Implementation of a probabilistic method for biclustering
    adapted to overdispersed count data. It is a Gamma-Poisson Latent
    Block Model.  It also implements two selection criteria in order to
    select the number of biclusters.
Qualitatively Constrained (Regression) Smoothing Splines via
        Linear Programming and Sparse Matrices.
Contains the R functions needed to perform Cluster-Of-Clusters Analysis (COCA)  and Consensus Clustering (CC). For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.
A 'shiny' app to discover cocktails. The
    app allows one to search for cocktails by ingredient,
    filter on rating, and number of ingredients. The
    package also contains data with the ingredients of
    nearly 26 thousand cocktails scraped from the web.
A copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2016). "Clustering dependent observations with copula functions". Statistical Papers, p.1-17. <doi:10.1007/s00362-016-0822-3>.
Useful tools for fitting, validating, and forecasting of practical convolution-closed time series models for low counts are provided. Marginal distributions of the data can be modeled via Poisson and Generalized Poisson innovations. Regression effects can be modelled via time varying innovation rates. The models are described in Jung and Tremayne (2011) <doi:10.1111/j.1467-9892.2010.00697.x> and the model assessment tools are presented in Czado et al. (2009) <doi:10.1111/j.1541-0420.2009.01191.x>, Gneiting and Raftery (2007) <doi:10.1198/016214506000001437> and, Tsay (1992) <doi:10.2307/2347612>.
Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data.
Statistical tests for the comparison between two correlations based on either independent or dependent groups. Dependent correlations can either be overlapping or nonoverlapping. A web interface is available on the website <http://comparingcorrelations.org>. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from <https://rkward.kde.org> to use this feature. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard.
Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.
Provides a set of functions to implement the Combined Compromise Solution (CoCoSo) Method created by Yazdani, Zarate, Zavadskas and Turskis (2019) <doi:10.1108/MD-05-2017-0458>. This method is based on an integrated simple additive weighting and compromise exponentially weighted product model.
A common misconception is that the Hochberg procedure comes up with adequate overall type I error control when test statistics are positively correlated. However, unless the test statistics follow some standard distributions, the Hochberg procedure requires a more stringent positive dependence assumption, beyond mere positive correlation, to ensure valid overall type I error control. To fill this gap, we formulate statistical tests grounded in rank correlation coefficients to validate fulfillment of the positive dependence through stochastic ordering (PDS) condition.
    See Gou, J., Wu, K. and Chen, O. Y. (2024). Rank correlation coefficient based tests on positive dependence through stochastic ordering with application in cancer studies, Technical Report.
Statistical tests for the comparison between two or more alpha
    coefficients based on either dependent or independent groups of individuals.
    A web interface is available at http://comparingcronbachalphas.org. A plugin
    for the R GUI and IDE RKWard is included. Please install RKWard from https://
    rkward.kde.org to use this feature. The respective R package 'rkward' cannot be
    installed directly from a repository, as it is a part of RKWard.
Provides functions for summarizing and plotting the
	output from Markov Chain Monte Carlo (MCMC) simulations, as
	well as diagnostic tests of convergence to the equilibrium
	distribution of the Markov chain.
A minimum set of functions to perform compositional data analysis
    using the log-ratio approach introduced by John Aitchison (1982). Main functions
    have been implemented in c++ for better performance.
Functions for microbiome data analysis that take into account its compositional nature. Performs variable selection through penalized regression for both, cross-sectional and longitudinal studies, and for binary and continuous outcomes.
In the context of high-throughput genetic data,
    CoDaCoRe identifies a set of sparse biomarkers that are
    predictive of a response variable of interest (Gordon-Rodriguez 
    et al., 2021) <doi:10.1093/bioinformatics/btab645>. More 
    generally, CoDaCoRe can be applied to any regression problem 
    where the independent variable is Compositional (CoDa), to 
    derive a set of scale-invariant log-ratios (ILR or SLR) that 
    are maximally associated to a dependent variable.
Provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) <doi:10.1016/j.csda.2024.107945>.
    The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities.
    These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares.
    'CoDaImpact' is designed to be used with the 'compositions' package and its ecosystem.
Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2021) <doi:10.1111/biom.13465>
    for transformation-free linear regression for compositional outcomes and predictors.
Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) <doi:10.1177/0962280217710835> and Dumuid et al. (2017b) <doi:10.1177/0962280217737805>.
Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.
Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis.
Easily automate the following tasks to describe data frames:
		Summarise the distributions, and labelled missings of variables graphically
		and using descriptive statistics.
		For surveys, compute and summarise reliabilities (internal consistencies, 
		retest, multilevel) for psychological scales.
		Combine this information with metadata (such as item labels and labelled 
		values) that is derived from R attributes.
		To do so, the package relies on 'rmarkdown' partials, so you can generate 
		HTML, PDF, and Word documents. 
		Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so
		that search engines can find your data and index the metadata.
		The metadata are also available at your fingertips via RStudio Addins.
Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame.
Logic game in the style of the early 1980s home computers 
    that can be played in the R console. This game is inspired by 
    Mastermind, a game that became popular in the 1970s.
    Can you break the code?
Includes several classifications such as International Statistical Classification of  Diseases and Related Health Problems 10th Revision (ICD10),  Anatomical Therapeutic Chemical (ATC) Classification,  The International Classification of Diseases for Oncology (ICD-O-3), and  International Classification of Primary Care (ICPC). Includes function that adds descriptive label to code value.  Depending on classification following languages are available: English, Finnish, Swedish, and Latin.
Data analysis frequently requires coding, in particular when data are collected by interviews, by observations or even by questionnaires. Therefore, code counting and data preparation are necessary phases to carry out the analysis. Thus, the analysts will wish to count the codes inserted in a text (tokenization and counting of a list of pre-established codes) and to carry out the preparation of the data (feature scaling min-max normalization, Zscore, Box and Cox transformation, non parametric bootstrap). For Box and Cox (1964) <https://www.jstor.org/stable/2984418> transformation, optimal Lambda is calculated by log-likelihood. Non parametric bootstrap is based on randomly sampling data with replacement. Package for educational purposes.
Generate a candidate code list for the Observational Medical Outcomes Partnership (OMOP) common data model based on string matching. For a given search strategy, a candidate code list will be returned.
The 'Codemeta' Project defines a 'JSON-LD' format
    for describing software metadata, as detailed at
    <https://codemeta.github.io>. This package provides core utilities
    to generate this metadata with a minimum of dependencies.
The 'Codemeta' Project defines a 'JSON-LD' format
    for describing software metadata, as detailed at
    <https://codemeta.github.io>. This package provides utilities to
    generate, parse, and modify 'codemeta.json' files automatically for R
    packages, as well as tools and examples for working with
    'codemeta.json' 'JSON-LD' more generally.
This creates code names that a user can consider for their organizations, their projects, themselves, people
    in their organizations or projects, or whatever else. The user can also supply a numeric seed (and even a character seed)
    for maximum reproducibility. Use is simple and the code names produced come in various types too, contingent on what the
    user may be desiring as a code name or nickname.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
Fast categorization of items based on external code data identified by 
  regular expressions. A typical use case considers patient with medically coded 
  data, such as codes from the International Classification of Diseases ('ICD') or 
  the Anatomic Therapeutic Chemical ('ATC') classification system. 
  Functions of the package relies on a triad of objects: (1) case data with unit 
  id:s and possible dates of interest; (2) external code data for corresponding 
  units in (1) and with optional dates of interest and; (3) a classification 
  scheme ('classcodes' object) with regular expressions to identify and 
  categorize relevant codes from (2). 
  It is easy to introduce new classification schemes ('classcodes' objects) or  
  to use default schemes included in the package. Use cases includes patient 
  categorization based on 'comorbidity indices' such as 'Charlson', 'Elixhauser', 
  'RxRisk V', or the 'comorbidity-polypharmacy' score (CPS), as well as adverse 
  events after hip and knee replacement surgery.
Predicts 3 to 12 months prognosis in Chronic Obstructive Pulmonary Disease (COPD) patients hospitalized for severe exacerbations, as described in Almagro et al. (2014) <doi:10.1378/chest.13-1328>.
Proposes Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence.
Augment clinical data with metadata to create
    output used in conventional publications and reports.
Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.
A collection of coding functions as alternatives to the standard
    functions in the stats package, which have names starting with 'contr.'.  Their
    main advantage is that they provide a consistent method for defining marginal
    effects in factorial models.  In a simple one-way ANOVA model the
    intercept term is always the simple average of the class means.
Univariate and multivariate temporal and spatial diversity indices, 
    rank abundance curves, and community stability measures. The functions 
    implement measures that are either explicitly temporal and include the 
    option to calculate them over multiple replicates, or spatial and include 
    the option to calculate them over multiple time points. Functions fall into 
    five categories: static diversity indices, temporal diversity indices, 
    spatial diversity indices, rank abundance curves, and community stability 
    measures. The diversity indices are temporal and spatial analogs to 
    traditional diversity indices. Specifically, the package includes functions 
    to calculate community richness, evenness and diversity at a given point in 
    space and time. In addition, it contains functions to calculate species 
    turnover, mean rank shifts, and lags in community similarity between two 
    time points. Details of the methods are available in
    Hallett et al. (2016) <doi:10.1111/2041-210X.12569> and Avolio 
    et al. (2019) <doi:10.1002/ecs2.2881>.
Provide a series of functions to conduct a meta analysis of 
    factor analysis based on co-occurrence matrices. The tool can be used to 
    solve the factor structure (i.e. inner structure of a construct, or scale) 
    debate in several disciplines, such as psychology, psychiatry, management, 
    education so on. References: Shafer (2005) <doi:10.1037/1040-3590.17.3.324>; 
    Shafer (2006) <doi:10.1002/jclp.20213>; Loeber and Schmaling (1985) <doi:10.1007/BF00910652>.
Cronbach's alpha and McDonald's omega are widely used reliability or internal consistency measures in social, behavioral and education sciences. Alpha is reported in nearly every study that involves measuring a construct through multiple test items. The package 'coefficientalpha' calculates coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Since Version 0.5, the bootstrap confidence intervals were added.
Plots the coefficients from model objects. This very quickly shows the user the point estimates and confidence intervals for fitted models.
Simulate species occurrence and abundances (counts) along
    gradients.
Simulates the composition of samples of vegetation
        according to gradient-based vegetation theory.  Features a
        flexible algorithm incorporating competition and complex
        multi-gradient interaction.
Contrast analysis for factorial designs is an alternative to the classical ANOVA approach with the advantage of testing focused hypothesis. The method is mainly based on Rosenthal, Rosnow and Rubin (2000, ISBN:978-0521659802) and Sedlmeier and Renkewitz (2018, ISBN:978-3868943214).
Generate cofeature (feature by sample) matrices. The package 
  utilizes ggplot2::geom_tile() to generate the matrix allowing for easy
  additions from the base matrix.
While individual calibrated radiocarbon dates can span several centuries, combining multiple dates together with any chronological constraints can make a chronology much more robust and precise. This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 <doi:10.11141/ia.13.2>. Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 <doi:10.1017/S0003598X00080534>; Nicholls and Jones 2001 <doi:10.1111/1467-9876.00250>).
Calculates complete functional regulation analysis and visualize
    the results in a single heatmap. The provided example data is for biological
    data but the methodology can be used for large data sets to compare quantitative
    entities that can be grouped. For example, a store might divide entities into
    cloth, food, car products etc and want to see how sales changes in the groups
    after some event. The theoretical background for the calculations are provided
    in New insights into functional regulation in MS-based drug profiling, Ana Sofia
    Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports <doi:10.1038/srep18826>.
Evaluation for density and distribution function of convolution of gamma
    distributions in R. Two related exact methods and one approximate method are
    implemented with efficient algorithm and C++ code. A quick guide for choosing
    correct method and usage of this package is given in package vignette. For the
    detail of methods used in this package, we refer the user to
    Mathai(1982)<doi:10.1007/BF02481056>,
    Moschopoulos(1984)<doi:10.1007/BF02481123>,
    Barnabani(2017)<doi:10.1080/03610918.2014.963612>,
    Hu et al.(2020)<doi:10.1007/s00180-019-00924-9>.
Reconstruct networks from multi-omics data sets with the
    collaborative graphical lasso (coglasso) algorithm described in Albanese, A.,
    Kohlen, W., and Behrouzi, P. (2024) <arXiv:2403.18602>. Build multiple networks
    using the coglasso() function, select the best one with stars_coglasso().
Functions for building cognitive maps based on qualitative data. Inputs are textual sources (articles, transcription of qualitative interviews of agents,...). These sources have been coded using relations and are linked to (i) a table describing the variables (or concepts) used for the coding and (ii) a table describing the sources (typology of agents, ...). Main outputs are Individual Cognitive Maps (ICM), Social Cognitive Maps (all sources or group of sources) and a list of quotes linked to relations. This package is linked to the work done during the PhD of Frederic M. Vanwindekens (CRA-W / UCL) hold the 13 of May 2014 at University of Louvain in collaboration with the Walloon Agricultural Research Centre (project MIMOSA, MOERMAN fund).
Provides authentication for Shiny applications using 'Amazon Cognito' ( <https://aws.amazon.com/es/cognito/>).
Computing Cohen's d_p in any experimental designs (between-subject, within-subject, and single-group design). Cousineau (2022) <https://github.com/dcousin3/CohensdpLibrary>; Cohen (1969, ISBN: 0-8058-0283-5).
This software tool is designed to generate new cohorts utilizing data from 
    previously instantiated cohorts. It employs interval algebra operators such as UNION, 
    INTERSECT, and MINUS to manipulate the data within the instantiated cohorts and 
    create new cohorts.
Common API for filtering data stored in different data models.
    Provides multiple filter types and reproducible R code.
    Works standalone or with 'shinyCohortBuilder' as the GUI for interactive Shiny apps.
This software tool is designed to extract data from a randomized subset of individuals 
    within a cohort and make it available for exploration in a shiny application environment. It 
    retrieves date-stamped, event-level records from one or more data sources that represent patient 
    data in the Observational Medical Outcomes Partnership (OMOP) data model format. This tool 
    features a user-friendly interface that enables users to efficiently explore the extracted 
    profiles, thereby facilitating applications, such as reviewing structured profiles.
A collection of functions dedicated to simulating staggered entry platform 
    trials whereby the treatment under investigation is a combination of two 
    active compounds. In order to obtain approval for this combination therapy,
    superiority of the combination over the two active compounds and superiority
    of the two active compounds over placebo need to be demonstrated. 
    A more detailed description of the design can be found in 
    Meyer et al. <DOI:10.1002/pst.2194> and a manual in Meyer et al. <arXiv:2202.02182>.
Functions to simplify the process of preparing event and transaction for cohort analysis.
Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts.
Functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes 'Epi' package 'Lexis' data.
Designed for the cleaning, contextualization and assessment of cytochrome c 
    oxidase I DNA barcode data (COI-5P, or the five prime portion of COI). It contains 
    functions for placing COI-5P barcode sequences into a common reading frame, 
    translating DNA sequences to amino acids and for assessing the likelihood that a 
    given barcode sequence includes an insertion or deletion error. The error assessment 
    relies on the comparison of input sequences against nucleotide and amino acid profile
    hidden Markov models (PHMMs) (for details see Durbin et al. 1998, ISBN: 9780521629713) 
    trained on a taxonomically diverse set of reference sequences. The functions are 
    provided as a complete pipeline and are also available individually for efficient and
    targeted analysis of barcode data.
Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications.
Conditional inference procedures for the general independence
             problem including two-sample, K-sample (non-parametric ANOVA),
             correlation, censored, ordered and multivariate problems described
             in <doi:10.18637/jss.v028.i08>.
Extract and monitor price and market cap of 'Cryptocurrencies' from 'Coin Market Cap' <https://coinmarketcap.com/api/>.
Builds the 
  coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016) 
  <doi:10.1016/j.spl.2015.11.008>.
  This methodology studies the relationship between a couple of
  time series based on the the set of turning points of each
  time series. The coincident profile establishes if two time
  series are coincident, or one of them leads the second.
A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment"
    for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation,
    data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for
    principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single
    hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons.
    It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.
We propose a consistent monitoring procedure to detect a
    structural change from a cointegrating relationship to a spurious
    relationship. The procedure is based on residuals from modified least
    squares estimation, using either Fully Modified, Dynamic or Integrated
    Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in
    that it is based on parameter estimation on a pre-break "calibration" period
    only, rather than being based on sequential estimation over the full sample.
    See the discussion paper <DOI:10.2139/ssrn.2624657> for further information.
    This package provides the monitoring procedures for both the cointegration
    and the stationarity case (while the latter is just a special case of the
    former one) as well as printing and plotting methods for a clear
    presentation of the results.
Cointegration methods are widely used in empirical macroeconomics
    and empirical finance. It is well known that in a cointegrating
    regression the ordinary least squares (OLS) estimator of the
    parameters is super-consistent, i.e. converges at rate equal to the
    sample size T. When the regressors are endogenous, the limiting
    distribution of the OLS estimator is contaminated by so-called second
    order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>.
    The presence of these bias terms renders inference difficult. Consequently,
    several modifications to OLS that lead to zero mean Gaussian mixture
    limiting distributions have been proposed, which in turn make
    standard asymptotic inference feasible. These methods include
    the fully modified OLS (FM-OLS) approach of Phillips and Hansen
    (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips
    and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991)
    <DOI:10.1017/S0266466600004217> and Stock and Watson (1993)
    <DOI:10.2307/2951763> and the new estimation approach called integrated
    modified OLS (IM-OLS) of Vogelsang and Wagner (2014)
    <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented
    partial sum (integration) transformation of the regression model. IM-OLS is
    similar in spirit to the FM- and D-OLS approaches, with the key difference
    that it does not require estimation of long run variance matrices and avoids
    the need to choose tuning parameters (kernels, bandwidths, lags). However,
    inference does require that a long run variance be scaled out.
    This package provides functions for the parameter estimation and inference
    with all three modified OLS approaches. That includes the automatic
    bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and
    of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of
    the long run variance.
Performs regression analysis for longitudinal count data,  
   allowing for serial dependence among observations from a given 
   individual and two dimensional random effects on the linear predictor. 
   Estimation is via maximization of the exact likelihood of a suitably 
   defined model. Missing values and unbalanced data are allowed. 
   Details can be found in the accompanying scientific papers: 
   Goncalves & Cabral (2021, Journal of Statistical Software, 
   <doi:10.18637/jss.v099.i03>) and Goncalves et al. 
   (2007, Computational Statistics & Data Analysis, 
   <doi:10.1016/j.csda.2007.03.002>).
Performs least squares constrained optimization on a linear objective function. It contains
    a number of algorithms to choose from and offers a formula syntax similar to lm().
A C/C++ based package for advanced data transformation and 
    statistical computing in R that is extremely fast, class-agnostic, robust and 
    programmer friendly. Core functionality includes a rich set of S3 generic grouped 
    and weighted statistical functions for vectors, matrices and data frames, which 
    provide efficient low-level vectorizations, OpenMP multithreading, and skip missing 
    values by default. These are integrated with fast grouping and ordering algorithms 
    (also callable from C), and efficient data manipulation functions. The package also 
    provides a flexible and rigorous approach to time series and panel data in R. 
    It further includes fast functions for common statistical procedures, detailed 
    (grouped, weighted) summary statistics, powerful tools to work with nested data, 
    fast data object conversions, functions for memory efficient R programming, and 
    helpers to effectively deal with variable labels, attributes, and missing data. 
    It is well integrated with base R classes, 'dplyr'/'tibble', 'data.table', 'sf', 
    'plm' (panel-series and data frames), and 'xts'/'zoo'.
Contains functions to help in selecting and exploring features ( or variables ) in binary classification problems.
             Provides functions to compute and display information value and weight of evidence (WoE) of the variables , and to convert numeric variables to categorical variables by binning.
             Functions are also provided  to determine which levels ( or categories ) of a categorical variable can be collapsed (or combined ) based on their response rates.
             The functions provided only work for binary classification problems.
Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it.
    Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame.
    See 'collapsibleTree' website for more information and examples.
Map functions while capturing results, errors, warnings, messages and other output tidily, then filter and summarise data frames or lists on the basis of those side effects.
Provides the basic functionality to interact with the Collatz conjecture.
    The parameterisation uses the same (P,a,b) notation as Conway's generalisations.
    Besides the function and reverse function, there is also functionality to retrieve
    the hailstone sequence, the "stopping time"/"total stopping time", or tree-graph. 
    The only restriction placed on parameters is that both P and a can't be 0.
    For further reading, see <https://en.wikipedia.org/wiki/Collatz_conjecture>.
Provides high performance container data types such
    as queues, stacks, deques, dicts and ordered dicts. Benchmarks
    <https://randy3k.github.io/collections/articles/benchmark.html> have
    shown that these containers are asymptotically more efficient than
    those offered by other packages.
Check for namespace collisions between a string input (your function or package name) 
    and half a million packages and functions on CRAN.
Tool to assessing whether the results of a study could be influenced by
    collinearity. Simulations under a given hypothesized truth regarding effects of an
    exposure on the outcome are used and the resulting curves of lagged effects are
    visualized. A user's manual is provided, which includes detailed examples (e.g. a
    cohort study looking for windows of vulnerability to air pollution, a time series
    study examining the linear association of air pollution with hospital admissions,
    and a time series study examining the non-linear association between temperature and
    mortality). The methods are described in Basagana and Barrera-Gomez (2021) <doi:10.1093/ije/dyab179>.
System for seamless management of multicollinearity in data frames
    with numeric and/or categorical variables for statistical analysis and 
    machine learning modeling. The package combines bivariate correlation 
    (Pearson, Spearman, and Cramer's V) with variance inflation factor analysis, 
    target encoding to transform categorical variables into numeric (Micci-Barreca, D. 2001 
    <DOI:10.1145/507533.507538>), and a flexible 
    feature prioritization method, to deliver a comprehensive 
    multicollinearity management tool covering a wide range of use cases.
These functions implement collocation-inference
    for continuous-time and discrete-time stochastic processes.
    They provide model-based smoothing, gradient-matching,
    generalized profiling and forwards prediction error methods.
Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.
Provides some low level functions for processing PLINK input and output files.
Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21.
Performs the colocalisation tests described in
    Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>,
    Wallace (2020) <doi:10.1371/journal.pgen.1008720>,
    Wallace (2021) <doi:10.1371/journal.pgen.1009440>.
Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearson’s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.
Also abbreviates to "CCSeq". Finds clusters of colocalized sequences in .bed annotation files
    up to a specified cut-off distance. Two sequences are colocalized if they
    are within the cut-off distance of each other, and clusters are sets of
    sequences where each sequence is colocalized to at least one other sequence
    in the cluster. For a set of .bed annotation tables provided in a list 
    along with a cut-off distance, the program will output a file containing
    the locations of each cluster. Annotated .bed files are from 
    the 'pwmscan' application at <https://ccg.epfl.ch/pwmtools/pwmscan.php>. 
    Personal machines might crash or take excessively long depending 
    on the number of annotated sequences in each file and whether chromsearch() 
    or gensearch() is used.
Automate the co-localization analysis of fluorescence microscopy 
  images. Selecting regions of interest, extract pixel intensities from 
  the image channels and calculate different co-localization statistics. The
  methods implemented in this package are based on Dunn et al. (2011) 
  <doi:10.1152/ajpcell.00462.2010>.
Compare color palettes with simulations of color vision deficiencies - deuteranopia, protanopia, and tritanopia.
        It includes calculation of distances between colors, and creating summaries of differences between a color palette and simulations of color vision deficiencies.
        This work was inspired by the blog post at <http://www.vis4.net/blog/2018/02/automate-colorblind-checking/>.
Provide the safe color set for color blindness, 
         the simulator of protanopia, deuteranopia.
         The color sets are collected from:
         Wong, B. (2011) <doi:10.1038/nmeth.1618>, and
         <http://mkweb.bcgsc.ca/biovis2012/>.
         The simulations of the appearance of the colors to color-deficient
         viewers were based on algorithms in
         Vienot, F., Brettel, H. and Mollon, J.D. (1999) 
         <doi:10.1002/(SICI)1520-6378(199908)24:4%3C243::AID-COL5%3E3.0.CO;2-3>.
         The cvdPlot() function to generate 'ggplot' grobs of simulations 
         were modified from <https://github.com/clauswilke/colorblindr>.
Colorful Data Frames in the terminal. The new class does
             change the behaviour of any of the objects, but adds a style
             definition and a print method. Using ANSI escape codes, it
             colors the terminal output of data frames. Some column types
             (such as p-values and identifiers) are automatically
             recognized.
Loads and displays images, selectively masks specified background
    colors, bins pixels by color using either data-dependent or
    automatically generated color bins, quantitatively measures color
    similarity among images using one of several distance metrics for
    comparing pixel color clusters, and clusters images by object color
    similarity. Uses CIELAB, RGB, or HSV color spaces. Originally written
    for use with organism coloration (reef fish color diversity, butterfly
    mimicry, etc), but easily applicable for any image set.
Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one.
    This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation).  The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.
Extracts colors from various image types, returns customized reports and plots treemaps 
    and 3D scatterplots of image compositions. Color palettes can also be created.
Build dendrograms with sample groups highlighted by different colors. Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters.
Color and visualize wildlife distributions in
    space-time using raster data. In addition to enabling display of
    sequential change in distributions through the use of small multiples,
    'colorist' provides functions for extracting several features of
    interest from a sequence of distributions and for visualizing those
    features using HCL (hue-chroma-luminance) color palettes. Resulting
    maps allow for "fair" visual comparison of intensity values (e.g.,
    occurrence, abundance, or density) across space and time and can be
    used to address questions about where, when, and how consistently a
    species, group, or individual is likely to be found.
Call the 'DeOldify' <https://github.com/jantic/DeOldify> image
    colorization API on 'DeepAI'<https://deepai.org/machine-learning-model/colorizer>
    to colorize black and white images.
Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'.
A tool for transforming coordinates in a color space to common
    color names using data from the Royal Horticultural Society and the
    International Union for the Protection of New Varieties of Plants.
Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values 
    within a single diagram. This is especially useful for rendering gene expression data as well as
    other types of differential experiments. In addition to different rendering methods (ggplot extensions)
    functionality for perceptually optimizing color palettes are provided.
    Furthermore the package provides extension methods of the colorspace color-class in order to
    simplify the work with palettes (a.o. length, as.list, and append are supported).
A simple set of classes and methods for mapping between scalar intensity values and colors. There is also support for layering maps on top of one another using alpha composition.
Color palettes for EPL, MLB, NBA, NHL, and NFL teams.
A color mapping is generated according to the break values and corresponding colors.
    Other colors are generated by interpolating in a certain color space. The functions were part
    of the 'circlize' package <https://CRAN.R-project.org/package=circlize>.
Generate multivariate color palettes to represent two-dimensional or three-dimensional data in graphics (in contrast to standard color palettes that represent just one variable). You tell 'colors3d' how to map color space onto your data, and it gives you a color for each data point. You can then use these colors to make plots in base 'R', 'ggplot2', or other graphics frameworks.
Methods and data for color science - color conversions by observer,
        illuminant, and gamma. Color matching functions and chromaticity diagrams.
        Color indices, color differences, and spectral data conversion/analysis.
Generation of random colors, possibly with a given hue or a
    given luminosity. This is a port of the JavaScript library
    'randomColor' <https://randomcolor.lllllllllllllllll.com/>.
Carries out mapping between assorted color spaces including RGB, HSV, HLS,
             CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB.
	     Qualitative, sequential, and diverging color palettes based on HCL colors
	     are provided along with corresponding ggplot2 color scales.
	     Color palette choice is aided by an interactive app (with either a Tcl/Tk
	     or a shiny graphical user interface) and shiny apps with an HCL color picker and a
	     color vision deficiency emulator. Plotting functions for displaying
	     and assessing palettes include color swatches, visualizations of the
	     HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation
	     functions include: desaturation, lightening/darkening, mixing, and
	     simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly).
	     Details can be found on the project web page at <https://colorspace.R-Forge.R-project.org/>
	     and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical
	     Software, <doi:10.18637/jss.v096.i01>).
Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners.
    Build complex systems from simpler parts using a spectral product algebra. For light sources,
    compute CCT, CRI, and SSI.  For object colors, compute optimal colors and Logvinenko coordinates.
    Work with the standard CIE illuminants and color matching functions, and read spectra from 
    text files, including CGATS files.  Estimate a spectrum from its response. A user guide and 9 vignettes are included.
Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Grey competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grant 80NSSC19M0161 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.
Can take in images in either .jpg, .jpeg, or .png format and creates a colour palette of the most frequent colours used in the image. Also provides some custom colour palettes.
Provides access to the COLOURlovers <https://www.colourlovers.com/>
    API, which offers color inspiration and color palettes.
A colour picker that can be used as an input in 'Shiny' apps
    or Rmarkdown documents. The colour picker supports alpha opacity, custom
    colour palettes, and many more options. A Plot Colour Helper tool is
    available as an 'RStudio' Addin, which helps you pick colours to use in your
    plots. A more generic Colour Picker 'RStudio' Addin is also provided to let 
    you select colours to use in your R code.
Maps one of the viridis colour palettes, or a user-specified palette to values. 
    Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, 
    and were set as the default palette for the 'Python' 'Matplotlib' library <https://matplotlib.org/>. 
    Other palettes available in this library have been derived from 
    'RColorBrewer' <https://CRAN.R-project.org/package=RColorBrewer> and 
    'colorspace' <https://CRAN.R-project.org/package=colorspace> packages.
Colour vision models, colour spaces and colour thresholds. Provides flexibility to build user-defined colour vision models for n number of photoreceptor types. Also includes Vorobyev & Osorio (1998) Receptor Noise Limited models <doi:10.1098/rspb.1998.0302>, Chittka (1992) colour hexagon <doi:10.1007/BF00199331>, and Endler & Mielke (2005) model <doi:10.1111/j.1095-8312.2005.00540.x>. Models have been extended to accept any number of photoreceptor types.
Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) <arXiv:2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".
Constrained ordinary least squares is performed. One constraint is that all beta coefficients (including the constant) cannot be negative. They can be either 0 or strictly positive. Another constraint is that the sum of the beta coefficients equals a constant. References: Hansen, B. E. (2022). Econometrics, Princeton University Press. <ISBN:9780691235899>.
Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes bivariate color palettes. Furthermore, a color for missing values is assigned to each palette.
A collection of command-line color styles based on the 'crayon'
  package. 'Colt' styles are defined in themes that can easily be switched, to
  ensure command line output looks nice on dark as well as light consoles.
Builds co-occurrence matrices based on spatial raster data.
    It includes creation of weighted co-occurrence matrices (wecoma) and 
    integrated co-occurrence matrices 
    (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).
Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps).
Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal.  Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>.
Fit and apply ComBat to harmonize magnetic resonance imaging (MRI) data from different sites. Briefly, ComBat is a batch adjustment method that removes additive and multiplicative differences between sites due to the use of different scanning devices. As detailed in the manual, the original function was first modified for the harmonization of MRI data (Fortin et al. (2017) <doi:10.1016/j.neuroimage.2017.11.024>) and then modified again to create separate functions for fitting and applying the harmonization and allow missing values and constant rows for its use within the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium (Radua et al. (2020) <doi:10.1016/j.neuroimage.2017.11.024>). This package includes the latter version.
Provides access to six fundamental statistics that can be used for the purpose of combination p-values. All methods used can referenced here: Heard & Rubin-Delanchy (2017) <arXiv:1707.06897>.
Determining the value of Stirling numbers of 1st kind and 2nd kind,references: Bóna,Miklós(2017,ISBN 9789813148840).
Includes functions to calculate scores and marks for track and
    field combined events competitions. The functions are based on the scoring
    tables for combined events set forth by the International Association of 
    Athletics Federation (2001).
Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested.
There are several non-functional-form-based interaction tests for testing interaction in unreplicated two-way layouts. However, no single test can detect all patterns of possible interaction and the tests are sensitive to a particular pattern of interaction. This package combines six non-functional-form-based interaction tests for testing additivity. These six tests were proposed by Boik (1993) <doi:10.1080/02664769300000004>, Piepho (1994), Kharrati-Kopaei and Sadooghi-Alvandi (2007) <doi:10.1080/03610920701386851>, Franck et al. (2013) <doi:10.1016/j.csda.2013.05.002>, Malik et al. (2016) <doi:10.1080/03610918.2013.870196> and Kharrati-Kopaei and Miller (2016) <doi:10.1080/00949655.2015.1057821>. The p-values of these six tests are combined by Bonferroni, Sidak, Jacobi polynomial expansion, and the Gaussian copula methods to provide researchers with a testing approach which leverages many existing methods to detect disparate forms of non-additivity. This package is based on the following published paper: Shenavari and Kharrati-Kopaei (2018) "A Method for Testing Additivity in Unreplicated Two-Way Layouts Based on Combining Multiple Interaction Tests". In addition, several sentences in help files or descriptions were copied from that paper.
Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design.
Provides functions and a workflow to easily and powerfully calculating specificity, sensitivity and ROC curves of biomarkers combinations. Allows to rank and select multi-markers signatures as well as to find the best performing sub-signatures, now also from single-cell RNA-seq datasets. The method used was first published as a Shiny app and described in Mazzara et al. (2017) <doi:10.1038/srep45477> and further described in Bombaci & Rossi (2019) <doi:10.1007/978-1-4939-9164-8_16>, and widely expanded as a package as presented in the bioRxiv pre print Ferrari et al. <doi:10.1101/2022.01.17.476603>.
Provides iterators for combinations, permutations, subsets, and
    Cartesian product, which allow one to go through all elements without creating a
    huge set of all possible values.
Use frequentist and Bayesian methods to estimate parameters from a
  binary outcome misclassification model. These methods correct for the problem
  of "label switching" by assuming that correct outcome classification occurs
  in at least 50% of observations. A description of the analysis methods is
  available in Hochstedler and Wells (2023) <arXiv:2303.10215>.
Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.
An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.
A convenient 'R' wrapper to the 'Comet' API, which is a cloud
    platform allowing you to track, compare, explain and optimize machine
    learning experiments and models. Experiments can be viewed on the 'Comet'
    online dashboard at <https://www.comet.com>.
Covariance measure tests for conditional independence testing 
    against conditional covariance and nonlinear conditional mean alternatives.
    Contains versions of the generalised covariance measure test 
    (Shah and Peters, 2020, <doi:10.1214/19-aos1857>) and projected covariance 
    measure test (Lundborg et al., 2023, <doi:10.48550/arXiv.2211.02039>). 
    Applications can be found in 
    Kook and Lundborg (2024, <doi:10.48550/arXiv.2402.14416>).
Calculation of various common and less common comfort indices such as predicted mean vote or the two node model. Converts physical variables such as relative to absolute humidity and evaluates the performance of comfort indices.
A set of common functions to be used for displaying messages, checking variables, 
  finding absolute paths, starting applications, etc. More functions will be added later.
Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.
Posterior inference under the convex mixture regression (CoMiRe) models introduced by Canale, Durante, and Dunson (2018) <doi:10.1111/biom.12917>.
Bayesian fit of a Dirichlet Process Mixture with hierarchical multivariate skew normal kernels and coarsened posteriors. For more information, see Gorsky, Chan and Ma (2020) <arXiv:2001.06451>.
Provides the "comma-free call" operator: '%(%'. Use it to call a
    function without commas between the arguments. Just replace the '('
    with '%(%' in a function call, supply your arguments as standard R
    expressions enclosed by '{ }', and be free of commas (for that call).
Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).
Contains functions for solving commonly encountered problems while
    programming in R. This package is intended to provide a lightweight 
    supplement to Base R, and will be useful for almost any R user.
Generates the scripts required to create an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database and associated documentation for supported database platforms. Leverages the 'SqlRender' package to convert the Data Definition Language (DDL) script written in parameterized Structured Query Language (SQL) to the other supported dialects.
Useful libraries for building a Java based GUI under R are provided.
The CommonMark specification defines a rationalized version of markdown
    syntax. This package uses the 'cmark' reference implementation for converting
    markdown text into various formats including html, latex and groff man. In
    addition it exposes the markdown parse tree in xml format. Also includes opt-in
    support for GFM extensions including tables, autolinks, and strikethrough text.
Estimate bivariate common mean vector under copula models with known correlation. In the current version, available copulas are the Clayton, Gumbel, Frank, Farlie-Gumbel-Morgenstern (FGM), and normal copulas. See Shih et al. (2019) <doi:10.1080/02331888.2019.1581782> and Shih et al. (2021) <under review> for details under the FGM and general copulas, respectively.
'Java' JAR files for the Apache Commons Mathematics Library for use by users and other packages.
Provides fast, easy feature extraction of human speech and model estimation
	     with hidden Markov models. Flexible extraction of phonetic features and their
	     derivatives, with necessary preprocessing options like feature standardization.
	     Communication can estimate supervised and unsupervised hidden Markov models with
	     these features, with cross validation and corrections for auto-correlation in
	     features. Methods developed in Knox and Lucas (2021) <doi:10.7910/DVN.8BTOHQ>.
Computing comorbidity indices and scores such as the weighted Charlson
  score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser
  comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>)
  using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>).
  Australian and Swedish modifications of the Charlson Comorbidity Index are available
  as well (Sundararajan, 2004 <doi:10.1016/j.jclinepi.2004.03.012> and Ludvigsson, 2021 
  <doi:10.2147/CLEP.S282475>), together with different weighting algorithms for both
  the Charlson and Elixhauser comorbidity scores.
Maps of Comoro Islands. Layers include the country coastline, each island coastline and administrative regions boundaries.
