parameters using Imprecise Priors." Submitted. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4973574>.
Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" <doi:10.1007/978-3-642-51175-2_24> ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" <doi:10.1007/978-3-642-59471-7_6> ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" <doi:10.1016/S0167-9473(00)00064-5> ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" <doi:10.1016/j.csda.2008.05.028> ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" <doi:10.1007/s11634-016-0263-9>.
Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.
A specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.
Fit logistic functions to observed dose-response continuous
    data and evaluate goodness-of-fit measures. See Malyutina A., Tang J.,
    and Pessia A. (2023) <doi:10.18637/jss.v106.i04>.
Implements the locally efficient doubly robust difference-in-differences (DiD)
    estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020)
    <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome
    regression estimators (also implemented in the package) to form estimators with
    more attractive statistical properties. Two different estimation methods can be used
    to estimate the nuisance functions.
While it has been well established that drugs affect and help
  patients differently, personalized drug response predictions remain 
  challenging. Solutions based on single omics measurements have been proposed, 
  and networks provide means to incorporate molecular interactions into reasoning. 
  However, how to integrate the wealth of information contained in multiple omics 
  layers still poses a complex problem. 
  We present a novel network analysis pipeline, DrDimont, Drug response prediction 
  from Differential analysis of multi-omics networks. It allows for comparative 
  conclusions between two conditions and translates them into differential drug 
  response predictions. DrDimont focuses on molecular interactions. It establishes 
  condition-specific networks from correlation within an omics layer that are 
  then reduced and combined into heterogeneous, multi-omics molecular networks. 
  A novel semi-local, path-based integration step ensures integrative conclusions. 
  Differential predictions are derived from comparing the condition-specific 
  integrated networks. DrDimont's predictions are explainable, i.e., molecular 
  differences that are the source of high differential drug scores can be retrieved.
  Our proposed pipeline leverages multi-omics data for differential predictions,
  e.g. on drug response, and includes prior information on interactions.
  The case study presented in the vignette uses data published by
  Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only
  to the software and explicitly not to the included data.
Implement the statistical test proposed in Weng et al. (2021) to test whether
    the average treatment effect curve is constant and whether a discrete covariate is a significant effect modifier.
A set of tools for relational and event analysis, including two- and one-mode network brokerage and structural measures, and helper functions optimized for relational event analysis with large datasets, including creating relational risk sets, computing network statistics, estimating relational event models, and simulating relational event sequences. For more information on relational event models, see Butts (2008) <doi:10.1111/j.1467-9531.2008.00203.x>, Lerner and Lomi (2020) <doi:10.1017/nws.2019.57>, Bianchi et al. (2024) <doi:10.1146/annurev-statistics-040722-060248>, and Butts et al. (2023) <doi:10.1017/nws.2023.9>. In terms of the structural measures in this package, see Leal (2025) <doi:10.1177/00491241251322517>, Burchard and Cornwell (2018) <doi:10.1016/j.socnet.2018.04.001>, and Fujimoto et al. (2018) <doi:10.1017/nws.2018.11>. This package was developed with support from the National Science Foundation’s (NSF) Human Networks and Data Science Program (HNDS) under award number 2241536 (PI: Diego F. Leal). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
Fits dose-response models utilizing a Bayesian model
  averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211>
  for both continuous and binary responses. Longitudinal dose-response modeling
  is also supported in a Bayesian model averaging framework as outlined in 
  Payne, Ray, and Thomann (2024)
  <doi:10.1080/10543406.2023.2292214>. Functions
  for plotting and calculating various posterior quantities
  (e.g. posterior mean, quantiles, probability of minimum efficacious dose,
  etc.) are also implemented.  Copyright Eli Lilly and Company (2019).
Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. 
    The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.
A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (2022) <doi:10.48550/arXiv.2005.14458>.
Estimates the conditional association between an exposure and an outcome given covariates. Three methods are implemented: O-estimation, where a nuisance model for the association between the covariates and the outcome is used; E-estimation where a nuisance model for the association between the covariates and the exposure is used, and doubly robust (DR) estimation where both nuisance models are used. In DR-estimation, the estimates will be consistent when at least one of the nuisance models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) <doi:10.1515/em-2014-0021>.
To overcome the memory limitations for fitting linear (LM) and Generalized Linear Models (GLMs) to large data sets, this package implements the Divide and Recombine (D&R) strategy. It basically divides the entire large data set into suitable subsets manageable in size and then fits model to each subset. Finally, results from each subset are aggregated to obtain the final estimate. This package also supports fitting GLMs to data sets that cannot fit into memory and provides methods for fitting GLMs under linear regression, binomial regression, Poisson regression, and multinomial logistic regression settings. Respective models are fitted using different D&R strategies as described by: Xi, Lin, and Chen (2009) <doi:10.1109/TKDE.2008.186>, Xi, Lin and Chen (2006) <doi:10.1109/TKDE.2006.196>, Zuo and Li (2018) <doi:10.4236/ojs.2018.81003>, Karim, M.R., Islam, M.A. (2019) <doi:10.1007/978-981-13-9776-9>.
Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) <doi:10.1016/j.aap.2019.105278> along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) <doi:10.1111/sjos.12255> to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) <doi:10.2307/3318678>. The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented.
Tutarials of R learning easily and happily.
Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, and set up color-blind palette, functions used in academic research of political psychology or political science in general.
Fit and explore Drift Diffusion Models (DDMs),
    a common tool in psychology for describing decision processes in simple
    tasks. It can handle both time-independent and time-dependent DDMs. You
    either choose prebuilt models or create your own, and the package takes
    care of model predictions and parameter estimation. Model predictions
    are derived via the numerical solutions provided by Richter, Ulrich, and
    Janczyk (2023, <doi:10.1016/j.jmp.2023.102756>).
Concept drift refers to the change in the data distribution or 
  in the relationships between variables over time.
  'drifter' calculates distances between variable distributions or 
  variable relations and identifies both types of drift. 
  Key functions are: 
  calculate_covariate_drift() checks distance between corresponding variables in two datasets,
  calculate_residuals_drift() checks distance between residual distributions for two models,
  calculate_model_drift() checks distance between partial dependency profiles for two models,
  check_drift() executes all checks against drift.
  'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
Performs the drifting Markov models (DMM) which are 
    non-homogeneous Markov models designed for modeling the heterogeneities of
    sequences in a more flexible way than homogeneous Markov chains or even 
    hidden Markov models. In this context, we developed an R package dedicated to 
    the estimation, simulation and the exact computation of associated reliability 
    of drifting Markov models. The implemented methods are described in
    Vergne, N. (2008), <doi:10.2202/1544-6115.1326> and
    Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> .
R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.
Cancer genomes contain large numbers of somatic alterations but few
    genes drive tumor development. Identifying cancer driver genes is critical 
    for precision oncology. Most of current approaches either identify driver 
    genes based on mutational recurrence or using estimated scores predicting 
    the functional consequences of mutations. 'driveR' is a tool for 
    personalized or batch analysis of genomic data for driver gene prioritization 
    by combining genomic information and prior biological knowledge. As features, 
    'driveR' uses coding impact metaprediction scores, non-coding impact scores, 
    somatic copy number alteration scores, hotspot gene/double-hit gene 
    condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG 
    pathways. It uses these features to estimate cancer-type-specific 
    probability for each gene of being a cancer driver using the related task of 
    a multi-task learning classification model. The method is described in detail 
    in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for 
    prioritizing cancer driver genes using somatic genomics data. BMC 
    Bioinformatics <doi:10.1186/s12859-021-04203-7>.
A toolkit for parsing dice notation, analyzing rolls,
    calculating success probabilities, and plotting outcome distributions.
Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) <DOI:10.24072/pcjournal.325> and Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.
This is the companion package to the Data Visualization Geometries Encyclopedia, providing seamless access to the associated data.
Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings.
Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi square), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) <https://www.researchgate.net/publication/223956222_Financial_incentives_personal_information_and_drop-out_in_online_studies>; Ulf-Dietrich Reips (2002) "Standards for Internet-Based Experimenting" <doi:10.1027//1618-3169.49.4.243>.
Generates simulated data representing the LOX drop testing
    process (also known as impact testing). A simulated process allows for
    accelerated study of test behavior. Functions are provided to simulate
    trials, test series, and groups of test series. Functions for creating plots
    specific to this process are also included. Test attributes and criteria can
    be set arbitrarily. This work is not endorsed by or affiliated with NASA.
    See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity
    of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized
    Liquid and Gaseous Oxygen Environments" <doi:10.1520/G0086-17>.
Efficient covariate-adjusted estimators of quantities that are useful for 
    establishing the effects of treatments on ordinal outcomes.
Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>.
Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) <arXiv:2104.14091>. The estimator is doubly robust against errors in the estimation of the intermediate nuisance parameters. Users can choose from the flexible estimation models provided in the package, or use any other preferred model.
Implementation of the Density Ratio Permutation Test for testing the goodness-of-fit of a hypothesised ratio of two densities, as described in Bordino and Berrett (2025) <doi:10.48550/arXiv.2505.24529>.
Several quality measurements for investigating the performance of dimensionality reduction methods are provided here. In addition a new quality measurement called Gabriel classification error is made accessible, which was published in Thrun, M. C., Märte, J., & Stier, Q: "Analyzing Quality Measurements for Dimensionality Reduction" (2023), Machine Learning and Knowledge Extraction (MAKE), <DOI:10.3390/make5030056>.
An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.
Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" <doi:10.48550/arXiv.2510.08438>.
Targeted minimum loss-based estimators of counterfactual means and
    causal effects that are doubly-robust with respect both to consistency and
    asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
    van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) <doi:10.18637/jss.v081.i12>). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) <doi:10.18637/jss.v027.i08>). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).
Plan optimal sample size allocation and go/no-go decision rules
  for phase II/III drug development programs with time-to-event, binary or
  normally distributed endpoints when assuming fixed treatment effects or a
  prior distribution for the treatment effect, using methods from Kirchner et al.
  (2016) <doi:10.1002/sim.6624> and Preussler (2020). Optimal is in the sense of
  maximal expected utility, where the utility is a function taking into account
  the expected cost and benefit of the program. It is possible to extend to more
  complex settings with bias correction (Preussler S et al. (2020)
  <doi:10.1186/s12874-020-01093-w>),  multiple phase III trials (Preussler et
  al. (2019) <doi:10.1002/bimj.201700241>), multi-arm trials (Preussler et al.
  (2019) <doi:10.1080/19466315.2019.1702092>), and multiple endpoints
  (Kieser et al. (2018) <doi:10.1002/pst.1861>).
Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.
A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. 
    Provides functionality for automated data processing, metadata extraction, and statistical 
    analysis of imaging results. The methodology implemented in this package is based on 
    Labrosse et al. (2024) <doi:10.1016/j.xpro.2024.103274> "Protocol for quantifying drug 
    sensitivity in 3D patient-derived ovarian cancer models", which describes the complete 
    workflow for drug sensitivity analysis in patient-derived cancer models.
A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity  in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs.
Summarise patient-level drug utilisation cohorts using data mapped 
    to the Observational Medical Outcomes Partnership (OMOP) common data model. 
    New users and prevalent users cohorts can be generated and their 
    characteristics, indication and drug use summarised.
Includes various functions for playing drum sounds. beat() plays a drum sound
  from one of the six included drum kits. tempo() sets spacing between calls to beat()
  in bpm. Together the two functions can be used to create many different drum patterns.
A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results.
Performs various analyzes of descriptive statistics, including correlations, graphics and tables.
All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2025, <doi:10.5281/zenodo.7229812>), freely available at <https://bookdown.org/hneth/ds4psy/>. The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in 'ds4psy' are designed to be explicit and instructive, rather than efficient or elegant.
Seasonal- and calendar adjustment of time series
    with daily frequency using the DSA approach developed by Ollech,
    Daniel (2018): Seasonal adjustment of daily time series. Bundesbank
    Discussion Paper 41/2018.
Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios.
    The purpose of the package is to help individuals learn 
    about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on what to do with the models.
Simulation models (apps) of various within-host immune response scenarios.
    The purpose of the package is to help individuals learn 
    about within-host infection and immune response modeling from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on what to do with the models.
Providing six different algorithms that can be used to split the 
    available data into training, test and validation subsets with similar distribution 
    for hydrological model developments. The dataSplit() function will help you divide 
    the data according to specific requirements, and you can refer to the par.default() 
    function to set the parameters for data splitting. The getAUC() function will help 
    you measure the similarity of distribution features between the data subsets.
    For more information about the data splitting algorithms, please refer to:
    Chen et al. (2022) <doi:10.1016/j.jhydrol.2022.128340>, 
    Zheng et al. (2022) <doi:10.1029/2021WR031818>.
Discretization-based random sampling algorithm that is useful for a complex model in high dimension is implemented. The normalizing constant of a target distribution is not needed. Posterior summaries are compared with those by 'OpenBUGS'. The method is described: Wang and Lee (2014) <doi:10.1016/j.csda.2013.06.011> and exercised in Lee (2009) <http://hdl.handle.net/1993/21352>.
This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mulè M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 <https://www.nature.com/articles/s41467-022-29356-8> for more details on the method.
Base 'DataSHIELD' functions for the server side. 'DataSHIELD' is a software package which allows
    you to do non-disclosive federated analysis on sensitive data. 'DataSHIELD' analytic functions have
    been designed to only share non disclosive summary statistics, with built in automated output
    checking based on statistical disclosure control. With data sites setting the threshold values for
    the automated output checks. For more details, see 'citation("dsBase")'.
Base 'DataSHIELD' functions for the client side. 'DataSHIELD' is a software package which allows
    you to do non-disclosive federated analysis on sensitive data. 'DataSHIELD' analytic functions have
    been designed to only share non disclosive summary statistics, with built in automated output
    checking based on statistical disclosure control. With data sites setting the threshold values for
    the automated output checks. For more details, see citation('dsBaseClient').
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881.
The D-score summarizes a child's performance on developmental milestones
    into a single number. Its key feature is its generic nature. The method
    does not depend on a specific measurement instrument. The statistical
    method underlying the D-score is described in van Buuren et al. (2025)
    <doi:10.1177/01650254241294033>. This package implements model keys to
    convert milestone scores to D-scores; maps instrument-specific item names
    to a generic 9-position naming convention; computes D-scores and their
    precision from a child's milestone scores; and converts D-scores to
    Development-for-Age Z-scores (DAZ) using age-conditional reference
    standards.
Implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) <doi:10.1038/s41598-024-54149-y>.
The models of probability density functions are Gaussian or
    exponential distributions with polynomial correction terms.
    Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian
    or exponential distributions together with degrees of polynomials by
    a grid search, and coefficient of polynomials by a variant of semidefinite
    programming. It adopts Akaike Information Criterion for model selection.
    See a vignette for a tutorial and more on our 'Github' repository 
    <https://github.com/tsuchiya-lab/dsdp/>.
Applies dynamic structural equation models to time-series data
	with generic and simplified specification for simultaneous and lagged
	effects. Methods are described in Thorson et al. (2024)
	"Dynamic structural equation models synthesize ecosystem dynamics 
	constrained by ecological mechanisms."
Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data. 
    This package defines the API that is to be implemented by 'DataSHIELD' compliant 
    data repositories.
Performs distance sampling simulations. 'dsims' repeatedly generates
    instances of a user defined population within a given survey region. It then 
    generates realisations of a survey design and simulates the detection process. 
    The data are then analysed so that the results can be compared for accuracy 
    and precision across all replications. This process allows users to optimise 
    survey designs for their specific set of survey conditions. The effects of 
    uncertainty in population distribution or parameters can be investigated
    under a number of simulations so that users can be confident that they have
    achieved a robust survey design before deploying vessels into the field. The
    distance sampling designs used in this package from 'dssd' are detailed in
    Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 
    978-0199225873). General distance sampling methods are detailed in Introduction 
    to Distance Sampling: Estimating Abundance of Biological Populations, Buckland 
    et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating 
    animal/plant abundance with distance sampling at <https://distancesampling.org/>.
Dataset containing information about job listings for data science job roles.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This 'DataSHIELD Interface' implementation is for analyzing datasets living
    in the current R session. The purpose of this is primarily for lightweight
    'DataSHIELD' analysis package development.
Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.
Performs parametric and non-parametric estimation and simulation of 
    drifting semi-Markov processes. The definition of parametric and non-parametric
    model specifications is also possible. Furthermore, three different types of
    drifting semi-Markov models are considered. These models differ in the number
    of transition matrices and sojourn time distributions used for the computation
    of a number of semi-Markov kernels, which in turn characterize the drifting 
    semi-Markov kernel. For the parametric model estimation and specification, 
    several discrete distributions are considered for the sojourn times: Uniform,
    Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric
    model specification makes no assumptions about the shape of the sojourn time
    distributions. Semi-Markov models are described in:
    Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>.
    Drifting Markov models are described in:
    Vergne, N. (2008) <doi:10.2202/1544-6115.1326>.
    Reliability indicators of Drifting Markov models are described in:
    Barbu, V. S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8>. 
    We acknowledge the DATALAB Project
    <https://lmrs-num.math.cnrs.fr/projet-datalab.html> (financed by the
    European Union with the European Regional Development fund (ERDF) and by
    the Normandy Region) and the HSMM-INCA Project (financed by the French
    Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005).
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This package is the 'DataSHIELD' interface implementation to analyze data
    shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a
    light-weight 'DataSHIELD' server using a file store and an 'RServe' server.
A collection of functions to search and download 
    Digital Surface Model (DSM) and Light Detection and Ranging (LiDAR) data via APIs, 
    including 'OpenTopography' <https://portal.opentopography.org/apidocs/> and 
    'TNMAccess' <https://apps.nationalmap.gov/tnmaccess/#/>, and canopy tree height data.
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This package is the 'DataSHIELD' interface implementation for 'Opal', which is
    the data integration application for biobanks by 'OBiBa'. Participant data, once
    collected from any data source, must be integrated and stored in a central
    data repository under a uniform model. 'Opal' is such a central repository.
    It can import, process, validate, query, analyze, report, and export data.
    'Opal' is the reference implementation of the 'DataSHIELD' infrastructure.
Test for no adverse shift in two-sample comparison when we
    have a training set, the reference distribution, and a test set. The
    approach is flexible and relies on a robust and powerful test
    statistic, the weighted AUC. Technical details are in Kamulete, V. M.
    (2021) <arXiv:1908.04000>. Modern notions of outlyingness such as
    trust scores and prediction uncertainty can be used as the underlying
    scores for example.
Provides efficient Markov chain Monte Carlo (MCMC) algorithms for 
    dynamic shrinkage processes, which extend global-local shrinkage priors to 
    the time series setting by allowing shrinkage to depend on its own past. 
    These priors yield locally adaptive estimates, useful for time series and 
    regression functions with irregular features. The package includes full MCMC 
    implementations for trend filtering using dynamic shrinkage on signal differences, 
    producing locally constant or linear fits with adaptive credible bands. 
    Also included are models with static shrinkage and normal-inverse-Gamma priors for comparison. 
    Additional tools cover dynamic regression with time-varying coefficients and 
    B-spline models with shrinkage on basis differences, allowing for flexible 
    curve-fitting with unequally spaced data. Some support for heteroscedastic errors, 
    outlier detection, and change point estimation.
    Methods in this package are described in Kowal et al. (2019) <doi:10.1111/rssb.12325>, 
    Wu et al. (2024) <doi:10.1080/07350015.2024.2362269>, Schafer and Matteson (2024)
    <doi:10.1080/00401706.2024.2407316>, and Cho and Matteson (2024) <doi:10.48550/arXiv.2408.11315>.
Discrete splines are a class of univariate piecewise polynomial
    functions which are analogous to splines, but whose smoothness is defined
    via divided differences rather than derivatives. Tools for efficient
    computations relating to discrete splines are provided here. These tools
    include discrete differentiation and integration, various matrix
    computations with discrete derivative or discrete spline bases matrices, and
    interpolation within discrete spline spaces. These techniques are described
    in Tibshirani (2020) <doi:10.48550/arXiv.2003.03886>.
You can retrieve 'Spotify' API Information such as artists, albums, tracks, features tracks, recommendations or related artists.
    This package allows you to search all the information by name and also includes a distance based algorithm to find similar songs.
	More information: <https://developer.spotify.com/documentation/web-api/> .
Perform a test of a simple null hypothesis about a 
    directly standardized rate and obtain the matching confidence 
    interval using a choice of methods.
The purpose of this package is to provide a comprehensive
    R interface to the Decision Support System for Agrotechnology
    Transfer Cropping Systems Model (DSSAT-CSM; see <https://dssat.net> for more information).
    The package provides cross-platform functions to read and
    write input files, run DSSAT-CSM, and read output files.
Creates survey designs for distance sampling surveys. These
    designs can be assessed for various effort and coverage statistics.
    Once the user is satisfied with the design characteristics they can 
    generate a set of transects to use in their distance sampling survey.
    Many of the designs implemented in this R package were first made 
    available in our 'Distance' for Windows software and are detailed in 
    Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, 
    ISBN-13: 978-0199225873). Find out more about estimating animal/plant 
    abundance with distance sampling at <https://distancesampling.org/>.
Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
Probability generating function,  formulae for the probabilities (discrete density) and random generation for discrete stable random variables.
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments.  A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>).  There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.
For an observational study with binary treatment, binary outcome and K strata, implements a d-statistic that uses those strata most insensitive to unmeasured bias in treatment assignment.<doi:10.1093/biomet/asaa032>  The package has one function, dstat2x2xk.
Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.
Implementation of selected 'Tidyverse' functions within 'DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the serverside package which should be installed on the server holding the data, and is used in conjuncture with the clientside package 'dsTidyverseClient' which is installed in the local R environment of the analyst. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.
Implementation of selected 'Tidyverse' functions within 'DataSHIELD', an open-source federated analysis solution in R. Currently, 'DataSHIELD' contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the clientside package which should be installed locally, and is used in conjuncture with the serverside package 'dsTidyverse' which is installed on the remote server holding the data. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.
Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
Data science methods used in wind energy applications. 
              Current functionalities include creating a multi-dimensional power curve model, 
              performing power curve function comparison, covariate matching, and energy decomposition. 
              Relevant works for the developed functions are: 
              funGP() - Prakash et al. (2022) <doi:10.1080/00401706.2021.1905073>, 
              AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, 
              tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, 
              ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>,
              deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
              syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
              imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
              All other functions - Ding (2019, ISBN:9780429956508).
Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny).
    The 'DataTables' library has been included in this R package. The
    package name 'DT' is an abbreviation of 'DataTables'.
Offers statistical methods to compare diagnostic performance between two binary diagnostic tests on the same subject in clinical studies. Includes functions for generating formatted tables to display diagnostic outcomes, facilitating a clear and comprehensive comparison directly through the R console. Inspired by and extending the functionalities of the 'DTComPair', 'tableone', and 'gtsummary' packages.
Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.
Function to create forest plots. Functions to use posterior samples from Bayesian bivariate meta-analysis model, Bayesian hierarchical summary receiver operating characteristic (HSROC) meta-analysis model or Bayesian latent class (LC) meta-analysis model to create Summary Receiver Operating Characteristic (SROC) plots using methods described by Harbord et al (2007)<doi:10.1093/biostatistics/kxl004>.
Dose Titration Algorithm Tuning (DTAT) is a methodologic framework
             allowing dose individualization to be conceived as a continuous
             learning process that begins in early-phase clinical trials and
             continues throughout drug development, on into clinical practice.
             This package includes code that researchers may use to reproduce
             or extend key results of the DTAT research programme, plus tools
             for trialists to design and simulate a '3+3/PC' dose-finding study.
             Please see Norris (2017a) <doi:10.12688/f1000research.10624.3> and
             Norris (2017c) <doi:10.1101/240846>.
To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method.
    The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.
Implement weighted higher-order initialization and angle-based iteration for multi-way spherical clustering under degree-corrected tensor block model. See reference Jiaxin Hu and Miaoyan Wang (2023) <doi:10.1109/TIT.2023.3239521>.
A system for combining two diagnostic tests using various approaches
              that include statistical and machine-learning-based methodologies. 
              These approaches are divided into four groups: linear combination 
              methods, non-linear combination methods, mathematical operators, 
              and machine learning algorithms. See 
              the <https://biotools.erciyes.edu.tr/dtComb/> website 
              for more information, documentation, and examples.
Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
Implementation of different algorithms for analyzing
        randomly truncated data, one-sided and two-sided (i.e. doubly)
        truncated data. It serves to compute empirical cumulative 
        distributions and also kernel density and hazard functions 
        using different bandwidth selectors.
      Several real data sets are included.
Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.
Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-Álvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.
Provides functions for planning clinical trials subject to a delayed
    treatment effect using assurance-based methods. Includes two 'shiny'
    applications for interactive exploration, simulation, and visualisation of
    trial designs and outcomes. The methodology is described in:
    Salsbury JA, Oakley JE, Julious SA, Hampson LV (2024)
    "Assurance methods for designing a clinical trial with a delayed treatment
    effect" <doi:10.1002/sim.10136>,
    Salsbury JA, Oakley JE, Julious SA, Hampson LV (2024)
    "Adaptive clinical trial design with delayed treatment effects using
    elicited prior distributions" <doi:10.48550/arXiv.2509.07602>.
Implements a Bayesian Optimal Phase II design (DTE-BOP2) for trials with delayed treatment effects, particularly relevant to immunotherapy studies where treatment benefits may emerge after a delay. The method builds upon the BOP2 framework and incorporates uncertainty in the delay timepoint through a truncated gamma prior, informed by expert knowledge or default settings. Supports two-arm trial designs with functionality for sample size determination, interim and final analyses, and comprehensive simulation under various delay and design scenarios. Ensures rigorous type I and II error control while improving trial efficiency and power when the delay effect is present. A manuscript describing the methodology is under development and will be formally referenced upon publication.
The Discrete Transmuted Generalized Inverse Weibull (DTGIW) distribution is a new distribution for count data analysis. The DTGIW is discrete distribution based on Atchanut and Sirinapa (2021). <DOI: 10.14456/sjst-psu.2021.149>.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
             modality, that measures diffusion of water in tissues like the human
             brain. The package contains R-functions to process diffusion-weighted
             data. The functionality includes diffusion tensor imaging (DTI),
             diffusion kurtosis imaging (DKI), modeling for high angular resolution
             diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
             tensor mixture models, several methods for structural adaptive
             smoothing including POAS and msPOAS, and a streamline fiber tracking
             for tensor and tensor mixture models.
             The package provides functionality to manipulate and visualize results
             in 2D and 3D.
A tool to calculate the correlation boundary for the correlation between the response rate and the log-rank test statistic for the binary surrogate endpoint and the time-to-event primary endpoint, as well as conduct simulation studies to obtain design operating characteristics of the drop-the-losers design.