Comparison of two ROC curves through the methodology proposed by Ana C. Braga.
Implements functions for comparing strings, sequences and 
    numeric vectors for clustering and record linkage applications. 
    Supported comparison functions include: generalized edit distances 
    for comparing sequences/strings, Monge-Elkan similarity for fuzzy 
    comparison of token sets, and L-p distances for comparing numeric 
    vectors. Where possible, comparison functions are implemented in 
    C/C++ to ensure good performance.
Functions to compare a model object to a comparison object.
  If the objects are not identical, the functions can be instructed to
  explore various modifications of the objects (e.g., sorting rows,
  dropping names) to see if the modified versions are identical.
Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.
Unified interface for the estimation of causal networks, including
    the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate
    additive noise model), 'bivariateCAM' (bivariate causal additive model),
    'CAM' (causal additive model) (from package 'CAM'; the package is 
    temporarily unavailable on the CRAN repository; formerly available versions 
    can be obtained from the archive), 'hiddenICP' (invariant
    causal prediction with hidden variables), 'ICP' (invariant causal prediction)
    (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence
    search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC
    Algorithm), 'FCI' (fast causal inference), 
    'RFCI' (really fast causal inference) (all from package 'pcalg') and
    regression.
It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. It also calculates the expected effect and the probability of observing the composite endpoint, among others. The methodology can be found in Bofill & Gómez (2019) <doi:10.1002/sim.8092> and Gómez & Lagakos (2013) <doi:10.1002/sim.5547>.
Compares two dataframes which have the same column
    structure to show the rows that have changed. Also gives a git style diff format
    to quickly see what has changed in addition to summary statistics.
Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the 'nimble' package or from other packages. Plug-ins for JAGS via 'rjags' and Stan via 'rstan' are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.
In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from 'AllMetrics' package. ‘input_df’ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular model’s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as ‘MIN’ and other values are denoted as ‘NA’. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as ‘MAX’ and other values are denoted as ‘NA’. ‘output_df’ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of ‘input_df’. Values in ‘output_df’ are corresponding ‘NA’, ‘MIN’ or ‘MAX’. Finally, the column containing minimum number of ‘NA’ values is denoted as the best column. ‘min_NA_col’ gives the name of the best column (model). ‘min_NA_values’ are the corresponding metrics values. ‘BestColumn_metrics’ is the data frame (dimension: 1*8) containing different metrics of the best column (model). ‘best_column_results’ is the final result (a list) containing all of these output elements. In special case, if two columns having equal 'NA', it will be checked among these two column which one is having least 'NA' in first five rows and will be inferred as the best. More details about 'AllMetrics' can be found in Garai (2023) <doi:10.13140/RG.2.2.18688.30723>.
Quickly run experiments to compare the run time and output of
    code blocks. The function mbc() can make fast comparisons of code,
    and will calculate statistics comparing the resulting outputs.
    It can be used to compare model fits to the same data or
    see which function runs faster.
    The R6 class ffexp$new() runs a function using all possible combinations
    of selected inputs. This is useful for comparing the effect of
    different parameter values. It can also run in parallel and
    automatically save intermediate results, which is very useful
    for long computations.
A standard test is observed on all specimens.  We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens.  Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control.  We treat the total sample as stratified two-phase sampling and use inverse probability weighting.  We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)).  See: Katki HA, Li Y, Edelstein DW, Castle PE.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations.
Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) <doi:10.1371/journal.pone.0116774> and Huang et al. (2020) <doi:10.1080/03610918.2020.1753075>, fixed-point tests in Klein et al. (2007) <doi:10.1002/sim.2864>, short-term tests, and long-term tests in Logan et al. (2008) <doi:10.1111/j.1541-0420.2007.00975.x>. Some commonly used descriptive statistics and plots are also included.
Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.
Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user.  Also fits the user specified distribution to a given data set.  More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017)  CompDist: Multisection composite distributions.
Compute ranking and rating based on competition
    results. Methods of different nature are implemented: with fixed
    Head-to-Head structure, with variable Head-to-Head structure and with
    iterative nature. All algorithms are taken from the book 'Who’s #1?:
    The science of rating and ranking' by Amy N. Langville and Carl D.
    Meyer (2012, ISBN:978-0-691-15422-0).
Tools for storing and managing competition results.
    Competition is understood as a set of games in which players gain some
    abstract scores.  There are two ways for storing results: in long (one
    row per game-player) and wide (one row per game with fixed amount of
    players) formats. This package provides functions for creation and
    conversion between them. Also there are functions for computing their
    summary and Head-to-Head values for players. They leverage grammar of
    data manipulation from 'dplyr'.
A graphical user interface for simulating the effects of mergers, tariffs, and quotas under an
             assortment of different economic models. The interface is powered by the 'Shiny' web application framework from 
             'RStudio'.
In computer experiments space-filling designs are having great impact. Most popularly used space-filling designs are Uniform designs (UDs), Latin hypercube designs (LHDs) etc. For further references one can see Mckay (1979) <DOI:10.1080/00401706.1979.10489755> and Fang (1980) <https://cir.nii.ac.jp/crid/1570291225616774784>. In this package, we have provided algorithms for generate efficient LHDs and UDs. Here, generated LHDs are efficient as they possess lower value of Maxpro measure, Phi_p value and Maximum Absolute Correlation (MAC) value. On the other hand, the produced UDs are having good space-filling property as they attained the lower bound of Discrete Discrepancy measure.
It is designed to streamline the process of calculating complete annual growth rates with user-friendly functions and robust algorithms. It enables researchers and analysts to effortlessly generate precise growth rate estimates for their data. For method details see, Sharma, M.K.(2013) <https://www.indianjournals.com/ijor.aspx?target=ijor:jfl&volume=26&issue=1and2&article=018>. It offers a comprehensive suite of functions and customisable parameters. Equipped to handle varying complexities in data structures. It empowers users to uncover insightful growth dynamics and make informed decisions.
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide <https://www.oecd-ilibrary.org/economics/handbook-on-constructing-composite-indicators-methodology-and-user-guide_9789264043466-en>, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.
It uses the first-order sensitivity index to measure whether the weights assigned by the creator of the composite indicator match the actual importance of the variables. Moreover, the variance inflation factor is used to reduce the set of correlated variables. In the case of a discrepancy between the importance and the assigned weight, the script determines weights that allow adjustment of the weights to the intended impact of variables. If the optimised weights are unable to reflect the desired importance, the highly correlated variables are reduced, taking into account variance inflation factor. The final outcome of the script is the calculated value of the composite indicator based on optimal weights and a reduced set of variables, and the linear ordering of the analysed objects.
It helps in development of a principal component analysis based composite index by assigning weights to variables and combining the weighted variables. For method details see Sendhil, R., Jha, A., Kumar, A. and Singh, S. (2018). <doi:10.1016/j.ecolind.2018.02.053>, and Wu, T. (2021). <doi:10.1016/j.ecolind.2021.108006>.
Retail shopping transactions for 2,469 households over one year.
    Originates from the 84.51° Complete Journey 2.0 source files 
    <https://www.8451.com/area51> which also includes useful metadata on 
    products, coupons, campaigns, and promotions.
Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.
Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'.
Providing a set of functions to easily generate and iterate complex networks.
 The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length.
 For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) <doi:10.1093/icb/icw068> and Ilany and Erol Akcay (2016) <doi:10.1101/026120>, which have inspired many methods in this package.
Extension of several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.
UpSet plots are an improvement over Venn Diagram for set overlap visualizations.
    Striving to bring the best of the 'UpSetR' and 'ggplot2', this package offers a way to create
    complex overlap visualisations, using simple and familiar tools, i.e. geoms of 'ggplot2'.
    For introduction to UpSet concept, see Lex et al. (2014) <doi:10.1109/TVCG.2014.2346248>.
Robust regression methods for compositional data.
    The distribution of the estimates can be approximated with various bootstrap
    methods. These bootstrap methods are available for the compositional as well
    as for standard robust regression estimates. This allows for direct
    comparison between them.
A suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package.
Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models
    to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The
    package provides functions for model estimation, dispersion testing, and
    diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016)
    <doi:10.1016/j.csda.2016.01.007> is also supported.
The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts.
Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>.
Provides functions for the consistent analysis of compositional 
  data (e.g. portions of substances) and positive numbers (e.g. concentrations) 
  in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions).
 Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>,
 statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>,
 Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al (2019)<DOI:10.1016/j.cmpb.2018.10.020>.
 Algorithms for generating correlated gene expressions are also available.
 Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for
 sensitivity analyses under dependent censoring (Yeh et al 2023) <DOI:10.3390/biomedicines11030797>.
Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and quantifying impacts of compound events in hydrology and climatology. Please see Hao Zengchao et al. (2019) <doi:10.1088/1748-9326/ab4df5>.
Contains the probability density function, cumulative distribution function, quantile function, and random number generator for composite and discrete composite distributions with Pareto tails. The detailed description of the methods and the applications of the methods can be found in Bowen Liu, Malwane M.A. Ananda (2023) <arXiv:2309.16443>.
Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.
Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
Provides 'Python'-style list comprehensions.
    List comprehension expressions use usual loops (for(), while() and repeat()) and
    usual if() as list producers. In many cases it gives more concise notation than
    standard "*apply + filter" strategy.
Several functions are available for calculating the most
    widely used effect sizes (ES), along with their variances, confidence
    intervals and p-values.  The output includes ES's of d (mean difference), g
    (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and
    OR (odds ratio and log odds ratio). In addition, NNT (number needed to
    treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are
    computed. This package uses recommended formulas as described in The
    Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, &
    Valentine, 2009).
Analysis of RNA crosslinking data for RNA structure prediction. The package is suitable for the analysis of RNA structure cross-linking data and chemical probing data.
Evaluates the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF), random numbers and maximum likelihood estimates (MLEs) of well-known complementary binomial-G, complementary negative binomial-G and complementary geometric-G families of distributions taking baseline models such as exponential, extended exponential,  Weibull,  extended Weibull, Fisk, Lomax, Burr-XII and Burr-X. The functions also allow computing the goodness-of-fit measures namely the Akaike-information-criterion (AIC), the Bayesian-information-criterion (BIC), the minimum value of the negative log-likelihood (-2L) function, Anderson-Darling (A) test, Cramer-Von-Mises (W) test, Kolmogorov-Smirnov test, P-value and convergence status. Moreover, some commonly used data sets from the fields of actuarial, reliability, and medical science are also provided. Related works include: 
  a) Tahir, M. H., & Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, 1-35. 
  <doi:10.1186/s40488-016-0052-1>.
Flexible framework for trait-based simulation of community assembly, where components could be replaced by user-defined function and that
             allows variation of traits within species.
Contains the basic functions to apply the unified framework
    for partitioning the drivers of stability of ecological communities. 
    Segrestin et al. (2024) <doi:10.1111/geb.13828>.
Interface with and extract data from the United Nations 'Comtrade' 
  API <https://comtradeplus.un.org/>. 'Comtrade' provides country level shipping 
  data for a variety of commodities, these functions allow for easy API query 
  and data returned as a tidy data frame.
To calculate the AQI (Air Quality Index) from pollutant concentration data.
    O3, PM2.5, PM10, CO, SO2, and NO2 are available currently.
    The method can be referenced at Environmental Protection Agency, United States as follows:
    EPA (2016) <https://www3.epa.gov/airnow/aqi-technical-assistance-document-may2016.pdf>.
Calculates the dutch air quality index (LKI). This index was 
    created on the basis of scientific studies of the health effects of air 
    pollution. From these studies it can be deduced at what concentrations a 
    certain percentage of the population can be affected. For more information 
    see: <https://www.rivm.nl/bibliotheek/rapporten/2014-0050.pdf>.
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
Concatenation of multiple sequence alignments based on a
    correspondence table that can be edited in Excel <doi:10.5281/zenodo.5130603>.
The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.
There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
Provides a function for fast computation of the connected
    components of an undirected graph (though not faster than the
    components() function of the 'igraph' package) from the edges or the
    adjacency matrix of the graph. Based on this one, a function to
    compute the connected components of a triangle 'rgl' mesh is also
    provided.
Analyzes data from a Conconi et al. (1996) <doi:10.1055/s-2007-972887>
    treadmill fitness test where speed is augmented by a constant amount every
    set number of seconds to estimate the anaerobic (lactate) threshold speed
    and heart rate. It reads a TCX file, allows optional removal observations
    from before and after the actual test, fits a change-point linear model
    where the change-point is the estimate of the lactate threshold, and plots
    the data points and fit model. Details of administering the fitness test are
    provided in the package vignette. Functions work by default for Garmin
    Connect TCX exports but may require additional data preparation for heart
    rate, time, and speed data from other sources.
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
A set of utilities for matching products in different
	     classification codes used in international trade
	     research. It supports concordance between the Harmonized
	     System (HS0, HS1, HS2, HS3, HS4, HS5, HS combined), the Standard 
	     International Trade Classification (SITC1, SITC2, SITC3, SITC4), 
	     the North American Industry Classification System (NAICS combined),
	     as well as the Broad Economic Categories (BEC), the International 
	     Standard of Industrial Classification (ISIC), and the Standard Industrial 
	     Classification (SIC). It also provides code nomenclature/descriptions 
	     look-up, Rauch classification look-up (via concordance to SITC2), and
	     trade elasticity look-up (via concordance to HS0 or SITC3
	     codes).
Lin's Concordance Correlation Coefficient (CCC) is a statistic 
  which measures the degree of agreement between two variables. The CCC is 
  useful for assessing (i) the measurement agreement between two variables 
  (typically outputs between two devices); (ii) the reproducibility between 
  two measurements obtained from the same device; and (iii) inter-rater 
  reliability. The 'concordancer' package provides a 'C++' implementation of 
  Lin's CCC via 'Rcpp'. In so doing, the ccc() function contained herein is 
  a much faster implementation than those contained in other R packages. For
  more details on Lin's CCC, please see <https://en.wikipedia.org/wiki/Concordance_correlation_coefficient>.
The Concordance Test is a non-parametric method for testing whether two o more samples originate from the same distribution. It extends the Kendall Tau correlation coefficient when there are only two groups. For details, see Alcaraz J., Anton-Sanchez L., Monge J.F. (2022) The Concordance Test, an Alternative to Kruskal-Wallis Based on the Kendall-tau Distance: An R Package. The R Journal 14, 26–53 <doi:10.32614/RJ-2022-039>.
Contains the CONCOR (CONvergence of iterated CORrelations) 
    algorithm and a series of supplemental functions for easy running, 
    plotting, and blockmodeling. The CONCOR algorithm is used on social network
    data to identify network positions based off a definition of structural 
    equivalence; see Breiger, Boorman, and Arabie (1975) 
    <doi:10.1016/0022-2496(75)90028-0> and Wasserman and Faust's book Social 
    Network Analysis: Methods and Applications (1994). This version allows 
    multiple relationships for the same set of nodes and uses both incoming and
    outgoing ties to find positions.
Based on individual market shares of all participants in a
    market or space, the package offers a set of different structural and
    concentration measures frequently - and not so frequently - used in research
    and in practice. Measures can be calculated in groups or individually.
    The calculated measure or the resulting vector in table format should help
    practitioners make more informed decisions. Methods used in this package are
    from:
    1.  Chang, E. J., Guerra, S. M., de Souza Penaloza, R. A. & Tabak, B. M.
    (2005) "Banking concentration: the Brazilian case".
    2.  Cobham, A. and A. Summer (2013). "Is It All About the Tails? The
    Palma Measure of Income Inequality".
    3.  Garcia Alba Idunate, P. (1994). "Un Indice de dominancia para el
    analisis de la estructura de los mercados".
    4.  Ginevicius, R. and S. Cirba (2009). "Additive measurement of market
    concentration" <doi:10.3846/1611-1699.2009.10.191-198>.
    5.  Herfindahl, O. C. (1950), "Concentration in the steel industry"
    (PhD thesis).
    6.  Hirschmann, A. O. (1945), "National power and structure of foreign
    trade".
    7.  Melnik, A., O. Shy, and R. Stenbacka (2008), "Assessing market dominance"
    <doi:10.1016/j.jebo.2008.03.010>.
    8.  Palma, J. G. (2006). "Globalizing Inequality: 'Centrifugal' and
    'Centripetal' Forces at Work".
    9.  Shannon, C. E. (1948). "A Mathematical Theory of Communication".
    10.  Simpson, E. H. (1949). "Measurement of Diversity"
    <doi:10.1038/163688a0>.
Implements a kernel-based association test for copy number 
  variation (CNV) aggregate analysis
  in a certain genomic region (e.g., gene set, chromosome, or genome) that is
  robust to the within-locus and across-locus etiological heterogeneity, and 
  bypass the need to define a "locus" unit for CNVs. 
  Brucker, A., et al. (2020) <doi:10.1101/666875>.
Higher order likelihood-based inference for logistic and 
  loglinear models.
Provides functions for the estimation of conditional copulas models,
    various estimators of conditional Kendall's tau
    (proposed in Derumigny and Fermanian (2019a, 2019b, 2020)
    <doi:10.1515/demo-2019-0016>,
    <doi:10.1016/j.csda.2019.01.013>,
    <doi:10.1016/j.jmva.2020.104610>),
    and test procedures for the simplifying assumption
    (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011>
    and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>).
Helps automate 'Quarto' website creation for small academic groups.
    Builds a database-like structure of people, projects and publications, linking
    them together with a string-based ID system. Then, provides functions to automate
    production of clean markdown for these structures, and in-built CSS formatting
    using CSS flexbox.
Apply and visualize conditional formatting to data frames in R.
    It renders a data frame with cells formatted according to
    criteria defined by rules, using a tidy evaluation syntax. The table is
    printed either opening a web browser or within the 'RStudio' viewer if
    available. The conditional formatting rules allow to highlight cells
    matching a condition or add a gradient background to a given column. This
    package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and
    exporting to an 'xlsx' file.
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.
Code for a variety of nonlinear conditional independence tests: 
  Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>),
  Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>),
  Invariant environment prediction,
  Invariant target prediction,
  Invariant residual distribution test,
  Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).
Set of functions for the easy analyses of conditioning data.
Impute the survival times for censored observations based on their conditional survival distributions derived from the Kaplan-Meier estimator. 'CondiS' can replace the censored observations with the best approximations from the statistical model, allowing for direct application of machine learning-based methods. When covariates are available, 'CondiS' is extended by incorporating the covariate information through machine learning-based regression modeling ('CondiS_X'), which can further improve the imputed survival time.
Provides ability to control how many times in function
    calls conditions are thrown (shown to the user). Includes control of
    warnings and messages.
Computes conditional multivariate normal densities, probabilities, and random deviates.
Computes conditional multivariate t probabilities, random deviates, and densities. It can also be used to create missing values at random in a dataset, resulting in a missing at random (MAR) mechanism. Inbuilt in the package are the Expectation-Maximization (EM), Monte Carlo EM, and Stochastic EM algorithms for imputation of missing values in datasets assuming the multivariate t distribution. See Kinyanjui, Tamba, Orawo, and Okenye (2020)<doi:10.3233/mas-200493>, and Kinyanjui, Tamba, and Okenye(2021)<http://www.ceser.in/ceserp/index.php/ijamas/article/view/6726/0> for more details.
Interact with 'Condor' from R via SSH connection. Files are first
  uploaded from user machine to submitter machine, and the job is then submitted
  from the submitter machine to 'Condor'. Functions are provided to submit,
  list, and download 'Condor' jobs from R. 'Condor' is an open source
  high-throughput computing software framework for distributed parallelization
  of computationally intensive tasks.
Trading of Condor Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Method to implement some newly developed methods for the
    estimation of the conditional survival function. See Meira-Machado, 
    Sestelo and Goncalves (2016) <doi:10.1002/bimj.201500038>.
Computes the density and probability for the conditional truncated multivariate normal (Horrace (2005) p. 4, <doi:10.1016/j.jmva.2004.10.007>). Also draws random samples from this distribution.
Enable the use of 'Shepherd.js' to create tours in 'Shiny' 
    applications.
Runs a function iteratively over each row of either a dataframe 
    or the results of a query.  Use the 'BigQuery' and 'DBI' wrappers to 
    iteratively pass each row of query results to a function.  If a field 
    contains a 'JSON' string, it will be converted to an object.  This is 
    helpful for queries that return 'JSON' strings that represent objects.  
    These fields can then be treated as objects by the pipeline.
Exploring fitted models by interactively taking 2-D and 3-D
  sections in data space.
Constructs a shiny app function with interactive displays for conditional visualization of models, 
     data and density functions. An extended version of package 'condvis'. 
    Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) <doi:10.1080/10618600.2021.1983439>.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.
This small library contains a series of simple tools for
        constructing and manipulating confounded and fractional
        factorial designs.
Calculate confidence and consistency that measure the
    goodness-of-fit and transferability of predictive/potential distribution
    models (including species distribution models) as described by Somodi &
    Bede-Fazekas et al. (2024) <doi:10.1016/j.ecolmodel.2024.110667>.
Functions for estimating and reporting multi-year averages and
    corresponding confidence intervals and distributions. A potential use case
    is reporting the chemical and ecological status of surface waters according
    to the European Water Framework Directive.
Manage configuration values across multiple environments (e.g.
  development, test, production). Read values using a function that determines
  the current environment and returns the appropriate value.
Enhances the 'ini' package by adding the ability to interpolate variables.
	     The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module)
	     and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.
Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.
R functions for criterion profile analysis, Davison and Davenport (2002) <doi:10.1037/1082-989X.7.4.468> and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2020) <doi:10.1037/met0000305>. Sensitivity analyses to aid in interpreting criterion profile analysis results are also included.
Produces descriptive interpretations of confidence intervals.
    Includes (extensible) support for various test types, specified as sets
    of interpretations dependent on where the lower and upper confidence limits
    sit. Provides plotting functions for graphical display of interpretations.
Calculates classic and/or bootstrap confidence intervals for
    many parameters such as the population mean, variance, interquartile
    range (IQR), median absolute deviation (MAD), skewness, kurtosis,
    Cramer's V, odds ratio, R-squared, quantiles (incl. median),
    proportions, different types of correlation measures, difference in
    means, quantiles and medians. Many of the classic confidence intervals
    are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap
    confidence intervals are calculated with the R package 'boot'. Both
    one- and two-sided intervals are supported.
The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the 'lme4' package and robust estimators from the rlmer() function in the 'robustlmm' package, as well as the varComprob() function in the 'robustvarComp' package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in \n Mason, F., Cantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and \n Mason, F., Cantoni, E., & Ghisletta, P. (2024) <doi:10.1037/met0000643>.
Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.
R's default conflict management system gives the most
    recently loaded package precedence. This can make it hard to detect
    conflicts, particularly when they arise because a package update
    creates ambiguity that did not previously exist. 'conflicted' takes a
    different approach, making every conflict an error and forcing you to
    choose which function to use.
Provides utilities for working with various 'Confluence' API 
    <https://docs.atlassian.com/ConfluenceServer/rest/latest/>, including a
    functionality to convert an R Markdown document to 'Confluence' format and
    upload it to 'Confluence' automatically.
Provides functions to construct finite-sample calibrated predictive 
    intervals for Bayesian models, following the approach in Barber et al. 
    (2021) <doi:10.1214/20-AOS1965>. These intervals are calculated efficiently
    using importance sampling for the leave-one-out residuals. By default,
    the intervals will also reflect the relative uncertainty in the Bayesian 
    model, using the locally-weighted conformal methods of Lei et al. (2018)
    <doi:10.1080/01621459.2017.1307116>.
Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.
It computes full conformal, split conformal and multi split
    conformal prediction regions when the response has functional nature.
    Moreover, the package also contain a plot function to visualize the
    output of the split conformal.
    To guarantee consistency, the package structure mimics the univariate 
    'conformalInference' package of professor Ryan Tibshirani.
    The main references for the code are: 
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, 
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>,
    Solari, and Djordjilovic (2021) <arXiv:2103.00627>.
It computes full conformal, split conformal and multi split
    conformal prediction regions when the response variable is
    multivariate (i.e. dimension is greater than one). Moreover, the
    package also contain plot functions to visualize the output of the
    full and split conformal functions. 
    To guarantee consistency, the package structure mimics the univariate 'conformalInference'
    package of professor Ryan Tibshirani.
    The main references for the code are: 
    Lei et al. (2016) <arXiv:1604.04173>,
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, 
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>,
    Solari, and Djordjilovic (2021) <arXiv:2103.00627>.
Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) <https://proceedings.mlr.press/v204/tyagi23a.html>.
An implementation of efficiency first conformal prediction (EFCP) and validity first conformal prediction (VFCP) that demonstrates both validity (coverage guarantee) and efficiency (width guarantee). To learn how to use it, check the vignettes for a quick tutorial. The package is based on the work by Yang Y., Kuchibhotla A.,(2021) <arxiv:2104.13871>.
Offers several functions for Configural Frequencies
    Analysis (CFA), which is a useful statistical tool for the analysis of
    multiway contingency tables. CFA was introduced by G. A. Lienert as
    'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). 
    Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. 
    Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
For multiple testing.
    Computes estimates and confidence bounds for the
    False Discovery Proportion (FDP), the fraction of false positives among
    all rejected hypotheses.
    The methods in the package use permutations of the data. Doing so, they
    take into account the dependence structure in the data.
Takes the outputs of a 'caret' confusion matrix and allows for the quick conversion of these list items to lists.
    The intended usage is to allow the tool to work with the outputs of machine learning classification models. 
    This tool works with classification problems for binary and multi-classification problems and allows for the record level conversion of the confusion matrix outputs.
    This is useful, as it allows quick conversion of these objects for storage in database systems and to track ML model performance over time.
    Traditionally, this approach has been used for highlighting model representation and feature slippage.
Narrow down the number of models to look at in model selection using the confidence envelopes based on the minimum ZIC (Generalized Information Criteria) values for regression and time series data. Functions involve the computation of multivariate normal-probabilities with covariance matrices based on minimum ZIC inverting the CDF of the minimum ZIC. It involves both the computation of singular and non-singular probabilities as described in Genz (1992) <[https:doi.org/10.2307/1390838]https:doi.org/10.2307/1390838>.
Download and read data on United States congressional proceedings. 
    Data is read from the Library of Congress's Congress.gov Application Programming 
    Interface (<https://github.com/LibraryOfCongress/api.congress.gov/>). Functions
    exist for all version 3 endpoints, including for bills, amendments, congresses, 
    summaries, members, reports, communications, nominations, and treaties.
Includes the 100 datasets simulated by Congreve and Lamsdell (2016)
  <doi:10.1111/pala.12236>, and analyses of the partition and quartet distance of
  reconstructed trees from the generative tree, as analysed by Smith (2019)
  <doi:10.1098/rsbl.2018.0632>.
Integrates two numerical omics data sets from the same samples using partial correlations. The output can be represented as a network, bipartite graph or a hypergraph structure. The method used in the package refers to Klaus et al (2021) <doi:10.1016/j.molmet.2021.101295>.
Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.
Provides a set of tools that can be used across 'data.frame' and
    'imputationList' objects.
This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method.
Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. This methodology is generic and therefore benefits both the academic and industrial research.
Functions calculating Conley (1999) <doi:10.1016/S0304-4076(98)00084-0> standard errors. The package started by merging and extending multiple packages and 
  other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in 
  the vignette.
With this package you can run 'ConMET' locally in R. 'ConMET' is an R-shiny application that facilitates performing and evaluating confirmatory factor analyses (CFAs) and is useful for running and reporting typical measurement models in applied psychology and management journals. 'ConMET' automatically creates, compares and summarizes CFA models. Most common fit indices (E.g., CFI and SRMR) are put in an overview table. ConMET also allows to test for common method variance. The application is particularly useful for teaching and instruction of measurement issues in survey research. The application uses the 'lavaan' package (Rosseel, 2012) to run CFAs.
The ConNEcT approach investigates the pairwise association strength of binary time series by calculating contingency measures and depicts the results in a network. The package includes features to explore and visualize the data. To calculate the pairwise concurrent or temporal sequenced relationship between the variables, the package provides seven contingency measures (proportion of agreement, classical & corrected Jaccard, Cohen's kappa, phi correlation coefficient, odds ratio, and log odds ratio), however, others can easily be implemented. The package also includes non-parametric significance tests, that can be applied to test whether the contingency value quantifying the relationship between the variables is significantly higher than chance level. Most importantly this test accounts for auto-dependence and relative frequency.See Bodner et al.(2021) <doi: 10.1111/bmsp.12222>.Finally, a network can be drawn. Variables depicted the nodes of the network, with the node size adapted to the prevalence. The association strength between the variables defines the undirected (concurrent) or directed (temporal sequenced) links between the nodes. The results of the non-parametric significance test can be included by depicting either all links or only the significant ones. Tutorial see Bodner et al.(2021) <doi:10.3758/s13428-021-01760-w>.
Provides a helpful 'R6' class and methods for interacting with 
    the 'Posit Connect' Server API along with some meaningful utility functions
    for regular tasks. API documentation varies by 'Posit Connect' installation
    and version, but the latest documentation is also hosted publicly at 
    <https://docs.posit.co/connect/api/>.
The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.
Enables 'DBI' compliant packages to integrate with the 'RStudio' connections 
  pane, and the 'pins' package. It automates the display of schemata, tables, views, as well 
  as the preview of the table's top 1000 records.
Connect to WFP's Moda platform to R, download data, and obtain the list of individuals with access to the project along with their access level.
A collection of helper functions and 'htmlwidgets' to help publishers
    curate content collections on 'Posit Connect'. The components,
    Card, Grid, Table, Search, and Filter can be used to produce a
    showcase page or gallery contained within a static or interactive
    R Markdown page.
Collects several different methods for analyzing and
    working with connectivity data in R.  Though primarily oriented towards
    marine larval dispersal, many of the methods are general and useful for
    terrestrial systems as well.
The congeneric normal-ogive model is a popular model for 
   psychometric data (McDonald, R. P. (1997) <doi:10.1007/978-1-4757-2691-6_15>).
   This model estimates the model, calculates theoretical and concrete 
   reliability coefficients, and predicts the latent variable of the model. 
   This is the companion package to Moss (2020) <doi:10.31234/osf.io/nvg5d>.
Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.
Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. 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, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.
Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures.
Extends 'ACER ConQuest' through a family of functions
    designed to improve graphical outputs and help with advanced analysis
    (e.g., differential item functioning).  Allows R users to call 
    'ACER ConQuest' from within R and read 'ACER ConQuest' System Files
    (generated by the command `put` <https://conquestmanual.acer.org/s4-00.html#put>).
    Requires 'ACER ConQuest' version 5.33.2 or later. 
    A demonstration version can be downloaded from <https://shop.acer.org/acer-conquest-5.html>.
Convert text into synthesized speech and get a list of supported voices for a region. 
    Microsoft's 'Cognitive Services Text to Speech REST' API <https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/rest-text-to-speech?tabs=streaming>  
    supports neural text to speech voices, which support specific languages and dialects that are identified by locale.
Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.
Helping biologists to choose the most suitable approach to link their research to conservation. After answering few questions on the data available, geographic and taxonomic scope, 'conserveR' ranks existing methods for conservation prioritization and systematic conservation planning by suitability. The methods data base of 'conserveR' contains 133 methods for conservation prioritization based on a systematic review of > 12,000 scientific publications from the fields of spatial conservation prioritization, systematic conservation planning, biogeography and ecology.
A fast way to loop a character vector or file names as a menu in the console for the
  user to choose an option.
To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved.
Shiny app for creating interactive consort flow diagrams and other types of flow diagrams, see Moher, Schulz and Altman (2001) <doi:10.1016/S0140-6736(00)04337-3>.
Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline.  Function outputs fits and various types of inference.
Compute the median ranking according to the Kemeny's axiomatic approach. 
    Rankings can or cannot contain ties, rankings can be both complete or incomplete. 
    The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. 
    The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. 
    The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient.
    This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions.
    Please type '?ConsRank-deprecated'
    Essential references:
    Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>; 
    D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>; 
    Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>; 
    D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>;
    Albano, A., and Plaia, A. (2021) <doi:10.1285/i20705948v14n1p117>.
Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering.
    It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). 
    The package depends on the 'ConsRank' R package.
    Options for validate the tree-based method are both test-set procedure and V-fold cross validation.
    The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index).
    Essential references:
    D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) <doi:10.1007/s00357-020-09367-0>
    D'Ambrosio, A., and Heiser, W.J. (2019) <doi:10.1007/s41237-018-0069-5>;    
    D'Ambrosio, A., and Heiser W.J. (2016) <doi:10.1007/s11336-016-9505-1>;
    Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) <doi:10.1109/TFUZZ.2011.2179303>.
Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. 
       The package makes it easy to incorporate constraints into the model's coefficients. 
          The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), 
          a vector of bound constraints 
          for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions
          among coefficients (i.e beta1 > beta2).
          The references of this packages are the same as 'stats' package for glm() and arima() functions.
          See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528).
          For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages.
CODATA internationally recommended values of the fundamental
    physical constants, provided as symbols for direct use within the R language.
    Optionally, the values with uncertainties and/or units are also provided if
    the 'errors', 'units' and/or 'quantities' packages are installed.
    The Committee on Data for Science and Technology (CODATA) is an
    interdisciplinary committee of the International Council for Science which
    periodically provides the internationally accepted set of values of the
    fundamental physical constants. This package contains the "2018 CODATA"
    version, published on May 2019:
    Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2020)
    <https://physics.nist.gov/cuu/Constants/>.
Examine any number of time series data frames to identify 
    instances in which various criteria are met within specified time
    frames. In clinical medicine, these types of events are often
    called "constellations of signs and symptoms", because a single 
    condition depends on a series of events occurring within a certain 
    amount of time of each other. This package was written to work with
    any number of time series data frames and is optimized for speed 
    to work well with data frames with millions of rows.
Provides functions for efficient computation of non-linear spatial predictions with local change of support (Hofer, C. and Papritz, A. (2011) "constrainedKriging: An R-package for customary, constrained and covariance-matching constrained point or block kriging" <doi:10.1016/j.cageo.2011.02.009>).  This package supplies functions for two-dimensional spatial interpolation by constrained (Cressie, N. (1993) "Aggregation in geostatistical problems" <doi:10.1007/978-94-011-1739-5_3>), covariance-matching constrained (Aldworth, J. and Cressie, N. (2003) "Prediction of nonlinear spatial functionals" <doi:10.1016/S0378-3758(02)00321-X>) and universal (external drift) Kriging for points or blocks of any shape from data with a non-stationary mean function and an isotropic weakly stationary covariance function.  The linear spatial interpolation methods, constrained and covariance-matching constrained Kriging, provide approximately unbiased prediction for non-linear target values under change of support.  This package extends the range of tools for spatial predictions available in R and provides an alternative to conditional simulation for non-linear spatial prediction problems with local change of support.
A method for modeling genetic data as a combination of discrete
    layers, within each of which relatedness may decay continuously with geographic
    distance. This package contains code for running analyses (which are implemented
    in the modeling language 'rstan') and visualizing and interpreting output. See the
    paper for more details on the model and its utility.
Prints code that can be used to recreate R objects. In a
    sense it is similar to 'base::dput()' or 'base::deparse()' but
    'constructive' strives to use idiomatic constructors.
Data package for the supplementary data in Prem et al. (2017)
    <doi:10.1371/journal.pcbi.1005697> and Prem et al. 
    <doi:10.1371/journal.pcbi.1009098>.
    Provides easy access to contact data for 177 countries, for use in
    epidemiological, demographic or social sciences research.
Extends the functionality of base 'R' lists and
    provides specialized data structures 'deque',
    'set', 'dict', and 'dict.table', the latter to extend the 'data.table'
    package.
Fits mixtures of multivariate contaminated normal distributions
        (with eigen-decomposed scale matrices) via the expectation conditional-
	maximization algorithm under a clustering or classification paradigm
	Methods are described in Antonio Punzo, Angelo Mazza, and Paul D McNicholas (2018) <doi:10.18637/jss.v085.i10>.
An interface for creating, registering, and resolving content-based
             identifiers for data management. Content-based identifiers rely on
             the 'cryptographic' hashes to refer to the files they identify, thus,
             anyone possessing the file can compute the identifier using a 
             well-known standard algorithm, such as 'SHA256'.  By registering
             a URL at which the content is accessible to a public archive (such as 
             Hash Archive) or depositing data in a scientific repository such 'Zenodo',
             'DataONE' or 'SoftwareHeritage', the content identifier can serve 
             many functions typically associated with A Digital Object Identifier
             ('DOI').  Unlike location-based identifiers like 'DOIs', content-based
             identifiers permit the same content to be registered in many locations.
A fast, flexible and transparent framework to estimate context-specific word and short document embeddings using the 'a la carte' 
    embeddings approach developed by Khodak et al. (2018) <arXiv:1805.05388> and evaluate hypotheses about covariate effects on embeddings using 
    the regression framework developed by Rodriguez et al. (2021)<https://github.com/prodriguezsosa/EmbeddingRegression>.
Various utilities for evaluating continued fractions.
Converts numbers to continued fractions and back again. A solver for Pell's Equation is provided.  The method for calculating roots in continued fraction form is provided without published attribution in such places as Professor Emeritus Jonathan Lubin, <http://www.math.brown.edu/jlubin/> and his post to StackOverflow, <https://math.stackexchange.com/questions/2215918> , or Professor Ron Knott, e.g., <https://r-knott.surrey.ac.uk/Fibonacci/cfINTRO.html> .
Provides an object class for dealing with many multivariate
    probability distributions at once, useful for simulation.
Provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.
Create contour lines for a non regular series of points, potentially from a non-regular canvas.