Create high-performance clinical reporting tables (TLGs) from
    ADaM-like inputs. The package provides a consistent, programmatic API
    to generate common tables such as demographics, adverse event incidence,
    and laboratory summaries, using 'data.table' for fast aggregation over
    large populations. Functions support flexible target-variable selection,
    stratification by treatment, and customizable summary statistics, and
    return tidy, machine-readable results ready to render with downstream
    table/formatting packages in analysis pipelines.
Allows humanitarian community, academia, media, government, and non-governmental organizations to utilize the data collected by the 'Displacement Tracking Matrix' (<https://dtm.iom.int>), a unit in the International Organization for Migration. This also provides non-sensitive Internally Displaced Person figures, aggregated at the country, Admin 1 (states, provinces, or equivalent), and Admin 2 (smaller administrative areas) levels.
A series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains.  Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s)  based on transition probabilities and an initial distribution.  The function FPTime determines the first passage time into each state.  The function statdistr determines the stationary distribution of a Markov Chain.
Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.
Provides the dose transition pathways (DTP) to project in advance
    the doses recommended by a model-based design for subsequent patients (stay,
    escalate, deescalate or stop early) using all the accumulated toxicity
    information; See Yap et al (2017) <doi: 10.1158/1078-0432.CCR-17-0582>. DTP
    can be used as a design and an operational tool and can be displayed as a
    table or flow diagram. The 'dtpcrm' package also provides the modified
    continual reassessment method (CRM) and time-to-event CRM (TITE-CRM) with
    added practical considerations to allow stopping early when there is
    sufficient evidence that the lowest dose is too toxic and/or there is a
    sufficient number of patients dosed at the maximum tolerated dose.
Provides a data.table backend for 'dplyr'. The goal of
    'dtplyr' is to allow you to write 'dplyr' code that is automatically
    translated to the equivalent, but usually much faster, data.table
    code.
Track and
    document 'dplyr' data pipelines. As you filter, mutate, and join your
    way through a data set, 'dtrackr' seamlessly keeps track of your data
    flow and makes publication ready documentation of a data pipeline simple.
You can load a schema from a DTR (data type registry) as an R
    object. Use this schema to write your data in JSON-LD (JavaScript
    Object Notation for Linked Data) format to make it machine readable.
Methods to estimate the optimal treatment regime among all linear regimes via smoothed estimation methods, and construct element-wise confidence intervals for the optimal linear treatment regime vector, as well as the confidence interval for the optimal value via wild bootstrap procedures, if the population follows treatments recommended by the optimal linear regime. See more details in: Wu, Y. and Wang, L. (2021), "Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes", Biometrics, 77: 465– 476, <doi:10.1111/biom.13337>.
We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.
Dynamic treatment regime estimation and inference via G-estimation, 
  dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via 
  bootstrap and recursive sandwich estimation. Estimation and 
  inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). 
  Extension to continuous treatment variables. Wallace et al. (2017) 
  <DOI:10.18637/jss.v080.i02>; Simoneau et al. (2020) 
  <DOI:10.1080/00949655.2020.1793341>.
Provides methods for estimating multi-stage optimal 
    dynamic treatment regimes for survival outcomes with dependent censoring.
    Cho, H., Holloway, S. T., and Kosorok, M. R. (2022) <doi:10.1093/biomet/asac047>.
Methods for evaluating the probability mass function, cumulative distribution function, and generating random samples from discrete tempered stable distributions. For more details see Grabchak (2021) <doi:10.1007/s11009-021-09904-3>.
It is a novel tool used to identify the candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) <doi: 10.1016/j.envres.2022.112890> and Feng et al. (2022) <doi: 10.7150/ijms.67815> for disease expression profiles; see Wishart et al. (2018) <doi: 10.1093/nar/gkx1037> and Gaulton et al. (2017) <doi: 10.1093/nar/gkw1074> for drug target information; see Kanehisa et al. (2021) <doi: 10.1093/nar/gkaa970> for the details of KEGG database.)
Basic time series functionalities such as listing of missing
    values, application of arbitrary aggregation as well as rolling (asymmetric)
    window functions and automatic detection of periodicity. As it is mainly
    based on 'data.table', it is fast and (in combination with the 'R6' package)
    offers reference semantics. In addition to its native R6 interface, it
    provides an S3 interface for those who prefer the latter. Finally yet
    importantly, its functional approach allows for incorporating
    functionalities from many other packages.
Provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).
Manipulates date ('Date'), date time ('POSIXct') and time
    ('hms') vectors.  Date/times are considered discrete and are floored
    whenever encountered.  Times are wrapped and time zones are maintained
    unless explicitly altered by the user.
High-frequency time-series support via 'nanotime' and 'data.table'.
A comprehensive implementation of dynamic time warping
    (DTW) algorithms in R.  DTW computes the optimal (least cumulative
    distance) alignment between points of two time series.  Common DTW
    variants covered include local (slope) and global (window)
    constraints, subsequence matches, arbitrary distance definitions,
    normalizations, minimum variance matching, and so on.  Provides
    cumulative distances, alignments, specialized plot styles, etc.,
    as described in Giorgino (2009) <doi:10.18637/jss.v031.i07>.
Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).
Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions. Implementations of
    DTW barycenter averaging, a distance based on global alignment kernels,
    and the soft-DTW distance and centroid routines are also provided. 
    All included distance functions have custom loops optimized for the 
    calculation of cross-distance matrices, including parallelization support.
    Several cluster validity indices are included.
Provides functionality for users who are learning R or the techniques of data analysis.  Written as a collection of wrapper functions, the 'DTwrapper' package facilitates many core operations of data processing.  This is achieved with relatively few requirements about the order of the processing steps or knowledge of specialized syntax.  'DTwrappers' creates coding results along with translations to data.table's code.  This enables users to benefit from the speed and efficiency of data.table's calculations.  Furthermore, the package also provides the translated code for educational purposes so that users can review working examples of coding syntax and calculations.
Offers functionality which provides methods for data analyses and cleaning that can be flexibly applied across multiple variables and in groups. These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data.
Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) <DOI:10.1109/MLSP.2017.8168165>: a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided.
Automatic differentiation is achieved by using dual numbers without
  providing hand-coded gradient functions. The output value of a mathematical 
  function is returned with the values of its exact first derivative 
  (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind
  (2018) <https://jmlr.org/papers/volume18/17-468/17-468.pdf>.
Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets.
An implementation of the decimated two-dimensional complex dual-tree wavelet transform as described in Kingsbury (1999) <doi:10.1098/rsta.1999.0447> and Selesnick et al. (2005) <doi:10.1109/MSP.2005.1550194>. Also includes the undecimated version and spectral bias correction described in Nelson et al. (2018) <doi:10.1007/s11222-017-9784-0>. The code is partly based on the 'dtcwt' Python library.
Create shareable data sets from raw data files that
	     contain protected elements. Relying on master crosswalk
	     files that list restricted variables, package functions
	     warn users about possible violations of data usage
	     agreement and prevent writing protected elements.
Provides an operator for assigning nested components of a list to
  names via a concise pattern matching syntax. This is especially convenient for
  assigning individual names to the multiple values that a function may return
  in the form of a list, and for extracting deeply nested list components.
The DuckDB project is an embedded analytical data management
    system with support for the Structured Query Language (SQL). This
    package includes all of DuckDB and an R Database Interface (DBI)
    connector.
Provides friendly wrappers for creating 'duckdb'-backed connections
  to tabular datasets ('csv', parquet, etc) on local or remote file systems.
  This mimics the behaviour of "open_dataset" in the 'arrow' package, 
  but in addition to 'S3' file system also generalizes to any list of 'http' URLs.
Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.
A drop-in replacement for 'dplyr', powered by 'DuckDB' for
    performance.  Offers convenient utilities for working with in-memory and
    larger-than-memory data while retaining full 'dplyr' compatibility.
Fast & memory-efficient functions to analyze and manipulate large 
             spatial data data sets. It leverages the fast analytical 
             capabilities of 'DuckDB' and its spatial extension (see <https://duckdb.org/docs/stable/core_extensions/spatial/overview>) 
             while maintaining compatibility with R’s spatial data ecosystem to 
             work with spatial vector data.
Analyzes non-verbal communication by processing data extracted 
    from video recordings of dyadic interactions. It supports integration 
    with open source tools, currently limited to 'OpenPose' (Cao et al. (2019) 
    <doi:10.1109/TPAMI.2019.2929257>), converting its outputs into CSV format 
    for further analysis. The package includes functions for data pre-processing, 
    visualization, and computation of motion indices such as velocity, acceleration, 
    and jerkiness (Cook et al. (2013) <doi:10.1093/brain/awt208>), facilitating 
    the analysis of non-verbal cues in paired interactions and contributing to 
    research on human communication dynamics.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
Provides functions to randomly select, return, and print
  quotes or entire scenes from the American version of
  the show the Office. Receive laughs from one of of the 
  greatest sitcoms of all time on demand. Add these functions 
  to your  '.Rprofile' to get a good laugh everytime you start
  a new R session.
A collection of functions to preprocess data and organize
    them in a format amenable to use by chevron.
Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for 0th-order stochastic dominance among k groups (Kruskal and Wallis, 1952). 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference and for mean difference. 'dunn.test' accounts for tied ranks.
We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) <doi:10.1371/journal.pone.0074388>.
Computes a new measure, DNSL betweenness, via the creation
    of a new graph from an existing one, duplicating nodes
    with self-loops. This betweenness centrality does not drop this
    essential information. Implements
    Merelo & Molinari (2024) <doi:10.1007/s42001-023-00245-4>.
Identifies code blocks that have a high level of similarity
  within a set of R files.
An easy-to-use yet powerful system for plotting grouped data effect sizes.
    Various types of effect size can be estimated, then plotted together with a
    representation of the original data. Select from many possible data representations
    (box plots, violin plots, raw data points etc.), and combine as desired.
    'Durga' plots are implemented in base R, so are compatible with base R methods
    for combining plots, such as 'layout()'. See Khan & McLean (2023) 
    <doi:10.1101/2023.02.06.526960>.
A suite of tools to help modelers and decision-makers effectively
 interpret and communicate decision risk when evaluating multiple policy options.
 It uses model outputs from uncertainty analysis for baseline scenarios and policy
 alternatives to generate visual representations of uncertainty and quantitative
 measures for assessing associated risks. For more details see 
 Wiggins and colleagues (2025) <doi:10.1371/journal.pone.0332522> and <https://dut.ihe.ca/>.
Creates a data frame containing the metadata associated with
    the documentation of a collection of R packages. It allows for linking
    topic names to their corresponding documentation online. If you
    maintain a universe meta-package, it helps create a comprehensive
    reference for its website.
Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.
Joint DNA-based disaster victim identification (DVI), as
    described in Vigeland and Egeland (2021)
    <doi:10.21203/rs.3.rs-296414/v1>. Identification is performed by
    optimising the joint likelihood of all victim samples and reference
    individuals. Individual identification probabilities, conditional on
    all available information, are derived from the joint solution in the
    form of posterior pairing probabilities. 'dvir' is part of the
    'pedsuite' collection of packages for pedigree analysis.
Contains functions that do something convenient (e.g. create BMI categories), functions for calculating moving-window statistics efficiently, and functions for generating various figures (e.g. histograms with fitted probability mass/density function).
A set of control charts for batch processes based on the VAR model. The package contains the implementation of T2.var and W.var control charts based on VAR model coefficients using the couple vectors theory. In each time-instant the VAR coefficients are estimated from a historical in-control dataset and a decision rule is made for online classifying of a new batch data. Those charts allow efficient online monitoring since the very first time-instant. The offline version is available too. In order to evaluate the chart's performance, this package contains functions to generate batch data for offline and online monitoring.See in Danilo Marcondes Filho and Marcio Valk (2020) <doi:10.1016/j.ejor.2019.12.038>.
Dual Wavelet based Nonlinear Autoregressive Distributed Lag model has been developed for noisy time series analysis. This package is designed to capture both short-run and long-run relationships in time series data, while incorporating wavelet transformations. The methodology combines the NARDL model with wavelet decomposition to better capture the nonlinear dynamics of the series and exogenous variables. The package is useful for analyzing economic and financial time series data that exhibit both long-term trends and short-term fluctuations. This package has been developed using algorithm of Jammazi et al. <doi:10.1016/j.intfin.2014.11.011>.
Edit and validate taxonomic data in compliance with Darwin Core
    standards (Darwin Core 'Taxon' class <https://dwc.tdwg.org/terms/#taxon>).
Solving large scale distance weighted discrimination.
  The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <doi:10.48550/arXiv.1604.05473> for more details.
The 'DWD' provides gridded radar data for Germany in binary format. 
  'dwdradar' reads these files and enables a fast conversion into numerical format.
This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) <doi:10.1119/1.15963> and Ripley & Thompson(1987) <doi:10.1039/AN9871200377>.
The rapid development of single-cell transcriptomic technologies 
    has helped uncover the cellular heterogeneity within cell populations. 
    However, bulk RNA-seq continues to be the main workhorse for quantifying 
    gene expression levels due to technical simplicity and low cost. To most 
    effectively extract information from bulk data given the new knowledge 
    gained from single-cell methods, we have developed a novel algorithm to 
    estimate the cell-type composition of bulk data from a single-cell 
    RNA-seq-derived cell-type signature. Comparison with existing methods using 
    various real RNA-seq data sets indicates that our new approach is more 
    accurate and comprehensive than previous methods, especially for the 
    estimation of rare cell types. More importantly,our method can detect 
    cell-type composition changes in response to external perturbations, 
    thereby providing a valuable, cost-effective method for dissecting the 
    cell-type-specific effects of drug treatments or condition changes. 
    As such, our method is applicable to a wide range of biological and 
    clinical investigations. Dampened weighted least squares ('DWLS') is an 
    estimation method for gene expression deconvolution, in which the cell-type 
    composition of a bulk RNA-seq data set is computationally inferred. 
    This method corrects common biases towards cell types that are 
    characterized by highly expressed genes and/or are highly prevalent, to 
    provide accurate detection across diverse cell types. See: 
    <https://www.nature.com/articles/s41467-019-10802-z.pdf> for more 
    information about the development of 'DWLS' and the methods behind our 
    functions.
Fit a Poisson regression to carcass distance data and integrate over the searched area at a wind farm to estimate the fraction of carcasses falling in the searched area and format the output for use as the dwp parameter in the 'GenEst' or 'eoa' package for estimating bird and bat mortality, following Dalthorp, et al. (2022) <arXiv:2201.10064>.
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
Provides methods for efficient algebraic operations and factorization of dyadic matrices using 'Rcpp' and 'RcppArmadillo'. The details of dyadic matrices and the corresponding methodology are described in Kos, M., Podgórski, K., and Wu, H. (2025) <doi:10.48550/arXiv.2505.08144>.
Estimates the Dyad Ratios Algorithm for pooling and smoothing poll estimates. The Dyad Ratios Algorithm smooths both forward and backward in time over polling results allowing differences in both question type and polling house. The result is an estimate of a single latent variable that describes the systematic trend over time in the (noisy) polling results. See James A. Stimson (2018) <doi:10.1177/0759106318761614> and the package's vignette for more details.
Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>), the multilevel p2 model (Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>, the (multilevel) b2 model.
Dynamic Reservoir Simulation Model (DYRESM) and Computational Aquatic Ecosystem Dynamics Model (CAEDYM) model development, including assisting with calibrating selected model parameters and visualising model output through time series plot, profile plot, contour plot, and scatter plot. For more details, see Yu et al. (2023) <https://journal.r-project.org/articles/RJ-2023-008/>.
Finds regular and chaotic intervals in the data using 
    the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) 
	<DOI:10.1137/080718851>.
An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.
Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.
Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.
Mechanistically models/predicts the phenology (macro-phases) of 10 crop plants (trained on a big dataset over 80 years derived from the German weather service (DWD) <https://opendata.dwd.de/>). Can be applied for remote sensing purposes, dynamically check the best subset of available covariates for the given dataset and crop.
The DYMO package provides tools for multi-feature time-series forecasting using a Dynamic Mode Decomposition (DMD) model combined with conformal predictive sampling for uncertainty quantification.
Time series regression.  The dyn class interfaces ts,
        irts(), zoo() and zooreg() time series classes to lm(), glm(),
        loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(),
        quantreg::rq(), randomForest::randomForest() and other regression
        functions allowing those functions to be used with time series
        including specifications that may contain lags, diffs and
        missing values.
Reaction rate dynamics can be retrieved from metabolite
  concentration time courses. User has to provide corresponding stoichiometric
  matrix but not a regulation model (Michaelis-Menten or similar).
  Instead of solving an ordinary differential equation (ODE) system describing the evolution of concentrations,
  we use B-splines to catch the concentration and rate dynamics then
  solve a least square problem on their coefficients with non-negativity
  (and optionally monotonicity) constraints.
  Constraints can be also set on initial values of concentration.
  The package 'dynafluxr' can be used as a library but also as
  an application with command line interface dynafluxr::cli("-h") or graphical user interface dynafluxr::gui().
While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).
Generalised model for population dynamics of invasive Aedes mosquitoes. Rationale and model structure are described here: Da Re et al. (2021) <doi:10.1016/j.ecoinf.2020.101180> and Da Re et al. (2022) <doi:10.1101/2021.12.21.473628>.
The goal of 'dynamicpv' is to provide a simple way to calculate (net) present values and outputs from health economic models (especially cost-effectiveness and budget impact) in discrete time that reflect dynamic pricing and dynamic uptake. Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) <doi:10.1515/fhep-2024-0014> provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) <doi:10.1016/j.jval.2024.03.006> reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges.
A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.
Contains methods for detection of clusters in hierarchical clustering dendrograms.
Easy-to-use and efficient interface for 
  Bayesian inference of complex panel (time series) data using dynamic 
  multivariate panel models by Helske and Tikka (2024) 
  <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of 
  multiple measurements per individual, time-varying and time-invariant 
  effects, and a wide range of discrete and continuous distributions. 
  Estimation of these dynamic multivariate panel models is carried out via 
  'Stan'. For an in-depth tutorial of the package, see 
  (Tikka and Helske, 2025) <doi:10.18637/jss.v115.i05>.
Provides functions for inferring longitudinal dominance hierarchies, which describe dominance relationships and their dynamics in a single latent hierarchy over time. Strauss & Holekamp (in press).
It allows running 'Dynare' program from base R, R Markdown and Quarto. 'Dynare' is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium ('DSGE') and overlapping generations ('OLG') models.  This package does not only integrate R and Dynare but also serves as a 'Dynare' Knit-Engine for 'knitr' package. The package requires 'Dynare' (<https://www.dynare.org/>) and 'Octave' (<https://www.octave.org/download.html>).  Write all your 'Dynare' commands in R or R Markdown chunk.
A set of tools to generate dynamic spectrogram visualizations in video format.
An R implementation and enhancement of the Dynamic TOPMODEL semi-distributed hydrological model originally proposed by Beven and Freer (2001) <doi:10.1002/hyp.252>. The 'dynatop' package implements code for simulating models which can be created using the 'dynatopGIS' package.
A set of algorithms based on Quinn et al. (1991) <doi:10.1002/hyp.3360050106> for processing river network and digital elevation data to build implementations of Dynamic TOPMODEL, a semi-distributed hydrological model proposed in Beven and Freer (2001) <doi:10.1002/hyp.252>. The 'dynatop' package implements simulation code for Dynamic TOPMODEL based on the output of 'dynatopGIS'.
Inference by sequential Monte Carlo for 
  dynamic tree regression and classification models
  with hooks provided for sequential design and optimization, 
  fully online learning with drift, variable selection, and 
  sensitivity analysis of inputs.  Illustrative 
  examples from the original dynamic trees paper 
  (Gramacy, Taddy & Polson (2011); <doi:10.1198/jasa.2011.ap09769>) are facilitated
  by demos in the package; see demo(package="dynaTree").
A GUI to solve dynamic biplots and classical biplot. Try matrices
    of 2-way and 3-way. The GUI can be run in multiple languages.
A two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
Provides density functions for the joint distribution of
    choice, response time and confidence for discrete confidence judgments
    as well as functions for parameter fitting, prediction and simulation
    for various dynamical models of decision confidence.  All models are
    explained in detail by Hellmann et al.  (2023;
    Preprint available at <https://osf.io/9jfqr/>, published version: <doi:10.1037/rev0000411>).  Implemented models are the dynaViTE model, 
    dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, <doi:10.1037/a0019737>),
    and various race models.  C++ code for dynWEV and 2DSD is based on the
    'rtdists' package by Henrik Singmann.
Computes dynamical correlation estimates and percentile
        bootstrap confidence intervals for pairs of longitudinal
        responses, including consideration of lags and derivatives.
Provides a common interface for applying dimensionality reduction methods,
  such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, 
  Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), 
  and Uniform Manifold Approximation and Projection ('UMAP'). 
  Has built-in support for sparse matrices.
Performs emulation of dynamic simulators using Gaussian process via one-step ahead approach. The package implements a flexible framework for approximating time-dependent outputs from computationally expensive dynamic systems. It is specifically designed for nonlinear dynamic systems where full simulations may be costly. The underlying Gaussian process model accounts for temporal dependency through the one-step-ahead formulation, allowing for accurate emulation of complex dynamics. Hyperparameters are estimated via maximum likelihood. For methodological details, see Heo (2025, <doi:10.48550/arXiv.2503.20250>) for exact method, and Mohammadi, Challenor, and Goodfellow (2019, <doi:10.1016/j.csda.2019.05.006>) for Monte Carlo method.
An anonymization algorithm to resist neighbor label attack in a dynamic network.
Based on random forest principle, 'DynForest' is able to include 
    multiple longitudinal predictors to provide individual predictions. 
    Longitudinal predictors are modeled through the random forest. The 
    methodology is fully described for a survival outcome in: 
    Devaux, Helmer, Genuer & Proust-Lima (2023) 
    <doi: 10.1177/09622802231206477>.
A novel, multi-modal simulation engine for
    studying dynamic cellular processes at single-cell resolution. 'dyngen'
    is more flexible than current single-cell simulation engines. It
    allows better method development and benchmarking, thereby stimulating
    development and testing of novel computational methods. Cannoodt et
    al. (2021) <doi:10.1038/s41467-021-24152-2>.
Dynamic linear models and time series regression.
Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) <doi:10.1016/j.apm.2013.05.038>; Nagy et al. (2011) <doi:10.1002/acs.1239>.
Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.
Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.
Provides tools for describing parameters of algorithms in an abstract way.
  Description can include an id, a description, a domain (range or list of values),
  and a default value. 'dynparam' can also convert parameter sets to a 'ParamHelpers' format, 
  in order to be able to use 'dynparam' in conjunction with 'mlrMBO'.
The dynpred package contains functions for dynamic prediction in survival analysis.
A domain-specific language for specifying translating recursions
    into dynamic-programming algorithms. See 
    <https://en.wikipedia.org/wiki/Dynamic_programming> for a description
    of dynamic programming.
Intensive longitudinal data have become increasingly prevalent in
    various scientific disciplines. Many such data sets are noisy, multivariate,
    and multi-subject in nature. The change functions may also be continuous,
    or continuous but interspersed with periods of discontinuities (i.e.,
    showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an
    R package that implements a set of computationally efficient algorithms for
    handling a broad class of linear and nonlinear discrete- and continuous-time
    models with regime-switching properties under the constraint of linear
    Gaussian measurement functions. The discrete-time models can generally
    take on the form of a state-space or difference equation model. The
    continuous-time models are generally expressed as a set of ordinary or
    stochastic differential equations. All estimation and computations are
    performed in C, but users are provided with the option to specify the
    model of interest via a set of simple and easy-to-learn model specification
    functions in R. Model fitting can be performed using single-subject time
    series data or multiple-subject longitudinal data. Ou, Hunter, & Chow
    (2019) <doi:10.32614%2FRJ-2019-012> provided a detailed introduction to the
    interface and more information on the algorithms.
Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.
Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in  Matias and Miele (2016) <doi:10.1111/rssb.12200>.
Dynamic simulations and graphical depictions of autoregressive
    relationships.
Time-varying coefficient models for interval censored and
    right censored survival data including
    1) Bayesian Cox model with time-independent, time-varying or
    dynamic coefficients for right censored and interval censored data studied by
    Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and
    Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>,
    2) Spline based time-varying coefficient Cox model for right censored data
    proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and
    3) Transformation model with time-varying coefficients for right censored data
    using estimating equations proposed by
    Peng and Huang (2007) <doi:10.1093/biomet/asm058>.
Performs calculations with tree taper (or stem
    profile) equations, including model fitting. The package
    implements the methods from García, O. (2015) "Dynamic
    modelling of tree form"
    <http://mcfns.net/index.php/Journal/article/view/MCFNS7.1_2>.
    The models are parsimonious, describe well the tree bole shape
    over its full length, and are consistent with wood formation
    mechanisms through time.
Methods to estimate dynamic treatment regimes using Interactive
  Q-Learning, Q-Learning, weighted learning, and value-search methods based on 
  Augmented Inverse Probability Weighted Estimators and Inverse Probability
  Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for 
  Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., 
  Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.
Provides common functionality for the 'dynverse' packages. 
  'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods.
  For more information, check out <https://dynverse.org>.
Provides functionality to infer trajectories from single-cell data,
  represent them into a common format, and adapt them. Other biological information
  can also be added, such as cellular grouping, RNA velocity and annotation.
  Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.
Scripting of structural equation models via 'lavaan' for
    Dyadic Data Analysis, and helper functions for supplemental
    calculations, tabling, and model visualization.
It is used to identify dysregulated pathways based on a pre-ranked gene pair list. A fast algorithm is used to make the computation really fast. The data in package 'DysPIAData' is needed.
This dataset includes Background and Pathway data used in package 'DysPIA'.
In practice, we will encounter problems where the longitudinal performance of processes needs to be monitored over time. 
  Dynamic screening systems (DySS) are methods that aim to identify and give signals to processes with poor performance as early as possible. 
  This package is designed to implement dynamic screening systems and the related methods.
  References:
  Qiu, P. and Xiang, D. (2014) <doi:10.1080/00401706.2013.822423>;
  Qiu, P. and Xiang, D. (2015) <doi:10.1002/sim.6477>;
  Li, J. and Qiu, P. (2016) <doi:10.1080/0740817X.2016.1146423>;
  Li, J. and Qiu, P. (2017) <doi:10.1002/qre.2160>;
  You, L. and Qiu, P. (2019) <doi:10.1080/00949655.2018.1552273>;
  Qiu, P., Xia, Z., and You, L. (2020) <doi:10.1080/00401706.2019.1604434>;
  You, L., Qiu, A., Huang, B., and Qiu, P. (2020) <doi:10.1002/bimj.201900127>;
  You, L. and Qiu, P. (2021) <doi:10.1080/00224065.2020.1767006>.
Functions for latent class analysis, short time Fourier
	     transform, fuzzy clustering, support vector machines,
	     shortest path computation, bagged clustering, naive Bayes
	     classifier, generalized k-nearest neighbour ...
Provides a framework to build and evaluate diagnosis or 
    prognosis models using stacking, voting, and bagging ensemble
    techniques with various base learners. The package also includes
    tools for visualization and interpretation of models. The development
    version of the package is available on 'GitHub' at 
    <https://github.com/xiaojie0519/E2E>. The methods 
    are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655>
    on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> 
    on stacking.
The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria et al. (2024) <doi:10.1007/s00180-022-01312-6>. It aims to explain and interpret decision tree ensemble models using a single tree-like structure. 'e2tree' is a new way of explaining an ensemble tree trained through 'randomForest' or 'xgboost' packages.
Fits dose-response models using an evolutionary
    algorithm to estimate the model parameters. The procedure currently
    can fit 3-parameter, 4-parameter, and 5-parameter log-logistic models
    as well as exponential models. Functions are also provided to plot,
    make predictions, and calculate confidence intervals for the resulting
    models. For details see "Nonlinear Dose-response Modeling of
    High-Throughput Screening Data Using an Evolutionary Algorithm",
    Ma, J., Bair, E., Motsinger-Reif, A.; Dose-Response
    18(2):1559325820926734 (2020) <doi:10.1177/1559325820926734>.
Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.
An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and  outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This  opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
Simulation-based evidence accumulation models for analyzing responses
  and reaction times in single- and multi-response tasks. The package includes
  simulation engines for five representative models: the Diffusion Decision
  Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator
  (LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and
  extends these frameworks to multi-response settings. The package supports
  user-defined functions for item-level parameterization and the incorporation
  of covariates, enabling flexible customization and the development of new
  model variants based on existing architectures. Inference is performed using
  simulation-based methods, including Approximate Bayesian Computation (ABC)
  and Amortized Bayesian Inference (ABI), which allow parameter estimation
  without requiring tractable likelihood functions. In addition to core
  inference tools, the package provides modules for parameter recovery,
  posterior predictive checks, and model comparison, facilitating the study of
  a wide range of cognitive processes in tasks involving perceptual decision
  making, memory retrieval, and value-based decision making. Key methods
  implemented in the package are described in Ratcliff (1978) <doi:10.1037/0033-295X.85.2.59>,
  Usher and McClelland (2001) <doi:10.1037/0033-295X.108.3.550>, Brown and Heathcote (2008)
  <doi:10.1016/j.cogpsych.2007.12.002>, Tillman, Van Zandt and Logan (2020)
  <doi:10.3758/s13423-020-01719-6>, Wieschen, Voss and Radev (2020)
  <doi:10.20982/tqmp.16.2.p120>, Csilléry, François and Blum (2012)
  <doi:10.1111/j.2041-210X.2011.00179.x>, Beaumont (2019)
  <doi:10.1146/annurev-statistics-030718-105212>, and Sainsbury-Dale,
  Zammit-Mangion and Huser (2024) <doi:10.1080/00031305.2023.2249522>.
A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.
Computes the most important properties of four 'Bayesian' early gating 
             designs (two single arm and two randomized controlled designs), such 
             as minimum required number of successes in the experimental group to 
             make a GO decision, operating characteristics and average operating 
             characteristics with respect to the sample size. 
             These might aid in deciding what design to use for the early phase trial.
Implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) <doi:10.1093/aje/kwt133>.
Build regression models using the techniques in Friedman's
    papers "Fast MARS" and "Multivariate Adaptive Regression
    Splines" <doi:10.1214/aos/1176347963>.
    (The term "MARS" is trademarked and thus not used in
    the name of the package.)
Providing easy, portable access to NASA 'EarthData' products
  through the use of bearer tokens. Much of NASA's public data catalogs hosted
  and maintained by its 12 Distributed Active Archive Centers ('DAACs') are
  now made available on the Amazon Web Services 'S3' storage.  However, 
  accessing this data through the standard 'S3' API is restricted to only to 
  compute resources running inside 'us-west-2' Data Center in Portland, Oregon,
  which allows NASA to avoid being charged data egress rates. This package
  provides public access to the data from any networked device by using the 
  'EarthData' login application programming interface (API),
  <https://www.earthdata.nasa.gov/data/earthdata-login>,
  providing convenient authentication and access to cloud-hosted NASA 'EarthData'
  products. This makes access to a wide range of earth observation data from 
  any location straight forward and compatible with R packages that are 
  widely used with cloud native earth observation data (such as 'terra',
  'sf', etc.)
This is a port of 'Fortran ETERNA 3.4' 
             <http://igets.u-strasbg.fr/soft_and_tool.php> by H.G. Wenzel
             for calculating synthetic Earth tides using the 
             Hartmann and Wenzel (1994) <doi:10.1029/95GL03324> or 
             Kudryavtsev (2004) <doi:10.1007/s00190-003-0361-2> tidal catalogs.
Downloads a satellite image via ESRI and maptiles (these are
    originally from a variety of aerial photography sources), 
    translates the image into a perceptually uniform color space,
    runs one of a few different clustering algorithms on the colors in the image 
    searching for a user-supplied number of colors,
    and returns the resulting color palette.
Implementation in a simple and efficient way of fully customisable population genetics simulations,
    considering multiple loci that have epistatic interactions. Specifically suited to the modelling of
    multilocus nucleocytoplasmic systems (with both diploid and haploid loci), it is nevertheless possible
    to simulate purely diploid (or purely haploid) genetic models.
	Examples of models that can be simulated with Ease are numerous, for example models of genetic
	incompatibilities as presented by Marie-Orleach et al. (2022) <doi:10.1101/2022.07.25.501356>.
	Many others are conceivable, although few are actually explored, Ease having been developed
	in particular to provide a solution so that these kinds of models can be simulated simply.
Some utility functions for validation and data manipulation. These functions can be helpful to reduce internal codes everywhere in package development.
Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Given the scores from decision makers, the analytic hierarchy process can be conducted easily.
Alluvial plots are similar to sankey diagrams and visualise categorical data 
    over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in 
    Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694> 
    Their graphical grammar however is a bit more complex then that of a regular x/y 
    plots. The 'ggalluvial' package made a great job of translating that grammar into 
    'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial 
    plot, however there still remains a multi-layered complexity that makes it difficult
    to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple 
    interface to this package that allows you to produce a decent alluvial plot from any 
    dataframe in either long or wide format from a single line of code while also handling 
    continuous data. It is meant to allow a quick visualisation of entire dataframes 
    with a focus on different colouring options that can make alluvial plots a great 
    tool for data exploration.
Perform analysis of variance and other important complementary
  analyses.  The functions are easy to use.  Performs analysis in various
  designs, with balanced and unbalanced data.
Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. 
    The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM', 
    as well as network comparison fitted using the 'bgms' and 'BBGM'. 
    The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the 
    posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior 
    structure space. Furthermore, the package comes with an extensive suite for visualizing results.
Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.
Extracting desired data using the proper Census variable names can 
    be time-consuming. This package takes the pain out of that process by 
    providing functions to quickly locate variables and download labeled tables 
    from the Census APIs (<https://www.census.gov/data/developers/data-sets.html>).
Get high-resolution (1 km) daily climate data (precipitation,
    minimum and maximum temperatures) for points and polygons within
    Europe.
Univariate and multivariate methods for compositional data 
    analysis, based on logratios. The package implements the approach in the
    book Compositional Data Analysis in Practice by Michael Greenacre (2018),
    where accent is given to simple pairwise logratios. Selection can be made
    of logratios that account for a maximum percentage of logratio variance.
    Various multivariate analyses of logratios are included in the package.
A unified interface for connecting to databases ('SQLite', 'MySQL', 'PostgreSQL').
    Just provide the database name and the package will ask you questions
    to help you configure the connection and setup your credentials.  Once
    database configuration and connection has been set up once, you won't
    have to do it ever again.
Descriptive analysis is essential for publishing medical articles.
  This package provides an easy way to conduct the descriptive analysis.
  1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test.
  2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, 'Tukey' for the numeric variables and 'FDR' for the factor variables.
  3. T test, ANOVA or Fisher test can be forced to apply.
  4. Mean and standard deviation can be forced to display.
Interactive labelling of scatter plots, volcano plots and 
    Manhattan plots using a 'shiny' and 'plotly' interface. Users can hover 
    over points to see where specific points are located and click points 
    on/off to easily label them. Labels can be dragged around the plot to place 
    them optimally. Plots can be exported directly to PDF for publication. For 
    plots with large numbers of points, points can optionally be rasterized as a 
    bitmap, while all other elements (axes, text, labels & lines) are preserved 
    as vector objects. This can dramatically reduce file size for plots with 
    millions of points such as Manhattan plots, and is ideal for publication.
Utilities for building certain kinds of common matrices and models in 
    the extended structural equation modeling package, 'OpenMx'.
Set of wrappers for the 'ncdf4' package to simplify and extend its
    reading/writing capabilities into/from multidimensional R arrays.
Provides a built-in Nemaplex database for nematodes, 
    which can be used to search for various nematodes. 
    Also supports various nematode community and functional 
    analyses such as nematode diversity, maturity index, metabolic footprint, 
    and functional guild. The methods are based on
     <https://shiny.wur.nl/ninja/>, Bongers, T. (1990)
     <doi:10.1007/BF00324627>, Ferris, H. (2010)
     <doi:10.1016/j.ejsobi.2010.01.003>, Wan, B. et al. (2022)
     <doi:10.1016/j.soilbio.2022.108695>, and Van Den Hoogen, J. et al.
     (2019) <doi:10.1038/s41586-019-1418-6>.
Creates text, 'LaTeX', Markdown, or Bootstrap-styled HTML-formatted odds ratio tables with confidence intervals for multiple logistic regression models.
Easily load and install multiple packages from different sources, 
    including CRAN and GitHub. The libraries function allows you to load or attach 
    multiple packages in the same function call. The packages function will load one 
    or more packages, and install any packages that are not installed on your system 
    (after prompting you). Also included is a from_import function that allows you 
    to import specific functions from a package into the global environment.
Power analysis is used in the estimation of sample sizes for
    experimental designs. Most programs and R packages will only output the highest
    recommended sample size to the user. Often the user input can be complicated
    and computing multiple power analyses for different treatment comparisons can
    be time consuming. This package simplifies the user input and allows the user
    to view all of the sample size recommendations or just the ones they want to see.
    The calculations used to calculate the recommended sample sizes are from the
    'pwr' package.
Provides various functions for reading and preparing the Panel Study of Income Dynamics (PSID) for longitudinal analysis, including functions that read the PSID's fixed width format files directly into R, rename all of the PSID's longitudinal variables so that recurring variables have consistent names across years, simplify assembling longitudinal datasets from cross sections of the PSID Family Files, and export the resulting PSID files into file formats common among other statistical programming languages ('SAS', 'STATA', and 'SPSS').
Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Makes difficult operations easy. Includes these types of functions: 
    shorthand, type conversion, data wrangling, and work flow. 
    Also includes some helpful data objects: NA strings, U.S. state list, color blind charting colors. 
    Built and shared by Oliver Wyman Actuarial Consulting. Accepting proposed contributions through GitHub.
Generate citations and references for R packages from CRAN or Bioconductor.
    Supports RIS and BibTeX formats with automatic DOI retrieval from GitHub repositories
    and published papers. Includes command-line interface for batch processing.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models.
Provides a simple interface to search and retrieve scientific articles
    from the 'SciELO' (Scientific Electronic Library Online) database <https://scielo.org>.
    It allows querying, filtering, and visualizing results in an interactive table.
The main function, ProtectTable(), performs table suppression according to a 
 frequency rule with a data set as the only required input. Within this function, 
 protectTable(), protect_linked_tables() or runArgusBatchFile() in package 'sdcTable' is called. 
 Lists of level-hierarchy (parameter 'dimList') and other required input to these functions 
 are created automatically. 
 The suppression method Gauss (default) is implemented independently of 'sdcTable'.
 The function, PTgui(), starts a graphical user interface based on the 'shiny' package.
A meta-package that installs and loads a set of packages from
    'easystats' ecosystem in a single step. This collection of packages provide
    a unifying and consistent framework for statistical modeling, visualization,
    and reporting. Additionally, it provides articles targeted at instructors for
    teaching 'easystats', and a dashboard targeted at new R users for easily
    conducting statistical analysis by accessing summary results, model fit indices,
    and visualizations with minimal programming.
Inspect survival data, plot Kaplan-Meier curves, assess the
    proportional hazards assumption, fit parametric survival models,
    predict and plot survival and hazards, and export the outputs to
    'Excel'.  A simple interface for fitting survival models using 
    flexsurv::flexsurvreg(), flexsurv::flexsurvspline(), 
    flexsurvcure::flexsurvcure(), and survival::survreg().
This SVG elements generator can easily generate 
    SVG elements such as rect, line, circle, ellipse, polygon, 
    polyline, text and group. Also, it can combine and 
    output SVG elements into a SVG file.
Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.
Offers a flexible and user-friendly interface for visualizing conditional 
 effects from a broad range of regression models, including mixed-effects and generalized 
 additive (mixed) models. Compatible model types include lm(), rlm(), glm(), glm.nb(),
 betareg(), and gam() (from 'mgcv'); nonlinear models via nls(); generalized least 
 squares via gls(); and survival models via coxph() (from 'survival'). 
 Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), 
 glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). 
 Plots are rendered using base R graphics with extensive customization options. 
 Approximate confidence intervals for nls() and betareg() models are computed using 
 the delta method. Robust standard errors for rlm() are computed using the sandwich 
 estimator (Zeileis 2004) <doi:10.18637/jss.v011.i10>. For beta regression using 
 'betareg', see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. For 
 mixed-effects models with 'lme4', see Bates et al. (2015) <doi:10.18637/jss.v067.i01>. 
 For models using 'glmmTMB', see Brooks et al. (2017) <doi:10.32614/RJ-2017-066>. 
 Methods for generalized additive models using 'mgcv' follow Wood (2017)
 <doi:10.1201/9781315370279>.
Functions are provided to determine production frontiers and technical 
    efficiency measures through non-parametric techniques based upon regression trees. 
    The package includes code for estimating radial input, output, directional and 
    additive measures, plotting graphical representations of the scores and the production 
    frontiers by means of trees, and determining rankings of importance of input variables 
    in the analysis. Additionally, an adaptation of Random Forest by a set of individual 
    Efficiency Analysis Trees for estimating technical efficiency is also included. More 
    details in: <doi:10.1016/j.eswa.2020.113783>.
Provides simple functions to create constraints for small test assembly problems 
    (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, 
    'GLPK', 'lpSolve', 'Symphony', and 'Gurobi' are supported as solvers. The 'gurobi' package is not available from 
    any mainstream repository; see <https://www.gurobi.com/downloads/>.
Use 'SQLite3' as a database system via a complete SQL free R interface, treating the data as if it was a single spreadsheet.
Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.
The univariate statistical quality control tool aims to address measurement error effects when constructing exponentially weighted moving average p control charts. The method primarily focuses on binary random variables, but it can be applied to any continuous random variables by using sign statistic to transform them to discrete ones. With the correction of measurement error effects, we can obtain the corrected control limits of exponentially weighted moving average p control chart and reasonably adjusted exponentially weighted moving average p control charts. The methods in this package can be found in some relevant references, such as Chen and Yang (2022) <arXiv: 2203.03384>; Yang et al. (2011) <doi: 10.1016/j.eswa.2010.11.044>; Yang and Arnold (2014) <doi: 10.1155/2014/238719>; Yang (2016) <doi: 10.1080/03610918.2013.763980> and Yang and Arnold (2016) <doi: 10.1080/00949655.2015.1125901>.
Replication methods to compute some basic statistic operations (means, standard deviations,
  frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models,
  and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed
  variables and/or a clustered sampling structure which both deserve special procedures at least in
  estimating standard errors. See the package documentation for a more detailed description along with references.
Miscellaneous functions for data cleaning and data analysis of educational assessments. Includes functions for descriptive 
   analyses, character vector manipulations and weighted statistics. Mainly a lightweight dependency for the packages 'eatRep', 
   'eatGADS', 'eatPrep' and 'eatModel' (which will be subsequently submitted to 'CRAN').
   The function for defining (weighted) contrasts in weighted effect coding refers to
   te Grotenhuis et al. (2017) <doi:10.1007/s00038-016-0901-1>.
   Functions for weighted statistics refer to
   Wolter (2007) <doi:10.1007/978-0-387-35099-8>.
Expert Algorithm Verbal Autopsy assigns causes of death to 2016 WHO Verbal Autopsy Questionnaire data. odk2EAVA() converts data to a standard input format for cause of death determination building on the work of Thomas (2021) <https://cran.r-project.org/src/contrib/Archive/CrossVA/>. codEAVA() uses the presence and absence of signs and symptoms reported in the Verbal Autopsy interview to diagnose common causes of death. A deterministic algorithm assigns a single cause of death to each Verbal Autopsy interview record using a hierarchy of all common causes for neonates or children 1 to 59 months of age.
Fitting and testing multi-attribute probabilistic choice
  models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
  Terry, 1952 <doi:10.1093/biomet/39.3-4.324>; Luce, 1959),
  elimination-by-aspects (EBA) models (Tversky, 1972 <doi:10.1037/h0032955>),
  and preference tree (Pretree) models (Tversky & Sattath, 1979
  <doi:10.1037/0033-295X.86.6.542>).
Package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2008, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
Estimate ecosystem metabolism in a Bayesian framework for
  individual water quality monitoring stations with continuous dissolved
  oxygen time series. A mass balance equation is used that provides
  estimates of parameters for gross primary production, respiration,
  and gas exchange. Methods adapted from Grace et al. (2015)
  <doi:10.1002/lom3.10011> and Wanninkhof (2014) <doi:10.4319/lom.2014.12.351>.
  Details in Beck et al. (2024) <doi:10.1002/lom3.10620>.
Empirical Bayes thresholding using the methods developed
    by I. M. Johnstone and B. W. Silverman. The basic problem is to
    estimate a mean vector given a vector of observations of the mean
    vector plus white noise, taking advantage of possible sparsity in
    the mean vector. Within a Bayesian formulation, the elements of
    the mean vector are modelled as having, independently, a
    distribution that is a mixture of an atom of probability at zero
    and a suitable heavy-tailed distribution. The mixing parameter can
    be estimated by a marginal maximum likelihood approach. This leads
    to an adaptive thresholding approach on the original data.
    Extensions of the basic method, in particular to wavelet
    thresholding, are also implemented within the package.
We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2020) <doi:10.1080/01621459.2020.1777137>.
Computes empirical Bayes confidence estimators and confidence
    intervals in a normal means model. The intervals are robust in the sense
    that they achieve correct coverage regardless of the distribution of the
    means. If the means are treated as fixed, the intervals have an average
    coverage guarantee. The implementation is based on Armstrong, Kolesár and
    Plagborg-Møller (2020) <arXiv:2004.03448>.
Estimate prior variable weights for Bayesian Additive Regression
    Trees (BART). These weights correspond to the probabilities of the variables
    being selected in the splitting rules of the sum-of-trees.
    Weights are estimated using empirical Bayes and external information on 
    the explanatory variables (co-data).
    BART models are fitted using the 'dbarts' 'R' package.
    See Goedhart and others (2023) <doi:10.1002/sim.70004> for details.
Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.
Provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies. 
    The package supports three modules: (1) bivariate correlation estimation for binary outcomes, (2) bivariate correlation estimation for continuous outcomes, and 
    (3) estimation of component-wise means and variances under a conditional two-component Gaussian mixture model for a continuous variable stratified by a binary class label.
    These methods enable privacy-preserving joint estimation when individual-level data are unavailable.
    The approaches are detailed in Shang, Tsao, and Zhang (2025a) <doi:10.48550/arXiv.2505.03995> and Shang, Tsao, and Zhang (2025b) <doi:10.48550/arXiv.2508.02057>.
Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
  package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Tools for accessing and analyzing eBird Status and
    Trends Data Products
    (<https://science.ebird.org/en/status-and-trends>). eBird
    (<https://ebird.org/home>) is a global database of bird observations
    collected by member of the public. eBird Status and Trends uses these
    data to model global bird distributions, abundances, and population trends 
    at a high spatial and temporal resolution.
An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) <doi:10.48550/arXiv.1909.09223> for details. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable.
Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.
Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.
Implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) <doi:10.1093/biomet/78.4.719>.
Provides simple, fast, and stable functions to fit the normal
    means model using empirical Bayes. For available models and details, see 
    function ebnm(). Our JSS article, 
    Willwerscheid, Carbonetto, and Stephens (2025) <doi:10.18637/jss.v114.i03>,
    provides a detailed introduction to the package.
EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy.  No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for  each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.
Implements the Ebrahim-Farrington goodness-of-fit test for logistic 
    regression models, particularly effective for sparse data and binary outcomes. 
    This test provides an improved alternative to the traditional Hosmer-Lemeshow 
    test by using a modified Pearson chi-square statistic with data-dependent 
    grouping. The test is based on Farrington (1996) theoretical framework but 
    simplified for practical implementation with binary data. Includes functions 
    for both the original Farrington test (for grouped data) and the new 
    Ebrahim-Farrington test (for binary data with automatic grouping). For more details see Hosmer (1980) <doi:10.1080/03610928008827941> and Farrington (1996) <doi:10.1111/j.2517-6161.1996.tb02086.x>.
Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors.  A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.
Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) <arXiv:1812.06948> for details.
Estimation tools for multidimensional Gaussian means using
    empirical Bayesian g-modeling. Methods are able to handle fully observed data as
    well as left-, right-, and interval-censored observations (Tobit
    likelihood); descriptions of these methods can be found in Barbehenn and
    Zhao (2023) <doi:10.48550/arXiv.2306.07239>. Additional, lower-level functionality based
    on Kiefer and Wolfowitz (1956) <doi:10.1214/aoms/1177728066> and Jiang and
    Zhang (2009) <doi:10.1214/08-AOS638> is provided that can be used to
    accelerate many empirical Bayes and nonparametric maximum likelihood
    problems.
The concept of Essential Biodiversity Variables (EBV, <https://geobon.org/ebvs/what-are-ebvs/>) 
    comes with a data structure based on the Network Common Data Form (netCDF). 
    The 'ebvcube' 'R' package provides functionality to easily create, access and 
    visualise this data. The EBV netCDFs can be downloaded from the EBV Data 
    Portal: Christian Langer/ iDiv (2020) <https://portal.geobon.org/>.
An implementation for estimating Effective control to 50% of growth
    inhibition (EC50) for multi isolates and stratified datasets. It implements 
    functions from the drc package in a way that is displayed a tidy data.frame 
    as output. Info about the drc package is available in Ritz C, Baty F, Streibig JC,
    Gerhard D (2015) <doi:10.1371/journal.pone.0146021>.
Support ecological analyses such as ordination and clustering. 
    Contains consistent and easy wrapper functions of 'stat', 'vegan', and 
    'labdsv' packages, and visualisation functions of ordination and clustering.
Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in 
             Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) <arXiv:2012.11767>.  
             Both the parametric and semi-parametric models are available.  The semi-parametric model 
             relies on 'INLA'.  The 'INLA' package can be obtained from <https://www.r-inla.org/>.
Provides an interface to the European Central Bank's Data
    Portal API, allowing for programmatic retrieval of a vast quantity
    of statistical data.
If translate English or Chinese sentence, there is a faster 
    way for R user. You can pass in an English or Chinese sentence, ecce 
    package support both English and Chinese translation. It also support 
    browse translation results in website. In addition, also support obtain 
    the pinyin of the Chinese character, you can more easily understand 
    the pronunciation of the Chinese character.
Data sets for econometrics, including political science.
Computes and plots a transformed empirical CDF (ecdf) as a
    diagnostic for heavy tailed data, specifically data with power law decay on the
    tails.  Routines for annotating the plot, comparing data to a model, fitting a
    nonparametric model, and some multivariate extensions are given.
Simulate ecological niche models using Mahalanobis distance, transform distances to 
    suitability with 1 - empirical cumulative distribution function and 1 - chi-squared, and 
    generate comparison figures.
The production of certified reference materials (CRMs) requires 
    various statistical tests depending on the task and recorded data to ensure 
    that reported values of CRMs are appropriate. 
    Often these tests are performed according to the procedures described in 
    'ISO GUIDE 35:2017'. The 'eCerto' package contains a 'Shiny' app which 
    provides functionality to load, process, report and backup data recorded 
    during CRM production and facilitates following the recommended procedures.
    It is described in Lisec et al (2023) <doi:10.1007/s00216-023-05099-3> and
    can also be accessed online <https://apps.bam.de/shn00/eCerto/> without 
    package installation.
Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on 'spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.
Functions and vignettes to update 
    data sets in 'Ecdat' and to create, manipulate, 
    plot, and analyze those and similar data sets.
Implementation of the Centre of Gravity method and the Extrapolated Centre of Gravity method. It supports replicated observations.
    Cameron, D.G., et al (1982) <doi:10.1366/0003702824638610>
    JCGM (2008) <doi:10.59161/JCGM100-2008E>.
A goodness-of-fit test for elliptical distributions with diagnostic
  capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2020) <doi:10.1016/j.jmva.2020.104602>.
Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
Easily create interactive charts by leveraging the 'Echarts Javascript' library which includes
    36 chart types, themes, 'Shiny' proxies and animations.
Deliver the full functionality of 'ECharts' with minimal overhead. 'echarty' users build R lists for 'ECharts' API. Lean set of powerful commands.
Functions for the echelon analysis proposed by Myers et al. (1997) <doi:10.1023/A:1018518327329>, and the detection of spatial clusters using echelon scan method proposed by Kurihara (2003) <doi:10.20551/jscswabun.15.2_171>.
Simulates cyclic voltammetry, linear-sweep voltammetry 
    (both with and without stirring of the solution), and single-pulse 
    and double-pulse chronoamperometry and chronocoulometry 
    experiments using the implicit finite difference method outlined in 
    Gosser (1993, ISBN: 9781560810261) and in Brown (2015) 
    <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide 
    ways to display and to examine the results of these simulations.
    The primary purpose of this package is to provide tools for
    use in courses in analytical chemistry.
Capture code evaluations and script executions by expressions,
    outputs, and condition calls for logging.
Provides a function (echo_find()) designed to find rhythms 
    from data using extended harmonic oscillators. For more information,
    see H. De los Santos et al. (2020) <doi:10.1093/bioinformatics/btz617> .