Provides functions for making contour plots.
  The contour plot can be created from grid data, a function,
  or a data set. If non-grid data is given, then a Gaussian
  process is fit to the data and used to create the contour plot.
Plots a set of x,y,z co-ordinates in a contour map. Designed to be similar to plots in base R so additional elements can be added using lines(), points() etc. This package is intended to be better suited, than existing packages, to displaying circular shaped plots such as those often seen in the semi-conductor industry.
One degree of freedom contrasts for 'lm', 'glm', 'gls', and 'geese' objects.
Contrast trees represent a new approach for assessing the
    accuracy of many types of machine learning estimates that are not
    amenable to standard (cross) validation methods; see "Contrast
    trees and distribution boosting", Jerome H. Friedman (2020)
    <doi:10.1073/pnas.1921562117>. In situations where inaccuracies
    are detected, boosted contrast trees can often improve
    performance. Functions are provided to to build such trees in
    addition to a special case, distribution boosting, an assumption
    free method for estimating the full probability distribution of an
    outcome variable given any set of joint input predictor variable
    values.
A Bayesian approach to using 
    predictive probability in an ANOVA construct with a continuous normal response, 
    when threshold values must be obtained for the question of interest to be 
    evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). 
    The Bayesian Mission Mean (BMM) is used to evaluate a question 
    of interest (that is, a mean that randomly selects combination of factor levels 
    based on their probability of occurring instead of averaging over the factor 
    levels, as in the grand mean). Under this construct, in contrast to a Gibbs 
    sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is 
    required. The nested sampler determines the conditional posterior distribution 
    of the model parameters, given Y, and the outside sampler determines the marginal 
    posterior distribution of Y (also commonly called the predictive distribution for Y). 
    This approach provides a sample from the joint posterior distribution of Y and 
    the model parameters, while also accounting for the threshold value that must be 
    obtained in order for the question of interest to be evaluated as successful.
Contribution table for credit assignment based on 'ggplot2'.
    This can improve the author contribution information in academic journals and personal CV.
Solves control systems problems relating to time/frequency response, LTI systems design and analysis, transfer function manipulations, and system conversion.
Inference with control function methods for nonlinear outcome models when the model is known ('Guo and Small' (2016) <arXiv:1602.01051>) and when unknown but semiparametric ('Li and Guo' (2021) <arXiv:2010.09922>).
Nonparametric two-sample procedure for comparing survival quantiles.
Graphically display the (causal) effect of a continuous variable on a time-to-event outcome
	using multiple different types of plots based on g-computation. Those functions
	include, among others, survival area plots, survival contour plots, survival quantile plots and
	3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally.
	For details, see Robin Denz, Nina Timmesfeld (2023) <doi:10.1097/EDE.0000000000001630>.
Implements the semiparametric efficient estimators of 
    continuous-time causal models for time-varying treatments and confounders 
    in the presence of dependent censoring (including structural failure time 
    model and Cox proportional hazards marginal structural model). 
    S. Yang, K. Pieper, and F. Cools (2019) <doi:10.1111/biom.12845>.
Convolute probabilistic distributions using the random generator 
 function of each distribution. A new random number generator function is created that 
 perform the mathematical operation on the individual random samples from the 
 random generator function of each distribution. See the documentation for examples.
Functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).
This is a pedagogical package, designed to help students understanding convergence of
             random variables. It provides a way to investigate interactively various modes of
	     convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d.
	     random variables. Visualisation of simulated sample paths is possible through interactive
	     plots. The approach is illustrated by examples and exercises through the function
	     'investigate', as described in
	     Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>.
	     The user can study his/her own sequences of random variables.
Indicators and measures by country and time describe
    what happens at economic and social levels. This package provides
    functions to calculate several measures of convergence after imputing
    missing values. The automated downloading of Eurostat data,
    followed by the production of country fiches and indicator fiches,
    makes possible to produce automated reports.
    The Eurofound report (<doi:10.2806/68012>)
    "Upward convergence in the EU: Concepts, measurements and indicators", 
    2018, is a detailed  presentation of  convergence.
Calculate the theoretical value of convertible bonds by given parameters, including B-S theory and Monte Carlo method.
Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in 'AnnotationDbi' in convertId2() but
    that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function
    in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the
    'biomaRt' package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes"
    can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol()
    attempts to determine the most likely current Gene Symbol.
A tool to estimate IRT item parameters (2 PL) using CTT-based item statistics from small samples via artificial neural networks and regression trees.
Provides conversion functionality between a broad range of
    scientific, historical, and industrial unit types.
Quantifies and assesses the significance of convergent evolution using multiple methods and measures as described in Stayton (2015) <DOI: 10.1111/evo.12729> and Grossnickle et al. 2023.  Also displays results in various ways.
Variance estimation on indicators of income concentration and
    poverty using complex sample survey designs. Wrapper around the
    'survey' package.
General functions for convolutions of data. Moving average, running median, and other filters are available.
    Bibliography regarding the functions can be found in the following text.
    Richard G. Brereton (2003) <ISBN:9780471489771>.
Fits convolution-based nonstationary
    Gaussian process models to point-referenced spatial data. The nonstationary
    covariance function allows the user to specify the underlying correlation
    structure and which spatial dependence parameters should be allowed to
    vary over space: the anisotropy, nugget variance, and process variance.
    The parameters are estimated via maximum likelihood, using a local
    likelihood approach. Also provided are functions to fit stationary spatial
    models for comparison, calculate the Kriging predictor and standard errors,
    and create various plots to visualize nonstationarity.
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
Computes a novel metric of affinity between two entities based on their co-occurrence 
  (using binary presence/absence data). The metric and its MLE, alpha hat, were advanced in 
  Mainali, Slud, et al, 2021 <doi:10.1126/sciadv.abj9204>. Various types of confidence intervals and median interval
  were developed in Mainali and Slud, 2022 <doi:10.1101/2022.11.01.514801>.
Generate project files and directories following a pre-made template.
  You can specify variables to customize file names and content, and
  flexibly adapt the template to your needs. 'cookiecutter' for 'R'
  implements a subset of the excellent 'cookiecutter' package for the
  'Python' programming language (<https://github.com/cookiecutter/>), and
  aims to be largely compatible with the original 'cookiecutter' template
  format.
A convenient tool to store and format browser cookies and use them 
    in 'HTTP' requests (for example, through 'httr2', 'httr' or 'curl').
Browser cookies are name-value pairs that are saved in a
    user's browser by a website. Cookies allow websites to persist
    information about the user and their use of the website. Here we
    provide tools for working with cookies in 'shiny' apps, in part by
    wrapping the 'js-cookie' JavaScript library
    <https://github.com/js-cookie/js-cookie>.
Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional 'ggtree' package can be obtained through Bioconductor.
Fast implementations of the co-operations: covariance,
    correlation, and cosine similarity.  The implementations are
    fast and memory-efficient and their use is resolved
    automatically based on the input data, handled by R's S3
    methods.  Full descriptions of the algorithms and benchmarks
    are available in the package vignettes.
The theory of cooperative games with transferable utility offers 
   useful insights into the way parties can share gains from cooperation and 
   secure sustainable agreements, see e.g. one of the books by Chakravarty, 
   Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, 
   ISBN:978-9027727299) for more details. A comprehensive set of tools for 
   cooperative game theory with transferable utility is provided. Users can 
   create special families of cooperative games, like e.g. bankruptcy games, 
   cost sharing games and weighted voting games. There are functions to check 
   various game properties and to compute five different set-valued solution 
   concepts for cooperative games. A large number of point-valued solution 
   concepts is available reflecting the diverse application areas of 
   cooperative game theory. Some of these point-valued solution concepts can 
   be used to analyze weighted voting games and measure the influence of 
   individual voters within a voting body. There are routines for visualizing 
   both set-valued and point-valued solutions in the case of three or four 
   players.
Computes cooperative games and allocation rules associated with linear production programming problems.
Automated flagging of common spatial and temporal
    errors in biological and paleontological collection data, for the use
    in conservation, ecology and paleontology. Includes automated tests to
    easily flag (and exclude) records assigned to country or province
    centroid, the open ocean, the headquarters of the Global Biodiversity
    Information Facility, urban areas or the location of biodiversity
    institutions (museums, zoos, botanical gardens, universities).
    Furthermore identifies per species outlier coordinates, zero
    coordinates, identical latitude/longitude and invalid coordinates.
    Also implements an algorithm to identify data sets with a significant
    proportion of rounded coordinates. Especially suited for large data
    sets. The reference for the methodology is: Zizka et al. (2019)
    <doi:10.1111/2041-210X.13152>.
Detects a variety of coordinated actions on social media and outputs the network of coordinated users along with related information.
Extensive functions for bivariate copula (bicopula) computations and related operations 
 for bicopula theory. The lower, upper, product, and select other bicopula are implemented along 
 with operations including the diagonal, survival copula, dual of a copula, co-copula, and
 numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical 
 derivatives and inverses of a bicopula are provided through which simulation is implemented. 
 Bicopula composition, convex combination, asymmetry extension, and products also are provided.
 Support extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau,
 Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer-
 Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and 
 positive/negative quadrant dependency, left (right) increasing (decreasing) are available. 
 Other features include Kullback-Leibler Divergence, Vuong Procedure, spectral measure, and 
 Lcomoments for inference, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit.
Provides tools for fitting the copCAR (Hughes, 2015)
    <DOI:10.1080/10618600.2014.948178> regression model for discrete
	areal data. Three types of estimation are supported (continuous
	extension, composite marginal likelihood, and distributional transform),
	for three types of outcomes (Bernoulli, negative binomial, and Poisson).
Correlates of protection (CoP) and correlates of risk (CoR) study the immune biomarkers associated with an infectious disease outcome, e.g. COVID or HIV-1 infection. This package contains shared functions for analyzing CoP and CoR, including bootstrapping procedures, competing risk estimation, and bootstrapping marginalized risks.
Semiparametric estimation for censored time series
    with lower detection limit. The latent response is a sequence of
    stationary process with Markov property
    of order one.
    Estimation of copula parameter(COPC) and Conditional quantile estimation
    are included for five available copula functions.
    Copula selection methods based on L2 distance from empirical copula function
    are also included.
Provides functions to  compute and plot Coverage
    Probability Excursion (CoPE) sets
    for real valued functions on a 2-dimensional domain. CoPE sets are obtained
    from repeated noisy observations of the function on the entire domain.
    They are designed to bound the excursion
    set of the target function at a given level from above and below with
    a predefined probability. The target
    function can be a parameter in spatially-indexed linear regression.
    Support by NIH grant R01 CA157528 is gratefully acknowledged.
The nonparametric methods for estimating copula entropy, transfer entropy, and the statistics for multivariate normality test and two-sample test are implemented. The methods for estimating transfer entropy and the statistics for multivariate normality test and two-sample test are based on the method for estimating copula entropy. Please refer to Ma and Sun (2011) <doi:10.1016/S1007-0214(11)70008-6>, Ma (2019) <arXiv:1910.04375>, Ma (2022) <arXiv:2206.05956>, and Ma (2023) <arXiv:2307.07247> for more information.
Copernicus Digital Elevation Model datasets (DEM) of 90 and 30 meters resolution using the 'awscli' command line tool. The Copernicus (DEM) is included in the Registry of Open Data on 'AWS (Amazon Web Services)' and represents the surface of the Earth including buildings, infrastructure and vegetation.
Subset and download data from EU Copernicus Marine
    Service Information: <https://data.marine.copernicus.eu>.
    Import data on the oceans physical and biogeochemical state
    from Copernicus into R without the need of external software.
A Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see   
     Manipur et al (2023) <doi:10.1101/2023.06.29.546856>.
The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. 
    The model was based on the evaluation of local alternatives, representing 
    regional potentialities, so as to fulfill demands of economic projects. 
    After defining demand profiles in terms of their technological coefficients, 
    the degree of importance of factors is defined so as to represent  
    the productive activity. The method can detect a surplus of supply without 
    the restriction of the distance of classical algebra, defining a hierarchy 
    of location alternatives. In COPPE-Cosenza Model, the distance between 
    factors is measured in terms of the difference between grades of memberships
    of the same factors belonging to two or more  sets under comparison. The 
    required factors are classified under the following linguistic variables: 
    Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant 
    (I). And the alternatives can assume the following linguistic variables: 
    Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and 
    Inexistent (In). The model also provides flexibility, allowing different 
    aggregation rules to be performed and defined by the Decision Maker. Such 
    feature is considered in this package, allowing the user to define other 
    aggregation matrices, since it considers the same linguistic variables 
    mentioned.
Performs Bayesian nonparametric density estimation using Martingale
 posterior distributions including the Copula Resampling (CopRe) algorithm. 
 Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and
 an extension to include full uncertainty quantification via a predictive 
 sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate 
 random nonparametric distributions as output, leading to complete nonparametric 
 inference on posterior summaries. Routines for calculating arbitrary 
 functionals from the sampled distributions are included as well as an important 
 algorithm for finding the number and location of modes, which can then be used 
 to estimate the clusters in the data using, for example, k-means.
 Implements work developed in Moya B., Walker S. G. (2022).
 <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) 
 <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) 
 <doi:10.1080/01621459.1995.10476550>.
Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with power functions and augment the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) for ratio, power, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://escholarship.org/uc/item/4ps3b5mj>), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745>), r-stress (de Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>), power stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>), restricted power stress, approximate power stress, power elastic scaling, power Sammon mapping (for all Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). All of these models can also solely be fit as MDS with power transformations. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>).
Implements the copula-based sensitivity analysis method, 
  as discussed in Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
  <arXiv:2102.09412>, with Gaussian copula adopted in particular.
Classes (S4) of commonly used elliptical, Archimedean,
 extreme-value and other copula families, as well as their rotations,
 mixtures and asymmetrizations. Nested Archimedean copulas, related
 tools and special functions. Methods for density, distribution, random
 number generation, bivariate dependence measures, Rosenblatt transform,
 Kendall distribution function, perspective and contour plots. Fitting of
 copula models with potentially partly fixed parameters, including
 standard errors. Serial independence tests, copula specification tests
 (independence, exchangeability, radial symmetry, extreme-value
 dependence, goodness-of-fit) and model selection based on
 cross-validation. Empirical copula, smoothed versions, and
 non-parametric estimators of the Pickands dependence function.
Estimation and statistical process control are performed under
 copula-based time-series models.
 Available are statistical methods in Long and Emura (2014 JCSA),
 Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>,
 Huang and Emura (2021 Commun Stat-Simul) <DOI:10.1080/03610918.2019.1602647>,
 Lin et al. (2021 Comm Stat-Simul) <DOI:10.1080/03610918.2019.1652318>,
 Sun et al. (2020 JSS Series in Statistics)<DOI:10.1007/978-981-15-4998-4>,
 and Huang and Emura (2021, in revision).
Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted).
Simulating bivariate survival data from copula models.
 Estimation of the association parameter in copula models.
 Two different ways to estimate the association parameter in copula models are implemented.
 A goodness-of-fit test for a given copula model is implemented.
 See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.
Additive copula regression for regression
    problems with binary outcome via gradient boosting 
    [Brant, Hobæk Haff (2022); <arXiv:2208.04669>]. The fitting process
    includes a specialised model selection algorithm for each component, where
    each component is found (by greedy optimisation) among all the D-vines with
    only Gaussian pair-copulas of a fixed dimension, as specified by the user.
    When the variables and structure have been selected, the algorithm then
    re-fits the component where the pair-copula distributions can be different
    from Gaussian, if specified.
Copula-based regression models for multivariate censored data, including 
 bivariate right-censored data, bivariate interval-censored data, and right/interval-censored 
 semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and 
 Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, 
 Gompertz) and semiparametric (Cox and transformation) models. Includes methods for 
 convenient prediction and plotting. Also provides a bivariate time-to-event simulation 
 function and an information ratio-based goodness-of-fit test for copula. Method details 
 can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, 
 Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and
 Sun et al. (2023+) JRSSC.
Data sets used for copula modeling in addition to those in
 the R package 'copula'.  These include a random subsample from the US National
 Education Longitudinal Study (NELS) of 1988 and nursing home data from
 Wisconsin.
Modelling of sensitivity and specificity on their natural scale
    using copula based bivariate beta-binomial distribution to yield marginal
    mean sensitivity and specificity. The intrinsic negative correlation between
    sensitivity and specificity is modelled using a copula function. A forest plot
    can be obtained for categorical covariates or for the model with intercept only.
    Nyaga VN, Arbyn M, Aerts M (2017) <doi:10.18637/jss.v082.c01>.
Provides a platform where EDAs (estimation of
    distribution algorithms) based on copulas can be implemented and
    studied. The package offers complete implementations of various
    EDAs based on copulas and vines, a group of well-known
    optimization problems, and utility functions to study the
    performance of the algorithms. Newly developed EDAs can be easily
    integrated into the package by extending an S4 class with generic
    functions for their main components.
Estimation of 2-level factor copula-based regression models  for clustered data where the response variable can be either discrete or continuous.
Estimation and goodness-of-fit functions for copula-based models of bivariate data with arbitrary distributions  (discrete, continuous, mixture of both types). The copula families considered here are the  Gaussian, Student, Clayton, Frank, Gumbel, Joe, Plackett, BB1, BB6, BB7,BB8, together with the following non-central squared copula families in Nasri (2020) <doi:10.1016/j.spl.2020.108704>: ncs-gaussian, ncs-clayton, ncs-gumbel, ncs-frank, ncs-joe, and ncs-plackett. For theoretical details, see, e.g., Nasri and Remillard (2023) <arXiv:2301.13408>.
Fits multivariate models in an R-vine pair copula construction framework,
    in such a way that the conditional copula can be easily evaluated.
    In addition, the package implements functionality to compute or approximate
    the conditional expectation via the conditional copula.
The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) <doi:10.1002/sim.6595>. The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) <doi:10.1177/0962280215596769> and also accounting for non-evaluable subjects in Nikoloulopoulos (2020) <doi:10.1515/ijb-2019-0107>. The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) <doi:10.1177/0962280216682376>. The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2019) <doi:10.1177/0962280218796685>. The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2020) <doi:10.1177/0962280220913898>. The one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests (2022) <doi:10.1111/rssa.12838>.
To optimize clinical trial designs and data analysis methods consistently through trial simulation, we need to simulate multivariate mixed-type virtual patient data independent of designs and analysis methods under evaluation. To make the outcome of optimization more realistic, relevant empirical patient level data should be utilized when it’s available. However, a few problems arise in simulating trials based on small empirical data, where the underlying marginal distributions and their dependence structure cannot be understood or verified thoroughly due to the limited sample size. To resolve this issue, we use the copula invariance property, which can generate the joint distribution without making a strong parametric assumption. The function copula.sim can generate virtual patient data with optional data validation methods that are based on energy distance and ball divergence measurement. The function compare.copula.sim can conduct comparison of marginal mean and covariance of simulated data. To simulate patient-level data from a hypothetical treatment arm that would perform differently from the observed data, the function new.arm.copula.sim can be used to generate new multivariate data with the same dependence structure of the original data but with a shifted mean vector.
Assembles two or more gene copies from short-read Next-Generation Sequencing data. Works best when there are only two gene copies and read length >=250 base pairs. High and relatively even coverage are important.
An algorithm of optimal subset selection, related to Covariance matrices, 
 Observation matrices and Response vectors (COR) to select the optimal subsets
 in distributed estimation. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
Duplicated publication data (pre-processed and formatted) for entity resolution. This data set contains a total of 1879 records. The following variables are included in the data set: id, title, book title, authors, address, date, year, editor, journal, volume, pages, publisher, institution, type, tech, note. The data set has a respective gold data set that provides information on which records match based on id.
Calculates the co-ranking matrix to assess the
    quality of a dimensionality reduction.
Allows the user to apply nice color gradients to 'shiny' elements.
    The gradients are extracted from the 'colorffy' website. See <https://www.colorffy.com/gradients/catalog>.
Provides a bundle of basic and fundamental bioinformatics tools,
    such as network querying and alignment, subnetwork extraction and search,
    network biomarker identification.
We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S.,  Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering.  This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions.  Read http://arxiv.org/abs/1508.01939 for more details.
Functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) <doi:10.1080/10618600.2017.1349664>. There is an R native version with methods for printing, summarizing, and plotting the result.
Given a collection of intervals with integer start and end 
    positions, find recurrently targeted regions and estimate the significance 
    of finding. Randomization is implemented by parallel methods, either 
    using local host machines, or submitting grid engine jobs.
Create a custom sized Core Collection based on a distance matrix and applying
  the A-NE (accession nearest entry), E-NE (entry nearest entry) or E-E (entry entry) method
  as introduced in Jansen and van Hintum (2007) <doi:10.1007/s00122-006-0433-9> and
  further elaborated on in Odong, T.L. (2012) <https://edepot.wur.nl/212422>.
  Optionally a list of preselected accessions to be included into the core can be set.
  For each accession in the computed core, if available nearby accessions are retrievable
  that can be used as an alternative.
Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.
Core Hunter is a tool to sample diverse, representative subsets from large germplasm
    collections, with minimum redundancy. Such so-called core collections have applications in plant
    breeding and genetic resource management in general. Core Hunter can construct cores based on
    genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many
    provided evaluation measures depending on the precise purpose of the core (e.g. high diversity,
    representativeness, or allelic richness). In addition, multiple measures can be simultaneously
    optimized as part of a weighted index to bring the different perspectives closer together.
    The Core Hunter library is implemented in Java 8 as an open source project (see
    <http://www.corehunter.org>).
A suite of machine learning algorithms written in C++ with the R 
 interface contains several learning techniques for classification and regression.
 Predictive models include e.g., classification and regression trees with
 optional constructive induction and models in the leaves, random forests, kNN, 
 naive Bayes, and locally weighted regression. All predictions obtained with these
 models can be explained and visualized with the 'ExplainPrediction' package.  
 This package is especially strong in feature evaluation where it contains several variants of
 Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, 
 information gain, MDL, and DKM. These methods can be used for feature selection 
 or discretization of numeric attributes.
 The OrdEval algorithm and its visualization is used for evaluation
 of data sets with ordinal features and class, enabling analysis according to the 
 Kano model of customer satisfaction. 
 Several algorithms support parallel multithreaded execution via OpenMP.  
 The top-level documentation is reachable through ?CORElearn.
The 'Certifiably Optimal RulE ListS (Corels)' learner by
 Angelino et al described in <arXiv:1704.01701> provides interpretable decision
 rules with an optimality guarantee, and is made available to R with this package.
 See the file 'AUTHORS' for a list of copyright holders and contributors.
The Core Microbiome refers to the group of microorganisms that are consistently present in a particular environment, habitat, or host species. These microorganisms play a crucial role in the functioning and stability of that ecosystem. Identifying these microorganisms can contribute to the emerging field of personalized medicine. The 'CoreMicrobiomeR' is designed to facilitate the identification, statistical testing, and visualization of this group of microorganisms.This package offers three key functions to analyze and visualize microbial community data. This package has been developed based on the research papers published by Pereira et al.(2018) <doi:10.1186/s12864-018-4637-6> and Beule L, Karlovsky P. (2020) <doi:10.7717/peerj.9593>.
Provides a minimal interface for applying
    annotators from the 'Stanford CoreNLP' java library. Methods
    are provided for tasks such as tokenisation, part of speech
    tagging, lemmatisation, named entity recognition, coreference
    detection and sentiment analysis.
Core functions for simulating quantities of interest
    from generalised linear models (GLM). This package will form the backbone of
    a series of other packages that improve the interpretation of GLM estimates.
Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features 
	using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables 
	using data transformations.
A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.
In metabolic flux experiments tracer molecules (often glucose
    containing labelled carbon) are incorporated in compounds measured
    using mass spectrometry. The mass isotopologue distributions of these
    compounds needs to be corrected for natural abundance of labelled
    carbon and other effects, which are specific on the compound and
    ionization technique applied. This package provides functions to
    correct such effects in gas chromatography atmospheric pressure
    chemical ionization mass spectrometry analyses.
Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.
Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'.

          The desktop application 'Cornerstone' (<https://www.camline.com/en/products/cornerstone/cornerstone-core.html>)
    is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster.
    The engineers incorporate intensified hands-on statistics into their projects.
    They benefit from an intuitive and uniquely designed graphical Workmap concept: you design
    experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon,
    immediately, interactively, and without any programming.

          While 'Cornerstone's' interface to the statistical programming language 'R' has been available
    since version 6.0, the latest interface with 'R' is even much more efficient.
    'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the
    standard 'Cornerstone' GUI.
    Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R'
    routines, immediately and without the need to deal with programming code.
    Additionally, your 'R' programming team develops corresponding 'R' packages detached from
    'Cornerstone' in their favorite 'R' environment.

          Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel 
    (<https://www.youtube.com/watch?v=HEQHwq_laXU>) (available in German).
Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2023, <doi:10.1080/02664763.2023.2233057>). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.
Provides 'coroutines' for R, a family of functions that can
    be suspended and resumed later on. This includes 'async' functions
    (which await) and generators (which yield). 'Async' functions are
    based on the concurrency framework of the 'promises' package.
    Generators are based on a dependency free iteration protocol defined
    in 'coro' and are compatible with iterators from the 'reticulate'
    package.
Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>.
Manipulate and view coronavirus data and other societally relevant data at a basic level.
Offers access to a database on government responses to the COVID-19 pandemic. 
    To date, the 'CoronaNet' dataset provides the most comprehensive and granular 
    documentation of such government policies in the world, capturing data for 
    20 broad policy categories alongside many other dimensions, including the 
    initiator, target, and timing of a policy. This package is a programmatic 
    front-end to up-to-date 'CoronaNet' policy records and the 'CoronaNet' policy intensity index scores. 
    For more information, see Cheng et al. (2020) <doi:10.1038/s41562-020-0909-7>.
Provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.
Implements a James-Stein-type shrinkage estimator for 
  the covariance matrix, with separate shrinkage for variances and correlations.  
  The details of the method are explained in Schafer and Strimmer (2005) 
  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) 
  <DOI:10.2202/1544-6115.1252>.  The approach is both computationally as well
  as statistically very efficient, it is applicable to "small n, large p" data, 
  and always returns a positive definite and well-conditioned covariance matrix.  
  In addition to inferring the covariance matrix the package also provides 
  shrinkage estimators for partial correlations and partial variances.  
  The inverse of the covariance and correlation matrix 
  can be efficiently computed, as well as any arbitrary power of the 
  shrinkage correlation matrix.  Furthermore, functions are available for fast 
  singular value decomposition, for computing the pseudoinverse, and for 
  checking the rank and positive definiteness of a matrix.
Utility functions for the statistical analysis of corpus frequency data.
        This package is a companion to the open-source course "Statistical Inference: 
        A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL').
Facilitates dynamic exploration of text collections through an
    intuitive graphical user interface and the power of regular expressions.
    The package contains 1) a helper function to convert a data frame to a
    'corporaexplorerobject', 2) a 'Shiny' app for fast and flexible exploration
    of a 'corporaexplorerobject', and 3) a 'Shiny' app for simple
    retrieval/extraction of documents from a 'corporaexplorerobject' in a
    reading-friendly format. The package also includes demo apps with which
    one can explore Jane Austen's novels and the State of the Union Addresses
    (data from the 'janeaustenr' and 'sotu' packages respectively).
Provides text analysis in R, focusing on the use of a tokenized text format. In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations).
    Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences),
    similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages,
    and the possibility to reconstruct original text from tokens to facilitate interpretation.
Implementation of two-dimensional (2D) correlation analysis based
    on the Fourier-transformation approach described by Isao Noda (I. Noda
    (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot
    functions for the resulting correlation matrix: The first one creates
    colored 2D plots, while the second one generates 3D plots.
The goal of 'corrarray' is to create a multi-sample
  correlation array by combining the correlation matrices of a data set
  stratified by a grouping variable. 
  For two specified levels of the variable, 'corrarray' displays one level's 
  correlation matrix in the lower triangular matrix and the other level's
  correlation matrix in the upper triangular matrix.
  Such an output can enable visualization of correlations from two samples
  in a single correlation matrix or corrgram.
Implements non-parametric analyses for clustered
    binary and multinomial data. The elements of the cluster are assumed
    exchangeable, and identical joint distribution (also known as marginal
    compatibility, or reproducibility) is assumed for clusters of different
    sizes. A trend test based on stochastic ordering is implemented. 
    Szabo A, George EO. (2010) <doi:10.1093/biomet/asp077>;
    George EO, Cheon K, Yuan Y, Szabo A (2016) <doi:10.1093/biomet/asw009>.
Using a computationally efficient method, the package can
    be used to find the corrected coverage estimate of a credible set 
    of putative causal variants from Bayesian genetic fine-mapping. 
    The package can also be used to obtain a corrected credible set
    if required; that is, the smallest set of variants required such 
    that the corrected coverage estimate of the resultant credible set is  
    within some user defined accuracy of the desired coverage.
    Maller et al. (2012) <doi:10.1038/ng.2435>,
    Wakefield (2009) <doi:10.1002/gepi.20359>,
    Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.
Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.
Correcting area under ROC (AUC) for measurement error based on probit-shift model.
There are many estimators of false discovery rate. In this package we compute the Nonlocal False
   Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery
   Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. 
   Bickel, D.R., Rahal, A. (2019) <https://tinyurl.com/kkdc9rk8>.
Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.
Calculate a set of corrected test statistics for cases when samples
    are not independent, such as when classification accuracy values are obtained
    over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> 
    and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.
Lightweight package for computing different kinds
    of correlations, such as partial correlations, Bayesian correlations,
    multilevel correlations, polychoric correlations, biweight
    correlations, distance correlations and more. Part of the 'easystats'
    ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.
Speeds up exploratory data analysis (EDA)
    by providing a succinct workflow and interactive visualization tools for understanding
    which features have relationships to target (response). Uses binary correlation analysis
    to determine relationship. Default correlation method is the Pearson method. 
    Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.
Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.
Routines for the graphical representation of correlation matrices by means of correlograms, MDS maps and biplots obtained by PCA, PFA or WALS (weighted alternating least squares); See Graffelman & De Leeuw (2023) <doi: 10.1080/00031305.2023.2186952>.
A candidate correspondence table between two classifications can be created when there are correspondence tables leading from the first classification to the second one via intermediate 'pivot' classifications. 
    The correspondence table between two statistical classifications can be updated when one of the classifications gets updated to a new version.
Many correlation coefficient related functions are offered, such as correlations, partial correlations and hypothesis testing using asymptotic tests and computer intensive methods (bootstrap and permutation). References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press.
Calculates correlation of variables and displays the results
    graphically. Included panel functions can display points, shading, ellipses, and
    correlation values with confidence intervals. See Friendly (2002) <doi:10.1198/000313002533>.
When exploring data or models we often examine variables one by one. 
  This analysis is incomplete if the relationship between these variables is 
  not taken into account. The 'corrgrapher' package facilitates simultaneous 
  exploration of the Partial Dependence Profiles and the correlation between 
  variables in the model.
  The package 'corrgrapher' is a part of the 'DrWhy.AI' universe.
Performs Correlated Meta-Analysis ('corrmeta') across multiple OMIC
        scans, accounting for hidden non-independencies between elements of the
        scans due to overlapping samples, related samples, or other information. 
        For more information about the method, refer to the paper
        Province MA. (2013) <doi:10.1142/9789814447973_0023>.
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. 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.
Provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.
A tool for exploring correlations.  It makes it possible to
    easily perform routine tasks when exploring correlation matrices such
    as ignoring the diagonal, focusing on the correlations of certain
    variables against others, or rearranging and visualizing the matrix in
    terms of the strength of the correlations.
Statistical summary of STRUCTURE output. STRUCTURE is a K-means clustering method for inferring population structure and assigning individuals to populations using genetic data. Pritchard JK, Stephens M, Donnelly PJ (2000) <DOI:10.1093/genetics/155.2.945>. <https://web.stanford.edu/group/pritchardlab/structure.html>.
After using this, a publication-ready correlation table with p-values
  indicated will be created.  The input can be a full data frame; any
  string and Boolean terms will be dropped as part of functionality.  Correlations and
  p-values are calculated using the 'Hmisc' framework.  Output of the 
  correlation_matrix() function is a table of strings; this gets saved out to a '.csv2'
  with the save_correlation_matrix() function for easy insertion into a paper.
  For more details about the process, consult
  <https://paulvanderlaken.com/2020/07/28/publication-ready-correlation-matrix-significance-r/>.
Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.
An investigative tool designed to help users visualize correlations between variables in their datasets. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results.
Set of methods to constrain numerical series and time series within
             arbitrary boundaries.
There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in  6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation.  ST5() and ST6() used two different ways to combine ST3() and ST4().  We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports.  Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.
Significance test of Spearman's Rho or Kendall's Tau 
             between short-range dependent random variables.
We present 'corto' (Correlation Tool), a simple package to infer
 gene regulatory networks and visualize master regulators from gene expression
 data using DPI (Data Processing Inequality) and bootstrapping to recover edges.
 An initial step is performed to calculate all significant
 edges between a list of source nodes (centroids) and target genes.
 Then all triplets containing two centroids and one target are tested
 in a DPI step which removes edges. A bootstrapping process then calculates
 the robustness of the network, eventually re-adding edges previously removed by DPI.
 The algorithm has been optimized to run outside a computing cluster, using a fast correlation
 implementation. The package finally provides functions to calculate network enrichment
 analysis from RNA-Seq and ATAC-Seq signatures as described in the article by
 Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.
Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'.
Implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes.
  Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) <doi:10.1080/19345747.2021.1947425>.
A set of simple functions that transforms longitudinal
    data to estimate the cosinor linear model as described in Tong (1976).
    Methods are given to summarize the mean, amplitude and acrophase, to
    predict the mean annual outcome value, and to test the coefficients.
Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). <doi:10.1186/1742-4682-11-16>.
A data package with 2 main package variables: 'signature' and 'etiology'. 
     The 'signature' variable contains the latest mutational signature profiles 
     released on COSMIC <https://cancer.sanger.ac.uk/signatures/> for 3 mutation
     types:
     * Single base substitutions in the context of preceding and following bases,
     * Doublet base substitutions, and
     * Small insertions and deletions.
    The 'etiology' variable provides the known or hypothesized causes of signatures. 
    'cosmicsig' stands for COSMIC signatures. Please run ?'cosmicsig' for more
    information.
Package encapsulates standard expressions for distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations.  Currently coded for a flat universe only.
Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).
The COSSO regularization method automatically
        estimates and selects important function components by a
        soft-thresholding penalty in the context of smoothing spline
        ANOVA models. Implemented models include mean regression,
        quantile regression, logistic regression and the Cox regression
        models.
Parameter estimation, one-step ahead forecast and new location
    prediction methods for spatio-temporal data.
Contains functions that can determine whether a time series
	is second-order stationary or not (and hence evidence for
	locally stationarity). Given two non-stationary series (i.e.
	locally stationary series) this package can then discover
	time-varying linear combinations that are second-order stationary.
	Cardinali, A. and Nason, G.P. (2013)
	<doi:10.18637/jss.v055.i01>.
Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation.
	Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest.
	Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers
	that don't accept observation weights.
The 'CoTiMA' package performs meta-analyses of correlation matrices of repeatedly measured variables taken from 
   studies that used different time intervals. Different time intervals between measurement occasions impose problems for 
   meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common 
   fixed or random effects analysis. However, continuous time math, which is applied in 'CoTiMA', can be used to extrapolate or 
   intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different 
   time intervals can be meta-analyzed. 'CoTiMA' fits models to empirical data using the structural equation model (SEM) package 
   'ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e., 
   continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical 
   model comparisons and significance tests are then performed on the continuous time parameter estimates. 'CoTiMA' also allows 
   analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power 
   (post hoc power, required sample sizes). See Dormann, C., Guthier, C., & Cortina, J. M. (2019) <doi:10.1177/1094428119847277>.
   and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) <doi:10.1037/bul0000304>.
Count transformation models featuring 
        parameters interpretable as discrete hazard ratios, odds ratios,
        reverse-time discrete hazard ratios, or transformed expectations. 
        An appropriate data transformation for a count outcome and
        regression coefficients are simultaneously estimated by maximising
        the exact discrete log-likelihood using the computational framework
        provided in package 'mlt', technical details are given in
        Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>. 
        The package also contains an experimental implementation of 
        multivariate count transformation models with an application
        to multi-species distribution models <arXiv:2201.13095>.
Implements cointegration/co-trending rank selection
        algorithm in Guo and Shintani (2013) "Consistent
        co-trending rank selection when both stochastic and nonlinear
        deterministic trends are present". 
        The Econometrics Journal 16: 473-483 
        <doi:10.1111/j.1368-423X.2012.00392.x>.
        Numbered examples correspond to Feb 2011 preprint 
        <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Counts colors within color range(s) in images, and
    provides a masked version of the image with targeted pixels
    changed to a different color. Output includes the locations
    of the pixels in the images, and the proportion of the image
    within the target color range with optional background masking.
    Users can specify multiple color ranges for masking.
The beta-binomial test is used for significance analysis of independent samples by Pham et al. (2010) <doi:10.1093/bioinformatics/btp677>. The inverted beta-binomial test is used for paired sample testing, e.g. pre-treatment and post-treatment data, by Pham and Jimenez (2012) <doi:10.1093/bioinformatics/bts394>.
The maximum likelihood estimation (MLE) of the count data models along with standard error of the estimates and Akaike information model section criterion are provided. The functions allow to compute the MLE for the following distributions such as the Bell distribution, the Borel distribution, the Poisson distribution, zero inflated Bell distribution, zero inflated Bell Touchard distribution, zero inflated Poisson distribution, zero one inflated Bell distribution and zero one inflated Poisson distribution. Moreover, the probability mass function (PMF), distribution function (CDF), quantile function (QF) and random numbers generation of the Bell Touchard and zero inflated Bell Touchard distribution are also provided.