Easily import multi-frequency acoustic data stored in 'HAC' files (see <doi:10.17895/ices.pub.5482> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.
Implements choice models based on economic theory, including
    estimation using Markov chain Monte Carlo (MCMC), prediction, and
    more. Its usability is inspired by ideas from 'tidyverse'. Models
    include versions of the Hierarchical Multinomial Logit and Multiple
    Discrete-Continous (Volumetric) models with and without screening. The
    foundations of these models are described in Allenby, Hardt and Rossi
    (2019) <doi:10.1016/bs.hem.2019.04.002>. Models with conjunctive
    screening are described in Kim, Hardt, Kim and Allenby (2022)
    <doi:10.1016/j.ijresmar.2022.04.001>. Models with set-size variation
    are described in Hardt and Kurz (2020) <doi:10.2139/ssrn.3418383>.
An R interface to United States Environmental 
    Protection Agency (EPA) Environmental Compliance 
    History Online ('ECHO') Application Program Interface
    (API). 'ECHO' provides information about EPA permitted 
    facilities, discharges, and other reporting info 
    associated with permitted entities. Data are obtained 
    from <https://echo.epa.gov/>.
Provides a lightweight implementation of functions and methods for
    fast and fully automatic time series modeling and forecasting using Echo
    State Networks (ESNs).
Extends the Changes-in-Changes model a la Athey and Imbens
    (2006) <doi:10.1111/j.1468-0262.2006.00668.x> to multiple cohorts and
    time periods, which generalizes difference-in-differences estimation
    techniques to the entire distribution. Computes quantile treatment
    effects for every possible two-by-two combination in ecic(). Then,
    aggregating all bootstrap runs adds the standard errors in
    summary_ecic(). Results can be plotted with plot_ecic() aggregated
    over all cohort-group combinations or in an event-study style for
    either individual periods or individual quantiles.
Provides a function that quickly computes the fine structure
    isotope patterns of a set of chemical formulas to a given degree of
    accuracy (up to the limit set by errors in floating point arithmetic). A
    data-set comprising the masses and isotopic abundances of individual
    elements is also provided and calculation of isotopic gross structures
    is also supported.
Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.
Functions for easy building of error correction models (ECM) for time series regression.
Programmatic interface to the European Centre for Medium-Range
    Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>)
    and Copernicus's Data Stores. Allows for easy downloads of weather 
    forecasts and climate reanalysis data in R. Data stores covered include the Climate Data Store (CDS; 
    <https://cds.climate.copernicus.eu>), Atmosphere Data Store (ADS; 
    <https://ads.atmosphere.copernicus.eu>) and Early Warning Data Store (CEMS; 
    <https://ewds.climate.copernicus.eu>).
Implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <doi:10.1093/pan/mpm017>) and (2011 <doi:10.18637/jss.v042.i05>) for ecological inference in 2 by 2 tables as well as the method of bounds introduced by Duncan and Davis (1953).  The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models).  For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models.
A system for calculating the optimal sampling effort, based on the ideas of 
    "Ecological cost-benefit optimization" as developed by A. Underwood (1997, 
    ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with
    prep_data() which formats and arranges the initial data, and then the 
    optimization follows the following procedure of four functions: (1) prep_data()
    takes the original dataset and creates simulated sets that can be used as a 
    basis for estimating statistical power and type II error. (2) sim_beta() is used
    to estimate the statistical power for the different sampling efforts specified
    by the user. (3) sim_cbo() calculates then the optimal sampling effort, based
    on the statistical power and the sampling costs. Additionally, (4) scompvar() 
    calculates the variation components necessary for (5) Underwood_cbo() to calculate
    the optimal combination of number of sites and samples depending on either 
    an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() 
    helps the user visualize the results of sim_beta().
Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that document biodiversity change. Freely available ecosystem remote sensing products (ERSP) are downloaded and integrated with data for national or regional domains to derive indicators for EBV in the class ecosystem structure (Pereira et al., 2013) <doi:10.1126/science.1229931>, including horizontal ecosystem extents, fragmentation, and information-theory indices. To process ERSP, users must provide a polygon or geographic administrative data map. Downloadable ERSP include Global Surface Water (Peckel et al., 2016) <doi:10.1038/nature20584>, Forest Change (Hansen et al., 2013) <doi:10.1126/science.1244693>, and Continuous Tree Cover data (Sexton et al., 2013) <doi:10.1080/17538947.2013.786146>.
Provides step-by-step automation for integrating biodiversity data from multiple online aggregators, merging and cleaning datasets while addressing challenges such as taxonomic inconsistencies, georeferencing issues, and spatial or environmental outliers. Includes functions to extract environmental data and to define the biogeographic ranges in which species are most likely to occur.
Work with the Ecological Community Data Design Pattern. 'ecocomDP' 
    is a flexible data model for harmonizing ecological community surveys, in a 
    research question agnostic format, from source data published across 
    repositories, and with methods that keep the derived data up-to-date as the 
    underlying sources change. Described in O'Brien et al. (2021), 
    <doi:10.1016/j.ecoinf.2021.101374>.
Creates 'graphs' of species associations (interactions) and ordination biplots from 
    co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in
    Popovic, GC., Hui, FKC., Warton, DI., (2018) <doi:10.1016/j.jmva.2017.12.002>.
A framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). 
    You create an ODE model, tells 'ecode' to explore its behaviour, and perform numerical 
    simulations on the model. 'ecode' also allows you to fit model parameters by machine learning 
    algorithms. Potential users include researchers who are interested in the dynamics of ecological 
    community and biogeochemical cycles.
Biotracers and stomach content analyses are combined in a Bayesian hierarchical model
    to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix 
    (all diet proportions).
    The package relies on the JAGS software and the 'jagsUI' package to run a Markov chain Monte Carlo 
    approximation of the different variables.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.
Computes alpha and beta diversity metrics using concurrent 'C' threads.
    Metrics include 'UniFrac', Faith's phylogenetic diversity, Bray-Curtis
    dissimilarity, Shannon diversity index, and many others.
    Also parses newick trees into 'phylo' objects and rarefies feature tables.
Fit and sample from the ensemble model described in Spence et al (2018): "A general framework for combining ecosystem models"<doi:10.1111/faf.12310>.
Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.
Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.
Provides measures to characterize the complexity of classification 
    and regression problems based on aspects that quantify the linearity of the 
    data, the presence of informative feature, the sparsity and dimensionality 
    of the datasets. This package provides bug fixes, generalizations and 
    implementations of many state of the art measures. The measures are 
    described in the papers: Lorena et al. (2019) <doi:10.1145/3347711> and 
    Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
  All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. 
  The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
  Solutions to model projects are in the same subdirectories.
Coalescent-Based Simulation of Ecological Communities as proposed
    by Munoz et al. (2018) <doi:10.1111/2041-210X.12918>. The package includes
    a tool for estimating parameters of community assembly by using Approximate 
    Bayesian Computation.
Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data building on Thomsen (1987) and Park (2008) approaches.
   References:
   Park, W.-H. (2008). ''Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. <https://deepblue.lib.umich.edu/bitstream/handle/2027.42/58525/wpark_1.pdf>
   Thomsen, S.R. (1987, ISBN:87-7335-037-2). ''Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark.
Functions and data sets to perform and demonstrate community ecology statistical tests, including Hutcheson's t-test (Hutcheson (1970) <doi:10.1016/0022-5193(70)90124-4>, Zar (2010) ISBN:9780321656865).
An intuitive and user-friendly package designed to aid undergraduate students in understanding and applying econometric methods in their studies,
    Tailored specifically for Econometrics and Regression Modeling courses, it provides a practical toolkit for modeling and analyzing econometric data with detailed inference capabilities.
Production efficiency and economic efficiency are crucial concepts in agriculture/horticulture for sustainable and profitable practices. It helps to determine the optimal use of resources to maximize outputs and profitability. Production efficiency focuses on the optimal use of resources to produce goods, while economic efficiency ensures these goods are produced and allocated in a way that maximizes economic welfare. Production efficiency and economic efficiency are calculated with the help of the formula given by (Kumar et al., 2017) <doi:10.21921/jas.v4i04.10202>.
Implements three complementary pipelines for causal analysis on macroeconomic time series:
    (1) Error-Correction Models with Multivariate Adaptive Regression Splines (ECM-MARS),
    (2) Bayesian Structural Time Series (BSTS), and 
    (3) Bayesian GLM with AR(1) errors validated with Leave-Future-Out (LFO). 
    Heavy backends (Stan) are optional and never used in examples or tests.
The 'EconDataverse' is a universe of open-source packages to work 
    seamlessly with economic data. This package is designed to make it easy to 
    download selected datasets that are preprocessed by 'EconDataverse' packages 
    and publicly hosted on 'Hugging Face'. Learn more about the 'EconDataverse' 
    at <https://www.econdataverse.org>.
The 'EconDataverse' is a universe of open-source packages to work 
    seamlessly with economic data. This package is designed to make it easy to 
    install and load multiple 'EconDataverse' packages in a single step. Learn
    more about the 'EconDataverse' at <https://www.econdataverse.org>.
Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette <doi:10.18637/jss.v102.i08>.
Randomly generate a wide range of interaction networks with
             specified size, average degree, modularity, and topological
             structure. Sample nodes and links from within simulated networks
             randomly, by degree, by module, or by abundance. Simulations
             and sampling routines are implemented in 'FORTRAN', providing
             efficient generation times even for large networks. Basic
             visualization methods also included. Algorithms implemented
             here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.
A collection of advanced tools, methods and models specifically  designed  for  analyzing different  types  of  ecological  networks - especially  antagonistic  (food  webs,  host-parasite),  mutualistic (plant-pollinator, plant-fungus, etc)  and competitive networks, as well as their variability in time and space. Statistical models are developed to describe and understand the mechanisms that determine species interactions, and to decipher the organization of these ecological networks (Ohlmann et al. (2019)  <doi:10.1111/ele.13221>, Gonzalez et al. (2020) <doi:10.1101/2020.04.02.021691>, Miele et al. (2021) <doi:10.48550/arXiv.2103.10433>, Botella et al (2021) <doi:10.1111/2041-210X.13738>).
Computes a series of indices commonly used in the fields of economic geography, economic complexity, and evolutionary economics to describe the location, distribution, spatial organization, structure, and complexity of economic activities. Functions include basic spatial indicators such as the location quotient, the Krugman specialization index, the Herfindahl or the Shannon entropy indices but also more advanced functions to compute different forms of normalized relatedness between economic activities or network-based measures of economic complexity. Most of the functions use matrix calculus and are based on bipartite (incidence) matrices consisting of region - industry pairs. These are described in Balland (2017) <http://econ.geo.uu.nl/peeg/peeg1709.pdf>.
Provides utility functions for standardizing economic entity 
  (economy, aggregate, institution, etc.) name and id in economic datasets such 
  as those published by the International Monetary Fund and World Bank. Aims to 
  facilitate consistent data analysis, reporting, and joining across datasets. 
  Used as a foundational building block in the 'EconDataverse' family of 
  packages (<https://www.econdataverse.org>).
A wrapper of different methods from Linear Algebra for the equations
  introduced in The Atlas of Economic Complexity and related literature. This
  package provides standard matrix and graph output that can be used seamlessly
  with other packages. See <doi:10.21105/joss.01866> for a summary
  of these methods and its evolution in literature.
Provides a consistent set of functions for enriching and analyzing 
    sovereign-level economic data. Economists, data scientists, and financial 
    professionals can use the package to add standardized identifiers, 
    demographic and macroeconomic indicators, and derived metrics such as 
    gross domestic product per capita or government expenditure shares.
Null models to analyse ecological networks (e.g. food webs, 
    flower-visitation networks, seed-dispersal networks) and detect resource 
    preferences or non-random interactions among network nodes. Tools are 
    provided to run null models, test for and plot preferences, plot and 
    analyse bipartite networks, and export null model results in a form 
    compatible with other network analysis packages. The underlying null model 
    was developed by Agusti et al. (2003) Molecular Ecology 
    <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to 
    ecological networks by Vaughan et al. (2018) econullnetr: an R package 
    using null models to analyse the structure of ecological networks and 
    identify resource selection. Methods in Ecology & Evolution, 
    <doi:10.1111/2041-210X.12907>.
Estimates power by simulation for multivariate
    abundance data to be used for sample size estimates. Multivariate
    equivalence testing by simulation from a Gaussian copula model.
    The package also provides functions for parameterising multivariate effect
    sizes and simulating multivariate abundance data jointly. The discrete
    Gaussian copula approach is described in
    Popovic et al. (2018) <doi:10.1016/j.jmva.2017.12.002>.
Estimating individual-level covariate-outcome associations 
 using aggregate data ("ecological inference") or a combination of 
 aggregate and individual-level data ("hierarchical related regression").
A toolbox for implementing the Ecological Dynamic Regime framework 
    (Sánchez-Pinillos et al., 2023 <doi:10.1002/ecm.1589>) to characterize and 
    compare groups of ecological trajectories in multidimensional spaces defined 
    by state variables. The package includes the RETRA-EDR algorithm to identify 
    representative trajectories, functions to generate, summarize, and visualize 
    representative trajectories, and several metrics to quantify the distribution 
    and heterogeneity of trajectories in an ecological dynamic regime and quantify 
    the dissimilarity between two or more ecological dynamic regimes. The package 
    also includes a set of functions to assess ecological resilience based on 
    ecological dynamic regimes (Sánchez-Pinillos et al., 2024 <doi:10.1016/j.biocon.2023.110409>).
Three sets of data and functions for informing ecosystem restoration
    decisions, particularly in the context of the U.S. Army Corps of Engineers.
    First, model parameters are compiled as a data set and associated metadata 
    for over 300 habitat suitability models developed by the U.S. Fish and 
    Wildlife Service (USFWS 1980, <https://www.fws.gov/policy-library/870fw1>).
    Second, functions for conducting habitat suitability analyses both for the 
    models described above as well as generic user-specified model parameterizations. 
    Third, a suite of decision support tools for conducting cost-effectiveness and 
    incremental cost analyses (Robinson et al. 1995, IWR Report 95-R-1, U.S. 
    Army Corps of Engineers).
Implementation of a modular framework for ecosystem risk assessments, 
    combining existing risk assessment approaches tailored to semi-quantitative and
    quantitative analyses.
API wrapper to download statistical information from the Economic
             Statistics System (ECOS) of the Bank of Korea
             <https://ecos.bok.or.kr/api/#/>.
Classes and methods for implementing aquatic ecosystem models,
  for running these models, and for visualizing their results.
R interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.
Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness.
    Functions are written so they will work in a parallel-computing environment.
Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.
Fits a state-space mass-balance model for marine ecosystems,
	which implements dynamics derived from 
	'Ecopath with Ecosim' ('EwE') <https://ecopath.org/>
	while fitting to time-series of fishery catch, biomass indices,
	age-composition samples, and weight-at-age data.  
	'Ecostate' fits biological parameters (e.g., equilibrium mass)
	and measurement parameters (e.g., catchability coefficients)
	jointly with residual variation in process errors, and can include
	Bayesian priors for parameters.
Functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.
Implementation of the scaling functions presented in "General statistical scaling laws for stability in ecological systems" by Clark et al in Ecology Letters <DOI:10.1111/ele.13760>. Includes functions for extrapolating variability, resistance, and resilience across spatial and ecological scales, as well as a basic simulation function for producing time series, and a regression routine for generating unbiased parameter estimates. See the main text of the paper for more details.
A collection of curated educational datasets for teaching ecology and
    agriculture concepts. Includes data on wildlife monitoring, plant treatments,
    and ecological observations with documentation and examples for educational use.
    All datasets are derived from published scientific studies and are available
    under CC0 or compatible licenses.
Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) <doi:10.1007/BF00048870>), fuzzy clustering (De Cáceres et al. (2010) <doi:10.1080/01621459.1963.10500845>), biodiversity indices (Jost (2006) <doi:10.1111/j.2006.0030-1299.14714.x>), and network analyses (Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided.
This is a collection of data files for 
  exploring sightings of wild things, relative to weather 
  and tourism patterns in Australia.
A simple approach to using a probit or logit analysis to calculate
        lethal concentration (LC) or time (LT) and the appropriate fiducial 
        confidence limits desired for selected LC or LT for
        ecotoxicology studies (Finney 1971; Wheeler et al. 2006; 
        Robertson et al. 2007). The simplicity of 'ecotox' comes from the 
        syntax it implies within its functions which are similar to functions 
        like glm() and lm(). In addition to the simplicity of the syntax, 
        a comprehensive data frame is produced which gives the user a 
        predicted LC or LT value for the desired level and a suite of important 
        parameters such as fiducial confidence limits and slope.   
        Finney, D.J. (1971, ISBN: 052108041X);
        Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>;
        Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K.
        (2007, ISBN: 0849323312).
Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis.
    Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book.
    Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included.
    Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard.
    Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
The US EPA ECOTOX database is a freely available database
    with a treasure of aquatic and terrestrial ecotoxicological data.
    As the online search interface doesn't come with an API, this
    package provides the means to easily access and search the database
    in R. To this end, all raw tables are downloaded from the EPA website
    and stored in a local SQLite database <doi:10.1016/j.chemosphere.2024.143078>.
Analysis of temporal changes (i.e. dynamics) of ecological entities, defined as trajectories on a chosen multivariate space, by providing a set of 
    trajectory metrics and visual representations [De Caceres et al. (2019) <doi:10.1002/ecm.1350>; 
    and Sturbois et al. (2021) <doi:10.1016/j.ecolmodel.2020.109400>]. Includes functions
    to estimate metrics for individual trajectories (length, directionality, angles, ...) as well as
    metrics to relate pairs of trajectories (dissimilarity and convergence). Functions are also
    provided to estimate the ecological quality of ecosystem with respect to reference conditions 
    [Sturbois et al. (2023)  <doi:10.1002/ecs2.4726>].
Computes temporal trends in environmental suitability obtained from ecological niche models, based on a set of species presence point coordinates and predictor variables.
An approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
Functions for evaluating and visualizing
  ecological assessment procedures for surface waters
  containing physical, chemical and biological assessments
  in the form of value functions.
Implements various procedures for finding 
	     multiple change-points from Matteson D. et al (2013)
	     <doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017) 
	     <doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019).
	     Two methods make use of dynamic  
	     programming and pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.
Computes the Extended Chen-Poisson (ecp) distribution, survival, 
    density, hazard, cumulative hazard and quantile functions. It also allows 
    to generate a pseudo-random sample from this distribution. The corresponding 
    graphics are available. Functions to obtain measures of skewness and 
    kurtosis, k-th raw moments, conditional k-th moments and mean 
    residual life function were added. For details about ecp distribution, see 
    Sousa-Ferreira, I., Abreu, A.M. & Rocha, C. (2023). 
    <doi:10.57805/revstat.v21i2.405>.
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Easy and rapid quantitative estimation of small terrestrial ectotherm temperature regulation effectiveness in R. ectotemp is built on classical formulas that evaluate temperature regulation by means of various indices, inaugurated by Hertz et al. (1993) <doi: 10.1086/285573>. Options for bootstrapping and permutation testing are included to test hypotheses about divergence between organisms, species or populations.
The cointegration based support vector regression model enables researchers to use data obtained from the cointegrating vector as input in the support vector regression model.
This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.
We implement (or re-implements in R) a variety of statistical
    tools. They are focused on non-parametric two-sample (or k-sample)
    distribution comparisons in the univariate or multivariate case. See
    the vignette for more info.
Reproducibility assessment is essential in extracting reliable scientific 
    insights from high-throughput experiments.  While  the  Irreproducibility 
    Discovery   Rate   (IDR)   method  has  been  instrumental  in  assessing 
    reproducibility,  its  standard implementation is constrained to handling
    only two  replicates. Package 'eCV' introduces an enhanced Coefficient of
    Variation  (eCV)  metric  to assess the likelihood of omic features being 
    reproducible. Additionally, it offers alternatives  to  the  Irreproducible
    Discovery  Rate  (IDR)  calculations for multi-replicate experiments. 
    These  tools  are  valuable for analyzing high-throughput data in genomics
    and  other  omics  fields. The methods implemented in 'eCV' are  described 
    in Gonzalez-Reymundez et al., (2023) <doi:10.1101/2023.12.18.572208>.
Functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively
    Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by
    us, as well as logistic regression and isotonic regression. Besides, the package also supports
    comparison between two estimation results.
Provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.
A collection of convenient functions to facilitate common tasks in exploratory data analysis. 
          Some common tasks include generating summary tables of variables, 
          displaying tables as a 'flextable' or a 'kable' and visualising variables using 'ggplot2'. 
          Labels stating the source file with run time can be easily generated
          for annotation in tables and plots.
A convenient toolbox to import data exported from Electronic Data Capture (EDC) software 'TrialMaster'.
This is the course package for the exercise portion of the "Ecological Data Collection and Processing" course.
Compute a cyclist's Eddington number, including efficiently
    computing cumulative E over a vector. A cyclist's Eddington number
    <https://en.wikipedia.org/wiki/Arthur_Eddington#Eddington_number_for_cycling>
    is the maximum number satisfying the condition such that a cyclist has
    ridden E miles or greater on E distinct days. The algorithm in this package
    is an improvement over the conventional approach because both summary
    statistics and cumulative statistics can be computed in linear time, since
    it does not require initial sorting of the data. These functions may also be
    used for computing h-indices for authors, a metric described by Hirsch (2005)
    <doi:10.1073/pnas.0507655102>. Both are specific applications of computing
    the side length of a Durfee square <https://en.wikipedia.org/wiki/Durfee_square>.
Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package 'bupaR'.
Detects sustained change in digital bio-marker data using
    simultaneous confidence bands. Accounts for noise using an auto-regressive
    model. Based on Buehlmann (1998) "Sieve bootstrap for smoothing in
    nonstationary time series" <doi:10.1214/aos/1030563978>.
Import physiologic data stored in
    the European Data Format (EDF and EDF+) into R.
    Both EDF and EDF+ files are supported. Discontinuous
    EDF+ files are not yet supported.
Provides easy access to tidy education finance data using Bellwether's methodology 
    to combine NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE),
    and community data from the ACS 5-Year Estimates. The package simplifies
    downloading, caching, and filtering education finance data by year and state,
    enabling researchers and analysts to explore K-12 education funding patterns,
    revenue sources, expenditure categories, and demographic factors across 
    U.S. school districts.
Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>,
    BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>,
    and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>.
    The files are read in two steps: first the header is read
    and then the signals (using the header object as a parameter).
Easily creating empirical distribution functions from data: 'dfun', 'pfun',
    'qfun' and 'rfun'.
In the USA, companies file different forms with the U.S. 
    Securities and Exchange Commission (SEC) through EDGAR (Electronic 
    Data Gathering, Analysis, and Retrieval system). The EDGAR 
    database automated system collects all the different necessary 
    filings and makes it publicly available. This package facilitates
    retrieving, storing, searching, and parsing of all the available 
    filings on the EDGAR server. It downloads filings from SEC 
    server in bulk with a single query. Additionally, it provides 
    various useful functions: extracts 8-K triggering events, extract
    "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)"
    sections of annual statements, searches filings for desired 
    keywords, provides sentiment measures, parses filing header 
    information, and provides HTML view of SEC filings.
A set of methods to access and parse live filing information from the
    U.S. Securities and Exchange Commission (SEC - <https://www.sec.gov/>) including
    company and fund filings along with all associated metadata.
Implements several algorithms for bundling edges in networks and flow and metro map layouts. This includes force directed edge bundling <doi:10.1111/j.1467-8659.2009.01450.x>, a flow algorithm based on Steiner trees<doi:10.1080/15230406.2018.1437359> and a multicriteria optimization method for metro map layouts <doi:10.1109/TVCG.2010.24>.
Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.
Facilitates basic spatial edge correction to point pattern data.
Datasets from most recent CCIIO DIY entry
  in a tidy format. These support the Centers for Medicare and Medicaid
  Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows
  health insurance issuers to calculate member risk profiles under the Health
  and Human Services-Hierarchical Condition Categories (HHS-HCC) regression
  model. This regression model is used to calculate risk adjustment transfers.
  Risk adjustment is a selection mitigation program implemented under the
  Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA.
  Under the ACA, health insurance issuers submit claims data to CMS	in order
  for CMS to calculate a risk score under the HHS-HCC regression model.
  However, CMS does not inform issuers of their average risk score until after
  the data submission deadline. These data sets can be used by issuers to
  calculate their average risk score mid-year. More information about risk
  adjustment and the HHS-HCC model can be found here:
  <https://www.cms.gov/mmrr/Articles/A2014/MMRR2014_004_03_a03.html>.
Enables R users to run large language models locally using 'GGUF' model files
    and the 'llama.cpp' inference engine. Provides a complete R interface for loading models,
    generating text completions, and streaming responses in real-time. Supports local
    inference without requiring cloud APIs or internet connectivity, ensuring complete
    data privacy and control. Based on the 'llama.cpp' project by Georgi Gerganov (2023) <https://github.com/ggml-org/llama.cpp>.
A system to facilitate designing comparative (and non-comparative) experiments using the 
  grammar of experimental designs <https://emitanaka.org/edibble-book/>. 
  An experimental design is treated as an intermediate, mutable object that is 
  built progressively by fundamental experimental components like units, treatments, and their relation.
  The system aids in experimental planning, management and workflow.
The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) <doi:10.12688/openreseurope.16054.1>.
Perform a Bayesian estimation of the exploratory 
    deterministic input, noisy and gate (EDINA)
    cognitive diagnostic model described by Chen et al. (2018)
    <doi:10.1007/s11336-017-9579-4>.
Package EDISON (Estimation of Directed Interactions from
    Sequences Of Non-homogeneous gene expression) runs an MCMC
    simulation to reconstruct networks from time series data, using
    a non-homogeneous, time-varying dynamic Bayesian network.
    Networks segments and changepoints are inferred concurrently,
    and information sharing priors provide a reduction of the
    inference uncertainty.
The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in 'shiny'. By the use of generic 'dplyr' methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case.
An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame'
    without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. 
    These modules are intended for reuse across applications.
Runs the eDITH (environmental DNA Integrating Transport 
  and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) 
  transport at a river network scale coupled with a species distribution model 
  to obtain maps of species distribution. eDITH can work with both eDNA concentration 
  (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding 
  (read count) data. Parameter estimation can be performed via Bayesian techniques 
  (via the 'BayesianTools' package) or optimization algorithms. An interface to the 
  'DHARMa' package for posterior predictive checks is provided. See Carraro and 
  Altermatt (2024) <doi:10.1111/2041-210X.14317> for a package introduction; 
  Carraro et al. (2018) <doi:10.1073/pnas.1813843115> and Carraro et al. (2020) 
  <doi:10.1038/s41467-020-17337-8> for methodological details.
Please note: active development has moved to packages 'validate'
    and 'errorlocate'. Facilitates reading and manipulating (multivariate) data
    restrictions (edit rules) on numerical and categorical data. Rules can be
    defined with common R syntax and parsed to an internal (matrix-like format).
    Rules can be manipulated with variable elimination and value substitution
    methods, allowing for feasibility checks and more. Data can be tested against
    the rules and erroneous fields can be found based on Fellegi and Holt's
    generalized principle. Rules dependencies can be visualized with using the
    'igraph' package.
A client for the Environmental Data Initiative repository REST API. The 'EDI' data repository <https://portal.edirepository.org/nis/home.jsp> is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the 'PASTA+' software stack <https://pastaplus-core.readthedocs.io/en/latest/index.html#> and was developed in collaboration with the US 'LTER' Network <https://lternet.edu/>. 'EDIutils' includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users.
Error-driven learning (based on the Widrow & Hoff (1960)<https://isl.stanford.edu/~widrow/papers/c1960adaptiveswitching.pdf> learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, <doi:10.1037/a0023851>) can be used to explain bottom-up human learning (Hoppe et al, <doi:10.31234/osf.io/py5kd>), but is also at the core of artificial neural networks applications in the form of the Delta rule. This package provides a set of functions for building small-scale simulations to investigate the dynamics of error-driven learning and it's interaction with the structure of the input. For modeling error-driven learning using the Rescorla-Wagner equations the package 'ndl' (Baayen et al, 2011, <doi:10.1037/a0023851>) is available on CRAN at <https://cran.r-project.org/package=ndl>. However, the package currently only allows tracing of a cue-outcome combination, rather than returning the learned networks. To fill this gap, we implemented a new package with a few functions that facilitate inspection of the networks for small error driven learning simulations. Note that our functions are not optimized for training large data sets (no parallel processing), as they are intended for small scale simulations and course examples. (Consider the python implementation 'pyndl' <https://pyndl.readthedocs.io/en/latest/> for that purpose.)
Bindings to edlib, a lightweight performant C/C++ library for exact pairwise sequence alignment using edit distance (Levenshtein distance). The algorithm computes the optimal alignment path, but also can be used to find only the start and/or end of the alignment path for convenience. Edlib was designed to be ultrafast and require little memory, with the capability to handle very large sequences. Three alignment methods are supported: global (Needleman-Wunsch), infix (Hybrid Wunsch), and prefix (Semi-Hybrid Wunsch). The original C/C++ library is described in "Edlib: a C/C++ library for fast, exact sequence alignment using edit distance", M. Šošić, M. Šikić, <doi:10.1093/bioinformatics/btw753>.
Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
Collection of data sets from various assessments that can be used to 
    evaluate psychometric models. These data sets have been analyzed in the
    following papers that introduced new methodology as part of the application section:
    Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) <doi:10.1111/bmsp.12307>,
    Culpepper, S. A., & Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>,
    Yinghan Chen et al. (2021) <doi:10.1007/s11336-021-09750-9>,
    Yinyin Chen et al. (2020) <doi:10.1007/s11336-019-09693-2>,
    Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>,
    Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>,
    Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>,
    Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>,
    and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.
A series of R functions that come in handy while working with metabarcoding data.
  The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way.
  In a way it is all about putting together the grammar of the  'tidyverse' from Wickham et al.(2019) <doi:10.21105/joss.01686> 
  with the functions we have used in community ecology compiled in packages like 'vegan' from Dixon (2003) <doi:10.1111/j.1654-1103.2003.tb02228.x>
  and 'phyloseq' McMurdie & Holmes (2013) <doi:10.1371/journal.pone.0061217>.
  The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader' and 'fastq_reader'),
  to process 'cutadapt' Martin (2011) <doi:10.14806/ej.17.1.200> 'info-file' output. When it comes to sequence counts across samples, the package
  works with the long format in mind (a three column 'tibble' with Sample, Sequence and counts ), with functions to move from there to the wider format.
Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: <https://ednajoint.netlify.app/>). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and gear scaling coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language.
Package implements the EDNE-test for equivalence
    according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
    "EDNE" abbreviates "Euclidean Distance between the 
    Non-standardized Expected values".
    The EDNE-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Euclidean distance.
    The test is an asymptotically valid test for the family of distributions 
    fulfilling the assumptions of the multivariate central limit theorem 
    (see Hoffelder et al.,2015).
    The function EDNE.EQ() implements the EDNE-test for equivalence 
    according to Hoffelder et al. (2015).
    The function EDNE.EQ.dissolution.profiles() implements a variant 
    of the EDNE-test for equivalence analyses of dissolution profiles
    (see Suarez-Sharp et al.,2020 <DOI:10.1208/s12248-020-00458-9>).
    EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the 
    differences of the expected values of both dissolution profile populations 
    is statistically significantly smaller than 10 [\% of label claim]. 
    The current regulatory standard approach for equivalence analyses of 
    dissolution profiles is the similarity factor f2.
    The statistical hypotheses underlying EDNE.EQ.dissolution.profiles()
    coincide with the hypotheses for f2 (see Hoffelder et al.,2015, 
    Suarez-Sharp et al., 2020).
A non-parametric framework based on  estimation statistics principle. Its main purpose is to  infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of  domination  of  categories  and  representing  orders  in  the form of a graph; 2) estimating  magnitude  of  difference  between  a  pair  of categories in forms of mean-difference confidence intervals; and 3) visualizing  domination  orders  and  magnitudes  of  difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) <doi:10.1016/j.heliyon.2020.e05435>.
A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance.
Streamlines the fitting of common Bayesian item response models 
    using Stan.
Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd).
Allows R users to retrieve and parse data from the Urban 
    Institute's Education Data API <https://educationdata.urban.org/> into a 
    'data.frame' for analysis.
Provides a comprehensive collection of datasets related to education, 
    covering topics such as student performance, learning methods, test scores, 
    absenteeism, and other educational metrics. This package serves as a resource 
    for educational researchers, data analysts, and statisticians to explore and 
    analyze data in the field of education.
Easily compute education inequality measures and the distribution 
  of educational attainments for any group of countries, using the data set 
  developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>. 
  The package offers the possibility to compute not only the Gini index, but 
  also generalized entropy measures for different values of the sensitivity 
  parameter. In particular, the package includes functions to compute the 
  mean log deviation, which is more sensitive to the bottom part of the 
  distribution; the Theil’s entropy measure, equally sensitive to all parts 
  of the distribution; and finally, the GE measure when the sensitivity 
  parameter is set equal to 2, which gives more weight to differences in 
  higher education. The decomposition of these measures in the components 
  between-country and within-country inequality is also provided. Two 
  graphical tools are also provided, to analyse the evolution of the
  distribution of educational attainments: The cumulative distribution 
  function and the Lorenz curve.
Implements the Edwards (1997) <doi:10.1002/j.1551-8833.1997.tb08229.x>
    Langmuir-based semi-empirical coagulation model, which predicts the concentration
    of organic carbon remaining in water after treatment with an Al- or Fe-based
    coagulant. Data and methods are provided to optimise empirical coefficients.
This software downloads and manages air quality data from the European Environmental Agency (EEA) dataflow (<https://www.eea.europa.eu/data-and-maps/data/aqereporting-9>).
    See the web page <https://eeadmz1-downloads-webapp.azurewebsites.net/> for details on the EEA's Air Quality Download Service.
    The package allows dynamically mapping the stations, summarising and time aggregating the measurements and building spatial interpolation maps.
    See the web page <https://www.eea.europa.eu/en> for further information on EEA activities and history.
    Further details, as well as, an extended vignette of the main functions included in the package, are available at the GitHub web page dedicated to the project.
Implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) <doi:10.1016/j.asoc.2023.110230>.
Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Read raw EEM data and prepares them for further analysis.
Forecasting time series with different decomposition based ARIMA models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.
Forecasting univariate time series with different decomposition based Extreme Learning Machine models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>, Parida M, Behera MK, Nayak N (2018). <doi:10.1109/ICSESP.2018.8376723>.
Application of Ensemble Empirical Mode Decomposition and its variant based Support Vector regression model for univariate time series forecasting. For method details see Das (2020).<http://krishi.icar.gov.in/jspui/handle/123456789/44138>.
Forecasting univariate time series with different decomposition based time delay neural network models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.
We introduced a novel ensemble-based explainable machine learning model using Model Confidence Set (MCS) and two stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The model combined the predictive capabilities of different machine-learning models and integrates the interpretability of explainability methods. To develop the proposed algorithm, a two-stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework was employed. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
Provides various tools for preprocessing Emission-Excitation-Matrix (EEM) for Parallel Factor Analysis (PARAFAC). Different
  methods are also provided to calculate common metrics such as humification index and fluorescence index.
Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, and identify cut points. Also includes three example data 
    sets of administrative education records for learning how to process records 
    with errors.
Provides functions to create simulated time series of environmental
    exposures (e.g., temperature, air pollution) and health outcomes for use in
    power analysis and simulation studies in environmental epidemiology. This
    package also provides functions to evaluate the results of simulation studies
    based on these simulated time series. This work was supported by a grant
    from the National Institute of Environmental Health Sciences (R00ES022631) and
    a fellowship from the Colorado State University Programs for Research and
    Scholarly Excellence.
Contains logic for computing sparse principal components via the EESPCA method, 
    which is based on an approximation of the eigenvector/eigenvalue identity. 
    Includes logic to support execution of the TPower and rifle sparse PCA methods,
    as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle
    via cross-validation to minimize the out-of-sample reconstruction error.
    H. Robert Frost (2021) <doi:10.1080/10618600.2021.1987254>.
A set of functions to estimate capture probabilities and
  densities from multipass pass removal data.
Functions for eleven procedures for determining the number of 
    factors, including functions for parallel analysis and the minimum average partial 
    test. There are also functions for conducting principal components analysis, principal 
    axis factor analysis, maximum likelihood factor analysis, image factor analysis, 
    and extension factor analysis, all of which can take raw data or correlation matrices 
    as input and with options for conducting the analyses using Pearson correlations, 
    Kendall correlations, Spearman correlations, gamma correlations, or polychoric 
    correlations. Varimax rotation, promax rotation, and Procrustes rotations can be
    performed. Additional functions focus on the factorability of a correlation matrix,
    the congruences between factors from different datasets, the assessment of local
    independence, the assessment of factor solution complexity, and internal consistency.
    Auerswald & Moshagen (2019, ISSN:1939-1463);
    Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2);
    Mulaik (2010, ISBN:978-1-4200-9981-2);
    O'Connor (2000, <doi:10.3758/bf03200807>);
    O'Connor (2001, ISSN:0146-6216).
Performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 
    <doi:10.1037/a0023353>) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 
    2011 <doi:10.1080/00273171.2011.564527>) for assessing the dimensionality
    of a set of variables using minimum rank factor analysis (see ten Berge & 
    Kiers, 1991 <doi:10.1007/BF02294464> for more information). The package 
    also includes the option to compute minimum rank factor analysis by 
    itself, as well as the greater lower bound calculation.
Provides a collection of standard factor retention methods in Exploratory Factor 
             Analysis (EFA), making it easier to determine the number of factors. Traditional 
             methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, 
             Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and 
             Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis 
             (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA 
             are readily available. This package also implements several newer methods, such as 
             the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) 
             <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) 
             <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) 
             <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like 
             Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) 
             <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner 
             (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network 
             (DNN) trained on large-scale datasets that can efficiently and reliably determine 
             the number of factors.
Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++.
A number of utility function for exploratory factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions.
Implements the exponential Factor Copula Model (eFCM) of Castro-Camilo, D. and Huser, R. (2020) for spatial extremes, with tools for dependence estimation, tail inference, and visualization. The package supports likelihood-based inference, Gaussian process modeling via Matérn covariance functions, and bootstrap uncertainty quantification. See Castro-Camilo and Huser (2020) <doi:10.1080/01621459.2019.1647842>.
An implementation of European Forestry Dynamics Model (EFDM) and
    an estimation algorithm for the transition probabilities.
    The EFDM is a large-scale forest model that simulates the development of
    the forest and estimates volume of wood harvested for any given forested
    area. This estimate can be broken down by, for example, species, site
    quality, management regime and ownership category.
    See Packalen et al. (2015) <doi:10.2788/153990>.
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
    in an image. The image is first transformed into the wavelet domain in
    order to decorrelate any noise components, following which the coefficients
    at each resolution are standardised. Statistical tests (in a multiple
    hypothesis testing setting) are then carried out to find the anomalies. The
    power of EFDR exceeds that of standard FDR, which would carry out tests on
    every wavelet coefficient: EFDR choose which wavelets to test based on a
    criterion described in Shen et al. (2002). The package also provides
    elementary tools to interpolate spatially irregular data onto a grid of the
    required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
    'Nonparametric hypothesis testing for a spatial signal.' Journal of the
    American Statistical Association 97.460 (2002): 1122-1140.
Estimate a total causal effect from observational data under 
    linearity and causal sufficiency. The observational data is supposed to 
    be generated from a linear structural equation model (SEM) with independent 
    and additive noise. The underlying causal DAG associated the SEM is required
    to be known up to a maximally oriented partially directed graph (MPDAG), 
    which is a general class of graphs consisting of both directed and 
    undirected edges, including CPDAGs (i.e., essential graphs) and DAGs. Such
    graphs are usually obtained with structure learning algorithms with added 
    background knowledge. The program is able to estimate every identified 
    effect, including single and multiple treatment variables. Moreover, the 
    resulting estimate has the minimal asymptotic covariance (and hence 
    shortest confidence intervals) among all estimators that are based on the 
    sample covariance.
Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) <doi:10.1162/REST_a_00639>.  The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method.
Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.
High-performance implementation of various effect plots
    useful for regression and probabilistic classification tasks.  The
    package includes partial dependence plots (Friedman, 2021,
    <doi:10.1214/aos/1013203451>), accumulated local effect plots and
    M-plots (both from Apley and Zhu, 2016, <doi:10.1111/rssb.12377>), as
    well as plots that describe the statistical associations between model
    response and features.  It supports visualizations with either
    'ggplot2' or 'plotly', and is compatible with most models, including
    'Tidymodels', models wrapped in 'DALEX' explainers, or models with
    case weights.
Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.
Provide utilities to work with indices of effect size for a wide
    variety of models and hypothesis tests (see list of supported models using
    the function 'insight::supported_models()'), allowing computation of and
    conversion between indices such as Cohen's d, r, odds, etc.
    References: Ben-Shachar et al. (2020) <doi:10.21105/joss.02815>.
Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.
Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.
Provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package.
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
An implementation for using efficient initials to compute the
    maximal eigenpair in R. It provides three algorithms to find the efficient
    initials under two cases: the tridiagonal matrix case and the general matrix
    case. Besides, it also provides two algorithms for the next to the maximal eigenpair under
    these two cases.
A collection of functions to compute the standardized 
  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). 
  The computation algorithms have been optimized to allow efficient computation even 
  with very large data sets.
The elliptical factor model, as an extension of the traditional factor model,  effectively overcomes the limitations of the traditional model when dealing with heavy-tailed characteristic data. This package implements sparse principal component methods (SPC) and bi-sparse online principal component estimation (SPOC) for parameter estimation. Includes functionality for calculating mean squared error, relative error, and loading matrix sparsity.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
Interact with the FRED API, <https://fred.stlouisfed.org/docs/api/fred/>, 
      to fetch observations across economic series;
      find information about different economic sources, releases, series, etc.;
      conduct searches by series name, attributes, or tags; and determine the 
      latest updates. Includes functions for creating panels of related variables 
      with minimal effort and datasets containing data sources, releases, and 
      popular FRED tags.
Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids
    in both mg/mL and mmol/L units.
Implements the Exploratory Graph Analysis (EGA) framework for dimensionality
             and psychometric assessment. EGA estimates the number of dimensions in
	     	 psychological data using network estimation methods and community detection
             algorithms. A bootstrap method is provided to assess the stability of dimensions
	     	 and items. Fit is evaluated using the Entropy Fit family of indices. Unique 
             Variable Analysis evaluates the extent to which items are locally dependent (or
             redundant). Network loadings provide similar information to factor loadings and
	     	 can be used to compute network scores. A bootstrap and permutation approach are
             available to assess configural and metric invariance. Hierarchical structures
             can be detected using Hierarchical EGA. Time series and intensive longitudinal 
	     	 data can be analyzed using Dynamic EGA, supporting individual, group, and 
             population level assessments.
Miscellaneous functions to help customise 'ggplot2' objects. High-level functions are provided to post-process 'ggplot2' layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom 'geom', and helper functions to enforce symmetric scales or add tags to facetted plots.
An implementation of Bayesian hierarchical models
    for faecal egg count data to assess anthelmintic
    efficacy. Bayesian inference is done via MCMC sampling using 'Stan' <https://mc-stan.org/>.
Fits a variety of hidden Markov models, structured
	in an extended generalized linear model framework.  See
	T. Rolf Turner, Murray A. Cameron, and Peter J. Thomson
	(1998) <doi:10.2307/3315677>, and
	Rolf Turner (2008) <doi:10.1016/j.csda.2008.01.029>
	and the references cited therein.
A system for importing electrophysiological signal, based
    on the 'Waveform Database (WFDB)' software package, written by Moody et al 
    2022 <doi:10.13026/gjvw-1m31>. A R-based system to utilize 'WFDB' functions
    for reading and writing signal data, as well as functions for visualization
    and analysis are provided. A stable and broadly compatible class for working
    with signal data, supporting the reading in of cardiac electrophysiological
    files such as intracardiac electrograms, is introduced.
User friendly interface based on the R package 'gstat' to fit
    exponential parametric models to empirical semi-variograms in order to
    model the spatial correlation structure of health data. Geo-located
    health outcomes of survey participants may be used to model spatial
    effects on health in an ego-centred approach.  The package contains a
    range of functions to help explore the spatial structure of the data
    as well as visualize the fit of exponential models for various
    metaparameter combinations with respect to the number of lag intervals
    and maximal distance.  Furthermore, the outcome of interest can be
    adjusted for covariates by fitting a linear regression in a
    preliminary step before the semi-variogram fitting process.
Tools for importing, analyzing and visualizing ego-centered
    network data. Supports several data formats, including the export formats of
    'EgoNet', 'EgoWeb 2.0' and 'openeddi'. An interactive (shiny) app for the
    intuitive visualization of ego-centered networks is provided. Also included
    are procedures for creating and visualizing Clustered Graphs 
    (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).
Statistics and graphics for streamflow history,
    water quality trends, and the statistical modeling algorithm: Weighted
    Regressions on Time, Discharge, and Season (WRTDS).
Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis. The WRTDS modeling
    method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>,
    and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The 
    paper describing the uncertainty and confidence interval calculations 
    is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.
Gene regulatory network constructed using combined score obtained from 
  individual network inference method. The combined score measures the significance 
  of edges in the ensemble network. Fisher's weighted method has been implemented to 
  combine the outcomes of different methods based on the probability values. 
  The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.
Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.
Parametric proportional hazards fitting with left truncation and
        right censoring for common families of distributions, piecewise constant 
        hazards, and discrete models. Parametric accelerated failure time models
        for left truncated and right censored data. Proportional hazards
        models for tabular and register data. Sampling of risk sets in Cox 
        regression, selections in the Lexis diagram, bootstrapping. 
        Broström (2022) <doi:10.1201/9780429503764>.
Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE).
A tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) <doi:10.1093/gigascience/giad030>). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 
     'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies.
By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions. The package also provides additional functions to mimic the R basic example() function and the prototyping of packages.
Process and analyze electronic health record (EHR) data. The 'EHR'
    package provides modules to perform diverse medication-related studies using
    data from EHR databases. Especially, the package includes modules to perform
    pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in
    Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, 
    Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional 
    modules will be added in future. In addition, this package provides various 
    functions useful to perform Phenome Wide Association Study (PheWAS) to explore 
    associations between drug exposure and phenotypes obtained from EHR data, as 
    outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) 
    <doi:10.1093/bioinformatics/bty306>.
Comprehensive toolkit for addressing selection 
    bias in binary disease models across diverse non-probability samples, each 
    with unique selection mechanisms. It utilizes Inverse Probability Weighting 
    (IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce 
    selection bias effectively in multiple non-probability cohorts by integrating 
    data from either individual-level or summary-level external sources. The 
    package also provides a variety of variance estimation techniques. Please 
    refer to Kundu et al. <doi:10.48550/arXiv.2412.00228>.
Implements methods for functional data analysis based on the epigraph 
  and hypograph indices. These methods transform 
  functional datasets, whether in one or multiple dimensions, into multivariate 
  datasets. The transformation involves applying the epigraph, hypograph, and 
  their modified versions to both the original curves and their first and second 
  derivatives. The calculation of these indices is tailored to the dimensionality 
  of the functional dataset, with special considerations for dependencies between 
  dimensions in multidimensional cases. This approach extends traditional multivariate
  data analysis techniques to the functional data setting. A key application of 
  this package is the EHyClus method, which enhances clustering analysis for 
  functional data across one or multiple dimensions using the epigraph and 
  hypograph indices. See Pulido et al. (2023) <doi:10.1007/s11222-023-10213-7>
  and Pulido et al. (2024) <doi:10.48550/arXiv.2307.16720>.
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.
Provides more than 550 data sets of actual election results. 
    Each of the data sets includes aggregate party and candidate outcomes at the voting
    unit (polling stations) level and two-way cross-tabulated results at the district level.
    These data sets can be used to assess ecological inference algorithms devised 
    for estimating RxC (global) ecological contingency tables using exclusively aggregate results 
    from voting units.
    Reference:
    Pavía (2022) <doi:10.1177/08944393211040808>.
Provides API access to data from the U.S. Energy Information Administration ('EIA') <https://www.eia.gov/>.
    Use of the EIA's API and this package requires a free API key obtainable at <https://www.eia.gov/opendata/register.php>.
    This package includes functions for searching the EIA data directory and returning time series and geoset time series datasets.
    Datasets returned by these functions are provided by default in a tidy format, or alternatively, in more raw formats.
    It also offers helper functions for working with EIA date strings and time formats and for inspecting different summaries of series metadata.
    The package also provides control over API key storage and caching of API request results.
Provides a function to query and extract data from the 'US Energy Information Administration' ('EIA') API V2  <https://www.eia.gov/opendata/>. The 'EIA' API provides a variety of information, in a time series format, about the energy sector in the US. The API is open, free, and requires an access key and registration at <https://www.eia.gov/opendata/>.
Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data using the model described in Forcina et al. (2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates.
   References:
   Brown, P. and Payne, C. (1986). ''Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453–460. <DOI:10.1080/01621459.1986.10478290>.
   Forcina, A., Gnaldi, M. and Bracalente, B. (2012). ''A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109–119. <DOI:10.1007/s10260-011-0184-x>.
Model fitting and species biotic interaction network topology selection for explicit
  interaction community models. Explicit interaction community models are an extension of binomial
  linear models for joint modelling of species communities, that incorporate both the effects of
  species biotic interactions and the effects of missing covariates. Species interactions are modelled
  as direct effects of each species on each of the others, and are estimated alongside the effects of
  missing covariates, modelled as latent factors. The package includes a penalized maximum likelihood
  fitting function, and a genetic algorithm for selecting the most parsimonious species interaction
  network topology.
Extract features from tabular data in a declarative fashion, with a focus on processing medical records. Features are specified as JSON and are independently processed before being joined. Input data can be provided as CSV files or as data frames. This setup ensures that data is transformed in a modular and reproducible manner, and allows the same pipeline to be easily applied to new data.
Implements two estimations related to the foundations of info metrics applied to ecological inference. These methodologies assess the lack of disaggregated data and provide an approach to obtaining disaggregated territorial-level data. For more details, see the following references: Fernández-Vázquez, E., Díaz-Dapena, A., Rubiera-Morollón, F. et al. (2020) "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach." <doi:10.1007/s11205-020-02455-z>. Díaz-Dapena, A., Fernández-Vázquez, E., Rubiera-Morollón, F., & Vinuela, A. (2021) "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom." <doi:10.1111/rsp3.12379>.
Augments the 'eiCompare' package's Racially Polarized Voting (RPV) 
  functionality to streamline analyses and visualizations used to support 
  voting rights and redistricting litigation. The package implements methods 
  described in Barreto, M., Collingwood, L., Garcia-Rios, S., & Oskooii, K. A. (2022). 
  "Estimating Candidate Support in Voting Rights Act Cases: Comparing Iterative 
  EI and EI-R×C Methods" <doi:10.1177/0049124119852394>.
Estimation of the parameters in a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accommodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification. See Hoff (2007) <doi:10.48550/arXiv.0711.1146>.
        for details on the model.
Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in <https://www.jmlr.org/papers/v11/mazumder10a.html>, which achieves highly computational advantage by truncated singular-value decomposition.
Methods and utilities for causal emergence.
    Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.
Perform tensor operations using a concise yet expressive syntax inspired by the Python library of the same name.
    Reshape, rearrange, and combine multidimensional arrays for scientific computing, machine learning, and data analysis.
    Einops simplifies complex manipulations, making code more maintainable and intuitive.
    The original implementation is demonstrated in Rogozhnikov (2022) <https://openreview.net/forum?id=oapKSVM2bcj>.
The summation notation suggested by Einstein (1916) <doi:10.1002/andp.19163540702> is a 
  concise mathematical notation that implicitly sums over repeated indices of n-dimensional arrays. Many ordinary
  matrix operations (e.g. transpose, matrix multiplication, scalar product, 'diag()', trace etc.)
  can be written using Einstein notation. The notation is particularly convenient for 
  expressing operations on arrays with more than two dimensions because the 
  respective operators ('tensor products') might not have a standardized name.
Provides EIOPA (European Insurance And Occupational Pensions Authority) risk-free rates. Please note that the author of this package is not affiliated with EIOPA. The data is accessed through a REST API available at <https://mehdiechchelh.com/api/>.
Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) 
             from aggregate data by simultaneously minimizing Euclidean row-standardized 
             unit-to-global distances.
             Acknowledgements:
             The authors wish to thank Generalitat Valenciana, Consellería de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031) for supporting this research.
Provides methods for analyzing R by C ecological contingency
        tables using the extreme case analysis, ecological regression,
        and Multinomial-Dirichlet ecological inference models.  Also
        provides tools for manipulating higher-dimension data objects.
Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Bulut, Gorgun, & Yildirim-Erbasli (2021) <doi:10.3390/psych3030023>, Stanke & Bulut (2019) <doi:10.21449/ijate.515085>, and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.
Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.
Structure mining from 'XGBoost' and 'LightGBM' models.