A simple countdown timer for slides and HTML documents
    written in 'R Markdown' or 'Quarto'. Integrates fully into 'Shiny'
    apps. Countdown to something amazing.
Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.
Modular and unified R6-based interface for counterfactual explanation methods.
    The following methods are currently implemented: Burghmans et al. (2022) <arXiv:2104.07411>, 
    Dandl et al. (2020) <doi:10.1007/978-3-030-58112-1_31> and Wexler et al. (2019) <doi:10.1109/TVCG.2019.2934619>.
    Optional extensions allow these methods to be applied to a variety of models and use cases.
    Once generated, the counterfactuals can be analyzed and visualized by provided functionalities.
Randomization-Based Inference for customized experiments.
  Computes Fisher-Exact P-Values alongside null randomization 
  distributions. Retrieves counternull sets and generates counternull 
  distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. 
  Package includes visualization of randomization distributions and Fisher 
  Intervals. Users can input custom test statistics and their own methods for 
  randomization. 
  Rosenthal and Rubin (1994) <doi:10.1111/j.1467-9280.1994.tb00281.x>.
A large number of measurements generate count data. This is a statistical data type 
    that only assumes non-negative integer values and is generated by counting. Typically, counting 
    data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. 
    The number of DNA double-strand breaks can be counted in individual cells using various 
    bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of 
    the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. 
    Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software 
    offers functions for a comprehensive automated evaluation of distribution models of count 
    data. In addition to programmatic interaction, a graphical user interface (web server) 
    is included, which enables fast and interactive data-scientific analyses. The user is supported 
    in selecting the most suitable counting distribution for his own data set.
Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.
A set of functions for applying a 
    restricted linear algebra to the analysis of 
    count-based data. See the accompanying preprint
    manuscript: "Normalizing need not be the norm:
    count-based math for analyzing single-cell data"
    Church et al (2022) <doi:10.1101/2022.06.01.494334>
    This tool is specifically designed to analyze 
    count matrices from single cell RNA sequencing 
    assays. The tools implement several count-based
    approaches for standard steps in single-cell 
    RNA-seq analysis, including scoring genes and cells, 
    comparing cells and clustering, calculating differential 
    gene expression, and several methods for rank 
    reduction. There are many opportunities for further
    optimization that may prove useful in the analysis of
    other data. We provide the source code freely
    available at <https://github.com/shchurch/countland>
    and encourage users and developers to fork the code for 
    their own purposes.
Calculates metrics of proportionality using the logit-normal multinomial model.  It can also provide empirical and plugin estimates of these metrics.
Flexible univariate count models based on renewal processes. The
    models may include covariates and can be specified with familiar formula
    syntax as in glm() and package 'flexsurv'.  The methodology is described by
    Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette
    'Countr_guide' in the package).
    If the suggested package 'pscl' is not available from CRAN, it can be
    installed with 'remotes::install_github("cran/pscl")'. It is no longer used
    by the functions in this package but is needed for some of the extended
    examples.
Wrangle country data more effectively and quickly. 
    This package contains functions to easily identify and convert country names,
    download country information, merge country data from different sources, 
    and make quick world maps.
Standardize country names, convert them into one of 40
    different coding schemes, convert between coding schemes, and assign
    region descriptors.
Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .
Implements the count splitting methodology from Neufeld et al. (2022) <doi:10.1093/biostatistics/kxac047> and Neufeld et al. (2023) <arXiv:2307.12985>. Intended for turning a matrix of single-cell RNA sequencing counts, or similar count datasets, into independent folds that can be used for training/testing or cross validation. Assumes that the entries in the matrix are from a Poisson or a negative binomial distribution.
For Bayesian and classical inference and prediction with count-valued data,
    Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable,
    and easy-to-use approach. STAR models the observed count data using a rounded 
    continuous data model and incorporates a transformation for greater flexibility.
    Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of 
    (i) transforming count-valued data and subsequently 
    (ii) modeling the transformed data using Gaussian models. 
    STAR is well-defined for count-valued data, which is reflected in predictive accuracy, 
    and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. 
    Importantly, STAR is easy to combine with existing MCMC or point estimation
    methods for continuous data, which allows seamless adaptation of continuous data
    models (such as linear regressions, additive models, BART, random forests,
    and gradient boosting machines) for count-valued data. The package also includes several
    methods for modeling count time series data, namely via warped Dynamic Linear Models. 
    For more details and background on these methodologies, see the works of 
    Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, 
    Kowal and Wu (2022) <doi:10.1111/biom.13617>, 
    King and Kowal (2022) <arXiv:2110.14790>, and 
    Kowal and Wu (2023) <arXiv:2110.12316>.
Implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.
Provide data transformation functions to transform counts in RNA-seq data analysis. Please see the reference: Zhang Z, Yu D, Seo M, Hersh CP, Weiss ST, Qiu W. (2019) <doi.org/10.1038/s41598-019-41315-w>.
A specialized tool is designed for assessing contextual bandit algorithms, particularly those aimed at handling overdispersed and zero-inflated count data. It offers a simulated testing environment that includes various models like Poisson, Overdispersed Poisson, Zero-inflated Poisson, and Zero-inflated Overdispersed Poisson. The package is capable of executing five specific algorithms: Linear Thompson sampling with log transformation on the outcome, Thompson sampling Poisson, Thompson sampling Negative Binomial, Thompson sampling Zero-inflated Poisson, and Thompson sampling Zero-inflated Negative Binomial. Additionally, it can generate regret plots to evaluate the performance of contextual bandit algorithms. This package is based on the algorithms by Liu et al. (2023) <arXiv:2311.14359>.
Implementing seven Covariate-Adaptive Randomization to assign patients to two treatments. 
    Three of these procedures can also accommodate quantitative and mixed covariates. Given a set of covariates, the user can
    generate a single sequence of allocations or replicate the design multiple times by simulating the patients' covariate
    profiles. At the end, an extensive assessment of the performance of the randomization procedures is provided, calculating
    several imbalance measures. See Baldi Antognini A, Frieri R, Zagoraiou M and Novelli M (2022) <doi:10.1007/s00362-022-01381-1> for details.
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
Tests on properties of space-time covariance functions.
    Tests on symmetry, separability and for assessing 
    different forms of non-separability are available. Moreover tests on 
    some classes of covariance functions, such that the classes of 
    product-sum models, Gneiting models and integrated product models have 
    been provided.  It is the companion R package to the papers of  
    Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability 
    and some classes of space-time covariance function models <doi:10.1007/s00477-017-1472-2>
    and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for
    selecting a class of space-time covariance functions <doi:10.18637/jss.v094.i01>.
Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.
Combine partial covariance matrices using a Wishart-EM algorithm. 
    Methods are described in the November 2019 article by Akdemir et al. 
    <https://www.biorxiv.org/content/10.1101/857425v1>.
    It can be used to combine partially overlapping covariance matrices 
    from independent trials, partially overlapping multi-view relationship
    data from genomic experiments, partially overlapping Gaussian graphs
    described by their covariance structures. 
    High dimensional covariance estimation, 
    multi-view data integration.
    high dimensional covariance graph estimation.
A covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates.
Produce forest plots to visualize covariate effects using either
    the command line or an interactive 'Shiny' application.
Computes p-values using the largest root test using 
    an approximation to the null distribution by Johnstone (2008) <DOI:10.1214/08-AOS605>.
Process Digital Cover Photography images of tree canopies to get canopy attributes like Foliage Cover and Leaf Area Index. 
    Detailed description of the methods in Chianucci et al. (2022) <doi:10.1007/s00468-018-1666-3>.
Converts any word, sentence or speech into Trump's infamous
  "covfefe" format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>.
  Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>.
Direct sparse covariance matrix estimation via the covariance graphical lasso by Bien, Tibshirani (2011) <doi:10.1093/biomet/asr054> using the fast coordinate descent algorithm of Wang (2014) <doi:10.1007/s11222-013-9385-5>.
Provides a daily summary of COVID-19 cases, deaths, recovered, tests,
  vaccinations, and hospitalizations for 230+ countries, 760+ regions, 
  and 12000+ administrative divisions of lower level. 
  Includes policy measures, mobility data, and geospatial identifiers.
  Data source: COVID-19 Data Hub <https://covid19datahub.io>.
Load and analyze updated time series worldwide data of reported cases for the Novel Coronavirus Disease (COVID-19) from different sources, including the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository <https://github.com/CSSEGISandData/COVID-19>, "Our World in Data" <https://github.com/owid/> among several others. The datasets reporting the COVID-19 cases are available in two main modalities, as a time series sequences and aggregated data for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread.
Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at <https://covid.saude.gov.br/>, is available at country, region, state, and city-levels. The package also downloads the world-level COVID-19 data from the John Hopkins University's repository.
Dataset with strategic information about COVID-19 in Brazil.
    Data for municipalities, states, region and Brazil. Data source:
    Sistema Unico de Saude - SUS.
Provides different datasets parsed from 'Drugbank' 
    <https://www.drugbank.ca/covid-19> database using 'dbparser' package. 
    It is a smaller version from 'dbdataset' package. It contains only information
    about COVID-19 possible treatment.
Imports and cleans 'opencovid19-fr'
    <https://github.com/opencovid19-fr/data> data on COVID-19 in France.
Pull raw and pre-cleaned versions of national and state-level 
    COVID-19 time-series data from covid19india.org <https://www.covid19india.org>. 
    Easily obtain and merge case count data, testing data, and vaccine data. 
    Also assists in calculating the time-varying effective reproduction number 
    with sensible parameters for COVID-19.
Provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile <https://www.protezionecivile.it/>.
Provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division <https://datasf.org/opendata/>.
Provides a daily counts of the Coronavirus (COVID19) cases by districts and country. Data source: Epidemiological Unit, Ministry of Health, Sri Lanka <https://www.epid.gov.lk/web/>.
Provides a daily summary of the Coronavirus (COVID-19) cases in Switzerland cantons and Principality of Liechtenstein. Data source: Specialist Unit for Open Government Data Canton of Zurich <https://www.zh.ch/de/politik-staat/opendata.html>.
Data personally collected about the spread of COVID-19 (SARS-COV-2) in Tunisia
    <https://github.com/MounaBelaid/covid19datatunisia>.
A wrapper around the 'COVID Tracking Project API'
    <https://covidtracking.com/api/> providing data on cases of COVID-19
    in the US.
Tools for Delphi's 'COVIDcast Epidata' API: data access, maps and
    time series plotting, and basic signal processing. The API includes a
    collection of numerous indicators relevant to the COVID-19 pandemic in the
    United States, including official reports, de-identified aggregated medical
    claims data, large-scale surveys of symptoms and public behavior, and
    mobility data, typically updated daily and at the county level. All data
    sources are documented at
    <https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html>.
Provides tools for downloading, reading and analyzing the COVID19 National
  Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute
  of Geography and Statistics - IBGE. The data must be downloaded from the official
  website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
A feasible framework for mutation analysis and reverse transcription 
  polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including 
  mutation profile visualization, statistics and mutation ratio of each assay. 
  The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in 
  large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020) 
  <doi:10.20944/preprints202004.0529.v1>.
Herramientas para el análisis de datos de COVID-19 en México. Descarga y analiza 
  los datos para COVID-19 de la Direccion General de Epidemiología de México (DGE) 
  <https://www.gob.mx/salud/documentos/datos-abiertos-152127>,
  la Red de Infecciones Respiratorias Agudas Graves (Red IRAG)
  <https://www.gits.igg.unam.mx/red-irag-dashboard/reviewHome> y la Iniciativa Global 
  para compartir todos los datos de influenza (GISAID)
  <https://gisaid.org/>. 
  English: Downloads and analyzes data  of COVID-19 from the  Mexican General 
  Directorate of Epidemiology (DGE), the Network of 
  Severe Acute Respiratory  Infections (IRAG network),and the Global 
  Initiative on Sharing All Influenza Data GISAID.
Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination.
We propose a method to estimate the probability of an 
    undetected case of COVID-19 in a defined setting, when a given number of 
    people have been exposed, with a given pretest probability of having 
    COVID-19 as a result of that exposure. Since we are interested in undetected
    COVID-19, we assume no person has developed symptoms (which would warrant 
    further investigation) and that everyone was tested on a given day, and all 
    tested negative.
The COVID Symptom Study is a non-commercial project that uses a free mobile app to facilitate real-time data collection of symptoms, exposures, and risk factors related to COVID19. The package allows easy access to summary statistics data from COVID Symptom Study Sweden.
Matrix-variate covariance estimation via the Kronecker-core decomposition. Computes the Kronecker and core covariance matrices corresponding to an arbitrary covariance matrix, and provides an empirical Bayes covariance estimator that adaptively shrinks towards the space of separable covariance matrices. For details, see Hoff, McCormack and Zhang (2022) <arXiv:2207.12484> "Core Shrinkage Covariance Estimation for Matrix-variate data".
Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' <https://about.codecov.io> or
    'Coveralls' <https://coveralls.io>. Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.
Covariance Regression with Random Forests (CovRegRF) is a
    random forest method for estimating the covariance matrix of a
    multivariate response given a set of covariates. Random forest trees
    are built with a new splitting rule which is designed to maximize the
    distance between the sample covariance matrix estimates of the child
    nodes. The method is described in Alakus et al. (2023)
    <doi:10.1186/s12859-023-05377-y>. 'CovRegRF' uses 'randomForestSRC' package
    (Ishwaran and Kogalur, 2022) 
    <https://cran.r-project.org/package=randomForestSRC> by freezing at the
    version 3.1.0. The custom splitting rule feature is utilised to apply the
    proposed splitting rule. The 'randomForestSRC' package implements 'OpenMP' 
    by default, contingent upon the support provided by the target architecture 
    and operating system. In this package, 'LAPACK' and 'BLAS' libraries are 
    used for matrix decompositions.
The cov.nnve() function implements robust covariance estimation
        by the nearest neighbor variance estimation (NNVE) method of
        Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.
Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
Functions for testing if the covariance structure of 2-dimensional data
    (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) =
    C_1 x C_2.
    A complete descriptions of the implemented tests can be found in the paper
    Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in
    nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017),
    no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.
Random sampling from distributions with user-specified population covariance matrix. Marginal information may be fully
        specified, for which the package implements the VITA (VIne-To-Anything) algorithm
        Grønneberg and Foldnes (2017) <doi:10.1007/s11336-017-9569-6>.  See also Grønneberg, Foldnes and Marcoulides (2022) <doi:10.18637/jss.v102.i03>.
        Alternatively, marginal skewness and kurtosis may be specified, for which the package
        implements the IG (independent generator) and PLSIM (piecewise linear) algorithms, see Foldnes and Olsson (2016) <doi:10.1080/00273171.2015.1133274> and
        Foldnes and Grønneberg (2021) <doi:10.1080/10705511.2021.1949323>, respectively.
Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance
  matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of 
  covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix
  tests use C++ to speed performance and allow larger data sets.
Covariance is of universal prevalence across various disciplines within statistics.
    We provide a rich collection of geometric and inferential tools for convenient analysis of
    covariance structures, topics including distance measures, mean covariance estimator,
    covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
    For an introduction to covariance in multivariate statistical analysis,
    see Schervish (1987) <doi:10.1214/ss/1177013111>.
Implements a specific form of segmented linear regression
    with two independent variables. The visualization of that function looks 
    like a quarter segment of a cowbell giving the package its name. 
    The package has been specifically constructed for the case where minimum 
    and maximum value of the dependent and two independent variables 
    are known a prior, which is usually the case
    when those values are derived from Likert scales.
Provides various features that help with creating publication-quality figures
    with 'ggplot2', such as a set of themes, functions to align plots and arrange
    them into complex compound figures, and functions that make it easy to annotate
    plots and or mix plots with images. The package was originally written for
    internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package).
    It has also been used extensively in the book Fundamentals of Data
    Visualization.
Allows printing of character strings as messages/warnings/etc.
    with ASCII animals, including cats, cows, frogs, chickens, ghosts,
    and more.
Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) <doi:10.48550/arXiv.2206.02296>; Rava (2021) <https://escholarship.org/uc/item/8h1846gs>.
The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).
Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) <doi:10.1017/S000712341700045X> and Harden and Kropko (2018) <doi:10.1017/psrm.2018.19>.
Perform the functional modeling methods of Huang and Wang (2018) <doi:10.1111/biom.12741> to accommodate dependent error in covariates of the proportional hazards model. The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available.
Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below.
Fit Cox proportional hazards models containing both 
 fixed and random effects.  The random effects can have a general form, of which
 familial interactions (a "kinship" matrix) is a particular special case. 
 Note that the simplest case of a mixed effects Cox model, i.e. a single random 
 per-group intercept, is also called a "frailty" model.  The approach is based
 on Ripatti and Palmgren, Biometrics 2002.
This software package provides Cox survival analysis for high-dimensional and multiblock datasets. 
             It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis,
             including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, 
             Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, 
             and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle 
             high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources 
             for interpreting results. While references are available within the corresponding functions, 
             key literature is mentioned below.
             Terry M Therneau (2024) <https://CRAN.R-project.org/package=survival>,
             Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>,
             Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>,
             Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>,
             Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>,
             Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>,
             Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.
Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
  which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see 
  Heinze and Schemper (2001) and Heinze and Dunkler (2008).
  The program fits profile penalized likelihood confidence intervals which were proved to outperform
  Wald confidence intervals.
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
Implements weighted estimation in Cox regression as proposed by
             Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, 
             <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and 
             Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). 
             Weighted Cox regression provides unbiased average hazard ratio 
             estimates also in case of non-proportional hazards. 
             Approximated generalized concordance probability an effect size measure for clear-cut
             decisions can be obtained.
             The package provides options to estimate time-dependent effects conveniently by
             including interactions of covariates with arbitrary functions of time, with or without
             making use of the weighting option.
A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables.
Calculate the R-squared, aka explained randomness, based on the partial likelihood ratio statistic under the Cox Proportional Hazard model [J O'Quigley, R Xu, J Stare (2005) <doi:10.1002/sim.1946>].
An implementation of robust estimation in Cox model. Functionality includes fitting efficiently and robustly Cox proportional hazards regression model in its basic form, where explanatory variables are time independent with one event per subject.  Method is based on a smooth modification of the partial likelihood.
Fits Cox regression based on retrospectively ascertained times-to-event. The method uses Inverse-Probability-Weighting estimating equations.
Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Functions for calculating the conditional power
             for different models in survival time analysis
             within randomized clinical trials
             with two different treatments to be compared
             and survival as an endpoint.
Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.
Functions to test and compare causal models using Confirmatory Path Analysis.
Implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.
A Bayesian meta-analysis method for studying cross-phenotype
    genetic associations. It uses summary-level data across multiple phenotypes to
    simultaneously measure the evidence of aggregate-level pleiotropic association
    and estimate an optimal subset of traits associated with the risk locus. CPBayes
    is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) <doi:10.1371/journal.pgen.1007139>.
Implements cluster-polarization coefficient for measuring distributional
	polarization in single or multiple dimensions, as well as associated functions.
	Contains support for hierarchical clustering, k-means, partitioning around medoids,
	density-based spatial clustering with noise, and manually imposed cluster membership.
	Mehlhaff (forthcoming) <https://imehlhaff.net/files/CPC_note.pdf>.
Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.
Probability mass function, distribution function, quantile function and random generation for the Complex Triparametric Pearson (CTP) and Complex Biparametric Pearson (CBP) distributions developed by Rodriguez-Avi et al (2003) <doi:10.1007/s00362-002-0134-7>, Rodriguez-Avi et al (2004) <doi:10.1007/BF02778271> and Olmo-Jimenez et al (2018) <doi:10.1080/00949655.2018.1482897>. The package also contains maximum-likelihood fitting functions for these models.
Concordance probability estimate (CPE) is a commonly used performance measure in survival analysis that evaluates the predictive accuracy of a survival model.  It measures how well a model can distinguish between pairs of individuals with different survival times.  Specifically, it calculate the proportion of all pairs of individuals whose predicted survival times are correctly ordered.
Classification using Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array. See Harshman and Lundy (1994): <doi:10.1016/0167-9473(94)90132-5>. Uses component weights from one mode of a Parafac or Parafac2 model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Allows for constraints on different tensor modes. Supports penalized logistic regression, support vector machine, random forest, feed-forward neural network, and regularized discriminant analysis. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Implements parallel computing via the 'parallel' and 'doParallel' packages.
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
Functions to generate ensembles of generalized linear models using 
             competing proximal gradients. The optimal sparsity and diversity
             tuning parameters are selected via an alternating grid search.
A general test for conditional independence in supervised learning 
  algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>. 
  Implements a conditional variable importance measure which can be applied to any supervised 
  learning algorithm and loss function. Provides statistical inference procedures without 
  parametric assumptions and applies equally well to continuous and categorical predictors 
  and outcomes.
Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <doi:10.1007/s11222-012-9343-7>.
Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include bar plots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms.
Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. A full description of this package is available in Ross, G.J (2015) - "Parametric and nonparametric sequential change detection in R" available at <https://www.jstatsoft.org/article/view/v066i03>.
A big data version for fitting cumulative probability models using the orm() function from the 'rms' package.  See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.
We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022)<doi:10.1101/2022.06.23.497396>. This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances.  The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes.
Connectome Predictive Modelling (CPM) (Shen et al. (2017)
    <doi:10.1038/nprot.2016.178>) is a method to predict individual
    differences in behaviour from brain functional connectivity. 'cpmr'
    provides a simple yet efficient implementation of this method.
Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data.
Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) <doi:10.1080/10618600.2018.1512868>. This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope.
CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.
Provides a header only, C++11 interface to R's C
    interface.  Compared to other approaches 'cpp11' strives to be safe
    against long jumps from the C API as well as C++ exceptions, conform
    to normal R function semantics and supports interaction with 'ALTREP'
    vectors.
Allow to run 'Cppcheck' (<https://cppcheck.sourceforge.io/>)
    on 'C' and 'C++' files with a 'R' command or a 'RStudio' addin. The report
    appears in the 'RStudio' viewer pane as a formatted 'HTML' file. It is
    also possible to get this report with a 'shiny' application. 'Cppcheck' 
    can spot many error types and it can also give some recommendations on the 
    code.
Compare double-precision floating point vectors using
    relative differences. All equality operations are calculated using
    'cpp11'.
Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm.
    Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>),
    bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://archive.siam.org/meetings/alenex05/papers/03agoldberg.pdf>),
    A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>),
    new bidirectional A* (Pijls & Post (2009) <https://repub.eur.nl/pub/16100/ei2009-10.pdf>),
    Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) <doi:10.1007/978-3-540-68552-4_24>),
    PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) <doi:10.1016/j.jpdc.2012.02.007>).
    Algorithms for solving the traffic assignment problem are All-or-Nothing assignment,
    Method of Successive Averages,
    Frank-Wolfe algorithm (M. Fukushima (1984) <doi:10.1016/0191-2615(84)90029-8>),
    Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) <doi:10.1287/trsc.1120.0409>),
    Algorithm-B (R. B. Dial (2006) <doi:10.1016/j.trb.2006.02.008>).
Implementation of the Control Polygon Reduction and Control Net
    Reduction methods for finding parsimonious B-spline regression models.
Implements the three-step workflow for robust analysis of change in 
    two repeated measurements of continuous outcomes, described in Ning et al.
    (in press), "Robust estimation of the effect of an exposure on the change in
    a continuous outcome", BMC Medical Research Methodology.
Calculate date of birth, age, and gender, and generate anonymous
    sequence numbers from CPR numbers.
    <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>.
The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arXiv:1511.04992>. If the 'rho=' argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <DOI:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.
Load Current Population Survey (CPS) microdata into R using the
    'Census Bureau Data' API
    (<https://www.census.gov/data/developers/data-sets.html>), including basic
    monthly CPS and CPS ASEC microdata.
Implements multiple change searching algorithms for a variety of 
    frequently considered parametric change-point models. In particular, it 
    integrates a criterion proposed by Zou, Wang and Li (2020) 
    <doi:10.1214/19-AOS1814> to select the number of change-points in a 
    data-driven fashion. Moreover, it also provides interfaces for 
    user-customized change-point models with one's own cost function and 
    parameter estimation routine. It is easy to get started with the 
    cpss.* set of functions by accessing their documentation pages 
    (e.g., ?cpss).
Nonparametric change point estimation for survival data based on p-values of exact binomial tests.
Simulates time-to-event data
    with type I right censoring using two methods: the inverse CDF
    method and our proposed memoryless method. The latter method
    takes advantage of the memoryless property of survival and
    simulates a separate distribution between change-points. We
    include two parametric distributions: exponential and Weibull.
    Inverse CDF method draws on the work of Rainer Walke (2010), 
    <https://www.demogr.mpg.de/papers/technicalreports/tr-2010-003.pdf>.
Provides automated methods for downloading, recoding, and merging 
    selected years of the Current Population Survey's Voting and Registration 
    Supplement, a large N national survey about registration, voting, and 
    non-voting in United States federal elections. Provides documentation for 
    appropriate use of sample weights to generate statistical estimates, 
    drawing from Hur & Achen (2013) <doi:10.1093/poq/nft042> and McDonald (2018) 
    <http://www.electproject.org/home/voter-turnout/voter-turnout-data>.
Non-parametric test for equality of multivariate distributions.  Trains a classifier to classify (multivariate) observations as coming from one of several distributions.  If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.
Incorporates colour gradients from the 'cpt-city' web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>.
Implements the nonparametric moving sum procedure for detecting 
    changes in the joint characteristic function (NP-MOJO) for multiple change
    point detection in multivariate time series. See McGonigle, E. T., Cho, H. 
    (2023) <arXiv:2305.07581> for description of the NP-MOJO methodology.
Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).
Estimation, based on conditional maximum likelihood, of the quadratic exponential 
             model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> 
             and of a simplified and a modified version of this model. The quadratic exponential model 
             is suitable for the analysis of binary longitudinal data when state dependence (further 
             to the effect of the covariates and a time-fixed individual intercept) has to be taken 
             into account. Therefore, this is an alternative to the dynamic logit model having the 
             advantage of easily allowing conditional inference in order to eliminate the individual 
             intercepts and then getting consistent estimates of the parameters of main interest 
             (for the covariates and the lagged response). The simplified version of this model 
             does not distinguish, as the original model does, between the last time occasion 
             and the previous occasions. The modified version formulates in a different way the 
             interaction terms and it may be used to test in a easy way state dependence as shown 
             in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. 
             The package also includes estimation of the dynamic logit model by a pseudo conditional 
             estimator based on the quadratic exponential model, as proposed by 
             Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. 
             For large time dimensions of the panel, the computation of the proposed models involves a 
             recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal 
             Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C.
             (2021, Computational Economics <DOI:10.1007/s10614-021-10218-2>.
Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on 'Liang and Zeger' (1986) <doi:10.1093/biomet/73.1.13> and Bell and 'McCaffrey' <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0>, Huang, 'Wiedermann', and 'Zhang' <doi:10.1080/00273171.2022.2077290>, and Huang, 'Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness).
Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) <doi:10.1038/s41586-020-2176-1>.
Implements Cragg-Donald (1993) <doi:10.1017/S0266466600007519> and Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006> tests for weak instruments in R.
Provides R routine for the so called two-sample
        Cramer-Test.  This nonparametric two-sample-test on equality
        of the underlying distributions can be applied to 
        multivariate data as well as univariate data. It offers two 
        possibilities to approximate the critical value both of which 
        are included in this package.
The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <arXiv:2008.03073>, of which functions are provided.
Functions for completing and recalculating rankings and sorting.
A set of functions to manage 'CRAN'-like repositories
    efficiently.
'API' to the database of 'CRAN' package downloads from the
    'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>,
    see <https://github.com/r-hub/cranlogs.app> for the raw 'API'.
Core visualizations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organizing the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, producing package dependence trees, and for computing useful summaries and producing interactive visualizations from the resulting networks and summaries. The resulting networks can be coerced to 'igraph' <https://CRAN.R-project.org/package=igraph> objects for further analyses and modelling.
One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.
Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.
Fit continuous-time correlated random walk models with time indexed
    covariates to animal telemetry data. The model is fit using the Kalman-filter on
    a state space version of the continuous-time stochastic movement process.
The crayon package is now superseded. Please use the 'cli' package
    for new projects.
    Colored terminal output on terminals that support 'ANSI'
    color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI'
    color support is automatically detected. Colors and highlighting can
    be combined and nested. New styles can also be created easily.
    This package was inspired by the 'chalk' 'JavaScript' project.
Provides color palettes based on crayon colors since the early 1900s. 
    Colors are based on various crayon colors, sets, and promotional palettes, most of
    which can be found at <https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors>. All 
    palettes are discrete palettes and are not necessarily color-blind friendly. 
    Provides scales for 'ggplot2' for discrete coloring.
Hardware-based support for 'CRC32C' cyclic redundancy checksum function
  is made available for 'x86_64' systems with 'SSE2' support as well as for 'arm64', 
  and detected at build-time via 'cmake' with a software-based fallback.  This
  functionality is exported at the 'C'-language level for use by other packages.
  'CRC32C' is described in 'RFC 3270' at <https://datatracker.ietf.org/doc/html/rfc3720>
  and is based on 'Castagnoli et al' <doi:10.1109/26.231911>.
Different approaches to censored or truncated regression with 
  conditional heteroscedasticity are provided. First, continuous 
  distributions can be used for the (right and/or left censored or truncated)
  response with separate linear predictors for the mean and variance. 
  Second, cumulative link models for ordinal data
  (obtained by interval-censoring continuous data) can be employed for
  heteroscedastic extended logistic regression (HXLR). In the latter type of
  models, the intercepts depend on the thresholds that define the intervals.
  Infrastructure for working with censored or truncated normal, logistic,
  and Student-t distributions, i.e., d/p/q/r functions and distributions3
  objects.
Frequentist statistical inference for cluster randomised trials with
  multiple outcomes that controls the family-wise error rate and provides nominal
  coverage of confidence sets. A full description of the methods can be found in Watson et al. (2023) <doi:10.1002/sim.9831>.
Provides a new method for interpretable heterogeneous 
    treatment effects characterization in terms of decision rules 
    via an extensive exploration of heterogeneity patterns by an 
    ensemble-of-trees approach, enforcing high stability in the 
    discovery. It relies on a two-stage pseudo-outcome regression, and 
    it is supported by theoretical convergence guarantees. Bargagli-Stoffi, 
    F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: 
    Interpretable Discovery and Inference of Heterogeneous Treatment Effects.  
    arXiv preprint <arXiv:2009.09036>.
Provides a new method for identification of clusters of genomic
 regions within chromosomes. Primarily, it is used for calling clusters of 
 cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions
 in the tissue or cell type of interest, such as those generated from chromatin-based 
 assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements 
 within chromosomes of a given sample to identify COREs in the following steps:
 1) It identifies window size or the maximum allowed distance between the elements 
 within each CORE, 2) It identifies number of elements which should be clustered 
 as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which 
 does not pass the threshold considered in the approach.
Create Pairwise Comparison Matrices for use in the Analytic Hierarchy Process. The Pairwise Comparison Matrix created will be a logical matrix, which unlike a random comparison matrix, is similar to what a rational decision maker would create on the basis of a preference vector for the alternatives considered.
Setup and retrieve HTTPS and SSH credentials for use with 'git' and 
    other services. For HTTPS remotes the package interfaces the 'git-credential' 
    utility which 'git' uses to store HTTP usernames and passwords. For SSH 
    remotes we provide convenient functions to find or generate appropriate SSH 
    keys. The package both helps the user to setup a local git installation, and
    also provides a back-end for git/ssh client libraries to authenticate with 
    existing user credentials.
A tiny package to generate CRediT author statements (<https://credit.niso.org/>). 
    It provides three functions: create a template, read it back and generate the CRediT author statement in a text file.
Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),<DOI:10.1016/0098-3004(84)90020-7>.
Population ratio estimator (calibrated) under two-phase random sampling design has gained enormous popularity in the recent time. This package provides functions for estimation population ratio (calibrated) under two phase sampling design, including the approximate variance of the ratio estimator. The improved ratio estimator can be applicable for both the case, when auxiliary data is available at unit level or aggregate level (eg., mean or total) for first phase sampled. Calibration weight of each unit of the second phase sample was calculated. Single and combined inclusion probabilities were also estimated for both phases under two phase random [simple random sampling without replacement (SRSWOR)] sampling. The improved ratio estimator's percentage coefficient of variation was also determined as a measure of accuracy. This package has been developed based on the theoretical development of Islam et al. (2021) and Ozgul (2020) <doi:10.1080/00949655.2020.1844702>.
Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method. Package documentation includes the vignette included in this package, and the paper by Schnell, Fiecas, and Carlin (2020, <doi:10.18637/jss.v094.i07>).
It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.
Simple tidying, analysis, and visualization of conjoint (factorial) experiments, including estimation and visualization of average marginal component effects ('AMCEs') and marginal means ('MMs') for weighted and un-weighted survey data, along with useful reference category diagnostics and statistical tests. Estimation of 'AMCEs' is based upon methods described by Hainmueller, Hopkins, and Yamamoto (2014) <doi:10.1093/pan/mpt024>.
In computationally demanding analysis projects,
  statisticians and data scientists asynchronously
  deploy long-running tasks to distributed systems,
  ranging from traditional clusters to cloud services.
  The 'NNG'-powered 'mirai' R package by Gao (2023)
  <doi:10.5281/zenodo.7912722> is a sleek
  and sophisticated scheduler that
  efficiently processes these intense workloads.
  The 'crew' package extends 'mirai' with a unifying
  interface for third-party worker launchers.
  Inspiration also comes from packages.
  'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>,
  'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>,
  'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>),
  and 'batchtools' by Lang, Bischel, and Surmann (2017)
  <doi:10.21105/joss.00135>.
In computationally demanding analysis projects,
  statisticians and data scientists asynchronously
  deploy long-running tasks to distributed systems,
  ranging from traditional clusters to cloud services.
  The 'crew.aws.batch' package extends the 'mirai'-powered
  'crew' package with a worker launcher plugin for AWS Batch.
  Inspiration also comes from packages 'mirai' by Gao (2023)
  <https://github.com/shikokuchuo/mirai>,
  'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>,
  'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>,
  'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>),
  and 'batchtools' by Lang, Bischl, and Surmann (2017).
  <doi:10.21105/joss.00135>.
In computationally demanding analysis projects,
  statisticians and data scientists asynchronously
  deploy long-running tasks to distributed systems,
  ranging from traditional clusters to cloud services.
  The 'crew.cluster' package extends the 'mirai'-powered
  'crew' package with worker launcher plugins for traditional
  high-performance computing systems.
  Inspiration also comes from packages 'mirai' by Gao (2023)
  <https://github.com/shikokuchuo/mirai>,
  'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>,
  'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>,
  'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>),
  and 'batchtools' by Lang, Bischl, and Surmann (2017).
  <doi:10.21105/joss.00135>.
Implements modeling and computational tools for conditional
    random fields (CRF) model as well as other probabilistic undirected
    graphical models of discrete data with pairwise and unary potentials.
Wraps the 'CRFsuite' library <https://github.com/chokkan/crfsuite> allowing users 
    to fit a Conditional Random Field model and to apply it on existing data.
    The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models 
    for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application
    is included in the package to allow you to easily construct training data.
Data on international and other major cricket matches from
  ESPNCricinfo <https://www.espncricinfo.com> and Cricsheet <https://cricsheet.org>.
  This package provides some functions to download the data into tibbles ready
  for analysis.
Tools for analyzing performances of cricketers based on stats in
    ESPN Cricinfo Statsguru. The toolset can  be used for analysis of Tests,ODIs 
    and Twenty20 matches of both batsmen and bowlers. The package can also be used to
    analyze team performances.
A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories.
Gives convenient access to publicly available police-recorded open
    crime data from large cities in the United States that are included in the
    Crime Open Database <https://osf.io/zyaqn/>.
A collection of functions that make it easier to understand crime (or other)
    data, and assist others in understanding it. The package helps you read data 
    from various sources, clean it, fix column names, and graph the data.
Implements convex regression with interpretable sharp partitions
    (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
Designs guide sequences for CRISPR/Cas9 genome editing and 
    provides information on sequence features pertinent to guide 
    efficiency. Sequence features include annotated off-target 
    predictions in a user-selected genome and a predicted efficiency 
    score based on the model described in Doench et al. (2016) 
    <doi:10.1038/nbt.3437>. Users are able to import additional genomes 
    and genome annotation files to use when searching and annotating 
    off-target hits. All guide sequences and off-target data can be 
    generated through the 'R' console with sgRNA_Design() or through 
    'crispRdesignR's' user interface with crispRdesignRUI(). CRISPR 
    (Clustered Regularly Interspaced Short Palindromic Repeats) and the 
    associated protein Cas9 refer to a technique used in genome editing.
A collection of functions for modeling fissile material operations in nuclear facilities,
    based on Zywiec et al (2021) <doi:10.1016/j.ress.2020.107322>.
An R implementation of the Critical Path Method (CPM).