    Key functionalities of this package cover: visualisation of tree-based ensembles models,
    identification of interactions, measuring of variable importance,
    measuring of interaction importance, explanation of single prediction 
    with break down plots (based on 'xgboostExplainer' and 'iBreakDown' packages). 
    To download the 'LightGBM' use the following link: <https://github.com/Microsoft/LightGBM>.
    'EIX' is a part of the 'DrWhy.AI' universe.
Emissions are the mass of pollutants released into the atmosphere. Air quality models need emissions data, with spatial and temporal distribution, to represent air pollutant concentrations. This package, eixport, creates inputs for the air quality models 'WRF-Chem' Grell et al (2005) <doi:10.1016/j.atmosenv.2005.04.027>, 'MUNICH' Kim et al (2018) <doi:10.5194/gmd-11-611-2018> , 'BRAMS-SPM' Freitas et al (2005) <doi:10.1016/j.atmosenv.2005.07.017> and 'RLINE' Snyder et al (2013) <doi:10.1016/j.atmosenv.2013.05.074>. See the 'eixport' website (<https://atmoschem.github.io/eixport/>) for more information, documentations and examples. More details in Ibarra-Espinosa et al (2018) <doi:10.21105/joss.00607>.
Functions for estimating catalytic constant and Michaelis-Menten constant
            for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs 
            sampler based on the Bayesian framework.
Extensions of the kernel smoothing functions from the 'ks' package for compatibility with the tidyverse and geospatial ecosystems <doi:10.1007/s00180-024-01543-9>.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. EL calculations are based on J. Valeinis, E. Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.
Functions for computing critical values and implementing the one-sided/two-sided EL tests.
A novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.
Provides functions to align curves and to compute mean curves based on the 
    elastic distance defined in the square-root-velocity framework. For more details on 
    this framework see Srivastava and Klassen (2016, <doi:10.1007/978-1-4939-4020-2>). 
    For more theoretical details on our methods and algorithms see 
    Steyer et al. (2023, <doi:10.1111/biom.13706>) and Steyer et al. (2023, <arXiv:2305.02075>).
Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.
Provides functions for the computation of functional elastic shape 
  means over sets of open planar curves. The package is particularly suitable for 
  settings where these curves are only sparsely and irregularly observed. It uses 
  a novel approach for elastic shape mean estimation, where planar curves are
  treated as complex functions and a full Procrustes mean is estimated from the
  corresponding smoothed Hermitian covariance surface. This is combined with the 
  methods for elastic mean estimation proposed in Steyer, Stöcker, Greven	(2022)
  <doi:10.1111/biom.13706>. See Stöcker et. al. (2022) <arXiv:2203.10522> for details.
Connect to 'Elasticsearch' and 'OpenSearch', 'NoSQL' databases 
    built on the 'Java' Virtual Machine and using the 'Apache' 'Lucene' library. 
    Interacts with the 'Elasticsearch' 'HTTP' API' 
    (<https://www.elastic.co/elasticsearch/>) and the
    'OpenSearch' 'HTTP' 'API' (<https://opensearch.org/>). 
    Includes functions for setting connection details to 'Elasticsearch' and 
    'OpenSearch' 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 APIs
    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).
    Laakso & Taagepera (1979) <https://escholarship.org/uc/item/703827nv>.
    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) <doi:10.1177/1354068809339538>.
    Golosov (2014) <doi:10.1177/1354068814549342>.
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/>.
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>.
A toolset for generating Ecological Limit Function (ELF) models and evaluating potential species loss resulting from flow change, based on the 'elfgen' framework. ELFs describe the relation between aquatic species richness (fish or benthic macroinvertebrates) and stream size characteristics (streamflow or drainage area). Journal publications are available outlining framework methodology (Kleiner et al. (2020) <doi:10.1111/1752-1688.12876>) and application (Rapp et al. (2020) <doi:10.1111/1752-1688.12877>).
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.
This comprehensive toolkit for Distributed Elliptical model is designated as "ELIC" (The LIC for Distributed Elliptical Model Analysis) analysis. It is predicated on the assumption that the error term adheres to a Elliptical distribution. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.
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.
Tools for transforming 'R' expressions. Provides functions for 
 finding, extracting, and replacing patterns in 'R' language objects, similarly 
 to how regular expressions can be used to find, extract, and replace patterns 
 in text. Also provides functions for generating code using specially-formatted 
 template files and for translating 'R' expressions into similar expressions in 
 other programming languages. The package may be helpful for advanced uses of 
 'R' expressions, such as developing domain-specific languages.
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.
Provides empirical likelihood-based methods for the inference of variance components in linear mixed-effects models.
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) model. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
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.
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.
Calculate and analyze household energy burden using the Net Energy
    Return aggregation methodology. Functions support weighted statistical
    calculations across geographic and demographic cohorts, with utilities for
    formatting results into publication-ready tables. Methods are based on
    Scheier & Kittner (2022) <doi:10.1038/s41467-021-27673-y>.
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>.
Provides computational methods for detecting adverse high-order drug interactions
         from individual case safety reports using statistical techniques,
         allowing the exploration of higher-order interactions among drug cocktails.
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.
Analyzes and quantifies ecosystem multifunctionality with functions to calculate multifunctionality richness (MFric), multifunctionality divergence (MFdiv), and multifunctionality regularity (MFreg). These indices help assess the relationship between biodiversity and multiple ecosystem functions. For more details, see Byrnes et al. (2014) <doi:10.1111/2041-210X.12143> and Chao et al. (2024) <doi:10.1111/ele.14336>.
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.
Tools to fit Mixture Cure Rate models via the Expectation-Maximization (EM) algorithm, allowing for flexible link functions in the cure component and various survival distributions in the latency part. The package supports user-specified link functions, includes methods for parameter estimation and model diagnostics, and provides residual analysis tailored for cure models. The classical theory methods used are described in Berkson, J. and Gage, R. P. (1952) <doi:10.2307/2281318>, Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) <https://www.jstor.org/stable/2984875>, Bazán, J., Torres-Avilés, F., Suzuki, A. and Louzada, F. (2017)<doi:10.1002/asmb.2215>.
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>.
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 representing linear dynamical systems. Currently, two such algorithms (one offline and one online) are implemented for the single-output cumulative structural equation model with an additive-noise output measurement equation and assumptions of normality and independence. The corresponding scientific papers are referenced in the descriptions of the functions implementing these algorithms.
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.
Access and interrogate 'EMODnet' (European Marine Observation
    and Data Network) Web Feature Service data <https://emodnet.ec.europa.eu/en/emodnet-web-service-documentation#data-download-services>. 
    This includes listing existing data sources, and getting data from each of them.
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.
Lactation curves describe temporal changes in milk yield and are key to breeding and managing dairy animals more efficiently. The use of ensemble modeling, which consists of combining predictions from multiple models, has the potential to yields more accurate and robust estimates of lactation patterns than relying solely on single model estimates. The package EMOTIONS fits 47 models for lactation curves and creates ensemble models using model averaging based on Akaike information criterion (AIC), Bayesian information criterion (BIC), root mean square percentage error (RMSPE) and mean squared error (MAE), variance of the predictions, cosine similarity for each model's predictions, and Bayesian Model Average (BMA). The daily production values predicted through the ensemble models can be used to estimate resilience indicators in the package. The package allows the graphical visualization of the model ranks and the predicted lactation curves. Additionally, the packages allows the user to detect milk loss events and estimate residual-based resilience indicators.
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>.
A comprehensive toolkit for discovering differential and difference
    equations from empirical time series data using symbolic regression. The package
    implements a complete workflow from data preprocessing (including Total Variation
    Regularized differentiation for noisy economic data), visual exploration of
    dynamical structure, and symbolic equation discovery via genetic algorithms.
    It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide
    industrial-grade robustness, physics-informed constraints, and rigorous
    out-of-sample validation. Designed for economists, physicists, and researchers
    studying dynamical systems from observational data.
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>.
Compute the empirical likelihood ratio, -2LogLikRatio (Wilks) statistics,
             based on current status data for the hypothesis about the parameters of mean
			 or probability or weighted cumulative hazard.
Provides functions for computing test reliability and conditional
    standard error of measurement (CSEM) based on the methods described in
    the Reliability in Educational Measurement chapter of the 5th edition of 
    "Educational Measurement" by Lee and Harris (2025, ISBN:9780197654965).
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 easy computation of Euclidean Minimum Spanning Trees (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.
Descarga, lee y analiza bases de la Encuesta Nacional de Hogares (ENAHO) y otras encuestas del Instituto Nacional de Estadística e Informática (INEI) del Perú. (Downloads, reads, and combines data from the Peruvian Home National Survey and other surveys from the National Institute for Statistics (INEI).)
Implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2022) <Journal of the Korean Statistical Society, 51(1), pp.65-86.>.
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.
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.
Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap.
Given two samples of size n_1 and n_2 from a data set where each sample consists of K functional observations (channels), each recorded on T grid points,  the function energy method implements a hypothesis test of equality of channel-wise mean at each channel using the bootstrapped distribution of maximum energy to control family wise error. The function energy_method_complex accomodates complex valued functional observations.
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
 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 through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). 
 <doi:10.1093/jrsssb/qkae108>.
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Special functions that enhance other mixed effect model packages by creating overlayed, reduced rank, and reduced model matrices together with multiple data sets to practice the use of these models. For more details see Covarrubias-Pazaran (2016) <doi:10.1371/journal.pone.0156744>.
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.
Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'.
    Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA).
    The multilevel GSEA algorithm is derived from the 'fgsea' package.
    Methods are described in Subramanian et al. (2005) <doi:10.1073/pnas.0506580102> and Korotkevich et al. (2021) <doi:10.1101/060012>.
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>.
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>.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
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.
Displays for model fits of multiple models and their ensembles. For classification models, the plots are heatmaps, for regression, scatterplots.
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.
An assistant built on large language models that helps interpret statistical model outputs in R by generating concise, audience-specific explanations.
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.
Methods for data analysis from an entropic perspective. These methods are nonparametric and perform well on non-ordinal data. Currently includes '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 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>).
The purpose of this package is to support the setup the R environment.  
    The two main features are 'autos', to automatically source files and/or 
    directories into your environment, and 'paths' to consistently set path objects 
    across projects for input and output. Both are implemented using a configuration 
    file to allow easy, custom configurations that can be used for multiple 
    or all projects.
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 <https://www.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. Has functions for extracting
  and manipulating parameter estimates and predicted values (ci.lin
  and its cousins), 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>.
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>.
The 'R4EPIs' project <https://r4epi.github.io/sitrep/> seeks
    to provide a set of standardized tools for analysis of outbreak and
    survey data in humanitarian aid settings. This package currently
    provides standardized data dictionaries from Medecins Sans Frontieres
    Operational Centre Amsterdam for outbreak scenarios (Acute Jaundice
    Syndrome, Cholera, Diphtheria, Measles, Meningitis) and surveys
    (Retrospective mortality and access to care, Malnutrition, Vaccination
    coverage and Event Based Surveillance) - as described in the following
    <https://scienceportal.msf.org/assets/standardised-mortality-surveys?utm_source=chatgpt.com>.
    In addition, a data generator from these dictionaries is provided. It
    is also possible to read in any Open Data Kit format data dictionary.
Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) <doi:10.1201/b16018> and Wickham et al. (2019, ISBN:9781492052040>.
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.
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).
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 
  formatting proportions with confidence intervals. This is part of the 
  'R4Epis' project <https://r4epi.github.io/sitrep/>.
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>.
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.
Provides functions for estimating plant pathogen parameters from
    access period (AP) experiments. Separate functions are implemented for
    semi-persistently transmitted (SPT) and persistently transmitted (PT)
    pathogens. The common AP experiment exposes insect cohorts to infected source
    plants, healthy test plants, and intermediate plants (for PT pathogens). The
    package allows estimation of acquisition and inoculation rates during feeding,
    recovery rates, and latent progression rates (for PT pathogens). Additional
    functions support inference of epidemic risk from pathogen and local
    parameters, and also simulate AP experiment data. The functions implement
    probability models for epidemiological analysis, as derived in Donnelly et
    al. (2025), <doi:10.32942/X29K9P>. These models were originally
    implemented in the 'EpiPv' 'GitHub' 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.
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 Fox M.P., MacLehose R.F., and Lash T.L. "Applying
    Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('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.
The 'EpiSimR' package provides an interactive 'shiny' app based on deterministic compartmental 
    mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. 
    It allows users to explore various intervention strategies, including vaccination and isolation, 
    by adjusting key epidemiological parameters. The methodology follows the approach described by 
    Brauer (2008) <doi:10.1007/978-3-540-78911-6_2>. Thanks to 'shiny' package.
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'.
Produces tables for descriptive epidemiological analysis.
    These tables include attack rates, case fatality ratios, and mortality
    rates (with appropriate confidence intervals), with additional
    functionality to calculate Mantel-Haenszel odds, risk, and incidence
    rate ratios. The methods implemented follow standard epidemiological
    approaches described in Rothman et al. (2008, ISBN:978-0-19-513554-2).
    This package is part of the 'R4EPIs' project <https://R4EPI.github.io/sitrep/>.
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 fast, flexible tool for generating disease surveillance
    reports from data exported from 'EpiTrax', a central repository for
    epidemiological data used by public health officials. It provides
    functions to manipulate 'EpiTrax' datasets, tailor reports to internal
    or public use, and export reports in CSV, Excel 'xlsx', or PDF formats.
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.
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>.
Analysis of items and persons in data. To identify and remove person misfit in polytomous
    item-response data using either 'mokken' or a graded response model (GRM, via 'mirt').
    Provides automatic thresholds, visual diagnostics (2D/3D), and export utilities. 
    Methods build on Mokken scaling as in Mokken (1971, ISBN:9789027968821) and on the
    graded response model of Samejima (1969) <doi:10.1007/BF03372160>.
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 tools for working with iEEG matrix data, including downloading curated iEEG data from OSF (The Open Science Framework <https://osf.io/>) (EpochDownloader()), making new objects (Epoch()), processing (crop() and resample()), and visualizing the data (plot()).
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>.
Simulating multi-arm cluster-randomized, multi-site, and simple randomized trials. Includes functions for conducting multilevel analyses using both Bayesian and Frequentist methods. Supports futility and superiority analyses through Bayesian approaches, along with visualization tools to aid interpretation and presentation of results.
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'.
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).
This framework enables forecasting and extrapolating measures of conditional risk
    (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities,
    using extreme value statistics and flexible neural network architectures.
    It allows for capturing complex multivariate dependencies,
    including dependencies between observations, such as sequential dependence (time-series).
    The methodology was introduced in Pasche and Engelke (2024) <doi:10.1214/24-AOAS1907>
    (also available in preprint: Pasche and Engelke (2022) <doi:10.48550/arXiv.2208.07590>).
Contains several functions for equivalence testing and practical significance testing. First, the tsti() command provides an automatic computation of three-sided testing results for a given estimate, standard error, and region of practical equivalence. For details, see Goeman, Solari, & Stijnen (2010) <doi:10.1002/sim.4002> and Isager & Fitzgerald (2024) <doi:10.31234/osf.io/8y925>. Second, the lddtest() command performs logarithmic density discontinuity equivalence testing for regression discontinuity designs. For reference, see Fitzgerald (2025) <doi:10.31222/osf.io/2dgrp_v1>.
Support functions for R-based "EQUALCompareImages - Compare similarity between and within images" shiny application which allow researchers without coding skills or expertise in image comparison algorithms to compare images. Gurusamy,K (2025)<doi:10.5281/zenodo.16994047>.
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 "EQUALencrypt - Encrypt and decrypt whole files" and "EQUALencrypt - Encrypt and decrypt columns of data" shiny applications which allow researchers without coding skills or expertise in encryption algorithms to share data after encryption. Gurusamy,K (2025)<doi:10.5281/zenodo.16743676> and Gurusamy,K (2025)<doi:10.5281/zenodo.16744058>.
Functions that help with analysis of prognostic study data. This allows users with little experience of developing models to develop models and assess the performance of the prognostic models. This also summarises the information, so the performance of multiple models can be displayed simultaneously. Gurusamy, K (2026)<https://github.com/kurinchi2k/EQUALPrognosis>.
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>).
Equating of multiple forms using Item Response Theory (IRT) methods. See Battauz (2025) <doi:10.18637/jss.v115.i11> for a detailed description of the package. See Battauz M. (2017) <doi:10.1007/s11336-016-9517-x>, Battauz and 'Leoncio' (2023) <doi:10.1177/01466216231151702> and Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>) for the methods to link multiple test forms.
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>.
Estimation of unknown historical or archaeological dates subject to relationships with other relative dates and absolute constraints, derived as marginal densities from the full joint conditional, using a two-stage Gibbs sampler with consistent batch means to assess convergence. Features reporting on Monte Carlo standard errors, as well as tools for rule-based estimation of dates of production and use of artifact types, aligning and checking relative sequences, and evaluating the impact of the omission of relative/absolute events upon one another.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression, proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/00949655.2013.876024>. The code is based on the 'gbm' package originally developed by Greg Ridgeway.
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>.
Extends the 'ergm.multi' packages from the Statnet suite to fit (temporal) exponential-family random graph models for signed networks. The framework models positive and negative ties as interdependent, which allows estimation and testing of structural balance theory. The package also includes options for descriptive summaries, visualization, and simulation of signed networks. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2025) <doi:10.1017/pan.2024.21>.
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.
Estimation of fully and partially observed Exponential-Family Random Network Models (ERNM). Exponential-family Random Graph Models (ERGM) and Gibbs Fields are special cases of ERNMs and can also be estimated with the package. Please cite Fellows and Handcock (2012), "Exponential-family Random Network Models" available at <doi:10.48550/arXiv.1208.0121>.
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>.
Implementation of the Edge Selection Algorithm for undirected graph selection.  The least angle regression-based algorithm selects edges of an undirected graph based on the projection of the current residuals on the two dimensional edge-planes.  The algorithm selects symmetric adjacency matrix, which many other regression-based undirected graph selection procedures cannot do.
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) <doi:10.48550/arXiv.1301.2267>. The ESS algorithm
	     is used for model selection in decomposable graphical models.
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) .
Standardises and facilitates the use of eleven established stability properties that have been used to assess systems’ responses to press or pulse disturbances at different ecological levels (e.g. population, community). There are two sets of functions. The first set corresponds to functions that measure stability at any level of organisation, from individual to community and can be applied to a time series of a system’s state variables (e.g., body mass, population abundance, or species diversity). The properties included in this set are: invariability, resistance, extent and rate of recovery, persistence, and overall ecological vulnerability. The second set of functions can be applied to Jacobian matrices. The functions in this set measure the stability of a community at short and long time scales. In the short term, the community’s response is measured by maximal amplification, reactivity and initial resilience (i.e. initial rate of return to equilibrium). In the long term, stability can be measured as asymptotic resilience and intrinsic stochastic invariability. Figueiredo et al. (2025) <doi:10.32942/X2M053>.
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Fast and memory-less computation of the energy statistics related quantities for vectors and matrices. References include: Szekely G. J. and Rizzo M. L. (2014), <doi:10.1214/14-AOS1255>. Szekely G. J. and Rizzo M. L. (2023), <ISBN:9781482242744>. Tsagris M. and Papadakis M. (2025). <doi:10.48550/arXiv.2501.02849>.
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.
Implements the Polynomial Maximization Method ('PMM') for parameter estimation
    in linear and time series models when error distributions deviate from normality.
    The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary
    least squares ('OLS') when errors exhibit significant skewness. Includes methods
    for linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference.
    Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) <doi:10.1007/978-3-319-77179-3_75>,
    Zabolotnii, Tkachenko, and Warsza (2022) <doi:10.1007/978-3-031-03502-9_37>, and
    Zabolotnii, Tkachenko, and Warsza (2023) <doi:10.1007/978-3-031-25844-2_21>.
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).
Performs analyzes and estimates of environmental covariates 
    and genetic parameters related to selection strategies and development 
    of superior genotypes. It has two main functionalities, the first being 
    about prediction models of covariates and environmental processes, while 
    the second deals with the estimation of genetic parameters and selection 
    strategies. Designed for researchers and professionals in genetics and 
    environmental sciences, the package combines statistical methods
    for modeling and data analysis. This includes the plastochron estimate 
    proposed by Porta et al. (2024) <doi:10.1590/1807-1929/agriambi.v28n10e278299>,
    Stress indices for genotype selection referenced by Ghazvini et al. (2024)
    <doi:10.1007/s10343-024-00981-1>, the Environmental Stress Index described by
    Tazzo et al. (2024) <https://revistas.ufg.br/vet/article/view/77035>,
    industrial quality indices of wheat genotypes (Szareski et al., 2019), 
    <doi:10.4238/gmr18223>, Ear Indexes estimation (Rigotti et al., 2024),
    <doi:10.13083/reveng.v32i1.17394>, Selection index for protein and grain yield
    (de Pelegrin et al., 2017), <doi:10.4236/ajps.2017.813224>, Estimation of the
    ISGR - Genetic Selection Index for Resilience for environmental resilience
    (Bandeira et al., 2024) <https://www.cropj.com/Carvalho_18_12_2024_825_830.pdf>,
    estimation of Leaf Area Index (Meira et al., 2015) 
    <https://www.fag.edu.br/upload/revista/cultivando_o_saber/55d1ef202e494.pdf>,
    Restriction of control variability (Carvalho et al., 2023) 
    <doi:10.4025/actasciagron.v45i1.56156>, Risk of Disease Occurrence in Soybeans
    described by Engers et al. (2024) <doi:10.1007/s40858-024-00649-1> and 
    estimation of genetic parameters for selection based on balanced experiments
    (Yadav et al., 2024) <doi:10.1155/2024/9946332>.
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 error (SEM), spatial Durbin (SDM),
    spatial error Durbin (SDEM) 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.
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>.
A plotting package for climate science and services. Provides a set
    of functions for visualizing climate data, including maps, time series, 
    scorecards and other diagnostics. Some functions are adapted and extended 
    from the 's2dv' and 'CSTools' packages (Manubens et al. (2018)
    <doi:10.1016/j.envsoft.2018.01.018>; Pérez-Zanón et al. (2022)
    <doi:10.5194/gmd-15-6115-2022>), with more consistent and integrated 
    functionalities.
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 <https://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.
Compute common data quality metrics for accuracy, precision and data loss
  for screen-based eye trackers. Supports input data both in pixels on the screen and
  in degrees, output measures are (where appropriate) expressed as angles in degrees.
A robust and efficient solution for working with Ethiopian dates. It can seamlessly convert to and from Gregorian dates. 
    It is designed to be compatible with the 'tidyverse' data workflow, including plotting with 'ggplot2'.
    It ensures lightning-fast computations by integrating high-performance 'C++' code through 'Rcpp' package.
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." Journal of Computational and Graphical Statistics <doi:10.1080/10618600.2025.2535600> and 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.
Provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086,  <doi:10.1002/bimj.70086>. A tutorial for this package can be found at <https://www.laylaparast.com/etsi>.
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>.
Access data related to the European union from 'GISCO'
  <https://ec.europa.eu/eurostat/web/gisco>, the Geographic Information
  System of the European Commission, via its rest API at
  <https://gisco-services.ec.europa.eu>.
  This package tries to make it easier to get these data into R.
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> .
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.
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 heterogeneous treatment effects (HTE) in randomized experiments. The package includes tools to estimate uniform confidence bands for estimation of the group average treatment effect sorted by generic machine learning algorithms (GATES). It also provides the tools to identify a subgroup of individuals who are likely to benefit from a treatment the most "exceptional responders" or those who are harmed by it. Detailed reference in Imai and Li (2023) <doi:10.48550/arXiv.2310.07973>.
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.
Evaluate diagnostic test performance using data from laboratory or diagnostic research. It supports both binary and continuous test variables. It allows users to compute key performance indicators and visualize Receiver Operating Characteristic (ROC) curves, determine optimal cut-off thresholds, display confusion matrix, and export publication-ready plot. It aims to facilitate the application of statistical methods in diagnostic test evaluation by healthcare professionals. The methodology used to compute the performance indicators follows the overview described by Habibzadeh (2025) <doi:10.11613/BM.2025.010101>. Thanks to 'shiny' package.
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, Mathur, & VanderWeele (2021) <doi:10.1097/EDE.0000000000001380>). 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., <doi:10.1093/ije/dyac073>).
A comprehensive collection of utility functions for data analysis
    and visualization in R. The package provides 55+ functions for data
    manipulation, file handling, color palette management, bioinformatics
    workflows, plotting, and package management. Features include void value
    handling, custom infix operators, flexible file I/O, and publication-ready
    visualizations with sensible defaults. Implementation follows tidyverse
    principles (Wickham et al. (2019) <doi:10.21105/joss.01686>) and incorporates
    best practices from the R community.
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, combined type, and radiation-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.
Provides a principled framework for sampling Virtual Control Group (VCG) using energy distance-based covariate balancing. The package offers visualization tools to assess covariate balance and includes a permutation test to evaluate the statistical significance of observed deviations.
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.
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.
The confusion matrix (CM) is used to get a classifier's
             evaluation measure in order to select a method among many.
	     A stochastic matrix and its transformation are computed from
	     the CM. The eigenvalues of the transformed symmetric matrix
	     are used to get an entropy which appears to be a good
	     evaluation measure. Many other measures, commonly used, are
	     provided for comparison purpose.
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>.
Diagnose, visualize, and aggregate event report 
    level data to the event level. Users provide an event report level 
    dataset, specify their aggregation rules, and the package produces a
    dataset aggregated at the event level. Also includes the Modes and 
    Agents of Election-Related Violence in Côte d'Ivoire and Kenya (MAVERICK) dataset,
    an event report level dataset that records all documented instances of 
    electoral violence from the first multiparty election to 2022 in 
    Côte d'Ivoire (1995-2022) and Kenya (1992-2022).
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>.
Implements likelihood-based evidence ratios for unified reporting
    in classical statistical testing. The package reports effect estimates,
    uncertainty intervals, and likelihood ratios on the log 10 scale derived
    from a single statistical model. It applies to standard normal mean tests,
    contingency tables, and regression coefficients, and provides a direct
    evidential measure while retaining classical error guarantees. 
    For the Evidence Ratio Reporting Standard see Lawless (2026) 
    <doi:10.5281/zenodo.18261076>.
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.
Performs likelihood-based extreme value inferences with 
    adjustment for the presence of missing values based on Simpson and 
    Northrop (2026). A Generalised Extreme Value
    distribution is fitted to block maxima using maximum likelihood estimation, 
    with the location and scale parameters reflecting the numbers of 
    non-missing raw values in each block. A Bayesian version is also provided. 