    CPM is a method used to estimate the minimum project duration and determine 
    the amount of scheduling flexibility on the logical network paths within the 
    schedule model. The flexibility is in terms of early start, early finish, 
    late start, late finish, total float and free float. Beside, it permits 
    to quantify the complexity of network diagram through the analysis of 
    topological indicators. Finally, it permits to change the activities duration 
    to perform what-if scenario analysis. The package was built based on following 
    references: To make topological sorting and other graph operation, we use 
    Csardi, G. & Nepusz, T. (2005) 
    <https://www.researchgate.net/publication/221995787_The_Igraph_Software_Package_for_Complex_Network_Research>;
    For schedule concept, the reference was Project Management Institute (2017) 
    <https://www.pmi.org/pmbok-guide-standards/foundational/pmbok>;
    For standards terms, we use Project Management Institute (2017) 
    <https://www.pmi.org/pmbok-guide-standards/lexicon>;
    For algorithms on Critical Path Method development, we use 
    Vanhoucke, M. (2013) <doi:10.1007/978-3-642-40438-2> and 
    Vanhoucke, M. (2014) <doi:10.1007/978-3-319-04331-9>; 
    And, finally, for topological definitions, we use
    Vanhoucke, M. (2009) <doi:10.1007/978-1-4419-1014-1>.
Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation and Review Technique) and LESS (Least Cost Estimating and Scheduling) methods. The package sets the critical path, schedule and Gantt chart. In addition, it allows to draw a graph even with marked critical activities. For more information about project management see: Taha H. A. "Operations Research. An Introduction" (2017, ISBN:978-1-292-16554-7), Rama Murthy P. "Operations Research" (2007, ISBN:978-81-224-2944-2), Yuval Cohen & Arik Sadeh (2006) "A New Approach for Constructing and Generating AOA Networks", Journal of Engineering, Computing and Architecture 1. 1-13,     Konarzewska I., Jewczak M., Kucharski A. (2020, ISBN:978-83-8220-112-3), Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9).
Functions for phase I clinical trials using the continual reassessment method.
Sample and cell filtering as well as visualisation of output metrics from 'Cell Ranger' by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. 'CRMetrics' allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. 'CRMetrics' allows for easy removal of ambient RNA using 'SoupX' by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or 'CellBender' by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using 'Pagoda2' by Nikolas Barkas et al. (2021) <https://github.com/kharchenkolab/pagoda2> or 'Seurat' by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using 'Conos' by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using 'scrublet' by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or 'DoubletDetection' by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.
Implements the Cross-contribution Compensating Multiple
    standard Normalization (CCMN) method described in Redestig et
    al. (2009) Analytical Chemistry <doi:10.1021/ac901143w>
    and other normalization algorithms.
Implements a wide range of model-based dose
    escalation designs, ranging from classical and modern continual
    reassessment methods (CRMs) based on dose-limiting toxicity endpoints to
    dual-endpoint designs taking into account a biomarker/efficacy outcome. The
    focus is on Bayesian inference, making it very easy to setup a new design
    with its own JAGS code. However, it is also possible to implement 3+3
    designs for comparison or models with non-Bayesian estimation. The whole
    package is written in a modular form in the S4 class system, making it very
    flexible for adaptation to new models, escalation or stopping rules.
Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) <DOI:10.1016/j.csda.2020.106944>) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) <DOI:10.1007/s11222-018-9831-5>).
Functions to help implement the extraction / subsetting / indexing
    function '[' and replacement function '[<-' of custom matrix-like types
    (based on S3, S4, etc.), modeled as closely to the base matrix class as
    possible (with tests to prove it).
A dashboard supports the usage of 'cromwell'. 
 'Cromwell' is a scientific workflow engine for command line users.
 This package utilizes 'cromwell' REST APIs and provides these convenient 
 functions: timing diagrams for running workflows, 'cromwell' engine status, 
 a tabular workflow list. For more information about 'cromwell', 
 visit <http://cromwell.readthedocs.io>.
Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece.
Functions to carry out the most important crystallographic calculations for crystal 
             structures made of 1d Gaussian-shaped atoms, especially useful for methods development.
             Main reference: E. Smith, G. Evans, J. Foadi (2017) <doi:10.1088/1361-6404/aa8188>.
Creates an HTML vertical timeline from a data frame as an input for
    'rmarkdown' documents and 'shiny' applications.
Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers
    to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts.
A device closing function which is able to crop graphics (e.g.,
  PDF, PNG files) on Unix-like operating systems with the required underlying
  command-line tools installed.
Images are cropped to a circle with a transparent background. The function takes a
  vector of images, either local or from a link, and circle crops the image. Paths to the
  cropped image are returned for plotting with 'ggplot2'. Also includes cropping to a hexagon,
  heart, parallelogram, and square.
Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes
             6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially,  in excel files which has been transformed
             and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell.
             The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.
Estimation of crop water demand can be processed via this package. As example, the data  from 'TerraClimate' dataset (<https://www.climatologylab.org/terraclimate.html>) calibrated with automatic weather stations of National Meteorological Institute of Brazil is available in a coarse spatial resolution to do the crop water demand. However, the user have also the option to download the variables directly from 'TerraClimate' repository with the download.terraclimate function  and access the original 'TerraClimate' products. If the user believes that is necessary calibrate the variables, there is another function to do it. Lastly, the estimation of the crop water demand present in this package can be run for all the Brazilian territory with 'TerraClimate' dataset.
Calculate agrometeorological variables for crops
             including growing degree days (McMaster, GS & Wilhelm, WW
             (1997) <doi:10.1016/S0168-1923(97)00027-0>), cumulative
             rainfall, number of stress days and cumulative or mean
             radiation and evaporation. Convert dates to day of year
             and vice versa. Also, download curated and interpolated
             Australian weather data from the Queensland Government
             DES longpaddock website
             <https://www.longpaddock.qld.gov.au/>. This data is
             freely available under the Creative Commons 4.0 licence.
Implements the Changepoints for a Range of Penalties (CROPS) algorithm of Haynes et al. (2017) <doi:10.1080/10618600.2015.1116445> for finding all of the optimal segmentations for multiple penalty values over a continuous range.
Interface to easily access Cropland Data Layer (CDL) data for any area of interest via the 'CropScape' <https://nassgeodata.gmu.edu/CropScape/> web service.
Climate crop zoning based in minimum and maximum air temperature. The data used in the package are from 'TerraClimate' dataset (<https://www.climatologylab.org/terraclimate.html>), but, it have been calibrated with automatic weather stations  of National Meteorological Institute of Brazil.  The climate crop zoning of this package can be run for all the Brazilian territory.
Analyze data from a crossover design using generalized estimation equations (GEE), including carryover effects and various correlation structures based on the Kronecker product. It contains functions for semiparametric estimates of carry-over effects in repeated measures and allows estimation of complex carry-over effects. Related work includes: a) Cruz N.A., Melo O.O., Martinez C.A. (2023). "CrossCarry: An R package for the analysis of data from a crossover design with GEE". <arXiv:2304.02440v1>. b)  Cruz N.A., Melo O.O., Martinez C.A. (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures". <doi:10.1007/s00362-022-01391-z> and c) Cruz N.A., Melo O.O., Martinez C.A. (2023). "Semiparametric generalized estimating equations for repeated measurements in cross-over designs". <doi:10.1177/09622802231158736>.
Contains functions for the construction of carryover
        balanced crossover designs. In addition contains functions to
        check given designs for balance.
A local haplotyping visualization toolbox to capture major patterns 
    of co-inheritance between clusters of linked variants, whilst connecting findings 
    to phenotypic and demographic traits across individuals. 'crosshap' enables users 
    to explore and understand genomic variation across a trait-associated region. 
    For an example of successful local haplotype analysis, see Marsh et al. (2022) 
    <doi:10.1007/s00122-022-04045-8>.
Provides an extension to the 'purrr' family of mapping
    functions to apply a function to each combination of elements in a
    list of inputs.  Also includes functions for automatically detecting
    output type in mapping functions, finding every combination of
    elements of lists or rows of data frames, and applying multiple models
    to multiple subsets of a dataset.
Network meta-analysis and meta-regression (allows
  including up to three covariates) for individual participant data,
  aggregate data, and mixtures of both formats using the three-level
  hierarchical model.  Each format can come from randomized controlled
  trials or non-randomized studies or mixtures of both. Estimates are
  generated in a Bayesian framework using JAGS. The implemented models
  are described by Hamza et al. 2023 <DOI:10.1002/jrsm.1619>.
Generate and analyse crossover designs from combinatorial or search algorithms as well as from literature and a GUI to
    access them.
Joint distribution of number of crossings and the 
  longest run in a series of independent Bernoulli trials. The
  computations uses an iterative procedure where computations 
  are based on results from shorter series. The procedure 
  conditions on the start value and partitions by further 
  conditioning on the position of the first crossing (or none).
Create descriptive tables for continuous and categorical variables. 
    Apply summary statistics and counting function, with or without a grouping variable, and create beautiful reports using 'rmarkdown' or 'officer'.
    You can also compute effect sizes and statistical tests if needed.
Provides 'SPSS'- and 'SAS'-like output for cross tabulations of two
  categorical variables (CROSSTABS) and for hierarchical loglinear analyses 
  of two or more categorical variables (LOGLINEAR). The methods are described in
  Agresti (2013, ISBN:978-0-470-46363-5),
  Ajzen & Walker (2021, ISBN:9780429330308),
  Field (2018, ISBN:9781526440273),
  Norusis (2012, ISBN:978-0-321-74843-0),
  Nussbaum (2015, ISBN:978-1-84872-603-1),
  Stevens (2009, ISBN:978-0-8058-5903-4),
  Tabachnik & Fidell (2019, ISBN:9780134790541), and
  von Eye & Mun (2013, ISBN:978-1-118-14640-8).
Provides building blocks for allowing HTML widgets to communicate
    with each other, with Shiny or without (i.e. static .html files). Currently
    supports linked brushing and filtering.
Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022). 
             "Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.
Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (versions 1.4.1 & 1.5.1)
  or the WHO 2014 questionnaire for automated coding of Cause of Death using the InSilicoVA (data.type = "WHO2016") and
  InterVA5 algorithms. Previous versions of this package supported user-supplied mappings (via the map_records function), but
  this functionality has been removed.  This package is made available by WHO and the Bloomberg Data for Health Initiative.
Contains generic functions for performing 
  cross validation and for computing diagnostic errors.
Defines classes and methods to cross-validate various
  binary classification algorithms used for "class prediction"
  problems.
Implements the cross-validation methodology from Pein and Shah (2021) <arXiv:2112.03220>. Can be customised by providing different cross-validation criteria, estimators for the change-point locations and local parameters, and freely chosen folds. Pre-implemented estimators and criteria are available. It also includes our own implementation of the COPPS procedure <doi:10.1214/19-AOS1814>.
A pair of functions for renaming and encoding data frames
	     using external crosswalk files. It is especially useful when
	     constructing master data sets from multiple smaller data
	     sets that do not name or encode variables consistently
	     across files. Based on similar commands in 'Stata'.
Generate crosswords from a list of words.
Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2019) <DOI:10.1007/s11222-018-9842-2>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then one of the available methods will either establish it or drop the monotonicity assumption. Two monotonicity tests are also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.
In order to facilitate analysis of datasets hosted on the Crunch
    data platform <https://crunch.io/>, the 'crplyr' package implements 'dplyr'
    methods on top of the Crunch backend. The usual methods 'select', 'filter',
    'group_by', 'summarize', and 'collect' are implemented in such a way as to
    perform as much computation on the server and pull as little data locally
    as possible.
Auto, Cross and Multi-dimensional recurrence quantification analysis. 
    Different methods for computing recurrence, cross vs. multidimensional
    or profile iti.e., only looking at the diagonal recurrent points, 
    as well as functions for optimization and plotting are proposed.
    in-depth measures of the whole cross-recurrence plot,
    Please refer to Coco and others (2021) <doi:10.32614/RJ-2021-062>,
    Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510> 
    and Wallot (2018) <doi: 10.1080/00273171.2018.1512846>
    for further details about the method.
A user friendly function 'crrcbcv' to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.
Extension of 'cmprsk' to Stratified and Clustered data.    
      A goodness of fit test for Fine-Gray model is also provided.        
      Methods are detailed in the following articles: Zhou et al. (2011) <doi:10.1111/j.1541-0420.2010.01493.x>,
      Zhou et al. (2012) <doi:10.1093/biostatistics/kxr032>, 
      Zhou et al. (2013) <doi: 10.1002/sim.5815>.
Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.
Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <doi:10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss.
Obtain coordinate system metadata from various data formats. There 
 are functions to extract a 'CRS' (coordinate reference system, 
 <https://en.wikipedia.org/wiki/Spatial_reference_system>) in 'EPSG' (European 
 Petroleum Survey Group, <http://www.epsg.org/>), 'PROJ4' <https://proj.org/>, 
 or 'WKT2' (Well-Known Text 2, 
 <http://docs.opengeospatial.org/is/12-063r5/12-063r5.html>) forms. This is 
 purely for getting simple metadata from in-memory formats, please use other 
 tools for out of memory data sources.
Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
  Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.
An algorithm for identifying candidate driver combinations in cancer. CRSO 
  is based on a theoretical model of cancer in which a cancer rule 
  is defined to be a collection of two or more events (i.e., alterations) that are minimally 
  sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively 
  are assumed to account for all of ways to cause cancer in the population. In CRSO every 
  event is designated explicitly as a passenger or driver within each patient. 
  Each event is associated with a patient-specific, event-specific passenger penalty, 
  reflecting how unlikely the event would have happened by chance, i.e., as a passenger.
  CRSO evaluates each rule set by assigning all samples to a rule in the rule set,
  or to the null rule, and then calculating the total statistical penalty from all
  unassigned event. CRSO uses a three phase procedure find the best rule set of 
  fixed size K for a range of Ks. A core rule set is then identified from among
  the best rule sets of size K as the rule set that best balances rule set size and 
  statistical penalty. 
  Users should consult the 'crso' vignette for an example walk through of a full CRSO run.
  The full description, of the CRSO algorithm is presented in: 
  Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." 
  BioRxiv 674234 [Preprint]. June 19, 2019. <doi:10.1101/674234>.
  Please cite this article if you use 'crso'.
Tidies and performs preliminary analysis of 'Coursera' research
    export data. These export data can be downloaded by anyone who has classes
    on Coursera and wants to analyze the data. Coursera is one of the leading 
    providers of MOOCs and was launched in January 2012. With over 25 million 
    learners, Coursera is the most popular provider in the world being followed 
    by EdX, the MOOC provider that was a result of a collaboration between 
    Harvard University and MIT, with over 10 million users. Coursera has over 
    150 university partners from 29 countries and offers a total of 2000+ 
    courses from computer science to philosophy. Besides, Coursera offers 180+ 
    specialization, Coursera's credential system, and four fully online Masters 
    degrees. For more information about Coursera check Coursera's
    About page on <https://blog.coursera.org/about/>.
Uses data from the 'EPSG' Registry to look up suitable coordinate reference 
    system transformations for spatial datasets in R.  Returns a data frame with 'CRS' codes
    that can be used for 'CRS' transformation and mapping projects.  Please see 
    the 'EPSG' Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.
Computes p-value according to the CRT using the HierNet test statistic. For more details, see Ham, Imai, Janson (2022) "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" <arXiv:2201.08343>.
Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).
Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see <https://thomasasmith.github.io/index.html>).
A simple HTTP client, with tools for making HTTP requests,
    and mocking HTTP requests. The package is built on R6, and takes
    inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>).
    The package name is a play on curl, the widely used command line tool
    for HTTP, and this package is built on top of the R package 'curl', an
    interface to 'libcurl' (<https://curl.se/libcurl/>).
The Crunch.io service <https://crunch.io/> provides a cloud-based
    data store and analytic engine, as well as an intuitive web interface.
    Using this package, analysts can interact with and manipulate Crunch
    datasets from within R. Importantly, this allows technical researchers to
    collaborate naturally with team members, managers, and clients who prefer a
    point-and-click interface.
To facilitate building custom dashboards on the Crunch data
    platform <https://crunch.io/>, the 'crunchy' package provides tools for
    working with 'shiny'. These tools include utilities to manage authentication
    and authorization automatically and custom stylesheets to help match the
    look and feel of the Crunch web application. The package also includes
    several gadgets for use in 'RStudio'.
Functions for reading in and manipulating CRU TS3.21: Climatic
    Research Unit (CRU) Time-Series (TS) Version 3.21 data.
A utility program oriented to create maps, plot data, and do basic data summaries
    of 'DAS' data <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> 
    produced by 'WinCruz' from the Southwest Fisheries Science Center.
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
In meta regression sometimes the studies have multiple effects that are correlated. For this reason cluster robust standard errors must be computed. However, since the clusters are unbalanced the wild bootstrap is suggested. See Oczkowski E. and Doucouliagos H. (2015). "Wine prices and quality ratings: a meta-regression analysis". American Journal of Agricultural Economics, 97(1): 103--121. <doi:10.1093/ajae/aau057> and Cameron A. C., Gelbach J. B. and Miller D. L. (2008). "Bootstrap-based improvements for inference with clustered errors". The Review of Economics and Statistics, 90(3): 414--427. <doi:10.1162/rest.90.3.414>.
To re-calculate the coefficients and the standard deviation when changing the reference group.
Reading and writing of files in the most commonly used formats of
  structural crystallography. It includes functions to work with a variety
  of statistics used in this field and functions to perform basic
  crystallographic computing. References: D. G. Waterman, J. Foadi, G. Evans 
  (2011) <doi:10.1107/S0108767311084303>.
Retrieves crypto currency information and historical prices as well as information on the exchanges they are listed on. Historical data contains daily open, high, low and close values for all crypto currencies. All data is scraped from <https://coinmarketcap.com> via their 'web-api'.
Playfair, Four-Square, Scytale, Columnar Transposition and Autokey methods. Further explanation on methods of classical cryptography can be found at Wikipedia; (<https://en.wikipedia.org/wiki/Classical_cipher>).
This high-level API client offers a streamlined access to public cryptocurrency market data and sentiment indicators. It features OHLC-V (Open, High, Low, Close, Volume) that comes
  with granularity ranging from seconds to months and essential sentiment indicators to develop and backtest trading strategies, or conduct detailed market analysis. By interacting directly with
  the major cryptocurrency exchanges this package ensures a reliable, and stable, flow of market information, eliminating the need for complex, low-level API interactions or webcrawlers.
Providing data to quickly visualize and analyze data from several cryptocurrencies.
An API wrapper for 'Cryptowatch' to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See <https://docs.cryptowat.ch/rest-api> for a detailed documentation.
Performs cryptographic randomness tests on a sequence of random
    integers or bits. Included tests are greatest common divisor, birthday spacings,
    book stack, adaptive chi-square, topological binary, and three random walk
    tests (Ryabko and Monarev, 2005) <doi:10.1016/j.jspi.2004.02.010>. Tests except greatest common divisor and birthday spacings are not
    covered by standard test suites. In addition to the chi-square goodness-of-fit
    test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are
    also generated by some of the cryptographic randomness tests.
Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. 'csa' package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.
Helps create alerts and determine trends by using various methods to analyze public health surveillance data. The primary analysis method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.
Monte Carlo conditional inference for the parameters of a
  linear nonnormal regression model.
Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.
Calculates pointwise confidence intervals for the cumulative distribution function of the event time for current status data, data where each individual is assessed at one time to see if they had the event or not by the assessment time.
Germline and somatic locus data which contain the total read depth and B allele 
    read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster 
    model can deal with the SNVs mutation and the CNAs mutation.
Flexible tools to fit, tune and obtain absolute risk predictions from regularized cause-specific cox models with elastic-net penalty.
Datasets relating to population in municipalities, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2024.
Estimate, assess, test, and study linear, nonlinear, hierarchical 
  and multigroup structural equation models using composite-based approaches 
  and procedures, including estimation techniques such as partial least squares 
  path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), 
  generalized structured component analysis (GSCA), generalized structured 
  component analysis with uniqueness terms (GSCAm), generalized canonical 
  correlation analysis (GCCA), principal component analysis (PCA), 
  factor score regression (FSR) using sum score, regression or 
  bartlett scores (including bias correction using Croon’s approach), 
  as well as several tests and typical postestimation procedures 
  (e.g., verify admissibility of the estimates, assess the model fit, 
  test the model fit etc.).
Mines contiguous sequential patterns in text.
Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) <doi:10.1128/JCM.00696-14> that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping.
An implementation of calls designed to collect and organize in an easy way the data from the Steam API specifically for the Counter-Strike Global Offensive Game (CS Go) <https://developer.valvesoftware.com/wiki/Steam_Web_API>.
Package for CShapes 2.0, a GIS dataset of country borders (1886-today). Includes functions for data extraction and the computation of distance matrices and -lists.
A collection of user-submitted functions to aid in the analysis of hydrological data, particularly for users in Canada. The functions focus on the use of Canadian data sets, and are suited to Canadian hydrology, such as the important cold region hydrological processes and will work with Canadian hydrological models. The functions are grouped into several themes, currently including Statistical hydrology, Basic data manipulations, Visualization, and Spatial hydrology. Functions developed by the Floodnet project are also included. CSHShydRology has been developed with the assistance of the Canadian Society for Hydrological Sciences (CSHS) which is an affiliated society of the Canadian Water Resources Association (CWRA). As of version 1.2.6, functions now fail gracefully when attempting to download data from a url which is unavailable.
Set of generalised tools for the flexible computation of climate 
  related indicators defined by the user. Each method represents a specific 
  mathematical approach which is combined with the possibility to select an 
  arbitrary time period to define the indicator. This enables a wide range of 
  possibilities to tailor the most suitable indicator for each particular climate 
  service application (agriculture, food security, energy, water management, ...). 
  This package is intended for sub-seasonal, seasonal and decadal climate 
  predictions, but its methods are also applicable to other time-scales, 
  provided the dimensional structure of the input is maintained. Additionally, 
  the outputs of the functions in this package are compatible with 'CSTools'. 
  This package is described in 'Pérez-Zanón et al. (2023) 
  <doi:10.1016/j.cliser.2023.100393>' and it was developed in the context of 
  'H2020 MED-GOLD' (776467) and 'S2S4E' (776787) projects. See 'Lledó et al. (2019) 
  <doi:10.1016/j.renene.2019.04.135>' and 'Chou et al., 2023 
  <doi:10.1016/j.cliser.2023.100345>' for details.
Provides datasets containing preformatted maps of Norway at
    the county, municipality, and ward (Oslo only) level for redistricting in 
    2024, 2020, 2018, and 2017. Multiple layouts are provided (normal, split, 
    and with an insert for Oslo), allowing the user to rapidly create choropleth
    maps of Norway without any geolibraries.
Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.
Provides functions for computing the density
    and the log-likelihood function of closed-skew normal variates,
    and for generating random vectors sampled from this distribution.
    See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004).
    The closed skew normal distribution, 
    Skew-elliptical distributions and their applications: a journey beyond normality,
    Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Imports 'PxStat' data in JSON-stat format and (optionally) reshapes it into wide
	format. The Central Statistics Office (CSO) is the national statistical institute of Ireland
	and 'PxStat' is the CSOs online database of Official Statistics. This database contains current
	and historical data series compiled from CSO statistical releases and is accessed at
	<http://data.cso.ie>.
	The CSO 'PxStat' Application Programming Interface (API), which is accessed in this package, provides
	access to 'PxStat' data in JSON-stat format at <http://data.cso.ie>.
	This dissemination tool allows developers machine to machine access to CSO 'PxStat' data.
Calculate the predictive discrete Fourier transform, complete discrete Fourier transform, complete periodogram, and tapered complete periodogram. This algorithm is based on the preprint "Spectral methods for small sample time series: A complete periodogram approach" (2020) by Sourav Das, Suhasini Subba Rao, and Junho Yang.
A tool that imports, subsets, visualizes, and exports the Correlates of State Policy Project dataset assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy>. The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, create map visualizations, export the citations associated with their searches, and more.
Contains the Correlates of State Policy Project dataset (+ codebook) assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy> used by the 'cspp' package. The Correlates data contains over 3000 variables across more than 100 years that pertain to state politics and policy in the United States.
Account for uncertainty when working with ranks. 
    Estimate standard errors consistently in linear regression with ranked variables.
    Construct confidence sets of various kinds for positions of populations in a ranking 
    based on values of a certain feature and their estimation errors. 
    Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006> and
    Chetverikov and Wilhelm (2023) <arXiv:2310.15512>.
A collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.
Selection of the number of clusters in cluster analysis using
    stability methods.
A uniform  statistical inferential tool in making individualized treatment decisions, which implements the methods of Ma et al. (2017)<DOI:10.1177/0962280214541724> 
    and Guo et al. (2021)<DOI:10.1080/01621459.2020.1865167>. It uses a flexible semiparametric modeling strategy for heterogeneous treatment effect estimation in high-dimensional settings and can gave valid confidence bands. Based on it, one can find the subgroups of patients that benefit from each treatment, thereby making individualized treatment selection.
Helpful functions for the cleaning and manipulation of surveillance data, especially with regards to the creation and validation of panel data from individual level surveillance data.
Provides easy and consistent time conversion for public health purposes. The time conversion functions provided here are between date, ISO week, ISO yearweek, ISO year, calendar month/year, season, season week.
Exploits dynamical seasonal forecasts in order to provide
    information relevant to stakeholders at the seasonal timescale. The package
    contains process-based methods for forecast calibration, bias correction,
    statistical and stochastic downscaling, optimal forecast combination and
    multivariate verification, as well as basic and advanced tools to obtain
    tailored products. This package was developed in the context of the 'ERA4CS' 
    project 'MEDSCOPE' and the 'H2020 S2S4E' project and includes contributions from 
    'ArticXchange' project founded by 'EU-PolarNet 2'.
    'Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>'.
    'Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>'.
    'Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>'.
    'Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>'.
    'Straus et al. (2007) <doi:10.1175/JCLI4070.1>'.
    'Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>'.
    'Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>'.
    'D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>'.
    'Verfaillie et al. (2017) <doi:10.5194/gmd-10-4257-2017>'.
    'Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>'.
    'Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>'.
Domain mean estimation with monotonicity or block monotone constraints. See Xu X, Meyer MC and Opsomer JD (2021)<doi:10.1016/j.jspi.2021.02.004> for more details.
Utility functions that help with common base-R problems relating to lists.
    Lists in base-R are very flexible. This package provides functions to quickly and easily
    characterize types of lists. That is, to identify if all elements in a list
    are null, data.frames, lists, or fully named lists. Other functionality is provided
    for the handling of lists, such as the easy splitting of lists into equally sized
    groups, and the unnesting of data.frames within fully named lists.
Reads and writes CSV with selected conventions.
 Uses the same generic function for reading and writing to promote consistent formats.
Functions for loading large (10M+ lines) CSV
    and other delimited files, similar to read.csv, but typically faster and
    using less memory than the standard R loader. While not entirely general,
    it covers many common use cases when the types of columns in the CSV file
    are known in advance. In addition, the package provides a class 'int64',
    which represents 64-bit integers exactly when reading from a file. The
    latter is useful when working with 64-bit integer identifiers exported from
    databases. The CSV file loader supports common column types including
    'integer', 'double', 'string', and 'int64', leaving further type
    transformations  to the user.
Provide functions for reading and writing CSVW - i.e. CSV tables and JSON metadata.
    The metadata helps interpret CSV by setting the types and variable names.
Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.
Contingency table analysis is performed based on maximum likelihood (ML) fitting of 
             multinomial-Poisson homogeneous (MPH) and homogeneous linear predictor (HLP) models. 
             See Lang (2004) <doi:10.1214/aos/1079120140> and Lang (2005) <doi:10.1198/016214504000001042> 
             for MPH and HLP models. Objects computed include model goodness-of-fit statistics; likelihood-
             based (cell- and link-specific) residuals; and cell probability and expected count estimates along 
             with standard errors. This package can also compute test-inversion--e.g. Wald, profile likelihood, 
             score, power-divergence--confidence intervals for contingency table estimands, when table 
             probabilities are potentially subject to equality constraints. For test-inversion intervals, see 
             Lang (2008) <doi:10.1002/sim.3391> and Zhu (2020) <doi:10.17077/etd.005331>.
An interactive application for working with contingency Tables. The application has a template for solving contingency table problems like chisquare test of independence,association plot between two categorical variables. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/CategoricalDataAnalysis/>.
Clean, decompose and aggregate univariate time series following the procedure "Cyclic/trend decomposition using bin interpolation" and the Logbox method for flagging outliers, both detailed in Ritter, F.: Technical note: A procedure to clean, decompose, and aggregate time series, Hydrol. Earth Syst. Sci., 27, 349–361, <doi:10.5194/hess-27-349-2023>, 2023.
A method for pattern discovery in weighted graphs as outlined in Thistlethwaite et al. (2021) <doi:10.1371/journal.pcbi.1008550>. Two use cases are achieved: 1) Given a weighted graph and a subset of its nodes, do the nodes show significant connectedness? 2) Given a weighted graph and two subsets of its nodes, are the subsets close neighbors or distant?
Column Text Format (CTF) is a new tabular data format designed for simplicity and performance.
    CTF is the simplest column store you can imagine: plain text files for each column in a table, and a metadata file.
    The underlying plain text means the data is human readable and familiar to programmers, unlike specialized binary formats.
    CTF is faster than row oriented formats like CSV when loading a subset of the columns in a table.
    This package provides functions to read and write CTF data from R.
It is assumed that psychological distances between the categories are equal for the measurement instruments consisted of polytomously scored items. According to Muraki, this assumption must be tested. In the examination process of this assumption, the fit indexes are obtained and evaluated. This package provides that this assumption is removed. By with this package, the converted scale values of all items in a measurement instrument can be calculated by estimating a category parameter set for each item. Thus, the calculations can be made without any need to usage of the common category parameter set. Through this package, the psychological distances of the items are scaled. The scaling of a category parameter set for each item cause differentiation of score of the categories will be got from items. Also, the total measurement instrument score of an individual can be calculated according to the scaling of item score categories by with this package.This package provides that the place of individuals related to the structure to be measured with a measurement instrument consisted of polytomously scored items can be reveal more accurately. In this way, it is thought that the results obtained about individuals can be made more sensitive, and the differences between individuals can be revealed more accurately. On the other hand, it can be argued that more accurate evidences can be obtained regarding the psychometric properties of the measurement instruments.
A shortcut procedure is proposed to implement closed testing for large-scale multiple testings, especially with the global test. This shortcut is asymptotically equivalent to closed testing and post hoc. Users could detect any possible sets of features or pathways with family-wise error rate controlled. The global test is powerful to detect associations between a group of features and an outcome of interest.
Retrieves historical versions of clinical trial registry
    entries from <https://ClinicalTrials.gov>. Package functionality
    and implementation for v 1.0.0 is documented in Carlisle (2022)
    <DOI:10.1371/journal.pone.0270909>.
Estimation and inference methods for the continuous threshold expectile regression.
    It can fit the continuous threshold expectile regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."
Identification and network inference of genetic loci associated
  with correlation changes in quantitative traits (called correlated trait loci, CTLs).
  Arends et al. (2016) <doi:10.21105/joss.00087>.
The CTM package is designed to solve problems of text mining and is specific for Chinese document.
Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) <doi:10.1111/1467-9965.00114> as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) <doi:10.1111/j.1467-9868.2005.00508.x> are included.
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) <doi:10.1177/0962280217729845>) and the glmnet-C-TMLE algorithm (Ju et al. (2017) <arXiv:1706.10029>).
Implements a basis function or functional data analysis framework
             for several techniques of multivariate analysis in continuous-time 
             setting. Specifically, we introduced continuous-time analogues of
             several classical techniques of multivariate analysis, such as 
             principal component analysis, canonical correlation analysis, 
             Fisher linear discriminant analysis, K-means clustering, and so 
             on. Details are in Biplab Paul, Philip T. Reiss and Erjia Cui (2023) 
             "Continuous-time multivariate analysis" <doi:10.48550/arXiv.2307.09404>.
The Clinical Trials Network (CTN) of the U.S. National Institute
  of Drug Abuse sponsored the CTN-0094 research team to harmonize data sets
  from three nationally-representative clinical trials for opioid use disorder
  (OUD). The CTN-0094 team herein provides a coded collection of trial outcomes
  and endpoints used in various OUD clinical trials over the past 50 years.
  These coded outcome functions are used to contrast and cluster different
  clinical outcome functions based on daily or weekly patient urine screenings.
  Note that we abbreviate urine drug screen as "UDS" and urine opioid screen as
  "UOS". For the example data sets (based on clinical trials data harmonized by
  the CTN-0094 research team), UDS and UOS are largely interchangeable.
A system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) <doi:10.1101/2023.03.11.532179>.
Functions for identifying, fitting, and applying continuous-time stochastic models to phylogenetic data.
  The package is based on methods introduced in
  Noonan et al. (2021) <doi:10.1101/2021.05.21.445056>.
Estimation of quantile regression models for survival data.
A system for querying, retrieving and analyzing
        protocol- and results-related information on clinical trials from
        four public registers, the 'European Union Clinical Trials Register'
        ('EUCTR', <https://www.clinicaltrialsregister.eu/>), 
        'ClinicalTrials.gov' ('CTGOV', using the classic interface at 
        <https://classic.clinicaltrials.gov/>, and 'CTGOV2', using the 
        current interface at <https://www.clinicaltrials.gov/>), the 
        'ISRCTN' (<http://www.isrctn.com/>) and the
        'European Union Clinical Trials Information System'
        ('CTIS', <https://euclinicaltrials.eu/>). 
        Trial information is downloaded, converted and stored in a database 
        ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). 
        Documents in registers associated with trials can also be downloaded. 
        Other functions identify deduplicated records, 
        easily find and extract variables (fields) of interest even 
        from complex nested data as used by the registers, 
        merge variables and update queries. 
        The package can be used for meta-analysis and trend-analysis of
        the design and conduct as well as of the results of clinical trials.
Tools to create and query database from the U.S. National Library
  of Medicine's Clinical Trials database <https://clinicaltrials.gov/>. Functions
  provide access a variety of techniques for searching the data using range
  queries, categorical filtering, and by searching for full-text keywords. 
  Minimal graphical tools are also provided for interactively exploring the
  constructed data.
A simple way to assess the stability of candidate housekeeping genes is implemented in this package.
Hierarchical continuous (and discrete) time state space modelling, for linear
    and nonlinear systems measured by  continuous variables, with limited support for 
    binary data. The subject specific dynamic system is modelled as a stochastic 
    differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. 
    Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default.
    Nonlinearities,  (state dependent parameters) and random effects on all parameters
    are possible, using either max likelihood / max a posteriori optimization 
    (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. 
    See  <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf>
    for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian 
    linear SDE approach, 
    see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>.
    Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> .
    Stan based functions are not available on 32 bit Windows systems at present. 
    <https://cdriver.netlify.app/> contains some tutorial blog posts.
Original 'ctsem' (continuous time structural equation modelling)
    functionality, based on the 'OpenMx' software, as described in 
    Driver, Oud, Voelkle (2017) <doi:10.18637/jss.v077.i05>, with updated details in vignette. 
    Combines stochastic differential equations representing latent processes with 
    structural equation measurement models. These functions were split off from
    the main package of 'ctsem', as the main package uses the 'rstan' package as a backend now --
    offering estimation options from max likelihood to Bayesian.
    There are nevertheless use cases for the wide format SEM style approach as offered here, 
    particularly when there are no individual differences in observation timing and the
    number of individuals is large. For the main 'ctsem' package, see <https://cran.r-project.org/package=ctsem>.
An implementation of several functions for feature extraction in 
    categorical time series datasets. Specifically, some features related to 
    marginal distributions and serial dependence patterns can be computed. These 
    features can be used to feed clustering and classification algorithms for
    categorical time series, among others. The package also includes some
    interesting datasets containing biological sequences. Practitioners from a 
    broad variety of fields could benefit from the general framework provided 
    by 'ctsfeatures'.
An interactive document on  the topic of classification tree analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/CTShiny/>.
A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs.
A Shiny interface developed in close coordination with the CTT package, providing a GUI that guides the user through CTT analyses.
Interactive shiny application for running classical test theory (item analysis).
Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated
             packages (client-side tools), generating HTML pages and storing task view information in the repository
	     (server-side tools).
For ordinal rating data, estimate and test models within the family of CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions); Simulation routines, plotting facilities and fitting measures are also provided.
A suite of functions for rapid and flexible analysis of codon 
    usage bias. It provides in-depth analysis at the codon level, including 
    relative synonymous codon usage (RSCU), tRNA weight calculations, machine 
    learning predictions for optimal or preferred codons, and visualization of 
    codon-anticodon pairing. Additionally, it can calculate various gene-
    specific codon indices such as codon adaptation index (CAI), effective 
    number of codons (ENC), fraction of optimal codons (Fop), tRNA adaptation 
    index (tAI), mean codon stabilization coefficients (CSCg), and GC contents 
    (GC/GC3s/GC4d). It also supports both standard and non-standard genetic 
    code tables found in NCBI, as well as custom genetic code tables.
R wrappers around the cubature C library of Steven
    G. Johnson for adaptive multivariate integration over hypercubes
    and the Cuba C library of Thomas Hahn for deterministic and
    Monte Carlo integration. Scalar and vector interfaces for 
    cubature and Cuba routines are provided; the vector interfaces
    are highly recommended as demonstrated in the package
    vignette.
A spatiotemperal data object in a relational data structure to separate the recording of time variant/ invariant variables.
An implementation of a data cube extracted out of 'dplyr' for
    backward compatibility.
Creates a 3D data cube view of a RasterStack/Brick, typically a 
    collection/array of RasterLayers (along z-axis) with the same geographical 
    extent (x and y dimensions) and resolution, provided by package 'raster'. 
    Slices through each dimension (x/y/z), freely adjustable in location, 
    are mapped to the visible sides of the cube. The cube can be freely rotated. 
    Zooming and panning can be used to focus on different areas of the cube.
Estimating mutation and selection coefficients on synonymous
       codon bias usage based on models of ribosome overhead cost (ROC).
       Multinomial logistic regression and Markov Chain Monte Carlo are used to
       estimate and predict protein production rates with/without the presence
       of expressions and measurement errors. Work flows with examples for
       simulation, estimation and prediction processes are also provided
       with parallelization speedup. The whole framework is tested with
       yeast genome and gene expression data of Yassour, et al. (2009)
       <doi:10.1073/pnas.0812841106>.
Computation of a cubic B-spline basis for arbitrary knots. It also provides the 1st and 2nd derivatives, as well as the integral of the basis elements. It is used by the author to fit penalized B-spline models, see e.g. Jullion, A. and Lambert, P. (2006) <doi:10.1016/j.csda.2006.09.027>,  Lambert, P. and  Eilers, P.H.C. (2009) <doi:10.1016/j.csda.2008.11.022> and, more recently, Lambert, P. (2021) <doi:10.1016/j.csda.2021.107250>. It is inspired by the algorithm developed by de Boor, C. (1977) <doi:10.1137/0714026>.
Functions for visualizing, animating, solving and 
        analyzing the Rubik's cube. Includes data structures for
		solvable and unsolvable cubes, random moves and random 
		state scrambles and cubes, 3D displays and animations 
		using 'OpenGL', patterned cube generation, and lightweight 
		solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the
		Kociemba solver.
Regression modeling using rules with added instance-based
    corrections.
Write executable specifications in a natural language that describes how your code should behave.
    Write specifications in feature files using 'Gherkin' language and execute them using functions implemented in R.
    Use them as an extension to your 'testthat' tests to provide a high level description of how your code works.
R interface for RAPIDS cuML (<https://github.com/rapidsai/cuml>),
    a suite of GPU-accelerated machine learning libraries powered by CUDA
    (<https://en.wikipedia.org/wiki/CUDA>).
Utility functions that provides wrapper to descriptive base functions
  like cor, mean and table.  It makes use of the formula interface to pass
  variables to functions.  It also provides operators to concatenate (%+%), to
  repeat (%n%) and manage character vectors for nice display.
Provides tools for measuring the compositionality of signalling systems (in particular the information-theoretic measure due to Spike (2016) <http://hdl.handle.net/1842/25930> and the Mantel test for distance matrix correlation (after Dietz 1983) <doi:10.1093/sysbio/32.1.21>), functions for computing string and meaning distance matrices as well as an implementation of the Page test for monotonicity of ranks (Page 1963) <doi:10.1080/01621459.1963.10500843> with exact p-values up to k = 22.
Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.
Provides similar functionality to 'Microsoft Excel' 'CUMPRINC' function <https://support.microsoft.com/en-us/office/cumprinc-function-94a4516d-bd65-41a1-bc16-053a6af4c04d>. 
    Returns principal remaining at a given month, principal paid in a month, and accumulated principal paid at a given month based on original loan amount, 
    monthly interest rate, and term of loan.
Estimation of number and location of change points in
        mean-shift (piecewise constant) models. Particularly useful (but not confined) to
        model genomic sequences of continuous measurements.
Cumulative descriptive statistics for (arithmetic, geometric, harmonic) mean, median, mode, variance, skewness and kurtosis.
Access the 'Cumulocity' API and retrieve data on devices, measurements, and events. Documentation for the API can be found at <https://www.cumulocity.com/guides/reference/rest-implementation/>.
Calculates and visualises cumulative percent 'decay' curves,
    which are typically calculated from metagenomic taxonomic profiles.
    These can be used to estimate the level of expected 'endogenous' taxa
    at different abundance levels retrieved from metagenomic samples, when
    comparing to samples of known sampling site or source. Method
    described in Fellows Yates, J. A. et. al. (2021) Proceedings of the
    National Academy of Sciences USA <doi:10.1073/pnas.2021655118>.
Contains functions for estimating generalized parametric mixture and non-mixture cure models <doi:10.1016/j.cmpb.2022.107125>, loss of lifetime, mean residual lifetime, and crude event probabilities.
Estimate mixture cure models with subgroup survival probabilities as auxiliary information. A reference of the underlying methods is Jie Ding, Jialiang Li and Xiaoguang Wang (2024) <doi:10.1093/jrsssc/qlad106>.
Cure dependent censoring regression models for long-term survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) <doi:10.1007/s10651-022-00549-0>.
Expectation-Maximization (EM) algorithm for point estimation and variance estimation to
    the nonparametric maximum likelihood estimator (NPMLE) for 
    logistic-Cox cure-rate model with left truncation and right-
    censoring. See Hou, Chambers and Xu (2017) <doi:10.1007/s10985-017-9415-2>.
Creates 'ggplot2' Cumulative Residual (CURE) plots to check the goodness-of-fit of a count model; or the tables to create a customized version. A dataset of crashes in Washington state is available for illustrative purposes.
The curl() and curl_download() functions provide highly
    configurable drop-in replacements for base url() and download.file() with
    better performance, support for encryption (https, ftps), gzip compression,
    authentication, and other 'libcurl' goodies. The core of the package implements a
    framework for performing fully customized requests where data can be processed
    either in memory, on disk, or streaming via the callback or connection
    interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly
    web client see the 'httr' package which builds on this package with http
    specific tools and logic.
An R client for the 'currencyapi.com' currency conversion 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://currencyapi.com/docs> .
The currentSurvival package contains functions for
  the estimation of the current cumulative incidence (CCI) and
  the current leukaemia-free survival (CLFS). The CCI is the 
  probability that a patient is alive and in any disease 
  remission (e.g. complete cytogenetic remission in chronic 
  myeloid leukaemia) after initiating his or her therapy (e.g. 
  tyrosine kinase therapy for chronic myeloid leukaemia). The 
  CLFS is the probability that a patient is alive and in any 
  disease remission after achieving the first disease remission.
Implementations of the family of map() functions with frequent saving of the intermediate results. The contained functions let you start the evaluation of the iterations where you stopped  (reading the already evaluated ones from cache), and work with the currently evaluated iterations while remaining ones are running in a background job. Parallel computing is also easier with the workers parameter.
Partial application is the process of reducing the arity of a
    function by fixing one or more arguments, thus creating a new function
    lacking the fixed arguments. The curry package provides three different ways
    of performing partial function application by fixing arguments from either
    end of the argument list (currying and tail currying) or by fixing multiple
    named arguments (partial application). This package provides this
    functionality through the %<%, %-<%, and %><% operators which allows for
    a programming style comparable to modern functional languages. Compared
    to other implementations such a purrr::partial() the operators in curry
    composes functions with named arguments, aiding in autocomplete etc.
A toolbox for developing applications, games, simulations, or
    agent-based models in the R terminal. Included functions allow users to move the 
    cursor around the terminal screen, change text colors and attributes, clear the
    screen, hide and show the cursor, map key presses to functions, draw shapes and curves,
    among others. Most functionalities require users to be in a terminal (not the R GUI).
Computes the maximum likelihood estimator, the smoothed maximum likelihood
    estimator and pointwise bootstrap confidence intervals for the distribution 
    function under current status data. 
    Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>.
Finds single- and two-arm designs using stochastic curtailment, as described by Law et al. (2022) <doi:10.1080/10543406.2021.2009498> and Law et al. (2021) <doi:10.1002/pst.2067> respectively. Designs can be single-stage or multi-stage. Non-stochastic curtailment is possible as a special case. Desired error-rates, maximum sample size and lower and upper anticipated response rates are inputted and suitable designs are returned with operating characteristics. Stopping boundaries and visualisations are also available. The package can find designs using other approaches, for example designs by Simon (1989) <doi:10.1016/0197-2456(89)90015-9> and Mander and Thompson (2010) <doi:10.1016/j.cct.2010.07.008>. Other features: compare and visualise designs using a weighted sum of expected sample sizes under the null and alternative hypotheses and maximum sample size; visualise any binary outcome design.
Performs multiple comparison procedures on curve observations among different treatment groups. The methods are applicable in a variety of situations (such as independent groups with equal or unequal sample sizes, or repeated measures) by using parametric bootstrap. References to these procedures can be found at Konietschke, Gel, and Brunner (2014) <doi:10.1090/conm/622/12431> and Westfall (2011) <doi:10.1080/10543406.2011.607751>.
Data recorded as paths or trajectories may be suitably described 
  by curves, which are independent of their parametrization. For the space of 
  such curves, the package provides functionalities for reading curves, sampling 
  points on curves, calculating distance between curves and for computing Tukey
  curve depth of a curve w.r.t. to a bundle of curves. For details see 
  Lafaye De Micheaux, Mozharovskyi, and Vimond (2019) <arXiv:1901.00180>.
Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.
Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) <https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.
Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>;
        Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model 
        fitting, and several utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.
Use the high-precision arithmetic provided by the R package 'Rmpfr' 
    to compute a custom-made Gauss quadrature nodes and weights, 
    with up to 33 nodes, using a moment-based method via moment 
    determinants. Paul Kabaila (2022) <arXiv:2211.04729>.
A versatile R package for creating and pricing custom derivatives to suit your financial needs.
Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.
Customized training is a simple technique for transductive
    learning, when the test covariates are known at the time of training. The
    method identifies a subset of the training set to serve as the training set
    for each of a few identified subsets in the training set. This package
    implements customized training for the glmnet() and cv.glmnet() functions.
Calculate the distribution of costs for the installation of an elevator based on the different distribution rules.
Provides functions for constructing and evaluating
    CUSUM charts and RA-CUSUM charts with focus on false signal probability.
Create CUSUM (cumulative sum) statistics from a vector or dataframe.
    Also create single or faceted CUSUM control charts, with or without control limits.
    Accepts vector, dataframe, tibble or data.table inputs.
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.
Seek the significant cutoff value for a continuous variable, which will 
    be transformed into a classification, for linear regression, 
    logistic regression, logrank analysis and cox regression. First of all, 
    all combinations will be gotten by combn() function. Then n.per argument, 
    abbreviated of total number percentage, will be used to remove the combination 
    of smaller data group. In logistic, Cox regression and logrank analysis, 
    we will also use p.per argument, patient percentage, to filter the lower 
    proportion of patients in each group. Finally, p value in regression 
    results will be used to get the significant combinations and output 
    relevant parameters. In this package, there is no limit to the number of 
    cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, 
    typical Bonferroni and Duglas G (1994) <doi: 10.1093/jnci/86.11.829>, to 
    adjust the p value, Missing values will be deleted by na.omit() function 
    before analysis.
Estimate cutpoints that optimize a specified metric in binary classification tasks
    and validate performance using bootstrapping. Some methods for more robust cutpoint
    estimation are supported, e.g. a parametric method assuming normal distributions,
    bootstrapped cutpoints, and smoothing of the metric values per cutpoint using
    Generalized Additive Models. Various plotting functions are included. For an overview
    of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.
Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.
Cross-validation methods of regression models that exploit features of various
    modeling functions to improve speed. Some of the methods implemented in the package are
    novel, as described in the package vignettes; for general introductions to cross-validation,
    see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani 
    (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with 
    Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, 
    and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical 
    Learning, Second Edition".
Works with the Citizen Voting Age Population special tabulation from the US Census Bureau <https://www.census.gov/programs-surveys/decennial-census/about/voting-rights/cvap.html>. Provides tools to download and process raw data. Also provides a downloading interface to processed data. Implements a very basic approach to estimate block level citizen voting age population from block group data.
Compute expected shortfall (ES) and Value at Risk (VaR) from a
    quantile function, distribution function, random number generator or
    probability density function.  ES is also known as Conditional Value at
    Risk (CVaR). Virtually any continuous distribution can be specified.
    The functions are vectorized over the arguments. The computations are
    done directly from the definitions, see e.g. Acerbi and Tasche (2002)
    <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided,
    as well.
Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
An efficient cross-validated approach for covariance matrix
    estimation, particularly useful in high-dimensional settings. This
    method relies upon the theory of high-dimensional loss-based covariance
    matrix estimator selection developed by Boileau et al. (2022)
    <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator
    from among a prespecified set of candidates.
Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> 
            is suitable for cluster randomized trials (CRTs) with a
            small number of clusters (e.g., 20 or fewer). The procedure of
            constrained randomization is based on the baseline values of some
            cluster-level covariates specified. The intervention effect on
            the individual outcome can then be analyzed through
            clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. 
            Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline
            values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.
Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.
Implementation of Cross-Validated Kernel Ensemble (CVEK), 
  a flexible modeling framework for robust nonlinear regression and 
  hypothesis testing based on ensemble learning with kernel-ridge estimators 
  (Jeremiah et al. (2017) <arXiv:1710.01406> and 
  Wenying et al. (2018) <arXiv:1811.11025>). It allows user to conduct 
  nonlinear regression with minimal assumption on the function form by 
  aggregating nonlinear models generated from a diverse collection of kernel 
  families. It also provides utilities to test for the estimated nonlinear 
  effect under this ensemble estimator, using either the asymptotic or 
  the bootstrap version of a generalized score test.
Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.
Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>.
Estimates a lasso penalized precision matrix via the blockwise coordinate descent (BCD). This package is a simple wrapper around the popular 'glasso' package that extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations.
              See Friedman et al (2008) <doi:10.1093/biostatistics/kxm045> for details regarding the estimation method.
Produce an averaging estimate/prediction by combining all candidate models for partial linear functional additive models, 
    using multi-fold cross-validation criterion. More details can be referred to Shishi Liu and Jingxiao Zhang. (2021) <arXiv:2105.00966>.
Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828> (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) <doi:10.2307/3315735> and Lockhart, Spinelli and Stephens (2007) <doi:10.1002/cjs.5550350111> (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) <doi:10.2307/3316040>. Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values.
It is devoted to Cramer-von Mises goodness-of-fit tests.
    It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
Implementation of cross-validation method for testing the forecasting accuracy of several multi-population mortality models. The family of multi-population includes several multi-population mortality models proposed through the actuarial and demography literature. The package includes functions for fitting and forecast the mortality rates of several populations. Additionally, we include functions for testing the forecasting accuracy of different multi-population models.
  References.
  Atance, D., Debon, A., and Navarro, E. (2020) <doi:10.3390/math8091550>.
  Bergmeir, C. & Benitez, J.M. (2012) <doi:10.1016/j.ins.2011.12.028>.
  Debon, A., Montes, F., & Martinez-Ruiz, F. (2011) <doi:10.1007/s13385-011-0043-z>.
  Lee, R.D. & Carter, L.R. (1992) <doi:10.1080/01621459.1992.10475265>.
  Russolillo, M., Giordano, G., & Haberman, S. (2011) <doi:10.1080/03461231003611933>.
  Santolino, M. (2023) <doi:10.3390/risks11100170>.
Cross-validate one or multiple regression and classification models
    and get relevant evaluation metrics in a tidy format. Validate the
    best model on a test set and compare it to a baseline evaluation.
    Alternatively, evaluate predictions from an external model. Currently
    supports regression and classification (binary and multiclass).
    Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., 
    & Wambugu M. (2019, ISBN: 9781838550134).
Perform canonical variate regression (CVR) for two sets of covariates and a univariate
            response, with regularization and weight parameters tuned by cross validation.
Calculate various cardiovascular disease risk scores from the
    Framingham Heart Study (FHS), the American College of Cardiology (ACC),
    and the American Heart Association (AHA) as described in D’agostino, et al
    (2008) <doi:10.1161/circulationaha.107.699579>, Goff, et al (2013)
    <doi:10.1161/01.cir.0000437741.48606.98>, and Mclelland, et al (2015)
    <doi:10.1016/j.jacc.2015.08.035>.
The goal of 'cvsem' is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using 'lavaan' syntax. 'cvsem' computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) <doi:10.18637/jss.v048.i02>. The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.
The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
The level-dependent cross-validation method is implemented
        for the selection of thresholding
        value in wavelet shrinkage. This procedure is implemented
        by coupling a conventional cross validation with an
        imputation method due to a limitation of data length,
        a power of 2. It can be easily applied to classical
        leave-one-out and k-fold cross validation.
        Since the procedure is computationally fast,
        a level-dependent cross validation can be performed for
        wavelet shrinkage of various data such as a data
        with correlated errors.
Tools that allow developers to write functions for
    cross-validation with minimal programming effort and assist
    users with model selection.
Tools for performing cross-validation (CV). The main function is a 
    general purpose wrapper that performs k-fold CV for any tuning parameter in 
    any supervised learning method. The package also has a function that 
    computes the loss incurred by a set of predictions for a variety of loss 
    functions and model families.
An object-oriented modeling language for disciplined
    convex programming (DCP) as described in Fu, Narasimhan, and Boyd
    (2020, <doi:10.18637/jss.v094.i14>). It allows the user to
    formulate convex optimization problems in a natural way following
    mathematical convention and DCP rules. The system analyzes the
    problem, verifies its convexity, converts it into a canonical
    form, and hands it off to an appropriate solver to obtain the
    solution. Interfaces to solvers on CRAN and elsewhere are
    provided, both commercial and open source.
The 'Corpus Workbench' ('CWB', <https://cwb.sourceforge.io/>) offers a classic and mature
 approach for working with large, linguistically and structurally annotated corpora. The 'CWB'
 is memory efficient and its design makes running queries fast, see Evert (2011)
 <https://eprints.lancs.ac.uk/id/eprint/62721>. The 'cwbtools' package offers
 pure 'R' tools to create indexed corpus files as well as high-level wrappers for the original 'C'
 implementation of 'CWB' as exposed by the 'RcppCWB' package
 (<https://CRAN.R-project.org/package=RcppCWB>). Additional functionality to add and
 modify annotations of corpora from within 'R' makes working with 'CWB' indexed corpora
 much more flexible and convenient. The 'cwbtools' package in combination with the 'R' packages
 'RcppCWB' (<https://CRAN.R-project.org/package=RcppCWB>) and 'polmineR'
 (<https://CRAN.R-project.org/package=polmineR>) offers a lightweight infrastructure
 to support the combination of quantitative and qualitative approaches for working
 with textual data.
A flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) <doi:10.13140/RG.2.2.28323.53280>.
Computes the convex hull in arbitrary dimension, based on the
    Qhull library (<http://www.qhull.org>). The package provides a
    complete description of the convex hull: edges, ridges, facets,
    adjacencies. Triangulation is optional.
Recent developments in modern coexistence theory have
    advanced our understanding on how species are able to persist and
    co-occur with other species at varying abundances. However, applying
    this mathematical framework to empirical data is still challenging,
    precluding a larger adoption of the theoretical tools developed by
    empiricists. This package provides a complete toolbox for modelling
    interaction effects between species, and calculate fitness and niche
    differences.  The functions are flexible, may accept covariates, and
    different fitting algorithms can be used.  A full description of the
    underlying methods is available in García-Callejas, D., Godoy, O., and
    Bartomeus, I. (2020) <doi:10.1111/2041-210X.13443>.  Furthermore, the
    package provides a series of functions to calculate dynamics for
    stage-structured populations across sites.
Extend 'cxxfunction' by saving the dynamic shared objects
        for reusing across R sessions.
A suite of functions for analysing cycling data.
An interface to the cycle routing/data services provided by
    'CycleStreets', a not-for-profit social enterprise and advocacy
    organisation.  The application programming interfaces (APIs) provided
    by 'CycleStreets' are documented at
    (<https://www.cyclestreets.net/api/>).  The focus of this package is
    the journey planning API, which aims to emulate the routes taken by a
    knowledgeable cyclist.  An innovative feature of the routing service
    of its provision of fastest, quietest and balanced profiles.  These
    represent routes taken to minimise time, avoid traffic and compromise
    between the two, respectively.
Cyclomatic complexity is a software metric (measurement),
    used to indicate the complexity of a program. It is a quantitative
    measure of the number of linearly independent paths through a program's
    source code. It was developed by Thomas J. McCabe, Sr. in 1976.
Tools for calculating coordinate representations of
    hypocycloids, epicyloids, hypotrochoids, and epitrochoids
    (altogether called 'cycloids' here) with different scaling
    and positioning options. The cycloids can be visualised with
    any appropriate graphics function in R.
Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period.  The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) <doi:10.1111/2041-210X.13305>). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations, such that the respective periods of higher mortality can be identified as "mortality seasons".
This model fitting tool incorporates cyclic coordinate descent and
    majorization-minimization approaches to fit a variety of regression models
    found in large-scale observational healthcare data.  Implementations focus
    on computational optimization and fine-scale parallelization to yield
    efficient inference in massive datasets.  Please see:
    Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.
The cyclotomic numbers are complex numbers that can be
    thought of as the rational numbers extended with the roots of unity. They
    are represented exactly, enabling exact computations. They contain the
    Gaussian rationals (complex numbers with rational real and imaginary
    parts) as well as the square roots of all rational numbers. They also
    contain the sine and cosine of all rational multiples of pi. The
    algorithms implemented in this package are taken from the 'Haskell'
    package 'cyclotomic', whose algorithms are adapted from code by Martin
    Schoenert and Thomas Breuer in the 'GAP' project
    (<https://www.gap-system.org/>). Cyclotomic numbers have applications in
    number theory, algebraic geometry, algebraic number theory, coding 
    theory, and in the theory of graphs and combinatorics. They have 
    connections to the theory of modular functions and modular curves.
Wraps cytoscape.js as a shiny widget. cytoscape.js <https://js.cytoscape.org/> is a Javascript-based graph theory (network) library for visualization and analysis. This package supports the visualization of networks with custom visual styles and several available layouts. Demo Shiny applications are provided in the package code.
Classes (S4) of circular-linear, symmetric copulas with corresponding
    methods, extending the 'copula' package. These copulas are especially useful
    for modeling correlation in discrete-time movement data. Methods for density, 
    (conditional) distribution, random number generation, bivariate dependence
    measures and fitting parameters using maximum likelihood and other 
    approaches. The package also contains methods for visualizing movement data
    and copulas.
A toolkit for querying 'Team Cymru' <http://team-cymru.org> 'IP'
    address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon
    and 'Malware' Hash Data Services.
Encryption wrappers, using low-level support from
    'sodium' and 'openssl'.  'cyphr' tries to smooth over some pain
    points when using encryption within applications and data analysis
    by wrapping around differences in function names and arguments in
    different encryption providing packages.  It also provides
    high-level wrappers for input/output functions for seamlessly
    adding encryption to existing analyses.
The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.
Tools to interface with Cytobank's API via R, organized by
    endpoints that represent various areas of Cytobank functionality. Learn more
    about Cytobank at <https://www.beckman.com/flow-cytometry/software>.
An implementation of Fan plots for cytometry data in 'ggplot2'. 
    For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart 
    <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).
Given the hypothesis of a bi-modal distribution of cells for
    each marker, the algorithm constructs a binary tree, the nodes of which are
    subpopulations of cells. At each node, observed cells and markers are modeled
    by both a family of normal distributions and a family of bi-modal normal mixture
    distributions. Splitting is done according to a normalized difference of AIC
    between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, 
    Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.
Typical morphological profiling datasets have millions of cells
    and hundreds of features per cell. When working with this data, you must
    clean the data, normalize the features to make them comparable across
    experiments, transform the features, select features based on their
    quality, and aggregate the single-cell data, if needed. 'cytominer' makes
    these steps fast and easy. Methods used in practice in the field are
    discussed in Caicedo (2017) <doi:10.1038/nmeth.4397>. An overview of the
    field is presented in Caicedo (2016) <doi:10.1016/j.copbio.2016.04.003>.
Supervised learning from a source distribution (with known segmentation into cell sub-populations) 
             to fit a target distribution with unknown segmentation. It relies regularized optimal transport to directly 
             estimate the different cell population proportions from a biological sample characterized with flow cytometry 
             measurements. It is based on the regularized Wasserstein metric to compare cytometry measurements from 
             different samples, thus accounting for possible mis-alignment of a given cell population across sample 
             (due to technical variability from the technology of measurements). Supervised learning technique based 
             on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a 
             mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2021) <arXiv:2006.09003>.
Create simplex plots to visualize the similarity between 
    single-cells and selected clusters in a 1-/2-/3-simplex space.
    Velocity information can be added as an additional layer. 
    See Liu J, Wang Y et al (2023) <doi:10.1101/2023.12.07.570655> for more details.
Interface to interest and foreign exchange rates published by the Czech National Bank.
Get programmatic access to the open data provided by the
    Czech Statistical Office (CZSO, <https://czso.cz>).
Provides a novel framework to able to automatically develop and deploy
    an accurate Multiple Classifier System based on the feature-clustering 
    distribution achieved from an input dataset. 'D2MCS' was developed focused on 
    four main aspects: (i) the ability to determine an effective method to 
    evaluate the independence of features, (ii) the identification of the 
    optimal number of feature clusters, (iii) the training and tuning of ML 
    models and (iv) the execution of voting schemes to combine the outputs of 
    each classifier comprising the Multiple  Classifier System.
For identifying, estimating, and plotting descriptive multidimensional item response theory models, restricted to 3D and dichotomous or polytomous data that fit the two-parameter logistic model or the graded response model. The method is foremost explorative and centered around the plot function that exposes item characteristics and constructs, represented by vector arrows, located in a three-dimensional interactive space. The results can be useful for item-level analysis as well as test development.
This packages is intended to make it easy to create D3 JavaScript
    network, tree, dendrogram, and Sankey graphs from R using data frames.
    !!! NOTE: Active development has moved to the networkD3 package. !!!
Builds interactive 'd3.js' hierarchical visualisation easily. D3partitionR makes it easy to build and customize sunburst, circle treemap, treemap, partition chart, ...
Provides functions that offer seamless 'D3Plus' integration. The examples provided here are taken from the official 'D3Plus' website <http://d3plus.org>.
Apache licensed alternative to 'Highcharter' which provides 
  functions for both fast and beautiful interactive visualization for 'Markdown'
  and 'Shiny'.
Provides a suite of functions to help ease the use of 'd3.js' in R.
              These helpers include 'htmltools::htmlDependency' functions, hierarchy
              builders, and conversion tools for 'partykit', 'igraph,' 'table',
              and 'data.frame' R objects into the 'JSON' that 'd3.js' expects.
Create and customize interactive collapsible 'D3' trees using the 'D3'
    JavaScript library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
    When in Shiny the tree layout is observed by the server and can be used as a reactive filter
    of structured data.
Provides an interface to 'D4Science' 'StorageHub' API (<https://dev.d4science.org/>). Allows to get user profile, and perform 
 actions over the 'StorageHub' (workspace) including creation of folders, files management (upload/update/deletion/sharing), and listing of 
 stored resources.
Tools, methods and processes for the management 
  of analysis workflows. These lightweight solutions facilitate 
  structuring R&D activities. These solutions were developed to comply with FAIR 
  principles as discussed by Jacobsen et al. (2017) <doi:10.1162/dint_r_00024>, 
  and with ALCOA+ principles as proposed by the U.S. FDA.
Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.
Functions and data sets used in examples and exercises in the
        text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
        Analysis and Graphics Using R", and in an upcoming Maindonald,
        Braun, Andrews, and Narayan text that builds on this earlier text.
Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.
Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.
Data Analysis using Bootstrap-Coupled ESTimation.
  Estimation statistics is a simple framework that avoids the pitfalls of
  significance testing. It uses familiar statistical concepts: means,
  mean differences, and error bars. More importantly, it focuses on the
  effect size of one's experiment/intervention, as opposed to a false
  dichotomy engendered by P values.
  An estimation plot has two key features:
  1. It presents all datapoints as a swarmplot, which orders each point to
  display the underlying distribution.
  2. It presents the effect size as a bootstrap 95% confidence interval on a
  separate but aligned axes.
  Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105.
  <doi:10.1038/s41592-019-0470-3>.
  The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.
Provides functions to manage databases: select, update, insert,
    and delete records, list tables, backup tables as CSV files, and import
    CSV files as tables.
Conduct detection and attribution of climate change using methods including optimal fingerprinting via 
    generalized total least squares or estimating equation approach from Ma et al. (2023) <doi:10.1175/JCLI-D-22-0681.1>. 
    Provide shrinkage estimators for covariance matrix from Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>, 
    and Ledoit and Wolf (2017) <doi:10.2139/ssrn.2383361>.
An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (in prep).
The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
Displays a terrible joke, the kind only dads crack.
What is funnier than a dad joke?  A dad joke in R!  This package 
    utilizes the API for <https://icanhazdadjoke.com> and returns dad jokes from
    several API endpoints.
Este pacote traduz os seguintes 
    conjuntos de dados: 'airlines', 'airports', 'ames_raw', 'AwardsManagers',
    'babynames', 'Batting', 'diamonds', 'faithful', 'fueleconomy',
    'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers',
    'mpg', 'mtcars', 'atmos', 'penguins', 'People, 'Pitching', 'pixarfilms','planes',
    'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b',
    'table5', 'vehicles', 'weather', 'who'.  English: It provides a
    Portuguese translated version of the datasets listed above.
The content falls into the following groupings: (i) Data, (ii)
    Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v)
    Matrix functions, (vi) Projector and canonical efficiency functions, and (vii)
    Miscellaneous functions. There is a vignette describing how to use the 
    design functions for randomizing and assessing designs available as a 
    vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of 
    information when the 'Error' function has been used in the call to 'aov'. 
    The package 'dae' can also be installed from 
  <http://chris.brien.name/rpackages/>.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.
Diff, patch and merge for data frames. Document changes in data
    sets and use them to apply patches. Changes to data can be made visible by using
    render_diff(). The 'V8' package is used to wrap the 'daff.js' 'JavaScript' library
    which is included in the package.
Allows to download, clean and analyse raw Vessel Monitoring System, VMS, data from
    Mexican government. You can use the vms_download() function to
    download raw data, or you can use the sample_dataset provided within
    the package. You can follow the tutorial in the vignette available at
    <https://cbmc-gcmp.github.io/dafishr/index.html>.
Data and miscellanea to support the book "Introduction to Data analysis with R for Forensic Scientists." This book was written by James Curran and published by CRC Press in 2010 (ISBN: 978-1-4200-8826-7).
Compressed spatial vector data originally from <https://dawadocs.dataforsyningen.dk/> saved as Simple 
    Features, SF, objects with data on population, age and gender from Statistics Denmark <https://www.dst.dk/da/>.
A port of the web-based software 'DAGitty', available at 
    <https://dagitty.net>, for analyzing structural causal models 
    (also known as directed acyclic graphs or DAGs).
    This package computes covariate adjustment sets for estimating causal
    effects, enumerates instrumental variables, derives testable
    implications (d-separation and vanishing tetrads), generates equivalent
    models, and includes a simple facility for data simulation.
Draw, manipulate, and evaluate directed
        acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.
DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. Further description can be found in Haber et al (2022) <DOI:10.1016/j.annepidem.2022.01.001>.
Downloads the public data available from the Brazilian Access to Information Law and and performs a search on information requests and appeals made since 2015.
Reverse and model the effects of changing deposition rates on geological data and rates. Based on Hohmann (2018) <doi:10.13140/RG.2.2.23372.51841> .
Generate reports that enable quick visual review of 
    temporal shifts in record-level data. Time series plots showing aggregated 
    values are automatically created for each data field (column) depending on its 
    contents (e.g. min/max/mean values for numeric data, no. of distinct 
    values for categorical data), as well as overviews for missing values, 
    non-conformant values, and duplicated rows. The resulting reports are shareable 
    and can contribute to forming a transparent record of the entire analysis process. 
    It is designed with Electronic Health Records in mind, but can be used for 
    any type of record-level temporal data (i.e. tabular data where each row represents 
    a single "event", one column contains the "event date", and other columns 
    contain any associated values for the event).
R interface for the Google Cloud Services 'Document AI API'
    <https://cloud.google.com/document-ai/> with additional tools for
    output file parsing and text reconstruction. 'Document AI' is a
    powerful server-based OCR service that extracts text and tables from
    images and PDF files with high accuracy. 'daiR' gives R users
    programmatic access to this service and additional tools to handle
    and visualize the output. See the package website <https://dair.info/>
    for more information and examples.
Simulates and computes the (maximum) likelihood of a dynamical 
    model of island biota assembly through speciation, immigration and
    extinction. See Valente et al. (2015) <doi:10.1111/ele.12461>.
Extracts colonisation and branching times of island
    species to be used for analysis in the R package 'DAISIE'. It uses
    phylogenetic and endemicity data to extract the separate island colonists
    and store them.
Functions and an example dataset for the psychometric theory of
  knowledge spaces.  This package implements data analysis methods and
  procedures for simulating data and quasi orders and transforming different
  formulations in knowledge space theory.  See package?DAKS for an overview.
Any unverified black box model is the path to failure. Opaqueness leads to distrust. 
  Distrust leads to ignoration. Ignoration leads to rejection. 
  DALEX package xrays any model and helps to explore and explain its behaviour.
  Machine Learning (ML) models are widely used and have various applications in classification 
  or regression. Models created with boosting, bagging, stacking or similar techniques are often
  used due to their high performance. But such black-box models usually lack direct interpretability.
  DALEX package contains various methods that help to understand the link between input variables 
  and model output. Implemented methods help to explore the model on the level of a single instance 
  as well as a level of the whole dataset.
  All model explainers are model agnostic and can be compared across different models.
  DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration.
  Find more details in (Biecek 2018) <arXiv:1806.08915>.
Provides wrapper of various machine learning models. 
  In applied machine learning, there 
  is a strong belief that we need to strike a balance 
  between interpretability and accuracy. 
  However, in field of the interpretable machine learning, 
  there are more and more new ideas for explaining black-box models, 
  that are implemented in 'R'. 
  'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models
  including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. 
  Important part of the package is Champion-Challenger analysis and innovative approach
  to model performance across subsets of test data presented in Funnel Plot.
Fit of a double additive location-scale model with a nonparametric error distribution from possibly right- or interval censored data. The additive terms in the location and dispersion submodels, as well as the unknown error distribution in the location-scale model, are estimated using Laplace P-splines. For more details, see Lambert (2021) <doi:10.1016/j.csda.2021.107250>.
The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
An important aspect of data analytics is related to data management support for artificial intelligence. It is related to preparing data correctly. This package provides extensions to support data preparation in terms of both data sampling and data engineering. Overall, the package provides researchers with a comprehensive set of functionalities for data science based on experiment lines, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
The DALY Calculator is a free, open-source Graphical User
             Interface (GUI) for stochastic disability-adjusted life year
             (DALY) calculation.
A collection of functions which aim to assist common computational workflow for analysis of matabolomic data..
Define a spatial 'Area of Interest' (AOI) around a constructed dam using hydrology data.
  Dams have environmental and social impacts, both positive and negative.
  Current analyses of dams have no consistent way to specify at what spatial extent we should evaluate these impacts.
  'damAOI' implements methods to adjust reservoir polygons to match satellite-observed surface water areas, plot upstream and downstream rivers using elevation data and accumulated river flow, and draw buffers clipped by river basins around reservoirs and relevant rivers. 
  This helps to consistently determine the areas which could be impacted by dam construction, facilitating comparative analysis and informed infrastructure investments.
Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
A suite of functions for analyzing and visualizing the health economic outputs of mathematical models.
    This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the 
    National Institutes of Health under award no. R01AI138783. The content of this package is solely the 
    responsibility of the authors and does not necessarily represent the official views of the National Institutes 
    of Health. The theoretical underpinnings of 'dampack''s functionality are detailed in Hunink et al. (2014) 
    <doi:10.1017/CBO9781139506779>.
Loads behavioural data from the widely used Drosophila Activity Monitor System (DAMS, TriKinetics <https://trikinetics.com/>) into the rethomics framework.
The single largest source of dams in the United States is the
    National Inventory of Dams (NID) <http://nid.usace.army.mil> from the US
    Army Corps of Engineers. Entire data from the NID cannot be obtained all at
    once and NID's website limits extraction of more than a couple of thousand
    records at a time. Moreover, selected data from the NID's user interface
    cannot not be saved to a file. In order to make the analysis of this data
    easier, all the data from NID was extracted manually. Subsequently, the raw
    data was checked for potential errors and cleaned. This package provides
    sample cleaned data from the NID and provides functionality to access the
    entire cleaned NID data.
Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.
Provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size 
 calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to 
 design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.
Functions to accompany Wayne W. Daniel's
       Biostatistics: A Foundation for Analysis in the Health
       Sciences, Tenth Edition.
Discriminant Adaptive Nearest Neighbor Classification is a 
    variation of k nearest neighbors where the shape of the neighborhood is 
    data driven. This package implements dann and sub_dann from
    Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
The purpose of the package is to enable an R function interface into the Statistics Denmark Databank API mainly for research purposes. 
    The Statistics Denmark Databank API has four endpoints, see here for more information and testing the API in their console: <https://www.dst.dk/en/Statistik/brug-statistikken/muligheder-i-statistikbanken/api>.
    This package mimics the structure of the API and provides four main functions to match the functionality of the API endpoints.
An implementation of Discriminant Analysis via Projections (DAP) method for high-dimensional binary classification in the case of unequal covariance matrices. See Irina Gaynanova and Tianying Wang (2018) <arXiv:1711.04817v2>.
Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...).
Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution.  The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) <arXiv:2103.09872>).
The recovery of visual sensitivity in a dark environment is known
    as dark adaptation. In a clinical or research setting the recovery is typically
    measured after a dazzling flash of light and can be described by the Mahroo,
    Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take
    dark adaptation data and use nonlinear regression to find the parameters of the
    model that 'best' describe the data. They do this by firstly, generating rapid
    initial objective estimates of data adaptation parameters, then a multi-start
    algorithm is used to reduce the possibility of a local minimum. There is also a
    bootstrap method to calculate parameter confidence intervals. The functions rely
    upon a 'dark' list or object. This object is created as the first step in the
    workflow and parts of the object are updated as it is processed.