    For the purposes of comparison, there are options to make no adjustment for 
    missing values or to discard any block maximum for which greater than a 
    percentage of the underlying raw values are missing. Example datasets 
    containing missing values are 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 an evolutionary algorithm using the 'AlphaSimR' machinery <doi:10.1093/g3journal/jkaa017> .
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>.
Provides an 'R' interface to the 'Evolution API' <https://evoapicloud.com>, enabling sending and receiving 'WhatsApp' messages directly from 'R'. Functions include sending text, images, documents, stickers, geographic locations, and interactive messages (lists). Also includes 'webhook' parsing utilities and channel health checks.
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://www.netlogo.org/>) 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>.
Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations.
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 Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods are described in Hernandez et al. (2023) <doi:10.1111/2041-210X.14065>. 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.
Provides fast dynamic-programming algorithms in 'C++'/'Rcpp'
    (with pure 'R' fallbacks) for the exact finite-sample distributions
    and p-values of Christoffersen (1998) independence (IND) and
    conditional-coverage (CC) VaR backtests. For completeness, it also
    provides the exact unconditional-coverage (UC) test following
    Kupiec (1995) via a closed-form binomial enumeration. See
    Christoffersen (1998) <doi:10.2307/2527341> and Kupiec (1995)
    <doi:10.3905/jod.1995.407942>.
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.
A 'Shiny'-based toolkit for item/test analysis. It is designed for multiple-choice, true-false, and open-ended questions. The toolkit is usable with datasets in 1-0 or other formats. Key analyses include difficulty, discrimination, response-option analysis, reports. The classical test theory methods used are described in Ebel & Frisbie (1991, ISBN:978-0132892314).
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'.
Provides a small collection of datasets supporting Pearson correlation 
    and linear regression analysis. It includes the precomputed dataset 'sos100', 
    with integer values summing to zero and squared sum equal to 100. For other 
    values of 'n' and user-defined parameters, the 'sos()' function from the 
    'exams.forge' package can be used to generate datasets on the fly. In addition, 
    the package contains around 500 german R Markdown exercises that illustrate the usage 
    of 'exams.forge' commands.
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.
Ever read or wrote source files containing sectioning comments? 
    If these comments are markdown style section comments,
    you can excerpt them and set a table of contents using the
    'python' package 'excerpts' (<https://pypi.org/project/excerpts/>).
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.
Tools to analyse human and mosquito behavioral interactions and to compute exposure to mosquito bites estimates.
    Using behavioral data for human individuals and biting patterns for mosquitoes, you will be able to compute hourly exposure for bed net users and non-users, and summarize (e.g. proportion indoors and outdoors, proportion per time periods, and proportion prevented by bed nets) or visualize these dynamics across a 24-hour cycle.
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.
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) <https://pmc.ncbi.nlm.nih.gov/articles/PMC5543384/>). 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>.
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 (2020) <doi:10.1007/s11634-020-00403-w>.
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.
Survival analysis is employed to model time-to-event data. This package examines the relationship between survival and one or more predictors, termed as covariates, which can include both treatment variables (e.g., season of birth, represented by indicator functions) and continuous variables. To this end, the Cox-proportional hazard (Cox-PH) model, introduced by Cox in 1972, is a widely applicable and commonly used method for survival analysis. This package enables the estimation of the effect of randomization for the treatment variable to account for potential confounders, providing adjustment when estimating the association with exposure. It accommodates both fixed and time-dependent covariates and computes survival probabilities for lactation periods in dairy animals. The package is built upon the algorithm developed by Klein and Moeschberger (2003) <DOI:10.1007/b97377>.
Creates family objects identical to stats
    family but for new links.
Provides computational tools for working with the Extended Laplace distribution, including the probability density function, cumulative distribution function, quantile function, random variate generation based on convolution with Uniform noise and the quantile-quantile plot. Useful for modeling contaminated Laplace data and other applications in robust statistics. See Saah and Kozubowski (2025) <doi:10.1016/j.cam.2025.116588>.
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.
Estimates extinction risk from population time series under a
    drifted Wiener process using the w-z method for accurate confidence
    intervals.
Provides functions to extract and process data from the FDA Adverse Event Reporting System (FAERS). It facilitates the conversion of raw FAERS data published after 2014Q3 into structured formats for analysis. See Yang et al. (2022) <doi:10.3389/fphar.2021.772768> for related information.
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.
It is meant to be used with the 'extrafont' package. The 'extrafont' package contains the code to install and use fonts, while the 'extrafontdb' package contains the font database.
Provide estimation and data generation tools for new multivariate frailty models.
             This version includes the gamma, inverse Gaussian, weighted Lindley, Birnbaum-Saunders,
	     truncated normal, mixture of inverse Gaussian, mixture of Birnbaum-Saunders, 
             generalized exponential and Jorgensen-Seshadri-Whitmore as the distribution for
             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 et al. (2024) <doi:10.1007/s11222-024-10458-w>,
             Gallardo et al. (2025) <doi:10.1002/bimj.70044>, Kiprotich et al. (2025) <doi:10.1177/09622802251338984>
             and Gallardo et al. (2025) <doi:10.1038/s41598-025-15903-y>.
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.
The basic use of this package is with 3 sequential functions. First to generate a cell mean matrix. In case of a repeated measurements design also generate correlation and covariance matrices. This is followed by iterative experiment simulation. Finally, power is calculated from the simulated data. Features that may be considered in the model are interaction, measure correlation, non-normal and unbalanced designs distributions.
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.
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 (2022) <doi:10.3150/21-BEJ1375>, Davison et al. (2023) <doi:10.1080/07350015.2022.2078332>, 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>, Padoan and Rizzelli (2024) <doi:10.3150/23-BEJ1668>, Daouia et al. (2024) <doi:10.3150/23-BEJ1632>.
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) <https://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.
Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) <doi:10.1038/s41467-020-17851-9>; Kret & Sjak-Shie (2018) <doi:10.3758/s13428-018-1075-y>; Strauch (2024) <doi:10.1016/j.tins.2024.06.002>]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>]. 'eyeris' provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) <doi:10.1038/s41592-018-0235-4>; Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44>]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology.
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.
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 complete rewrite and reimagining of 'bakR' (see 'Vock et al.' (2025) <doi:10.1371/journal.pcbi.1013179>).
  Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any
	type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq),
	TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. 'EZbakR' extends
	standard NR-seq standard NR-seq mutational modeling to support multi-label analyses
	(e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better
	account for transcript-to-transcript variance in metabolic label incorporation.
	'EZbakR' also generalized dynamical systems modeling of NR-seq data to support analyses
	of premature mRNA processing and flow between subcellular compartments. Finally,
	'EZbakR' implements flexible and well-powered comparative analyses of all
	estimated parameters via design matrix-specified generalized linear modeling.
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>.
Provides tools to compute the neural fragility matrix from intracranial electrocorticographic (iEEG) recordings, enabling the analysis of brain dynamics during seizures. The package implements the method described by Li et al. (2017) <doi:10.23919/ACC.2017.7963378> and includes functions for data preprocessing ('Epoch'), fragility computation ('calcAdjFrag'), and visualization.
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.
Streamlines common steps for working with animal tracking data, 
    from raw telemetry points to summaries, interactive maps, and home range 
    estimates. Designed to be beginner-friendly, it enables rapid exploration 
    of spatial and movement data with minimal wrangling, providing a unified 
    workflow for importing, summarizing, and visualizing, and analyzing animal 
    movement datasets.
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 'Jolpica API' <https://jolpi.ca> and the unofficial API <https://www.formula1.com/en/timing/f1-live> via the 'fastf1' 'Python' library <https://docs.fastf1.dev/>.
Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2025 or higher). It also includes a function to visualize pit stop performance.
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/>.
Query data hosted in 'Microsoft Fabric'. Provides helpers to open 'DBI'
    connections to 'SQL' endpoints of 'Lakehouse' and 'Data Warehouse' items; submit
    'Data Analysis Expressions' ('DAX') queries to semantic model datasets in 'Microsoft
    Fabric' and 'Power BI'; read 'Delta Lake' tables stored in 'OneLake'
    ('Azure Data Lake Storage Gen2'); and execute 'Spark' code via the 'Livy API'.
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.
Calculate useful quantities for a user-defined differential equation
    model of infectious disease transmission among individuals in a healthcare
    facility. Input rates of transition between states of individuals with and
    without the disease-causing organism, distributions of states at facility
    admission, relative infectivity of transmissible states, and the facility length
    of stay distribution. Calculate the model equilibrium and the basic facility
    reproduction number, as described in Toth et al. (2025) 
    <doi:10.1371/journal.pcbi.1013577>.
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>.
Description: Provides comprehensive tools for analysing and characterizing
    mixed-level factorial designs arranged in blocks. Includes construction
    and validation of incidence structures, computation of C-matrices,
    evaluation of A-, D-, E-, and MV-efficiencies, checking of orthogonal
    factorial structure (OFS), diagnostics based on Hamming distance,
    discrepancy measures, B-criterion, Es^2 statistics, J2-distance and
    J2-efficiency, Phi-p optimality, and symmetry conditions for universal
    optimality. The methodological framework follows foundational work on
    factorial and mixed-level design assessment by Xu and Wu (2001) 
    <doi:10.1214/aos/1013699993>, and Gupta (1983) 
    <doi:10.1111/j.2517-6161.1983.tb01253.x>. These methods assist in selecting,
    comparing, and studying factorial block designs across a range of
    experimental situations.
Opens a 'shiny' app which supports theoretical and computational analysis of block designs for symmetrical and mixed level factorial experiments. This package includes tools to check whether a design has orthogonal factorial structure (OFS) with balance or not and is able to find the orthogonality deviation value if not having OFS. This package includes function  to evaluate efficiency factor of all factorial effects in two situations, in the first situation if the design is verified with OFS and balance then calculate the efficiencies of all factorial effects using a specific analytical procedure  and in the second situation if the design is verified with non-OFS and balance then a new general method has been developed and used to calculate efficiencies under the condition that the design should be proper and equi-replicated, See Gupta, S.C. and Mukerjee, R. (1987): "A Calculus for factorial arrangements".  Lecture Notes in Statistics. No. 59, Springer-Verlag, Berlin, New York, <doi:10.1007/978-1-4419-8730-3>. For the easy use of package, 'shiny' app is used for giving inputs and inputs validation.
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>.
Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.
Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2022) <doi:10.1017/pan.2020.40>, and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.
Multifactor nonparametric analysis of variance based on ranks.
    Builds on the Kruskal-Wallis H test and its 2x2 Scheirer-Ray-Hare
    extension to handle any factorial designs. Provides effect sizes,
    Dunn-Bonferroni pairwise-comparison matrices, and simple-effects
    analyses. Tailored for psychology and the social sciences, with
    beginner-friendly R syntax and outputs that can be dropped into
    journal reports. Includes helpers to export tab-separated results and
    compact tables of descriptive statistics (to APA-style reports).
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.
Implements the Factor-Augmented Clustering Tree (FACT) algorithm
    for clustering time series data. The method constructs a classification 
    tree where splits are determined by covariates, and the splitting criterion
    is based on a group factor model representation of the time series within 
    each node. Both threshold-based and permutation-based tests are supported 
    for splitting decisions, with an option for parallel computation.
    For methodological details, see Hu, Li, Luo, and Wang (2025, in preparation), 
    Factor-Augmented Clustering Tree for Time Series.
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>.
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.
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>.
Provides a comprehensive Shiny-based graphical user interface
    for conducting a wide range of factor analysis procedures. 'FAfA'
    (Factor Analysis for All) guides users through data uploading,
    assumption checking (descriptives, collinearity, multivariate
    normality, outliers), data wrangling (variable exclusion, data
    splitting), factor retention analysis (e.g., Parallel Analysis, Hull
    method, EGA), Exploratory Factor Analysis (EFA) with various rotation
    and extraction methods, Confirmatory Factor Analysis (CFA) for model
    testing, Reliability Analysis (e.g., Cronbach's Alpha, McDonald's
    Omega), Measurement Invariance testing across groups, and item
    weighting techniques. The application leverages established R packages
    such as 'lavaan' and 'psych' to perform these analyses, offering an
    accessible platform for researchers and students. Results are
    presented in user-friendly tables and plots, with options for
    downloading outputs.
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>.
Tools for training and analysing fairness-aware gated neural 
    networks for subgroup-aware prediction and interpretation in clinical datasets. 
    Methods draw on prior work in mixture-of-experts neural networks by
    Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>,
    fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>,
    and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) 
    <doi:10.1016/j.jpsychires.2016.03.016>.
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>".
A collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2024) <doi:10.48550/arXiv.2406.09307>.
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)
  <doi:10.1007/s11222-022-10143-w> 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)) <doi:10.48550/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) <doi:10.1093/jrsssc/qlad058>).
Generate privacy-preserving synthetic datasets that mirror structure, types, factor levels, and missingness; export bundles for 'LLM' workflows (data plus 'JSON' schema and guidance); and build fake data directly from 'SQL' database tables without reading real rows. Methods are related to approaches in Nowok, Raab and Dibben (2016) <doi:10.32614/RJ-2016-019> and the foundation-model overview by Bommasani et al. (2021) <doi:10.48550/arXiv.2108.07258>.
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.
Fits probability distributions to data and plugs into the
    'probaverse' suite of R packages so distribution objects are ready for
    further manipulation and evaluation. Supports methods such as maximum
    likelihood and L-moments, and provides diagnostics including empirical
    ranking and quantile score.
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>.
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 described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" <doi:10.1080/10618600.2023.2204136>.
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'.
Handy functions and data to support the course book 'Empirical Research in Accounting: Tools and Methods' (1st ed.). Chapman and Hall/CRC. <doi:10.1201/9781003456230> and <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>.
Provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.
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.
Implementation of the FASSTER (Forecasting with Additive Switching 
    of Seasonality, Trend, and Exogenous Regressors) model for forecasting time 
    series with multiple seasonal patterns. The model combines state space 
    methodology with a switching component in the observation equation to allow 
    flexible modeling of complex seasonal patterns, including time-varying 
    effects and multiple seasonalities.
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' <doi:10.1080/10618600.2018.1518238> as
    well as the specialized variant of 'Co-Factor Analysis of
    Citation Networks' <doi:10.1080/10618600.2024.2394464>.
    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>.
Provides a high-performance framework for deriving bioclimatic and custom summary variables from 
    large-scale climate raster data. The package features a dual-backend architecture that intelligently switches 
    between fast in-memory processing for smaller datasets (via the 'terra' package) and a memory-safe tiled approach 
    for massive datasets that do not fit in RAM (via 'exactextractr' and 'Rfast'). The main functions, 
    derive_bioclim() and derive_statistics(), offer a unified interface with advanced options for 
    custom time periods and static indices, making it suitable for a wide range of ecological and 
    environmental modeling applications. A software note is in preparation. In the meantime, you can visit 
    the package website <https://gepinillab.github.io/fastbioclim/> to find tutorials in English and Spanish.
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/>.
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: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().
Estimates the probability matrix for the R×C Ecological Inference problem using the Expectation-Maximization Algorithm with four approximation methods for the E-Step, and an exact method as well. It also provides a bootstrap function to estimate the standard deviation of the estimated probabilities. In addition, it has functions that aggregate rows optimally to have more reliable estimates in cases of having few data points. For comparing the probability estimates of two groups, a Wald test routine is implemented. The library has data from the first round of the Chilean Presidential Election 2021 and can also generate synthetic election data. Methods described in Thraves, Charles; Ubilla, Pablo; Hermosilla, Daniel (2024) ''A Fast Ecological Inference Algorithm for the R×C case'' <doi:10.2139/ssrn.4832834>.
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' <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' 8.0 or higher.
Implementation of the fast univariate inference approach (Cui et al. (2022) <doi:10.1080/10618600.2021.1950006>, Loewinger et al. (2024) <doi:10.7554/eLife.95802.2>, Xin et al. (2025)) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.
Provides fast moving-window ("focal") and buffer-based extraction 
    for raster data using the 'terra' package. Automatically selects between 
    a 'C++' backend (via 'terra') and a Fast Fourier Transform (FFT) backend 
    depending on problem size. The FFT backend supports sum and mean, while 
    other statistics (e.g., median, min, max, standard deviation) are handled 
    by the 'terra' backend. Supports multiple kernel types (e.g., circle, 
    rectangle, gaussian), with NA handling consistent with 'terra' via 
    'na.rm' and 'na.policy'. Operates on 'SpatRaster' objects and returns 
    results with the same geometry.
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>.
Converts R data frames and 'sf' spatial objects into 'JSON' and 
    'GeoJSON' strings. The core encoders are implemented in 'Rust' using the 
    'extendr' framework and are designed to efficiently serialize large 
    tabular and spatial datasets. Returns serialized 'JSON' text, allowing 
    applications such as 'shiny' or web APIs to transfer data to 
    client-side 'JavaScript' libraries without additional encoding overhead.
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.
Pairwise Hamming distances are computed between the rows of a binary (0/1) matrix using highly optimized 'C' code. The input is an integer matrix where each row represents a binary feature vector and returns a symmetric integer matrix of pairwise distances. Internally, rows are bit-packed into 64-bit words for fast XOR-based comparisons, with hardware-accelerated popcount operations to count differences. 'OpenMP' parallelization ensures efficient performance for large matrices.
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.
A joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>.
             The time-to-event data is modelled using a (cause-specific) Cox 
             proportional hazards regression model with time-fixed covariates. 
             The longitudinal biomarkers are modelled using a linear mixed 
             effects model. The association between the longitudinal submodel 
             and the survival submodel is captured through shared random 
             effects. It allows researchers to analyze large-scale data to 
             model biomarker trajectories, estimate their effects on event 
             outcomes, and dynamically predict future events from patients’ 
             past histories. A function for simulating survival and longitudinal 
             data for multiple biomarkers is also included alongside built-in 
             datasets.
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.
Provides core computational operations in C++ via 'RcppArmadillo', enabling faster performance than pure R, improved numerical stability, and parallel execution with OpenMP where available. On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with 'CVST' to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three scalable approximations—Nyström, Pivoted Cholesky, and Random Fourier Features—allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the 'tidymodels' ecosystem via the 'parsnip' model specification 'krr_reg', and the S3 method tunable.krr_reg(). To understand the theoretical background, one can refer to Wainwright (2019) <doi:10.1017/9781108627771>.
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 designed to speed up the computation of certain 
  matrix operations that are commonly used in statistics and econometrics. It provides 
  efficient implementations for the computation of several structured matrices, matrix 
  decompositions and statistical procedures, many of which have minimal memory overhead.
  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.
Provides a guarded resampling workflow for training and evaluating machine-learning models. 
	When the guarded resampling path is used, preprocessing and model fitting are re-estimated within 
	each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates 
	with established engines in the 'tidymodels' ecosystem, and aims to improve evaluation reliability by 
	coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight 
	AutoML-style workflow by automating model training, resampling, and tuning across multiple algorithms, 
	while keeping evaluation design explicit and user-controlled.
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/>.
Constructs optimal policy trees which provide a rule-based treatment prescription policy.
 Input is covariate and reward data, where, typically, the rewards will be doubly robust reward estimates.
 This package aims to construct optimal policy trees more quickly than the existing 'policytree' package and
 is intended to be used alongside that package.
 For more details see Cussens, Hatamyar, Shah and Kreif (2025) <doi:10.48550/arXiv.2506.15435>.
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.
Fast estimation algorithms to implement the Quantile Regression with Selection estimator and the multiplicative Bootstrap for inference. This estimator can be used to estimate models that feature sample selection and heterogeneous effects in cross-sectional data. For more details, see Arellano and Bonhomme (2017) <doi:10.3982/ECTA14030> and Pereda-Fernández (2024) <doi:10.48550/arXiv.2402.16693>.
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.
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.
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, <doi:10.48550/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.
Implements the algorithm by Briefs and Bläser (2025) <https://openreview.net/forum?id=8PHOPPH35D>, based on the approach of Gupta and Bläser (2024) <doi:10.1609/aaai.v38i18.30023>. It determines, for a structural causal model (SCM) whose directed edges form a tree, whether each parameter is unidentifiable, 1-identifiable or 2-identifiable (other cases cannot occur), using a randomized algorithm with provable running time O(n^3 log^2 n).
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>.
A collection of utility functions for working with
  Year Month Day objects. Includes functions for fast parsing of numeric
  and character input based on algorithms described in Hinnant, H. (2021) 
  <https://howardhinnant.github.io/date_algorithms.html> as well as a branchless
  calculation of leap years by Jerichaux (2025) <https://stackoverflow.com/a/79564914>.
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 
    finance, neuroscience and other disciplines. Two algorithms to estimate the
    distribution parameters, quantiles, value-at-risk and expected shortfall.
    IMPORTANT: Standardization has been changed in versions >= 2.0.0 to get 
	sd = 1 when kappa = Inf rather than 2*pi/sqrt(3) in versions <= 1.8.6. 
	This affects parameter g (other parameters stay unchanged). Do not update 
	if you need consistent comparisons with previous results for the g parameter.
A collection of functions designed to retrieve, filter and spatialize data from the Catálogo Taxônomico da Fauna do Brasil. For more information about the dataset, please visit <http://fauna.jbrj.gov.br/fauna/listaBrasil/>.
Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://scienceverse.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 (2025) <doi:10.1073/pnas.2413211122>.
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).
Validate function arguments succinctly with informative error messages and optional automatic type casting and size recycling. Enable schema-based assertions by attaching reusable rules to data.frame and list objects for use throughout workflows.
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 uses moment
 fractional Bayes factors (MFBF) and is suitable for learning sparse graphs.
 The algorithm is implemented 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 the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <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).
Parses financial condition and performance data (Call Reports) for
    institutions in the United States Farm Credit System. Contains functions for
    downloading files from the Farm Credit Administration (FCA) Call Report
    archive website and reading the files into tidy data frame format.
    The archive website can be found at
    <https://www.fca.gov/bank-oversight/call-report-data-for-download>.
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. Matrix factorization is provided by the GreConD+ algorithm (Belohlavek and Trneckova, 2024 <doi:10.1109/TFUZZ.2023.3330760>).
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.
Analyze functional data and its change points. Includes 
    functionality to store and process data, summarize and validate assumptions, 
    characterize and perform inference of change points, and provide 
    visualizations. Data is stored as discretely collected observations without
    requiring the selection of basis functions. For more details see chapter 8 
    of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers
    are forthcoming. Focused works are also included in the documentation of 
    corresponding functions.
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.
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>.
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.
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.
Implements the Fourier cumulative sum (CUSUM) cointegration test 
    for detecting cointegration relationships in time series data with 
    structural breaks. The test uses Fourier approximations to capture smooth 
    structural changes and CUSUM statistics to test for cointegration stability.
    Based on methodology described in Zaghdoudi (2025) 
    <doi:10.46557/001c.144076>. The corrected Akaike Information Criterion 
    (AICc) is used for optimal frequency selection.
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) <doi:10.48550/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.
Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elías, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2022.2070171>. Elías, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, <doi:10.1007/s00180-022-01282-9>. Elías, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, <doi:10.1007/s11749-024-00954-6>.
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>.
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>.
Perform optimal transport based tests in factorial designs as introduced in Groppe et al. (2025) <doi:10.48550/arXiv.2509.13970> via the FDOTT() function. These tests are inspired by ANOVA and its nonparametric counterparts. They allow for testing linear relationships in factorial designs between finitely supported probability measures on a metric space. Such relationships include equality of all measures (no treatment effect), interaction effects between a number of factors, as well as main and simple factor effects.
Constructs and visualises trade-off functions for f-differential 
    privacy (f-DP) as introduced by Dong et al. (2022) <doi:10.1111/rssb.12454>.
    Supports Gaussian differential privacy, the f-DP generalisation of
    (epsilon, delta)-differential privacy, and accepts user-specified optimal
    type I / type II errors from which the lower convex hull trade-off function
    is automatically constructed.
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.
Automated feature engineering functions tailored for credit scoring. It includes utilities for extracting structured features from timestamps, IP addresses, and email addresses, enabling enhanced predictive modeling for financial risk assessment.
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.
Provides tools for estimating causal effects in panel data using counterfactual methods, as well as other modern DID estimators. It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) <doi:10.48550/arXiv.2107.00856> and Chiu et al. (2025) <doi:10.48550/arXiv.2309.15983>. Optionally integrates with the "HonestDiDFEct" package for sensitivity analyses compatible with imputation estimators. "HonestDiDFEct" is not on CRAN but can be obtained from <https://github.com/lzy318/HonestDiDFEct>.
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.
Estimates the first-exposure effect (FEE) using a
    one-inflated positive Poisson model, or a one-inflated zero-truncated
    negative binomial model. In addition, estimates the marginal FEE, and
    standard errors for the FEE and marginal FEE.
Implement and fit a variety of short-memory (SM) and long-memory
  (LM) models from a very broad family of exponential generalized autoregressive
  conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified
  EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally
  integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the
  EGARCH family is discussed in Feng et al. (2023)
  <https://econpapers.repec.org/paper/pdnciepap/156.htm>. For convenience and
  the purpose of comparison, a variety of other popular SM and LM GARCH-type
  models, like an APARCH model, a fractionally integrated
  APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH
  (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH
  models, are implemented as well as dual models with simultaneous modelling of
  the mean, including dual long-memory models with a fractionally integrated
  autoregressive moving average (FARIMA) model in the mean and a long-memory
  model in the variance, and semiparametric volatility model extensions.
  Parametric models and parametric model parts are fitted through
  quasi-maximum-likelihood estimation.
  Furthermore, common forecasting and backtesting functions for value-at-risk
  (VaR) and expected shortfall (ES) based on the package's models are provided.
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.
Implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) <doi:10.1016/j.jeconom.2022.12.011>, allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by Słoczyński (2024) <doi:10.48550/arXiv.2011.06695> as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) <doi:10.1080/01621459.1995.10476535>.
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://developers.felt.com/rest-api/getting-started>). 
    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.
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/articles/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/>.
Calculates the fused extended two-way fixed effects (FETWFE) estimator for unbiased and efficient estimation of difference-in-differences in panel data with staggered treatment adoption. This estimator eliminates bias inherent in conventional two-way fixed effects estimators, while also employing a novel bridge regression regularization approach to improve efficiency and yield valid standard errors. Also implements extended TWFE (etwfe) and bridge-penalized ETWFE (betwfe). Provides S3 classes for streamlined workflow and supports flexible tuning (ridge and rank-condition guarantees), automatic covariate centering/scaling, and detailed overall and cohort-specific effect estimates with valid standard errors. Includes simulation and formatting utilities, extensive diagnostic tools, vignettes, and examples. See Faletto (2025) (<doi:10.48550/arXiv.2312.05985>).
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.