Estimation of dark diversity and site-specific species pools using species co-occurrences. 
    It includes implementations of probabilistic dark diversity based on the 
    Hypergeometric distribution, as well as estimations based on the Beals index,
    which can be transformed to binary predictions using different thresholds, 
    or transformed into a favorability index. All methods include the possibility of
    using a calibration dataset that is used to estimate the indication matrix 
    between pairs of species, or to estimate dark diversity directly on a single 
    dataset. See De Caceres and Legendre (2008) <doi:10.1007/s00442-008-1017-y>, 
    Lewis et al. (2016) <doi:10.1111/2041-210X.12443>, 
    Partel et al. (2011) <doi:10.1016/j.tree.2010.12.004>, Real et al. (2017) <doi:10.1093/sysbio/syw072>
    for further information.
Provides programmatic access to the 'Dark Sky' 'API' 
    <https://darksky.net/dev/docs>, which provides current or historical global 
    weather conditions.
Functions are provided that facilitate the import and analysis of
    'SNP' (single nucleotide polymorphism) and 'silicodart' (presence/absence) data. 
    The main focus is on data generated by 'DarT' (Diversity Arrays Technology), 
    however, data from other sequencing platforms can be used once 'SNP' or related 
    fragment presence/absence data from any source is imported. Genetic datasets 
    are stored in a derived 'genlight' format (package 'adegenet'), that allows for 
    a very compact storage of data and metadata. Functions are available for 
    importing and exporting of 'SNP' and 'silicodart' data, for reporting on and 
    filtering on various criteria (e.g. 'CallRate', heterozygosity, reproducibility, 
    maximum allele frequency). Additional functions are available for visualization 
    (e.g. Principle Coordinate Analysis) and creating a spatial representation 
    using maps. 'dartR' supports also the analysis of 3rd party software package 
    such as 'newhybrid', 'structure', 'NeEstimator' and 'blast'. Since version 
    2.0.3 we also implemented simulation functions, that allow to forward simulate
    'SNP' dynamics under different population and evolutionary dynamics. 
    Comprehensive tutorials and support can be found at our 'github' repository: 
    github.com/green-striped-gecko/dartR/. If you want to cite 'dartR', you find 
    the information by typing citation('dartR') in the console.
Facilitates the import and analysis of 'SNP' (single nucleotide 'polymorphism') 
    and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' 
    (Diversity Arrays Technology), however, data from other sequencing platforms can be used 
    once 'SNP' or related fragment presence/absence data from any source is imported. Genetic 
    datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for 
    a very compact storage of data and metadata. Functions are available for 
    importing and exporting of 'SNP' and 'silicodart' data, for reporting on and 
    filtering on various criteria (e.g. 'callrate', 'heterozygosity', 'reproducibility', 
    maximum allele frequency). Additional functions are available for visualization 
    (e.g. Principle Coordinate Analysis) and creating a spatial representation 
    using maps. 'dartR.base' is the 'base' package of the 'dartRverse' suits of packages. 
    To install the other packages, we recommend to install the 'dartRverse' package, that 
    supports the installation of all packages in the 'dartRverse'.
    If you want to cite 'dartR', you find the information by typing citation('dartR.base')
    in the console.
Functions are provided that facilitate the analysis of SNP 
    (single nucleotide polymorphism) data to answer questions regarding 
    captive breeding and relatedness between individuals. 'dartR.captive' 
    is part of the 'dartRverse' suit of packages.
    Gruber et al. (2018) <doi:10.1111/1755-0998.12745>.
    Mijangos et al. (2022) <doi:10.1111/2041-210X.13918>.
Data package for 'dartR'. Provides data sets to run examples in 'dartR'. This was necessary due to the size limit imposed by 'CRAN'. The data in 'dartR.data' is needed to run the examples provided in the 'dartR' functions. All available data sets are either based on actual data (but reduced in size) and/or simulated data sets to allow the fast execution of examples and demonstration of the functions.
Facilitates the analysis of SNP (single nucleotide polymorphism) 
    and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of 
    functions to analyse such data in a population genetics context. It provides
    several functions to calculate population genetic metrics and to study 
    population structure. Quite a few functions need additional software to be 
    able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description 
    in the help pages how to download and link the packages so the function can 
    run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages.
    Gruber et al. (2018) <doi:10.1111/1755-0998.12745>.
    Mijangos et al. (2022) <doi:10.1111/2041-210X.13918>.
Allows to simulate SNP data using genlight objects. For example, it is straight forward to simulate a simple drift scenario with exchange of individuals between two populations or create a new genlight object based on allele frequencies of an existing genlight object.
Provides landscape genomic functions to analyse 'SNP' (single nuclear
    polymorphism) data, such as least cost path analysis and isolation by distance. 
    Therefore each sample needs to have coordinate data attached (lat/lon) to be 
    able to run most of the functions. 'dartR.spatial' is a package that belongs
    to the 'dartRverse' suit of packages and depends on 'dartR.base' and 'dartR.data'.
Provides a single function that supports the installation of all packages 
    belonging to the 'dartRverse'. The 'dartRverse' is a set of packages that work 
    together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar 'look and feel' 
    and are based on the same type of data structure ('genlight'), with additional
    metadata for loci and individuals (samples). For more information visit the
    'GitHub' pages <https://github.com/green-striped-gecko/dartRverse>.
Allows manual creation of themes and logos to be used in
    applications created using the 'shinydashboard' package. Removes the need to
    change the underlying css code by wrapping it into a set of convenient R
    functions.
Provides methods for reading, displaying,
    processing and writing files originally arranged for
    the 'DSSAT-CSM' fixed width format. The 'DSSAT-CSM' 
    cropping system model is described at J.W. Jones,
    G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor,
    L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, 
    J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.
An implementation of common higher order functions with syntactic
    sugar for anonymous function. Provides also a link to 'dplyr' and
    'data.table' for common transformations on data frames to work around non
    standard evaluation by default.
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and traverse the
    tree in various orders. Aggregate, cumulate, print, plot, convert to and from
    data.frame and more. Useful for decision trees, machine learning, finance,
    conversion from and to JSON, and many other applications.
Validate dataset by columns and rows using convenient predicates inspired by 'assertr' package.
             Generate good looking HTML report or print console output to display in logs of your data processing pipeline.
Makes it easy to engage with the Application Program Interface (API)
    of the 'TCdata360' and 'Govdata360' platforms at <https://tcdata360.worldbank.org/>
    and <https://govdata360.worldbank.org/>, respectively.
    These application program interfaces provide access to over 5000 trade, competitiveness, and governance
    indicator data, metadata, and related information from sources
    both inside and outside the World Bank Group.
    Package functions include easier download of data sets, metadata, and
    related information, as well as searching based on user-inputted query.
An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 
    'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Snowflake', 'Spark', and 'SQLite'). Also includes support for
    fetching data as 'Andromeda' objects. Uses either 'Java Database Connectivity' ('JDBC') or other 'DBI' drivers to connect to databases.
Provides external JAR dependencies for the 'DatabaseConnector' package.
Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
'Databrary.org' is a restricted access repository for research data, especially video and audio.
  This package provides commands to interact with the data stored on 'Databrary.org'.
Easily define templates for lists and data frames that validate
    each element. Specify the expected type (i.e., character, numeric, etc),
    expected length, minimum and maximum values, allowable values, and more for
    each element in your data. Decide whether violations of these expectations
    should throw an error or a warning. This package is useful for validating
    data within R processes which pull from dynamic data sources such as
    databases and web APIs to provide an extra layer of validation around input
    and output data.
Includes functions that researchers or practitioners may use to clean
    raw data, transferring html, xlsx, txt data file into other formats. And it
    also can be used to manipulate text variables, extract numeric variables from
    text variables and other variable cleaning processes. It is originated from a
    author's project which focuses on creative performance in online education
    environment. The resulting paper of that study will be published soon.
Flexible and efficient cleaning of data with interactivity.
  'datacleanr' facilitates best practices in data analyses and reproducibility with built-in features and by translating interactive/manual operations to code. 
  The package is designed for interoperability, and so seamlessly fits into reproducible analyses pipelines in 'R'.
Tools for combining and cleaning data sets, particularly
    with grouped and time series data.
Easy comparison of two tabular data
    objects in R. Specifically designed to show differences between two sets of
    data in a useful way that should make it easier to understand the differences,
    and if necessary, help you work out how to remedy them. Aims
    to offer a more useful output than all.equal() when your two data sets do not
    match, but isn't intended to replace all.equal() as a way to test for equality.
Supports the process of applying a cut to Standard Data Tabulation Model (SDTM),
    as part of the analysis of specific points in time of the data, normally as part of 
    investigation into clinical trials. The functions support different approaches of
    cutting to the different domains of SDTM normally observed.
Creates a data dictionary from any dataframe or tibble in your R environment. 
    You can opt to add variable labels. You can write the object directly to Excel.
Query for metrics from 'Datadog' (<https://www.datadoghq.com/>) via its API.
An interactive editor built on 'rhandsontable' to allow the 
  interactive viewing, entering, filtering and editing of data in R 
  <https://dillonhammill.github.io/DataEditR/>.
Automated data exploration process for analytic tasks and predictive modeling, so
    that users could focus on understanding data and extracting insights. The package scans and
    analyzes each variable, and visualizes them with typical graphical techniques. Common
    data processing methods are also available to treat and format data.
Based on provided database description and/or database connection
  generate data sample preserving source structure.
Real life data is muddy, fuzzy and unpredictable. This makes data manipulations tedious and bringing the data to right shape alone is a major chunk of work. Functions in this package help us get an understanding of dataframes to dramatically reduces data coding time.
Automatic generation of finite state machine models of dynamic 
    decision-making that both have strong predictive power and are 
    interpretable in human terms. We use an efficient model representation and 
    a genetic algorithm-based estimation process to generate simple 
    deterministic approximations that explain most of the structure of complex 
    stochastic processes. We have applied the software to empirical data, and 
    demonstrated it's ability to recover known data-generating processes by 
    simulating data with agent-based models and correctly deriving the 
    underlying decision models for multiple agent models and degrees of
    stochasticity.
A wrapper for the Data.gov.sg developer resources, which provide 
    real time and historical information, ranging from carpark availability to 
    weather forecasts. The functions makes the API calls for a given date and
    time, before returning the relevant information in a data frame. All APIs 
    are supported, less the IPOS one which is not returning any data. Relevant 
    information can be found here <https://data.gov.sg/developer>.
Functions to pipe data from 'R' to 'DataGraph', a graphing and analysis application for mac OS. Create a live connection using either '.dtable' or '.dtbin' files that can be read by 'DataGraph'. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://community.visualdatatools.com/datagraph/knowledge-base/r-package/>.
Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.
Data screening is an important first step of any statistical
    analysis. dataMaid auto generates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset.
Designed to create a basic data dictionary and append to the original dataset's attributes list. The package makes use of a tidy dataset and creates a data frame that will serve as a linker that will aid in building the dictionary. The dictionary is then appended to the list of the original dataset's attributes. The user will have the option of entering variable and item descriptions by writing code or use alternate functions that will prompt the user to add these.
Set of tools aimed at processing meteorological data, converting hourly recorded data to daily, monthly and annual data.
'Shiny' modules to import data into an application or 'addin'
    from various sources, and to manipulate them after that.
A grammar of data manipulation with 'data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.
Datasets and Functions to Accompany Salas-Eljatib (2021, ISBN: 9789566086109) 
    "Analisis de datos con el programa estadistico R: una introduccion aplicada". The package helps carry out data management, exploratory analyses, and model fitting.
Creating, and refining data nuggets. 
    Data nuggets reduce a large dataset into a small collection of nuggets of 
    data, each containing a center (location), weight (importance), and scale 
    (variability) parameter. Data nugget centers are created by choosing 
    observations in the dataset which are as equally spaced apart as possible. 
    Data nugget weights are created by counting the number observations 
    closest to a given data nugget’s center. We then say the data nugget 
    'contains' these observations and the data nugget center is recalculated 
    as the mean of these observations. Data nugget scales are created by 
    calculating the trace of the covariance matrix of the observations 
    contained within a data nugget divided by the dimension of the dataset. 
    Data nuggets are refined by 'splitting' data nuggets which have scales or 
    shapes (defined as the ratio of the two largest eigenvalues of the 
    covariance matrix of the observations contained within the data nugget) 
    Reference paper: [1] Cherasia, K. E., Cabrera, J., Fernholz, L. T., & Fernholz, R. (2022). Data Nuggets in Supervised Learning. \emph{In Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler} (pp. 429-449). Cham: Springer International Publishing. [2] Beavers, T., Cheng, G., Duan, Y., Cabrera, J., Lubomirski, M., Amaratunga, D., Teigler, J. (2023). Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure (Submitted for Publication).
Post Global Financial Crisis derivatives reforms have lifted
    the veil off over-the-counter (OTC) derivative markets. Swap Execution
    Facilities (SEFs) and Swap Data Repositories (SDRs) now publish data
    on swaps that are traded on or reported to those facilities
    (respectively). This package provides you the ability to get this data
    from supported sources.
Provides read and write access to data and metadata from
    the DataONE network <https://www.dataone.org> of data repositories.  
    Each DataONE repository implements a consistent repository application 
    programming interface. Users call methods in R to access these remote 
    repository functions, such as methods to query the metadata catalog, get 
    access to metadata for particular data packages, and read the data objects 
    from the data repository. Users can also insert and update data objects on 
    repositories that support these methods.
Provides a flexible container to transport and manipulate complex
    sets of data. These data may consist of multiple data files and associated
    meta data and ancillary files. Individual data objects have associated system
    level meta data, and data files are linked together using the OAI-ORE standard
    resource map which describes the relationships between the files. The OAI-
    ORE standard is described at <https://www.openarchives.org/ore/>. Data packages
    can be serialized and transported as structured files that have been created
    following the BagIt specification. The BagIt specification is described at
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.
RStudio addins and R functions that make copy-pasting vectors and tables to text painless.
Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools.
    These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping.
    Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data.
    They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods.
    Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation;
    thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed.
    Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation.
    These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.
Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of 'data.table' efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.
Data quality assessments guided by a
    'data quality framework introduced by Schmidt and colleagues, 2021' 
    <doi:10.1186/s12874-021-01252-7> target the
    data quality dimensions integrity, completeness, consistency, and
    accuracy. The scope of applicable functions rests on the
    availability of extensive metadata which can be provided in
    spreadsheet tables. Either standardized (e.g. as 'html5' reports) or
    individually tailored reports can be generated. For an introduction
    into the specification of corresponding metadata, please refer to the
    'package website'
    <https://dataquality.qihs.uni-greifswald.de/Annotation_of_Metadata.html>.
Explore data related to the Doctor Who TV series.
Data screening is an important first step of any statistical
    analysis. 'dataReporter' auto generates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset. See Petersen AH, Ekstrøm CT (2019). "dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R." _Journal of Statistical Software_, *90*(6), 1-38 <doi:10.18637/jss.v090.i06> for more information.
Quality control and formatting tools developed for the Copernicus Data Rescue Service. The package includes functions to handle the Station Exchange Format (SEF), various statistical tests for climate data at daily and sub-daily resolution, as well as functions to plot the data. For more information and documentation see <https://datarescue.climate.copernicus.eu/st_data-quality-control>.
Collection of functions to help retrieve U.S. Geological Survey
    and U.S. Environmental Protection Agency water quality and
    hydrology data from web services. Data are discovered from 
    National Water Information System <https://waterservices.usgs.gov/> and <https://waterdata.usgs.gov/nwis>. 
    Water quality data are obtained from the Water Quality Portal <https://www.waterqualitydata.us/>.
Contains data organized by topics: categorical data, regression model, 
            means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.
For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.
A row by row data processing tool. You can write data processing code in 'DataSailr' script which is specially intended for data manipulation. The package uses 'libsailr' (C/C++ library) for its 'DataSailr' code parsing and its execution.
The Datasaurus Dozen is a set of datasets with the same
    summary statistics. They retain the same summary statistics despite
    having radically different distributions.  The datasets represent a
    larger and quirkier object lesson that is typically taught via
    Anscombe's Quartet (available in the 'datasets' package). Anscombe's
    Quartet contains four very different distributions with the same
    summary statistics and as such highlights the value of visualisation
    in understanding data, over and above summary statistics. As well as
    being an engaging variant on the Quartet, the data is generated in a
    novel way. The simulated annealing process used to derive datasets
    from the original Datasaurus is detailed in "Same Stats, Different
    Graphs: Generating Datasets with Varied Appearance and Identical
    Statistics through Simulated Annealing" <doi:10.1145/3025453.3025912>.
Collects a diverse range of symbolic data and offers a comprehensive set of functions that facilitate the conversion of traditional data into the symbolic data format.
Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.
Read, construct and write CDISC (Clinical Data Interchange Standards Consortium) Dataset JSON (JavaScript Object Notation) files, while validating per the Dataset JSON schema file, as described in CDISC (2023) <https://www.cdisc.org/dataset-json>.
Graphical interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces.
Companion to the book "An Introduction to Clustering with R" by P. Giordani, M.B. Ferraro and F. Martella (Springer, Singapore, 2020). The datasets are used in some case studies throughout the text.
A collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses.
Provides a convenient API interface to access immunological data
    within 'the CAVD DataSpace'(<https://dataspace.cavd.org>), a data sharing 
    and discovery tool that facilitates exploration of HIV immunological data 
    from pre-clinical and clinical HIV vaccine studies.
The goal of 'dataspice' is to make it easier for researchers to
  create basic, lightweight, and concise metadata files for their datasets.
  These basic files can then be used to make useful information available during
  analysis, create a helpful dataset "README" webpage, and produce more complex
  metadata formats to aid dataset discovery. Metadata fields are based on
  the 'Schema.org' and 'Ecological Metadata Language' standards.
For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) <doi:10.20944/preprints201710.0098.v1>.
Based on a SAS data step. This allows for row-wise dynamic building
    of data, iteratively importing slices of existing dataframes, conducting
    analyses, and exporting to a results frame. This is particularly useful for
    differential or time-series analyses, which are often not well suited to vector-
    based operations.
Provides a set of functions and a class to connect, extract and
    upload information from the 'Refinitiv Datastream' database. This
    package uses the 'DSWS' API and server used by the 'Datastream DFO addin'.  
    Details of this API are available at <https://www.lseg.com/en/data-analytics>.
    Please report issues at <https://github.com/CharlesCara/DatastreamDSWS2R/issues>.
Pulls together a collection of datasets from Miguel de Carvalho research articles. Including, for example:
    - de Carvalho (2012) <doi:10.1016/j.jspi.2011.08.016>;
    - de Carvalho et al (2012) <doi:10.1080/03610926.2012.709905>;
    - de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>);
    - de Carvalho and Davison (2014) <doi:10.1080/01621459.2013.872651>;
    - de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>.
Allows you to define rules which can be used to verify a given
    dataset.
    The package acts as a thin wrapper around more powerful data packages such
    as 'dplyr', 'data.table', 'arrow', and 'DBI' ('SQL'), which do the heavy lifting.
Provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5),
    enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0,
    use the archived 'dvn' package <https://cran.r-project.org/package=dvn>.
Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
Gives access to data visualisation methods that are relevant from the statistician's point of view. Using 'D3''s existing data visualisation tools to empower R language and environment. The throw chart method is a line chart used to illustrate paired data sets (such as before-after, male-female).
A lightweight package to assist in key steps involved in any data 
    analysis workflow: (1) wrangling the raw data to get it in the needed form, 
    (2) applying preprocessing steps and statistical transformations, and 
    (3) compute statistical summaries of data properties and distributions. 
    It is also the data wrangling backend for packages in 'easystats' ecosystem.
    References: Patil et al. (2022) <doi:10.21105/joss.04684>.
Functions to download and treat data regarding the Brazilian
    Amazon region from a variety of official sources.
Works as a virtual CRAN snapshot for source packages.
    It automatically downloads and installs 'tar.gz' files with dependencies,
    all of which were available on a specific day.
There are many different formats dates are commonly
    represented with: the order of day, month, or year can differ,
    different separators ("-", "/", or whitespace) can be used, months can
    be numerical, names, or abbreviations and year given as two digits or
    four. 'datefixR' takes dates in all these different formats and
    converts them to R's built-in date class. If 'datefixR' cannot
    standardize a date, such as because it is too malformed, then the user
    is told which date cannot be standardized and the corresponding ID for
    the row. 'datefixR' also allows the imputation of missing days and
    months with user-controlled behavior.
Methods and workflows to get chronograms (i.e., phylogenetic trees with branch lengths
    proportional to time), using open, peer-reviewed, state-of-the-art scientific data on time of lineage divergence.
    This package constitutes the main underlying code of the DateLife web service
    at <https://www.datelife.org>. To obtain a single summary chronogram from a group of
    relevant chronograms, we implement the Super Distance Matrix (SDM) method
    described in Criscuolo et al. (2006) <doi:10.1080/10635150600969872>.
    To find the grove of chronograms with a sufficiently overlapping set of taxa
    for summarizing, we implement theorem 1.1. from Ané et al. (2009)
    <doi:10.1007/s00026-009-0017-x>.
    A given phylogenetic tree can be dated using time of lineage divergence data
    as secondary calibrations (with caution, see Schenk (2016) <doi:10.1371/journal.pone.0148228>).
    To obtain and apply secondary calibrations, the package implements the congruification method described
    in Eastman et al. (2013) <doi:10.1111/2041-210X.12051>. Tree dating can be performed with different methods
    including BLADJ (Webb et al. (2008) <doi:10.1093/bioinformatics/btn358>), PATHd8
    (Britton et al. (2007) <doi:10.1080/10635150701613783>), mrBayes (Huelsenbeck
    and Ronquist (2001) <doi:10.1093/bioinformatics/17.8.754>), and treePL (Smith
    and O'Meara (2012) <doi:10.1093/bioinformatics/bts492>).
A Shiny Input for date-ranges, which pops up two calendars for selecting dates, times, or predefined ranges like "Last 30 Days". It wraps the JavaScript library 'daterangepicker' which is available at <https://www.daterangepicker.com>.
Provides methods for working with nominal dates, times, and 
 durations. Base R has sophisticated facilities for handling time, but these 
 can give unexpected results if, for example, timezone is not handled properly. 
 This package provides a more casual approach to support cases which 
 do not require rigorous treatment.  It systematically deconstructs the 
 concepts origin and timezone, and de-emphasizes the display of seconds. It 
 also converts among nominal durations such as seconds, hours, days, and weeks.
 See '?datetime' and '?duration' for examples. Adapted from 'metrumrg' 
 <http://r-forge.r-project.org/R/?group_id=1215>.
Supports import/export for a number of datetime string standards 
    and R datetime classes often including
    lossless re-export of     
    any original reduced precision including 'ISO 8601' <https://en.wikipedia.org/wiki/ISO_8601> and 
    'pdfmark' <https://opensource.adobe.com/dc-acrobat-sdk-docs/library/pdfmark/> datetime strings.
    Supports local/global datetimes with optional UTC offsets and/or (possibly heterogeneous) time zones
    with up to nanosecond precision.
Provides a datetime range picker widget for usage in 'Shiny'.
    It creates a calendar allowing to select a start date and an end date
    as well as two fields allowing to select a start time and an end time.
Utilities for handling dates and times, such
   as selecting particular days of the week or month,
   formatting timestamps as required by RSS feeds, or
   converting timestamp representations of other software
   (such as 'MATLAB' and 'Excel') to R. The package is
   lightweight (no dependencies, pure R implementations) and
   relies only on R's standard classes to represent dates
   and times ('Date' and 'POSIXt'); it aims to provide
   efficient implementations, through vectorisation and the
   use of R's native numeric representations of timestamps
   where possible.
Transfer any date type to ISO standard. Package recognizes dates in given data frame and transform to ISO format. Only one date format can be applied within one data frame column.
Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.
A thin wrapper around the 'Datorama' API.
    Ideal for analyzing marketing data from <https://datorama.com>.
Provee una versión traducida de los siguientes
    conjuntos de datos: 'airlines', 'airports', 'AwardsManagers',
    'babynames', 'Batting', 'credit_data', 'diamonds', 'faithful', 'fueleconomy',
    'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers',
    'mpg', 'mtcars', 'atmos', 'palmerpenguins', 'People, 'Pitching', 'planes',
    'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b',
    'table5', 'vehicles', 'weather', 'who'.  English: It provides a
    Spanish translated version of the datasets listed above.
Converting date ranges into dating 'steps' eases the
    visualization of changes in e.g. pottery consumption, style and other
    variables over time. This package provides tools to process and
    prepare data for visualization and employs the concept of aoristic
    analysis.
Interface with the 'Dat' p2p network protocol <https://datproject.org>. Clone archives from the network, share your own files, and install packages from the network.
Profiles datasets (collecting statistics and informative summaries 
    about that data) on data frames and 'ODBC' tables: maximum, minimum, mean, standard deviation, nulls,
    distinct values, data patterns, data/format frequencies.
Phone numbers are often represented as strings because there
    is no obvious and suitable native representation for them.  This leads
    to high memory use and a lack of standard representation.  The package
    provides integer representation of Australian phone numbers with optional
    raw vector calling code. The package name is an extension of 'au' and 'ph'.
Various utilities for the Davies distribution.
In applications it is usual that some additional          
      information is available. This package dawai (an acronym 
      for Discriminant Analysis With Additional Information) 
      performs linear and quadratic discriminant analysis with 
      additional information expressed as inequality restrictions 
      among the populations means. It also computes several 
      estimations of the true error rate.
Programmatic interface to the 'Daymet' web services
    (<http://daymet.ornl.gov>). Allows for easy downloads of
    'Daymet' climate data directly to your R workspace or your computer.
    Routines for both single pixel data downloads and
    gridded (netCDF) data are provided.
Allows clinicians and researchers to compute daily dose (and subsequently days' supply) for prescription refills using the following methods: Fixed window, fixed tablet, defined daily dose (DDD), and Random Effects Warfarin Days' Supply (REWarDS). Daily dose is the computed dose that the patient takes every day. For medications with fixed dosing (e.g. direct oral anticoagulants) this is known and does not need to be estimated. For medications with varying dose such as warfarin, however, the daily dose should be assumed or estimated to allow measurement of drug exposure. Days’ supply is the number of days that patients’ supply of medication will last after each prescription fill. Estimating days’ supply is necessary to calculate drug exposure. The package computes days’ supply and daily dose at both the prescription and patient levels. Results at the prescription level are denoted with “-Rx-” and those at patient level are denoted with “-Pt-”.
Provides methods for (auto)covariance/correlation function estimation 
    in change point regression with stationary errors circumventing the pre-estimation
    of the underlying signal of the observations. Generic, first-order, (m+1)-gapped,
    difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) <doi:10.48550/arXiv.1905.04578>. Bias-reducing, second-order, (m+1)-gapped, 
    difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) 
    <doi:10.1111/sjos.12256>. Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) <doi:10.3150/15-BEJ782>. 
    It also includes a general projection-based method for covariance matrix estimation.
Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'.
A way to apply Distance-Based Common Spatial Patterns
    (DB-CSP) techniques in different fields, both classical Common Spatial
    Patterns (CSP) as well as DB-CSP. The method is composed of two
    phases: applying the DB-CSP algorithm and performing a classification.
    The main idea behind the CSP is to use a linear transform to project
    data into low-dimensional subspace with a projection matrix, in such a
    way that each row consists of weights for signals. This transformation
    maximizes the variance of two-class signal matrices.The dbcsp object
    is created to compute the projection vectors. For exploratory and
    descriptive purpose, plot and boxplot functions can be used. Functions
    train, predict and selectQ are implemented for the classification
    step.
Tools for working with a new versatile
	discrete distribution, the db ("discretised Beta")
	distribution.  This package provides density (probability),
	distribution, inverse distribution (quantile) and random
	data generation functions for the db family.  It provides
	functions to effect conveniently maximum likelihood
	estimation of parameters, and a variety of useful plotting
	functions.  It provides goodness of fit tests and functions
	to calculate the Fisher information, different estimates of
	the hessian of the log likelihood and Monte Carlo estimation
	of the covariance matrix of the maximum likelihood parameter
	estimates.  In addition it provides analogous tools for
	working with the beta-binomial distribution which has been
	proposed as a competitor to the db distribution.
A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.
Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) <doi:10.1037/1082-989X.5.1.87>. 
              The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small.
Reads and writes files to SQLite databases
    <https://www.sqlite.org/index.html> as flobs (a flob is a blob that
    preserves the file extension).
Contains functions that check for formatting of the Subject Phenotype data set and data dictionary as specified by the National Center for Biotechnology Information (NCBI) Database of Genotypes and Phenotypes (dbGaP) <https://www.ncbi.nlm.nih.gov/gap/docs/submissionguide/>.
Fast fitting of generalised linear models on moderately large datasets, by taking an initial sample, fitting in memory, then evaluating the score function for the full data in the database. Thomas Lumley <doi:10.1080/10618600.2019.1610312>.
Provides an implementation of a mixture of hidden Markov models 
    (HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering 
    (DBHC) algorithm. The DBHC algorithm is an HMM Clustering 
    algorithm that finds a mixture of discrete-output HMMs while using 
    heuristics based on Bayesian Information Criterion (BIC) to search for the 
    optimal number of HMM states and the optimal number of clusters.
Client for programmatic access to the South Florida Water
  Management District's 'DBHYDRO' database at 
  <https://www.sfwmd.gov/science-data/dbhydro>, with functions
  for accessing hydrologic and water quality data.
A database interface definition for communication between R
    and relational database management systems.  All classes in this
    package are virtual and need to be extended by the various R/DBMS
    implementations.
A helper that tests DBI back ends for conformity to the
    interface.
Doubly censored data, as described in Chang and Yang (1987) <doi: 10.1214/aos/1176350608>), are commonly seen in many fields. We use EM algorithm to compute the non-parametric MLE (NPMLE) of the cummulative probability function/survival function and the two censoring distributions. One can also specify a constraint F(T)=C, it will return the constrained NPMLE and the -2 log empirical likelihood ratio for this constraint. This can be used to test the hypothesis about the constraint and, by inverting the test, find confidence intervals for probability or quantile via empirical likelihood ratio theorem. Influence functions of hat F may also be calculated, but currently, the it may be slow.
It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode  and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).
Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single 
  logistic regression model, but not a sequence of logistic regression models. The fitted model
  object returned from the model training consists of two tables. One table is used to give the
  boundaries of bins for each continuous variable as well as the corresponding coefficients,
  and the other one is used for discrete variables. This package can also be used for binning
  continuous variables for other statistical analysis.
Implements the de-biased estimator for low-rank matrix completion and provides confidence intervals for entries of interest. See: by Chen et al. (2019) <doi:10.1073/pnas.1910053116>, Mai (2021) <arXiv:2103.11749>.
Perform model selection using distribution and probability-based methods,
	including standardized AIC, BIC, and AICc. These standardized information criteria
	allow one to perform model selection in a way similar to the prevalent "Rule of 2"
	method, but formalize the method to rely on probability theory. A novel goodness-of-fit
	procedure for assessing linear regression models is also available. This test relies on
	theoretical properties of the estimated error variance for a normal linear regression
	model, and employs a bootstrap procedure to assess the null hypothesis that the fitted
	model shows no lack of fit. For more information, see Koeneman and Cavanaugh (2023)
	<arXiv:2309.10614>. Functionality to perform all subsets linear or generalized linear
	regression is also available.
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>.
  Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). 
  Relies on 'spatstat' for some core calculation.
It allows to learn the structure of univariate time series, learning parameters and forecasting. 
             Implements a model of Dynamic Bayesian Networks with temporal windows, 
             with collections of linear regressors for Gaussian nodes, 
             based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and 
             Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.
Given the non-negative data and its distribution, the package estimates the rank parameter for Non-negative Matrix Factorization. The method is based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately despite the large amount of optimization error. The distribution of the non-negative data can be either Normal distributed or Poisson distributed.
Learning and inference over dynamic Bayesian networks of arbitrary 
    Markovian order. Extends some of the functionality offered by the 'bnlearn' 
    package to learn the networks from data and perform exact inference. 
    It offers three structure learning algorithms for dynamic Bayesian networks:
    Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014)
    <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021)
    <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform 
    forecasts of arbitrary length. A tool for visualizing the structure of the 
    net is also provided via the 'visNetwork' package.
This tool is for parsing public drug databases such as 'DrugBank' XML database <https://go.drugbank.com/>.
    The parsed data are then returned in a proper 'R' object called 'dvobject'.
Leverages 'dplyr' to process the calculations of a plot inside a database. 
    This package provides helper functions that abstract the work at three levels:
    outputs a 'ggplot', outputs the calculations, outputs the formula
    needed to calculate bins.
A 'dplyr' back end for databases that allows you to work with
    remote database tables as if they are in-memory data frames.  Basic
    features works with any database that has a 'DBI' back end; more
    advanced features require 'SQL' translation to be provided by the
    package author.
Calculates lower bound on power, upper bound on power, and exact power (small sets only) for detection boundary tests (e.g. Berk-Jones, Generalized Berk-Jones, innovated Berk-Jones) used in set-based inference studies. These detection boundary tests are described in Sun et al., (2019) <doi:10.1080/01621459.2019.1660170>.
Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses.
  Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler 
  (Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler 
  (Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).
A fast reimplementation of several density-based algorithms of
    the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based 
    spatial clustering of applications with noise) and HDBSCAN (hierarchical 
    DBSCAN), the ordering algorithm OPTICS (ordering points to identify the 
    clustering structure), shared nearest neighbor clustering, and the outlier 
    detection algorithms LOF (local outlier factor) and GLOSH (global-local 
    outlier score from hierarchies). The implementations use the kd-tree data 
    structure (from library ANN) for faster k-nearest neighbor search. An R 
    interface to fast kNN and fixed-radius NN search is also provided. 
    Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.
Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.
Offers meta programming style tools to generate configurable R functions that produce HTML forms based on table input and SQL meta data. 
    Also generates functions for collecting the parameters of those HTML forms after they are submitted. Useful for 
    quickly generating HTML forms based on existing SQL tables. To use the resultant functions, the output files containing those functions must 
    be read into the R environment (perhaps using base::source()).
Provides select, insert, update, upsert, and delete database operations. Supports 'PostgreSQL', 'MySQL', 'SQLite', and more, and plays nicely with the 'DBI' package.
Performs differential network analysis to infer disease specific gene networks.
A collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA). 
    This package implements the results presented in Prass, T.S. and Pumi, G. (2019). "On the behavior of the DFA and DCCA in trend-stationary processes" <arXiv:1910.10589>.
Functions for analyzing dichotomous choice contingent 
   valuation (CV) data. It provides functions for estimating parametric and 
   nonparametric models for single-, one-and-one-half-, and double-bounded 
   CV data. For details, see Aizaki et al. (2022) <doi:10.1007/s42081-022-00171-1>.
Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) <doi:10.1080/07350015.2013.771027>, the DCC-MIDAS of Colacito et al. (2011) <doi:10.1016/j.jeconom.2011.02.013>, the Asymmetric DCC of Cappiello et al. <doi:10.1093/jjfinec/nbl005>, and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) <doi:10.1080/07350015.2011.652048>. 'dccmidas' offers the possibility of including standard GARCH <doi:10.1016/0304-4076(86)90063-1>, GARCH-MIDAS <doi:10.1162/REST_a_00300> and Double Asymmetric GARCH-MIDAS <doi:10.1016/j.econmod.2018.07.025> models in the univariate estimation. Moreover, also the scalar and diagonal BEKK <doi:10.1017/S0266466600009063> models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications.
Fast computation of the distance covariance 'dcov' and distance correlation 'dcor'.  The computation cost is only O(n log(n)) for the distance correlation (see Chaudhuri, Hu (2019) <arXiv:1810.11332> <doi:10.1016/j.csda.2019.01.016>). The functions are written entirely in C++ to speed up the computation.
Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering 
    big data (gaussian mixture models for both multivariate and univariate datasets). This version 
    implements the faster alternative-EM* that expedites convergence via structure based data segregation. 
    The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, 
    Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban, 
    Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.
Prepare the results of a DCE to be analysed through choice models.'DCEmgmt' reshapes DCE data from wide to long format considering the special characteristics of a DCE. 'DCEmgmt' includes the function 'DCEestm' which estimates choice models once the database has been reshaped with 'DCEmgmt'.
Design, conduct and analyze 'DCEs' from a virtual interface in shiny. Reference: Perez-Troncoso, D. (2022) <https://github.com/danielpereztr/DCEtool>.
Data cloud geometry (DCG) applies random walks in finding community structures for social networks. 
    Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).
Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing 
    attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic 
    dynamic systems which can behave like an erratic and apparently random motion. A relevant field
    inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour 
    from empirical time series data. One of the main features of chaos is the well known initial value 
    sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify 
    the initial value sensitive property estimating the Lyapunov exponents. The DChaos package 
    provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos 
    based on the Lyapunov exponent in order to know if the data generating process behind time series 
    behave chaotically or not.
Functions for reading DCP and CDF.bin files generated by the dChip software.
An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. 
    Gerlovina et al. (2022) <doi:10.1093/genetics/iyac126>.
Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
R codes for distance based cell lineage reconstruction. Our methods won both sub-challenges 2 and 3 of the Allen Institute Cell Lineage Reconstruction DREAM Challenge in 2020. 
        References: Gong et al. (2021) <doi:10.1016/j.cels.2021.05.008>, Gong et al. (2022) <doi:10.1186/s12859-022-04633-x>.