Tools to support systematic and reproducible workflows for both
	stationary and nonstationary flood frequency analysis, with applications
	extending to other hydroclimate extremes, such as precipitation frequency
	analysis. This package implements the FFA framework proposed by Vidrio-
	Sahagún et al. (2024) <doi:10.1016/j.envsoft.2024.105940>, originally
	developed in 'MATLAB', now adapted for the 'R' environment. This work was
	funded by the Flood Hazard Identification and Mapping Program of Environment
	and Climate Change Canada, as well as the Canada Research Chair (Tier 1)
	awarded to Dr. Pietroniro.
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).
Provides a simplified interface to the Central Data Repository 'REST
    API' service made available by the United States Federal Financial
    Institutions Examination Council ('FFIEC'). Contains functions to retrieve
    reports of Condition and Income (Call Reports) and Uniform Bank Performance
    Reports ('UBPR') in list or tidy data frame format for most 'FDIC' insured
    institutions. See
    <https://cdr.ffiec.gov/public/Files/SIS611_-_Retrieve_Public_Data_via_Web_Service.pdf>
    for the official 'REST API' documentation published by the 'FFIEC'.
Provides quick and easy access to official spatial data from
    Germany’s Federal Agency for Cartography and Geodesy (BKG) <https://gdz.bkg.bund.de/>.
    Interfaces various web feature services (WFS) and download servers.
    Allows retrieval, caching and filtering with a wide range of open
    geodata products, including administrative or non-administrative boundaries,
    land cover, elevation models, geographic names, and points of interest
    covering Germany. Can be particularly useful for linking regional statistics
    to their spatial representations and streamlining workflows that involve
    spatial data of Germany.
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.
A typical gait analysis requires the examination of the motion of nine joint angles on the left-hand side and six joint angles on the right-hand side across multiple subjects. Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject’s gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. This R package provides tools for computing the Functional Gait Deviation Index, a novel index for quantifying gait pathology using multivariate functional principal component analysis. The package supports analysis at the level of both legs combined, individual legs, and individual joints/planes. It includes functions for functional data preprocessing, multivariate functional principal component decomposition, FGDI computation, and visualisation of gait abnormality scores. Further details can be found in Minhas, S. K., Sangeux, M., Polak, J., & Carey, M. (2025). The Functional Gait Deviation Index. Journal of Applied Statistics <doi:10.1080/02664763.2025.2514150>.
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.
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.
The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) <doi:10.1002/jmr.300080603>. Here, we introduce 'fibos', an 'R' and 'Python' package that extends the 'OS' methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) <doi:10.1101/2024.11.01.621530>.
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  Gaussian spatial process prediction (known as Kriging), cubic and thin plate splines, and compactly supported
 covariance functions for large data sets. The spline and spatial process
 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  maximum likelihood. For spatial process prediction
 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. As included are fast approximations for prediction
 and conditional simulation for larger data sets.
 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 based the  R sparse matrix package spam. Use
 help(fields) to get started and for an overview. All package graphics functions
 focus on  extending base R graphics and are easy to interpret and modify.
 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 of this package
 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.
For including external figures into an assembled 
    {patchwork}. This enables the creation of more complex figures that include 
    images alongside plots.
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.
Provides easy-to-understand and consistent interfaces for
    accessing data on the U.S. Congress. The functions in 'filibustr'
    streamline the process for importing data on Congress into R, removing
    the need to download and work from CSV files and the like. Data
    sources include 'Voteview' (<https://voteview.com/>), the U.S. Senate
    website (<https://www.senate.gov/>), and more.
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.
Tidy tools to apply filter-based supervised feature selection
    methods. These methods score and rank feature relevance using metrics
    such as p-values, correlation, and importance scores (Kuhn and Johnson
    (2019) <doi:10.1201/9781315108230>).
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.
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.
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.
Generate decision tables and simulate operating characteristics
    for phase I dose-finding designs to enable objective comparison across
    methods. Supported designs include the traditional 3+3, Bayesian Optimal
    Interval (BOIN) (Liu and Yuan (2015) <doi:10.1158/1078-0432.CCR-14-1526>),
    modified Toxicity Probability Interval-2 (mTPI-2) (Guo et al. (2017)
    <doi:10.1002/sim.7185>), interval 3+3 (i3+3) (Liu et al. (2020)
    <doi:10.1177/0962280220939123>), and Generalized 3+3 (G3). Provides
    visualization tools for comparing decision rules and operating
    characteristics across multiple designs simultaneously.
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). 
        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) <doi:10.48550/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.
"This package quantifies the provenance of sediments in a catchment or study area. Based on a characterization of the sediment sources and the end sediment mixtures, a mixing model algorithm is applied to the sediment mixtures to estimate the relative contribution of each potential source. The package includes several graphs to help users in their data understanding, such as box plots, correlation, PCA, and LDA graphs. In addition, new developments such as the Consensus Ranking (CR), Consistent Tracer Selection (CTS), and Linear Variability Propagation (LVP) methods are included to correctly apply the fingerprinting technique and increase dataset and model understanding. A new method based on Conservative Balance (CB) method has also been included to enable the use of isotopic tracers."
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!
Access and retrieve vocabulary data 'Finto' API <https://api.finto.fi/>, which is a centralized service for interoperable thesauri, ontology and classification schemes for different subject areas.
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 wrapper for the python module 'FIORA' as well as a 
    'shiny'-App to facilitate data processing and visualization. 'FIORA' allows 
    to predict Mass-Spectra based on the SMILES code of chemical compounds. It 
    is described in the Nature Communications article by Nowatzky (2025) 
    <doi:10.1038/s41467-025-57422-4>.
Provides a lightweight suite
             of functions for retrieving information
             about 5-digit or 2-digit US FIPS codes.
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.
A convenient and user-friendly interface to 
    interact with the 'Firebase Authentication REST API': <https://firebase.google.com/docs/reference/rest/auth>.
    It enables R developers to integrate 'Firebase Authentication' services 
    seamlessly into their projects, allowing for user authentication, account 
    management, and other authentication-related tasks.
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.
Methods for computing and visualizing wildfire ignition exposure 
    and directional vulnerability that are published in a series of scientific 
    publications are automated by the functions in this package. See Beverly 
    et al. (2010) <doi:10.1071/WF09071>, Beverly et al. (2021) 
    <doi:10.1007/s10980-020-01173-8>, and Beverly and Forbes (2023) 
    <doi:10.1007/s11069-023-05885-3> for background and methodology.
Provides a plugin for 'fiery' that supports various forms
    of authorization and authentication schemes. Schemes can be required in
    various combinations or by themselves and can be combined with scopes to
    provide fine-grained access control to the server.
Provide a range of plugins for 'fiery' web servers that handle
    different aspects of server-side web security. Be aware that security cannot
    be handled blindly, and even though these plugins will raise the security of
    your server you should not build critical infrastructure without the aid of
    a security expert.
Provides a persistent datastore for 'fiery' apps. The datastore is
    build on top of the 'storr' package and can thus be based on a variety of
    backends. The datastore contains both a global and session-scoped section.
Provides plugins for setting up 'fiery' apps as a reverse proxy.
    This allows you to use a 'fiery' server as a front for multiple services or
    even work as a load-balancer.
A model-independent factor importance ranking and selection procedure based on total Sobol' indices. Please see Huang and Joseph (2025) <doi:10.1080/00401706.2025.2483531>. 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.
Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering.
  Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics.
  Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster.
  Based on principals of combining CWT with k-means first developed by Sakamoto (2009) <doi:10.1371/journal.pone.0005379> and redeveloped by Beale (2026) <doi:10.21203/rs.3.rs-6907076/v1>.
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.
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.
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.
Generates predictive distributions based on calibrating priors
    for various commonly used statistical models, including models with 
    predictors. Routines for densities, probabilities, quantiles, random
    deviates and the parameter posterior are provided. The predictions are
    generated from the Bayesian prediction integral, with priors chosen to
    give good reliability (also known as calibration). For homogeneous models,
    the prior is set to the right Haar prior, giving predictions which are
    exactly reliable. As a result, in repeated testing, the frequencies of
    out-of-sample outcomes and the probabilities from the predictions agree.
    For other models, the prior is chosen to give good reliability. Where
    possible, the Bayesian prediction integral is solved exactly. Where exact
    solutions are not possible, the Bayesian prediction integral is solved
    using the Datta-Mukerjee-Ghosh-Sweeting (DMGS) asymptotic expansion.
    Optionally, the prediction integral can also be solved using posterior
    samples generated using Paul Northrop's ratio of uniforms sampling package
    ('rust'). Results are also generated based on maximum likelihood, for
    comparison purposes. Various model selection diagnostics and testing
    routines are included. Based on "Reducing reliability bias in assessments
    of extreme weather risk using calibrating priors", Jewson, S., Sweeting, T.
    and Jewson, L. (2024); <doi:10.5194/ascmo-11-1-2025>.
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.
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.
Provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) <doi:10.18637/jss.v064.i04>.
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>).
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>.
Provides functions for creating, analyzing, and visualizing event study models using fixed-effects regression. Supports staggered adoption, multiple confidence intervals, flexible clustering, and panel/time transformations in a simple workflow.
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.
Extends 'data.table' join functionality, lets it work with any
  data frame class, and provides a familiar 'x'/'y'-style interface, enabling
  broad use across R. Offers NA-safe matching by default, on-the-fly column
  selection, multiple match-handling on both sides, 'x' or 'y' row order, and a
  row origin indicator. Performs inner, left, right, full, semi- and anti-joins
  with equality and inequality conditions, plus cross joins. Specific support
  for 'data.table', (grouped) tibble, and 'sf'/'sfc' objects and their
  attributes; returns a plain data frame otherwise. Avoids data-copying of
  inputs and outputs. Allows displaying the 'data.table' code instead of (or as
  well as) executing it.
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 shape-based clustering algorithms for multidimensional longitudinal data based on the Fréchet distance. It implements two main methods: MFKmL (Multidimensional Fréchet distance-based K-means for Longitudinal data), an extension of the K-means algorithm using the Fréchet distance originally developed in the 'kmlShape' package, adapted for multidimensional trajectories; and SFKmL (Sparse multidimensional Fréchet distance-based K-medoids for Longitudinal data), a K-medoids-based clustering algorithm that incorporates variable selection. These tools are designed to enhance clustering performance in high-dimensional longitudinal data settings, particularly those with time delays, variations in trajectory speed, irregular sampling intervals, and noise. This package implements methods derived from Kang et al. (2023) <doi:10.1007/s11222-023-10237-z>.
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.
In order to achieve accurate estimation without sparsity assumption on the precision matrix, element-wise inference on the precision matrix, and joint estimation of multiple Gaussian graphical models, a novel method is proposed and efficient algorithm is implemented.
    FLAG() is the main function given a data matrix, and FlagOneEdge() will be used when one pair of random variables are interested where their indices should be given.
    Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications, see Qian Y (2023) <doi:10.14711/thesis-991013223054603412>, Qian Y, Hu X, Yang C (2023) <doi:10.48550/arXiv.2306.17584>.
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.
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>.
A set of methods to simulate from and fit computational models of
    attentional selectivity. The package implements the dual-stage two-phase
    (DSTP) model of Hübner et al. (2010) <doi:10.1037/a0019471>, and the
    shrinking spotlight (SSP) model of White et al. (2011)
    <doi:10.1016/j.cogpsych.2011.08.001>.
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) <doi:10.48550/arXiv.1612.08468>), further effects plots,
    interaction strength, and variable contribution breakdown (Gosiewska
    and Biecek (2019) <doi:10.48550/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.
Provides a suite of methods for detecting influential 
    subjects in longitudinal datasets, particularly when observations 
    occur at irregular time points. The methods identify individuals 
    whose response trajectories deviate significantly from the population 
    pattern, enabling detection of anomalies or subjects exerting undue 
    influence on model outcomes.
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 (2024) <doi:10.1515/ijb-2023-0059>.
The FLEX method, developed by Yoon and Choi (2013) <doi:10.1007/978-3-642-33042-1_21>, performs least squares estimation for fuzzy predictors and outcomes, generating crisp regression coefficients by minimizing the distance between observed and predicted outcomes. It also provides functions for fuzzifying data and inference tasks, including significance testing, fit indices, and confidence interval estimation.
A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020) <doi:10.1214/19-BA1195>), which uses regression tree ensembles to estimate the conditional average treatment effect of a binary treatment on a scalar output as a function of many covariates. This implementation avoids many redundant computations and memory allocations present in the original BCF implementation, allowing the model to be fit to larger datasets. The implementation was originally developed for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar et al. (2023) <doi:10.1353/obs.2023.0024> for more details.
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.
An implementation of multiple regression models for 
    count data. These include various forms of the negative binomial (NB-1, 
    NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other
    compound Poisson distributions, the Generalized Waring model, etc.
    Information on the different forms of the negative binomial are
    described by Greene (2008) <doi:10.1016/j.econlet.2007.10.015>. For
    treatises on count models, see Cameron and Trivedi (2013) 
    <doi:10.1017/CBO9781139013567> and Hilbe (2012)
    <doi:10.1017/CBO9780511973420>. For the implementation of
    under-reporting in count models, see Wood et al. (2016)
    <doi:10.1016/j.aap.2016.06.013>. For prediction methods in random
    parameter models, see Wood and Gayah (2025)
    <doi:10.1016/j.aap.2025.108147>. For estimating random parameters using
    maximum simulated likelihood, see Greene and Hill (2010)
    <doi:10.1108/S0731-9053(2010)26>; Gourieroux and Monfort (1996)
    <doi:10.1093/0198774753.001.0001>; or Hensher et al. (2015)
    <doi:10.1017/CBO9781316136232>.
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>.
Implements a fast, flexible method for simulating continuous
    variables with specified rank correlations using the Iman–Conover
    transformation (Iman & Conover, 1982 <doi:10.1080/03610918208812265>)
    and back-ranking. Includes plotting tools and error-diagnostics.
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.
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 color palettes designed to be reminiscent of text on paper.
    The color schemes were taken from <https://stephango.com/flexoki>.
    Includes discrete, continuous, and binned scales that are not necessarily 
    color-blind friendly. Simple scale and theme functions are available for use 
    with 'ggplot2'.
Extends the capabilities for flexible partitioning and model-based clustering
       available in the packages 'flexclust' and 'flexmix' to handle ordinal and
       mixed-with-ordinal data types via new distance, centroid and driver functions that
       make various assumptions regarding ordinality. Using them within the flex-scheme
       allows for easy comparisons across methods.
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:10.1093/jrsssc/qlaf016>); 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.
Enables the construction of flexible urban delineations that
    can be tailored to specific applications or research questions, see Van
    Migerode et al. (2024) <DOI:10.1177/23998083241262545> and Van Migerode
    et al. (2025) <DOI:10.5281/zenodo.15173220>.
    Originally developed to flexibly reconstruct the Degree of
    Urbanisation classification of cities,
    towns and rural areas developed by Dijkstra et al. 
    (2021) <DOI:10.1016/j.jue.2020.103312>. Now it also support a broader 
    range of delineation approaches, using multiple datasets – including
    population, built-up area, and night-time light grids – and different
    thresholding methods.
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.
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 operations on
	standard rings (the integers, the integers modulo n, finite
	fields, the rational, p-adic, real, and complex numbers) as well
	as matrices and polynomials 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 rounding and
	other 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 mirroring 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>.
Lints are code patterns that are not optimal because they are
    inefficient, forget corner cases, or are less readable. 'flir' provides a
    small set of functions to detect those lints and automatically fix them.
    It builds on 'astgrepr', which itself uses the 'Rust' crate 'ast-grep' to
    parse and navigate R code.
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.
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.
Provides functionality for clustering
    origin-destination (OD) pairs, representing desire lines (or flows).
    This includes creating distance matrices between OD pairs and passing
    distance matrices to a clustering algorithm. See the academic paper 
    Tao and Thill (2016) <doi:10.1111/gean.12100>
    for more details on spatial clustering of flows.
    See the paper on delineating demand-responsive operating areas
    by Mahfouz et al. (2025) <doi:10.1016/j.urbmob.2025.100135>
    for an example of how this package can be used to cluster flows for
    applied transportation research.
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.
High-performance tools for transport modeling - network processing, route 
    enumeration, and traffic assignment in R. The package implements the Path-Sized Logit 
    model for traffic assignment - Ben-Akiva and Bierlaire (1999) <doi:10.1007/978-1-4615-5203-1_2> - 
    an efficient route enumeration algorithm, and provides powerful utility functions for (multimodal) 
    network generation, consolidation/contraction, and/or simplification. The user is expected to provide 
    a transport network (either a graph or collection of linestrings) and an origin-destination (OD) 
    matrix of trade/traffic flows. Maintained by transport consultants at CPCS (cpcs.ca).
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.  
    Package update to version 2.0 made updates to read.flows function to allow 
    loading of GRDC and ROBIN streamflow record formats.
    This package uses the `changepoint` package for change point detection.
    For more information on change point methods, see the changepoint 
    package at <https://cran.r-project.org/package=changepoint>.
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>.
Specialized solvers for combinatorial optimization problems in the Subset Sum family. The solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value multisets with predefined subset sum errors, (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The multi-threaded framework for the latter offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. Historical updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) option of mapping floating-point instance to compressed 64-bit integer instance with user-controlled precision loss, which could yield substantial speedup due to the dimension reduction and efficient compressed integer arithmetic via bit-manipulations; (e) distributed computing infrastructure for multidimensional subset sum; (f) arbitrary-precision zero-margin-of-error multidimensional Subset Sum accelerated by a simplified Bloom filter. The package contains a copy of 'xxHash' from <https://github.com/Cyan4973/xxHash>. Package vignette (<doi:10.48550/arXiv.1612.04484>) detailed a few historical updates. Functions prefixed with 'aux' (auxiliary) are independent implementations of published algorithms for solving optimization problems less relevant to Subset Sum.
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.
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.
Parse static-chamber greenhouse gas measurement
    files generated by a variety of instruments; compute flux rates using
    multi-observation metadata; and generate diagnostic metrics and plots.
    Designed to be easy to integrate into reproducible scientific workflows.
Toolbox to process 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.
    Organized with one function per step, maximizing user flexibility and backwards
    compatibility. Different models to estimate the fluxes from the raw data are
    available: exponential as described in
    Zhao et al (2018) <doi:10.1016/j.agrformet.2018.08.022>, exponential as described in
    Hutchinson and Mosier (1981) <doi:10.2136/sssaj1981.03615995004500020017x>,
    quadratic, and linear. Other functions include quality assessment,
    plotting for visual check, calculation of fluxes based on the setup
    specific parameters (chamber size, plot area, ...), gross primary production
    and transpiration rate calculation, and light response curves.
Implements methods for multiple change point detection in multivariate
    time series with non-stationary dynamics and cross-correlations. The methodology
    is based on a model in which each component has a fluctuating mean represented by
    a random walk with occasional abrupt shifts, combined with a stationary vector
    autoregressive structure to capture temporal and cross-sectional dependence. The
    framework is broadly applicable to correlated multivariate sequences in which
    large, sudden shifts occur in all or subsets of components and are the primary
    targets of interest, whereas small, smooth fluctuations are not. Although random
    walks are used as a modeling device, they provide a flexible approximation for a
    wide class of slowly varying or locally smooth dynamics, enabling robust
    performance beyond the strict random walk setting.
An interactive 'shiny'-based tool for exploration and quality
    assurance and quality control (QA/QC) of eddy covariance flux tower
    data processing. It generates data-point removal code via
    user-directed selection from a scatterplot, and can export a cleaned
    .csv with removed points set to NA plus an R script for
    reproducibility. Reference: Key (2025) <DOI:10.5281/zenodo.15597159>.
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, probability-based social computing method
    using Masked Language Models to measure conceptual associations
    (e.g., attitudes, biases, stereotypes, social norms, cultural values)
    as propositional semantic representations 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.
Provides an implementation of finite mixture regression models for censored
    data under four distributional families: Normal (FM-NCR), Student t (FM-TCR),
    skew-Normal (FM-SNCR), and skew-t (FM-STCR). The package enables flexible
    modeling of skewness and heavy tails often observed in real-world data, while
    explicitly accounting for censoring. Functions are included for parameter
    estimation via the Expectation-Maximization (EM) algorithm, computation of standard errors, and model
    comparison criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), 
    and the Efficient Determination Criterion (EDC). The underlying methodology is described in Park et al. (2024)
    <doi:10.1007/s00180-024-01459-4>.
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.
Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage.
             Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future.
             References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication.
             Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication.
             Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation.
             Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication.
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>.
Generate planar and spherical triangle meshes,
    compute finite element calculations for 1-, 2-, and 3-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>.
Provides a suite of functions to test for Functional
    Measurement Invariance (FMI) between two groups. Implements
    hierarchical permutation tests for configural, metric, and scalar
    invariance, adapting concepts from Multi-Group Confirmatory
    Factor Analysis (MGCFA) to functional data.
    Methods are based on concepts from: 
    Meredith, W. (1993) <doi:10.1007/BF02294825>,5
    Yao, F., Müller, H. G., & Wang, J. L. (2005) <doi:10.1198/016214504000001745>,
    and Lee, K. Y., & Li, L. (2022) <doi:10.1111/rssb.12471>.
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) <doi:10.48550/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>.
Lightweight utilities to estimate autoregressive (AR) and
    autoregressive moving average (ARMA) noise models from residuals and apply
    matched generalized least squares to whiten functional magnetic resonance
    imaging (fMRI) design and data matrices. The ARMA estimator follows a
    classic 1982 approach <doi:10.1093/biomet/69.1.81>, and a restricted AR
    family mirrors workflows described by Cox (2012)
    <doi:10.1016/j.neuroimage.2011.08.056>.
Creates, manipulates, and evaluates hemodynamic response functions
    and event-related regressors for functional magnetic resonance imaging data
    analysis. Supports multiple basis sets including Canonical, Gamma, Gaussian,
    B-spline, and Fourier bases. Features decorators for time-shifting and blocking,
    and efficient convolution algorithms for regressor construction. Methods are based
    on standard fMRI analysis techniques as described in Jezzard et al. (2001, ISBN:9780192630711).
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>.
Methods and tools designed to improve the forecast accuracy for a linearly 
    constrained multiple time series, while fulfilling the linear/aggregation relationships 
    linking the components (Girolimetto and Di Fonzo, 2024 <doi:10.48550/arXiv.2412.03429>). 
    'FoCo2' offers multi-task forecast combination and reconciliation 
    approaches leveraging input from multiple forecasting models or experts and ensuring 
    that the resulting forecasts satisfy specified linear constraints. In addition, linear 
    inequality constraints (e.g., non-negativity of the forecasts) can be imposed, if needed.
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 with Paul Murrell's symbol extensions. Is is to be used with the **extrafont** package. When this font package is installed, the CM fonts will be available for PDF or Postscript output files; however, this will (probably) not make the font available for screen or bitmap output files.
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.
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.
Easily create graphs of the inter-relationships between
    functions in an environment.
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. It supports both maximum likelihood estimation (MLE, for 
             'static' models only) and Bayesian inference.
             For Bayesian methods, it incorporates several techniques:
             MCMC sampling with Hamiltonian Monte Carlo, variational inference using
             either the Pathfinder algorithm or Automatic Differentiation Variational
             Inference (ADVI), and the Laplace approximation.
             The package compiles all the 'CmdStan' models once during installation
             using the 'instantiate' package.
             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').
Provides a comprehensive set of datasets and tools for 'causal inference' research. 
    The package includes data from clinical trials, cancer studies, epidemiological surveys, environmental exposures, and health-related observational studies.
    Designed to facilitate causal analysis, risk assessment, and advanced statistical modeling, 
    it leverages datasets from packages such as 'causalOT', 'survival', 'causalPAF', 'evident', 'melt', and 'sanon'. 
    The package is inspired by the foundational work of Pearl (2009) <doi:10.1017/CBO9780511803161> on causal inference frameworks.
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.
Provides an interface to the 'FORCIS' database 
    (Chaabane et al. (2024) <doi:10.5281/zenodo.7390791>) on global foraminifera
    distribution. This package allows to download and to handle 'FORCIS' data.
    It is part of the FRB-CESAB working group FORCIS.
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/forcis/>.
Feature Ordering by Integrated R square Dependence (FORD) is a variable selection algorithm based on the new measure of dependence: Integrated R2 Dependence Coefficient (IRDC). For more information, see the paper: Azadkia and Roudaki (2025),"A New Measure Of Dependence: Integrated R2" <doi:10.48550/arXiv.2505.18146>.
Forest Many-Objective Robust Decision Making ('FoRDM') is a R toolkit for supporting robust forest management under deep uncertainty.
    It provides a forestry-focused application of Many-Objective Robust Decision Making ('MORDM') to forest simulation outputs, 
    enabling users to evaluate robustness using regret- and 'satisficing'-based measures. 'FoRDM' identifies robust solutions, 
    generates Pareto fronts, and offers interactive 2D, 3D, and parallel-coordinate visualizations.
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.
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(), snaive() and arfima() 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.
Runs multiple individual time series models, and combines them into an ensembles of time series models. This is mainly used to predict the results of the monthly labor market report from the 
    United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data.
    For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon.
Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.
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>.
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. 2024 <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.
Provides allele frequency data for Short Tandem Repeat human
             genetic markers commonly used in forensic genetics for human
             identification and kinship analysis. Includes published population
             frequency data from the US National Institute of Standards and
             Technology, Federal Bureau of Investigation and the UK government.
Statistical methods and simulation tools for the
        interpretation of forensic DNA mixtures. The methods
        implemented are described in
        Haned et al. (2011) <doi:10.1111/j.1556-4029.2010.01550.x>,
        Haned et al. (2012) <doi:10.1016/j.fsigen.2012.11.002> and
        Gill & Haned (2013) <doi:10.1016/j.fsigen.2012.08.008>.
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 throughout the U.S. 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 country. The 'forested' package contains data
    frames 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. Currently, the package provides data for
    Washington and Georgia.
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.
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.
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>).
Interface for accessing the 'Fortnite Ecosystem' API, allowing users
    to retrieve island metadata and engagement metrics. The package provides functions
    to search for 'Fortnite Creative' islands, retrieve detailed metadata about specific
    islands including titles, descriptions, and tags, and access engagement metrics
    such as daily active users and play duration. It supports pagination for large
    result sets and time-series analysis of island performance. The API endpoint is
    <https://api.fortnite.com/ecosystem/v1>.
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.
Play or simulate games of "Four in a Row" in the R console. This 
    package is designed for educational purposes, encouraging users to write
    their own functions to play the game automatically. It contains a 
    collection of built-in functions that play the game at various skill levels,
    for users to test their own functions against.
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>.
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. (2025) <doi:10.1016/j.physa.2024.130308>.
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.