Low level functions for implementing
    maximum likelihood estimating procedures for
    complex models using data cloning and Bayesian
    Markov chain Monte Carlo methods
    as described in Solymos 2010 <doi:10.32614/RJ-2010-011>.
    Sequential and parallel MCMC support
    for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.
Contains a single function dclust() for divisive hierarchical clustering based on 
    recursive k-means partitioning (k = 2). Useful for clustering large datasets
    where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records).
    For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.
A set of functions for the detection of spatial clusters
        of disease using count data. Bootstrap is used to estimate
        sampling distributions of statistics.
Model-based methods for the detection of disease clusters
  using GLMs, GLMMs and zero-inflated models. These methods are described
  in 'V. Gómez-Rubio et al.' (2019) <doi:10.18637/jss.v090.i14> and
  'V. Gómez-Rubio et al.' (2018) <doi:10.1007/978-3-030-01584-8_1>.
A collection of functions for calculating the M2 model fit
    statistic for diagnostic classification models as described by Liu et al.
    (2016) <DOI:10.3102/1076998615621293>. These functions provide multiple
    sources of information for model fit according to the M2 statistic,
    including the M2 statistic, the *p* value for that M2 statistic, and the
    Root Mean Square Error of Approximation based on the M2 statistic.
S4 classes around infrastructure provided by the
    'coda' and 'dclone' packages to make package development easy as a breeze
    with data cloning for hierarchical models.
Data cleaning scripts typically contain a lot of 'if this change that'
    type of statements. Such statements are typically condensed expert knowledge.
    With this package, such 'data modifying rules' are taken out of the code and
    become in stead parameters to the work flow. This allows one to maintain, document,
    and reason about data modification rules as separate entities.
Traversal graph algorithm for listing linear n-peptide constraints for overlapping protein regions. (Lebre and Gascuel, The combinatorics of overlapping genes, freely available from arXiv at : http://arxiv.org/abs/1602.04971).
Provides methods for distance covariance and distance correlation (Szekely, et al. (2007) <doi:10.1214/009053607000000505>), generalized version thereof (Sejdinovic, et al. (2013) <doi:10.1214/13-AOS1140>) and corresponding tests (Berschneider, Bottcher (2018) <arXiv:1808.07280>. Distance standard deviation methods (Edelmann, et al. (2020) <doi:10.1214/19-AOS1935>) and distance correlation methods for survival endpoints (Edelmann, et al. (2021) <doi:10.1111/biom.13470>) are also included.
The 'FBED' and 'mmpc' variable selection algorithms have been implemented using the distance correlation. The references include: Tsamardinos I., Aliferis C. F. and Statnikov A. (2003). "Time and sample efficient discovery of Markovblankets and direct causal relations". In Proceedings of the ninth ACM SIGKDD international Conference. <doi:10.1145/956750.956838>. Borboudakis G. and Tsamardinos I. (2019). "Forward-backward selection with early dropping". Journal of Machine Learning Research, 20(8): 1--39. <doi:10.48550/arXiv.1705.10770>. Huo X. and Szekely G.J. (2016). "Fast computing for distance covariance". Technometrics, 58(4): 435--447. <doi:10.1080/00401706.2015.1054435>.
Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) <doi:10.1214/009053607000000505>; Székely and Rizzo (2013) <doi:10.1016/j.jmva.2013.02.012>; Székely and Rizzo (2014) <doi:10.1214/14-AOS1255>; Huo and Székely (2016) <doi:10.1080/00401706.2015.1054435>.
Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: 
			       a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). 'An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. <doi:10.1111/insr.12294>.
             b) Fokianos Konstantinos and Pitsillou Maria (2018). 'Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. <doi:10.1093/biomet/asx082>.
             c) Fokianos Konstantinos and Pitsillou Maria (2017). 'Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. <doi:10.1080/00401706.2016.1156024>.
             d) Pitsillou Maria and Fokianos Konstantinos (2016). 'dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. <doi:10.32614/RJ-2016-049>.
Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data.  'DCPO' uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with 'Stan'.  References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.
Nonparametric smoothing techniques for data on a lattice and
    functional time series. Smoothing is done via kernel regression or
    local polynomial regression, a bandwidth selection procedure based on
    an iterative plug-in algorithm is implemented. This package allows for
    modeling a dependency structure of the error terms of the
    nonparametric regression model.  Methods used in this paper are
    described in Feng/Schaefer (2021)
    <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021)
    <https://ideas.repec.org/p/pdn/ciepap/143.html>.
Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.
Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) <doi:10.1073/pnas.0803205106>) varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension.
    The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics. 
    The goal of this package is to show an alternative to variable selection (columns) or individuals (rows). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation ||A-CUR||. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest.
A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by
  easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density,
  Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)
  <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of
  the loglikelihood and a random generator for Davidian curves.
Diagnostic and prognostic models are typically evaluated with
    measures of accuracy that do not address clinical consequences.
    Decision-analytic techniques allow assessment of clinical outcomes,
    but often require collection of additional information may be
    cumbersome to apply to models that yield a continuous result. Decision
    curve analysis is a method for evaluating and comparing prediction
    models that incorporates clinical consequences, requires only the data
    set on which the models are tested, and can be applied to models that
    have either continuous or dichotomous results. See the following references 
    for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>,
    Vickers (2008) <doi:10.1186/1472-6947-8-53>, 
    and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 <doi:10.18637/jss.v091.i05>).
A distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) <doi:10.1109/ICDMW.2017.11>).
Implements maximum likelihood and bootstrap methods based on
 the diversity-dependent birth-death process to test whether
 speciation or extinction are diversity-dependent, under various
 models including various types of key innovations.
 See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
 <DOI:10.1098/rspb.2011.1439>,
 Etienne & Haegeman 2012, Am. Nat. 180: E75-E89,
 <DOI:10.1086/667574>,
 Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099,
 <DOI:10.1111/2041-210X.12565> and
 Laudanno et al. 2021. Syst. Biol. 70: 389–407,
 <DOI:10.1093/sysbio/syaa048>.
 Also contains functions to simulate the diversity-dependent
 process.
Solves ordinary and delay differential equations, where
    the objective function is written in either R or C.  Suitable only
    for non-stiff equations, the solver uses a 'Dormand-Prince' method
    that allows interpolation of the solution at any point.  This
    approach is as described by Hairer, Norsett and Wanner (1993)
    <ISBN:3540604529>.  Support is also included for iterating
    difference equations.
Contains the normalizing and variance stabilizing
	Data-Driven Haar-Fisz algorithm. Also contains related algorithms
	for simulating from certain microarray gene intensity models and
	evaluation of certain transformations. Contains cDNA and shipping
	credit flow data.
Implements Meng's data defect index (ddi), which represents
    the degree of sample bias relative to an iid sample. The data defect 
    correlation (ddc) represents the correlation between the outcome of interest
    and the selection into the sample; when the sample selection is independent
    across the population, the ddc is zero. Details are in Meng (2018) 
    <doi:10.1214/18-AOAS1161SF>, "Statistical Paradises and Paradoxes in Big Data (I): 
    Law of Large Populations, Big Data Paradox, and the 2016 US Presidential 
    Election." Survey estimates from the Cooperative Congressional Election Study 
    (CCES) is included to replicate the article's results.
The Data Driven I-V Feature Extraction is used to extract 
    Current-Voltage (I-V) features from I-V curves. I-V curves indicate the 
    relationship between current and voltage for a solar cell or Photovoltaic (PV) 
    modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), 
    series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), 
    fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) 
    contain important information of the performance for PV modules. The traditional 
    method uses the single diode model to model I-V curves and extract I-V features. 
    This package does not use the diode model, but uses data-driven a method which 
    select different linear parts of the I-V curves to extract I-V features. 
    This method also uses a sampling method to calculate uncertainties when extracting 
    I-V features. Also, because of the partially shaded array, "steps" occurs in 
    I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves.
    This material is based upon work supported by the U.S. Department of Energy’s 
    Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy 
    Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be 
    found in the following paper.
    [1] Ma, X. et al, 2019. 
    <doi:10.1109/JPHOTOV.2019.2928477>.
Useful functions for various DDI (Data Documentation Initiative)
    related inputs and outputs. Converts data files to and from DDI, SPSS,
    Stata, SAS, R and Excel, including user declared missing values.
Statistical inference for the regression coefficients in high-dimensional linear models with hidden confounders. The Doubly Debiased Lasso method was proposed in <arXiv:2004.03758>.
A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.
Estimate common causal parameters using double/debiased machine 
    learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. 
    'ddml' simplifies estimation based on (short-)stacking as discussed in 
    Ahrens et al. (2024) <arXiv:2401.01645>, which leverages multiple base 
    learners to increase robustness to the underlying data generating process.
Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) <doi:10.1145/782010.782021>, Angiulli, F., & Pizzuti, C. (2002) <doi:10.1007/3-540-45681-3_2>, Hautamaki, V., & Ismo, K. (2004) <doi:10.1109/ICPR.2004.1334558> and Zhang, K., Hutter, M. & Jin, H. (2009) <doi:10.1007/978-3-642-01307-2_84>. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) <doi:10.1007/3-540-47887-6_53>, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) <doi:10.1007/11731139_68>, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) <doi:10.1137/1.9781611973440.63>, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) <doi:10.1007/978-3-540-73499-4_6>, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) <doi:10.1109/ICDE.2003.1260802>, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) <doi:10.1145/342009.335388>, Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) <doi:10.1145/1645953.1646195>, Zhu, Q., Feng, Ji. & Huang, J. (2016) <doi:10.1016/j.patrec.2016.05.007>, Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) <doi:10.1016/j.knosys.2015.10.014>, Tang, B. & Haibo, He. (2017) <doi:10.1016/j.neucom.2017.02.039> and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) <doi:10.1007/978-3-642-20847-8_23>.
The desirable Dietary Pattern (DDP)/ PPH score
  measures the variety of food consumption. The (weighted) score is calculated 
  based on the type of food. This package is intended to calculate
  the DDP/ PPH score that is faster than traditional method via 
  a manual calculation by BKP (2017) <http://bkp.pertanian.go.id/storage/app/uploads/public/5bf/ca9/06b/5bfca906bc654274163456.pdf> and is simpler than the nutrition survey 
  <http://www.nutrisurvey.de>. The database to create weights and baseline values
  is the Indonesia national survey in 2017.
Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>)  by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.
An interface to explore, analyze, and visualize droplet digital PCR
    (ddPCR) data in R. This is the first non-proprietary software for analyzing
    two-channel ddPCR data. An interactive tool was also created and is available
    online to facilitate this analysis for anyone who is not comfortable with
    using R.
Create 'D3' based 'SVG' ('Scalable Vector Graphics') graphics using a simple 'R' API. 
    The package aims to simplify
    the creation of many 'SVG' plot types using a straightforward 'R' API.  
    The package relies on the 'r2d3' 'R' package and the 'D3' 'JavaScript' library. 
    See <https://rstudio.github.io/r2d3/> and <https://d3js.org/> respectively.
A wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains, including medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented.
Functions designed to connect disease-related differential proteins and 
  co-expression network. It provides the basic statics analysis included t test, ANOVA analysis. 
  The network construction is not offered by the package, you can used 'WGCNA' package which you 
  can learn in Peter et al. (2008) <doi:10.1186/1471-2105-9-559>. It also provides module analysis 
  included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and 
  hub analysis.
Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.
A sparse Partial Least Squares implementation which uses soft-threshold
	estimation of the covariance matrices and therein introduces
	sparsity. Number of components and regularization coefficients are automatically set.
Smooth testing of goodness of fit. These tests are data
    driven (alternative hypothesis is dynamically selected based on data). In this
    package you will find various tests for exponent, Gaussian, Gumbel and uniform
    distribution.
Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. 
    Reference: Li et al. (2023) <arXiv:2306.04700>.
Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.
Bayesian networks with continuous and/or discrete
        variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
Set of functions for Data Envelopment Analysis. It runs both classic and fuzzy DEA models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). <doi:10.1287/mnsc.30.9.1078>, Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). <doi:10.1016/0377-2217(78)90138-8> and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). <doi:10.1287/mnsc.27.6.668>.
The 'debar' sequence processing pipeline is designed for denoising high throughput 
    sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package 
    is designed to detect and correct insertion and deletion errors within sequencer outputs. 
    This is accomplished through comparison of input sequences against a profile hidden Markov 
    model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, 
    ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted 
    for through the introduction of a placeholder character. Since the PHMM is a probabilistic 
    representation of the COI barcode, corrections are not always perfect. For this reason 
    'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder 
    characters (default is 7 base pairs in either direction, this feature can be disabled).
    Testing has shown that this censorship results in the correct sequence length being restored, 
    and erroneous base pairs being masked the vast majority of the time (>95%).
Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI.
A general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Efficiency gains of biomarker-guided trials can be quantitatively compared to the traditional all-comers design. For reference, see Zhang et al. (2024) <arXiv:2401.00540>.
A statistically and computationally efficient debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes.
    The reference paper is Zhang, Giessing, and Chen (2023) <arXiv:2309.06429>.
Shiny application that performs bifurcation and phaseplane analysis of systems of ordinary 
    differential equations. The package allows for computation of equilibrium curves as a function of 
    a single free parameter, detection of transcritical, saddle-node and hopf bifurcation points along 
    these curves, and computation of curves representing these transcritical, saddle-node and hopf 
    bifurcation points as a function of two free parameters. The shiny-based GUI allows visualization 
    of the results in both 2D- and 3D-plots. The implemented methods for solution localisation and curve
    continuation are based on the book "Elements of applied bifurcation theory" (Kuznetsov, Y. A., 1995;
    ISBN: 0-387-94418-4).
A Bayesian framework for parameter inference in differential equations.
    This approach offers a rigorous methodology for parameter inference as well as
    modeling the link between unobservable model states and parameters, and
    observable quantities. Provides templates for the DE model, the
    observation model and data likelihood, and the model parameters and their prior
    distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
    to estimate the posterior distributions of the parameters and any derived
    quantities, including the model trajectories. Further functionality is provided
    to facilitate MCMC diagnostics and the visualisation of the posterior distributions
    of model parameters and trajectories.
Analysis of historical non-decimal currencies and value
    systems that use tripartite or tetrapartite systems such as pounds,
    shillings, and pence. It introduces new vector classes to represent
    non-decimal currencies, making them compatible with numeric classes,
    and provides functions to work with these classes in data frames in
    the context of double-entry bookkeeping.
Orders a data-set consisting of an ensemble of probability density functions on the same x-grid.  Visualizes a box-plot of these functions based on the notion of distance determined by the user.  Reports outliers based on the distance chosen and the scaling factor for an interquartile range rule.  For further details, see: Alexander C. Murph et al. (2023). "Visualization and Outlier Detection for Probability Density Function Ensembles." <https://sirmurphalot.github.io/publications>.
Specify debug messages as special string constants, and
    control debugging of packages via environment variables.
Tool to print out the value of R objects/expressions while running
  an R script. Outputs can be made dependent on user-defined conditions/criteria. 
  Debug messages only appear when a global option for debugging is set. 
  This way, 'debugr' code can even remain in the debugged code for later use 
  without any negative effects during normal runtime.
Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) <doi:10.1080/01621459.2021.1909598>.
Calculates various estimates for measures of educational
        differentials, the relative importance of primary and secondary
        effects in the creation of such differentials and compares the
        estimates obtained from two datasets.
Provides constrained triangulation of polygons. Ear cutting (or 
 ear clipping) applies constrained triangulation by successively 'cutting'
 triangles from a polygon defined by path/s. Holes are supported by introducing
 a bridge segment between polygon paths. This package wraps the 'header-only' 
 library 'earcut.hpp' <https://github.com/mapbox/earcut.hpp.git> which includes
 a reference to the method used by Held, M. (2001) <doi:10.1007/s00453-001-0028-4>.
Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.
Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.
Makes 'deck.gl' <https://deck.gl/>, a WebGL-powered open-source JavaScript framework
  for visual exploratory data analysis of large datasets, available within R via the 'htmlwidgets' package.
  Furthermore, it supports basemaps from 'mapbox' <https://www.mapbox.com/> via
  'mapbox-gl-js' <https://github.com/mapbox/mapbox-gl-js>.
A zero dependency package containing functions to declare labels
    and missing values, coupled with associated functions to create (weighted)
    tables of frequencies and various other summary measures. 
    Some of the base functions have been rewritten to make use of the specific 
    information about the missing values, most importantly to distinguish
    between empty NA and declared NA values.
    Some functions have similar functionality with the corresponding ones from
    packages "haven" and "labelled". The aim is to ensure as much compatibility
    as possible with these packages, while offering an alternative in the
    objects of class "declared".
Researchers can characterize and learn about the properties of
    research designs before implementation using `DeclareDesign`. Ex ante
    declaration and diagnosis of designs can help researchers clarify the 
    strengths and limitations of their designs and to improve their 
    properties, and can help readers evaluate a research strategy prior
    to implementation and without access to results. It can also make it
    easier for designs to be shared, replicated, and critiqued.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Main function "decode" is used to decode coded key values to plain
    text. Function "code" can be used to code plain text to code if there is a
    1:1 relation between the two. The concept relies on 'keyvalue' objects used
    for translation. There are several 'keyvalue' objects included in the areas
    of geographical regional codes, administrative health care unit codes,
    diagnosis codes and more. It is also easy to extend the use by arbitrary 
    code sets.
Hybrid model is the most promising forecasting method by combining decomposition and deep learning techniques to improve the accuracy of time series forecasting. Each decomposition technique decomposes a time series into a set of intrinsic mode functions (IMFs), and the obtained IMFs are modelled and forecasted separately using the deep learning models. Finally, the forecasts of all IMFs are combined to provide an ensemble output for the time series. The prediction ability of the developed models are calculated using international monthly price series of maize in terms of evaluation criteria like root mean squared error, mean absolute percentage error and, mean absolute error. For method details see Choudhary, K. et al. (2023). <https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf>.
Predict future values with hybrid combinations of Pattern Sequence based
        Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode
        Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based
        hybrid methods.
Tools to apply Ensemble Empirical Mode 
    Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new 
    algorithm, extricate, that performs EEMD in seconds, a linear interpolation 
    algorithm  using the greatest rational common divisor of depth or time, 
    different algorithms to compute instantaneous amplitude, frequency and  
    ratios of frequencies, and functions to verify and visualise the outputs.
    The functions were developed during the CRASH project (Checking the 
    Reproducibility of Astrochronology in the Hauterivian). When using for 
    publication please cite Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, 
    A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2022, 
    "A decomposition approach to cyclostratigraphic signal processing". 
    Earth-Science Reviews 225 (103894).
    <doi:10.1016/j.earscirev.2021.103894>.
Three global value chain (GVC) decompositions are implemented. 
    The Leontief decomposition derives the value added origin of exports by 
    country and industry as in Hummels, Ishii and Yi (2001). The Koopman, 
    Wang and Wei (2014) decomposition splits country-level exports into 9 
    value added components, and the Wang, Wei and Zhu (2013) decomposition 
    splits bilateral exports into 16 value added components. Various GVC 
    indicators based on these decompositions are computed in the 
    complimentary 'gvc' package. 
    --- References: ---
    Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of 
       vertical specialization in world trade. Journal of international 
       Economics, 54(1), 75-96.
    Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double 
       counting in gross exports. American Economic Review, 104(2), 459-94.
    Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production 
       sharing at the bilateral and sector levels (No. w19677). 
       National Bureau of Economic Research.
A collection of functions to deal
        with nonparametric measurement error problems using
        deconvolution kernel methods. We focus two measurement error
        models in the package: (1) an additive measurement error model,
        where the goal is to estimate the density or distribution
        function from contaminated data; (2) nonparametric regression
        model with errors-in-variables. The R functions allow the
        measurement errors to be either homoscedastic or
        heteroscedastic. To make the deconvolution estimators
        computationally more efficient in R, we adapt the "Fast Fourier
        Transform" (FFT) algorithm for density estimation with
        error-free data to the deconvolution kernel estimation. Several
        methods for the selection of the data-driven smoothing
        parameter are also provided in the package. See details in:
        Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
        measurement error models: The R package decon. Journal of
        Statistical Software, 39(10), 1-24.
Takes sample information in the form of the fraction of mutations
    in each of 96 trinucleotide contexts and identifies the weighted combination
    of published signatures that, when summed, most closely reconstructs the
    mutational profile.
Empirical Bayes methods for learning prior distributions from data.
    An unknown prior distribution (g) has yielded (unobservable) parameters, each of
    which produces a data point from a parametric exponential family (f). The goal
    is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
    Bayes methods. Details and examples are in the paper by Narasimhan and Efron
    (2020, <doi:10.18637/jss.v094.i11>).
Retrieves code comment decorations for C++
    languages of the form '\\ [[xyz]]', which are used for automated
    wrapping of C++ functions.
DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
    of Artificial Training Examples) builds an ensemble of J48 trees by recursively
    adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) <DOI:10.1016/j.inffus.2004.04.001>").
A decorator is a function that receives a function, extends
    its behaviour, and returned the altered function. Any caller that uses
    the decorated function uses the same interface as it were the
    original, undecorated function. Decorators serve two primary uses: (1)
    Enhancing the response of a function as it sends data to a second
    component; (2) Supporting multiple optional behaviours. An example of
    the first use is a timer decorator that runs a function, outputs its
    execution time on the console, and returns the original function's
    result. An example of the second use is input type validation
    decorator that during running time tests whether the caller has passed
    input arguments of a particular class.  Decorators can reduce
    execution time, say by memoization, or reduce bugs by adding defensive
    programming routines.
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
A collection of methods for automated data cleaning where all actions are logged.
Attempt to repair inconsistencies and missing values in
        data records by using information from valid values and
        validation rules restricting the data.
Apply the Deductive Rational Method to a monthly series of flow or precipitation data to fill in missing data. The method is as described in: Campos, D.F., (1984, ISBN:9686194444).
Contains one main function deduped() which speeds up slow,
    vectorized functions by only performing computations on the unique values
    of the input and expanding the results at the end.
Duplicated data can exist in different rows and columns and user may need to
    treat observations (rows) connected by duplicated data as one observation,
    e.g. companies can belong to one family (and thus: be one company) by sharing
    some telephone numbers. This package allows to find connected rows
    based on data on chosen columns and collapse it into one row.
Explore neural networks in a layer oriented way, the framework
    is intended to give the user total control of the
    internals of a net without much effort. Use classes like PerceptronLayer
    to create a layer of Percetron neurons, and specify how many you want. The
    package does all the tricky stuff internally leaving you focused in what
    you want. I wrote this package during a neural networks course to help me
    with the problem set.
Provides tools for exploration of R package dependencies. 
    The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way.
    It also adds some popularity measures for the packages e.g. in the form of download count through the 'cranlogs' package. 
    Uses the CRAN metadata database <http://crandb.r-pkg.org> and Bioconductor metadata <https://bioconductor.org>.
    Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). 
    The deepdep_shiny() function runs shiny application that helps to produce a nice 'deepdep' plot.
Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. In addition novel ensemble models like 'deeptree' and 'deepforest' has been included which combines decision trees and neural network.
Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) 
    <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures 
    to multiple layers. Each layer contains a set of latent variables that follow a mixture of 
    Gaussian distributions. To avoid overparameterized solutions, dimension reduction is 
    applied at each layer by way of factor models.
Performs Bayesian posterior inference for deep Gaussian processes following 
    Sauer, Gramacy, and Higdon (2023, <arXiv:2012.08015>).  See Sauer (2023,
    <http://hdl.handle.net/10919/114845>) for comprehensive methodological details and
    <https://bitbucket.org/gramacylab/deepgp-ex/> for a variety of coding examples. 
    Models are trained through
    MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings
    sampling of kernel hyperparameters.  Vecchia-approximation for faster computation is implemented
    following Sauer, Cooper, and Gramacy (2022, <arXiv:2204.02904>).  Downstream tasks include
    sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, 
    Gramacy, and Higdon, 2023), optimization through expected improvement (EI; 
    Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>), and contour location through entropy
    (Sauer, 2023).  Models extend up to three layers deep; a one layer model is equivalent to 
    typical Gaussian process regression.  Incorporates OpenMP and SNOW parallelization and 
    utilizes C/C++ under the hood.
A wrapper for the 'DeepL' Pro API <https://www.deepl.com/docs-api>, a web service
    for translating texts between different languages. A DeepL API developer account is required
    to use the service (see <https://www.deepl.com/pro#developer>).
Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). <doi:10.1023/A:1007692713085>), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) <doi:10.1007/s11634-020-00399-3>) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) <doi:10.1007/s11222-020-09989-9>). There are also functions for graphical representation of clusters obtained.
Implement some deep learning architectures and neural network
    algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
A tool that contains trained deep learning models
    for predicting effector proteins. 'deepredeff' has been trained to
    identify effector proteins using a set of known experimentally
    validated effectors from either bacteria, fungi, or oomycetes.
    Documentation is available via several vignettes, and the paper by
    Kristianingsih and MacLean (2020) <doi:10.1101/2020.07.08.193250>.
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as 
    proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
    Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
Enhancing cross-language compatibility within the 'RStudio' environment and supporting seamless language understanding, 
    the 'deepRstudio' package leverages the power of the 'DeepL' API (see <https://www.deepl.com/docs-api>) to enable seamless, fast, 
    accurate, and affordable translation of code comments, documents, and text. This package offers the ability to translate selected text 
    into English (EN), as well as from English into various languages, namely Japanese (JA), Chinese (ZH), Spanish (ES), 
    French (FR), Russian (RU), Portuguese (PT), and Indonesian (ID). With much of the text being written in English, the emphasis 
    is on compatibility from English. It is also designed for developers working on multilingual projects and data analysts collaborating 
    with international teams, simplifying the translation process and making code more accessible and comprehensible to people with 
    diverse language backgrounds. This package uses the 'rstudioapi' package and 'DeepL' API, and is simply implemented, executed 
    from addins or via shortcuts on 'RStudio'. With just a few steps, content can be translated between supported languages, promoting better 
    collaboration and expanding the global reach of work. The functionality of this package works only on 'RStudio' using 'rstudioapi'.
Extends the functionality of other plotting packages like
    'ggplot2' and 'lattice' to help facilitate the plotting of data over long time
    intervals, including, but not limited to, geological, evolutionary, and ecological
    data. The primary goal of 'deeptime' is to enable users to add highly customizable
    timescales to their visualizations. Other functions are also included to assist
    with other areas of deep time visualization.
Allows for the specification of deep conditional transformation 
    models (DCTMs) and ordinal neural network transformation models, as 
    described in Baumann et al (2021) <doi:10.1007/978-3-030-86523-8_1> and 
    Kook et al (2022) <doi:10.1016/j.patcog.2021.108263>. Extensions such as
    autoregressive DCTMs (Ruegamer et al, 2022, <doi:10.48550/arXiv.2110.08248>)
    and transformation ensembles (Kook et al, 2022, <doi:10.48550/arXiv.2205.12729>)
    are implemented.
Abstract of Manuscript. Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user’s gene lists, aiding in hypothesis generation and data-driven literature review. Sokolowski, Dustin J., et al. "Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression." Nucleic Acids Research Genomics and Bioinformatics (2023).
Provides mean squared error (MSE) and plot the kernel densities related to extreme value distributions with their estimated values.
    By using Gumbel and Weibull Kernel. See Salha et al. (2014) <doi:10.4236/ojs.2014.48061> and Khan and Akbar (2021) <doi:10.4236/ojs.2021.112018 >.
A simple syntax to change the default values for function
  arguments, whether they are in packages or defined locally.
Parses command line arguments and supplies values to scripts. Users can specify names to which parsed inputs are assigned, value types into which inputs are cast, long options or short options, input splitters and callbacks that define how options should be specified and how input values are supplied.
Creates 'define.xml' documents used for regulatory 
    submissions based on spreadsheet metadata.  Can also help
    create metadata and generate HTML data explorer.
Use numerical optimization to fit ordinary differential equations (ODEs) to time series data to examine the dynamic relationships between variables or the characteristics of a dynamical system. It can now be used to estimate the parameters of ODEs up to second order, and can also apply to multilevel systems. See <https://github.com/yueqinhu/defit> for details.
Simple functions to deflate nominal Brazilian Reais using several popular price indexes downloaded from the Brazilian Institute for Applied Economic Research.
Implements the 'deflist' class, a read-only list-like object that accesses its elements via a function.
    The 'deflist' class can be used to model deferred access to data or computations by routing indexed list access to a function.
    This approach is particularly useful when sequential list-like access to data is required but holding all the data in memory at once is not feasible.
    The package also provides utilities for memoisation and caching to optimize access to frequently requested elements.
Implements two out-of box classifiers presented in <doi:10.48550/arXiv.2112.01063> for 
    distinguishing forest and non-forest terrain images. Under these algorithms, there are 
    frequentist approaches: one parametric, using stable distributions, and another one- 
    non-parametric, using the squared Mahalanobis distance. The package also contains functions for 
    data handling and building of new classifiers as well as some test data set.
Methods for fitting nonstationary Gaussian process models by spatial deformation, as introduced by Sampson and Guttorp (1992) <doi:10.1080/01621459.1992.10475181>, and by dimension expansion, as introduced by Bornn et al. (2012) <doi:10.1080/01621459.2011.646919>. Low-rank thin-plate regression splines, as developed in Wood, S.N. (2003) <doi:10.1111/1467-9868.00374>, are used to either transform co-ordinates or create new latent dimensions.
Numerical quadrature of functions of one variable over a finite
  or infinite interval with double exponential formulas.
Compute degree days from daily min and max temperatures for
    modeling plant and insect development.
Genes that are differentially expressed between two or more experimental conditions can be detected in RNA-Seq. A high biological variability may impact the discovery of these genes once it may be divergent between the fixed effects. However, this variability can be covered by the random effects. 'DEGRE' was designed to identify the differentially expressed genes considering fixed and random effects on individuals. These effects are identified earlier in the experimental design matrix. 'DEGRE' has the implementation of preprocessing procedures to clean the near zero gene reads in the count matrix, normalize by 'RLE' published in the 'DESeq2' package, 'Love et al. (2014)' <doi:10.1186/s13059-014-0550-8> and it fits a regression for each gene using the Generalized Linear Mixed Model with the negative binomial distribution, followed by a Wald test to assess the regression coefficients.
Likelihood-based inference for skewed count distributions, typically of degrees used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis. See Jones and Handcock <doi:10.1098/rspb.2003.2369>.
Estimation of a density from grouped (tabulated) summary statistics evaluated in each of the big bins (or classes) partitioning the support of the variable. These statistics include class frequencies and central moments of order one up to four. The log-density is modelled using a linear combination of penalised B-splines. The multinomial log-likelihood involving the frequencies adds up to a roughness penalty based on the differences in the coefficients of neighbouring B-splines and the log of a root-n approximation of the sampling density of the observed vector of central moments in each class. The so-obtained penalized log-likelihood is maximized using the EM algorithm to get an estimate of the spline parameters and, consequently, of the variable density and related quantities such as quantiles, see Lambert, P. (2021) <arXiv:2107.03883> for details.
Performs reference based multiple imputation of recurrent event data
    based on a negative binomial regression model, as described
    by Keene et al (2014) <doi:10.1002/pst.1624>.
Provides probability mass, distribution, quantile, random-variate
    generation, and method-of-moments parameter-estimation functions for the
    Delaporte distribution with parameterization based on Vose (2008)
    <isbn:9780470512845>. The Delaporte is a discrete probability distribution
    which can be considered the convolution of a negative binomial distribution
    with a Poisson distribution. Alternatively, it can be considered a counting
    distribution with both Poisson and negative binomial components. It has been
    studied in actuarial science as a frequency distribution which has more
    variability than the Poisson, but less than the negative binomial.
Mechanisms to parallelize dependent tasks in a manner that
    optimizes the compute resources available. It provides access to "delayed"
    computations, which may be parallelized using futures. It is, to an extent,
    a facsimile of the 'Dask' library (<https://www.dask.org/>), for the 'Python'
    language.
Provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is either homogeneous across the individual subject, or varies heterogeneously from individual to individual within a certain domain and following a specific pattern. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.
Calculates the Delaunay triangulation and the Dirichlet
	or Voronoi tessellation (with respect to the entire plane) of
	a planar point set. Plots triangulations and tessellations in
	various ways.  Clips tessellations to sub-windows. Calculates
	perimeters of tessellations.  Summarises information about
	the tiles of the tessellation.	Calculates the centroidal
	Voronoi (Dirichlet) tessellation using Lloyd's algorithm.
Measure of agreement delta was originally by Martín & Femia (2004) <DOI:10.1348/000711004849268>. 
    Since then has been considered as agreement measure for different 
    fields, since their behavior is usually better than the usual kappa index
    by Cohen (1960) <DOI:10.1177/001316446002000104>. The main issue with delta 
    is that can not be computed by hand contrary to kappa. The current algorithm
    is based on the Version 5 of the delta windows program that can be found on
    <https://www.ugr.es/~bioest/software/delta/cmd.php?seccion=downloads>.
Infer progression of circadian rhythms in transcriptome data in
  which samples are not labeled with time of day and coverage of the circadian
  cycle may be incomplete. See Shilts et al. (2018) <doi:10.7717/peerj.4327>.
Analysis of agreement for nominal data between two raters using the Delta model. This model is proposed as an alternative to the widespread measure Cohen kappa coefficient, which performs poorly when the marginal distributions are very asymmetric (Martin-Andres and Femia-Marzo (2004), <doi:10.1348/000711004849268>; Martin-Andres and Femia-Marzo (2008) <doi:10.1080/03610920701669884>). The package also contains a function to perform a massive analysis of multiple raters against a gold standard. A shiny app is also provided to obtain the measures of nominal agreement between two raters.
The deltaPlotR package implements Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported (Magis and Facon (2014) <doi:10.18637/jss.v059.c01>).
A collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. 
    Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull.
    For details see Chen (2000a,b), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.
The distributed expectation maximization algorithms are used to solve parameters of multivariate Gaussian mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.
Developed to Solve the Multi-Criteria Decision Making Problems with Decision Making Trial and Evaluation Laboratory Technique in R.
Multi-sample algorithm based on contigs and coverage values, to 
    infer the relative distances of contigs from the replication origin and to 
    accurately compare bacterial growth rates between samples. Yuan Gao and 
    Hongzhe Li (2018) <doi:10.1038/s41592-018-0182-0>.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Two general demographic decomposition methods are offered: Pseudo-continuous decomposition proposed by Horiuchi, Wilmoth, and Pletcher (2008) <doi:10.1353/dem.0.0033> and stepwise replacement decomposition proposed by Andreev, Shkolnikov and Begun (2002) <doi:10.4054/DemRes.2002.7.14>.
Designed to support the visualization, numerical computation,
     qualitative analysis, model-data fusion, and stochastic simulation for
     autonomous systems of differential equations. Euler and Runge-Kutta methods
     are implemented, along with tools to visualize the two-dimensional
     phaseplane. Likelihood surfaces and a simple Markov Chain Monte Carlo
     parameter estimator can be used for model-data fusion of differential
     equations and empirical models. The Euler-Maruyama method is provided for
     simulation of stochastic differential equations. The package was originally
     written for internal use to support teaching by Zobitz, and refined to
     support the text "Exploring modeling with data and differential equations
     using R" by John Zobitz (2021) <https://jmzobitz.github.io/ModelingWithR/index.html>.
Construction and analysis of matrix population models in R.
Connects to the <https://genderize.io/>, <https://agify.io/> and <https://nationalize.io/> APIs to estimate gender, age and nationality of a first name.
The Demographic Table in R combines contingency table for categorical variables, mean and standard deviation for continuous variables. t-test, chi-square test and Fisher's exact test calculated the p-value of two groups. The standardized mean difference were performed with 95 % confident interval, and writing table into document file.
Functions for creating demographic
     table with simple summary statistics, with
     optional comparison(s) over one or more
     groups.  Numeric variables are summarized in
     means, standard deviations, medians,
     inter-quartile-ranges (IQR), skewness,
     Shapiro-Wilk normality test and ranges, and
     compared using two-sample t-test, Wilcoxon
     test, ANOVA and/or Kruskal-Wallis test.
     Logical and factor variables are summarized
     in counts and percentages and compared using
     chi-squared test and/or Fisher's exact test.
Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.
Demonstration code showing how (univariate) kernel
	     density estimates are computed, at least conceptually,
	     and allowing users to experiment with different kernels,
	     should they so wish.  The method used follows directly
	     the definition, but gains efficiency by replacing the
	     observations by frequencies in a very fine grid covering
	     the sample range.  A canonical reference is
	     B. W. Silverman, (1998) <doi: 10.1201/9781315140919>.
	     NOTE: the density function in the
	     stats package uses a more sophisticated method based on the
	     fast Fourier transform and that function should be used if
	     computational efficiency is a prime consideration.
Estimate population kin counts and its distribution by type, age and sex.
    The package implements one-sex and two-sex framework for studying living-death availability,
    with time varying rates or not, and multi-stage model.
Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).
Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.
Implements the Determinantal point process (DPP) based optimal design
    emulator described in Pratola, Lin and Craigmile (2018) <arXiv:1804.02089> for
    Gaussian process regression models.  See <http://www.matthewpratola.com/software>
    for more information and examples.
Offers a set of functions for extending
    'dendrogram' objects in R, letting you visualize and compare trees of
    'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters
    - the color, size, type, etc of its branches, nodes and labels. (2)
    Visually and statistically compare different 'dendrograms' to one another.
There are various functions for managing and cleaning data before the application of different approaches. This includes identifying and erasing sudden jumps in dendrometer data not related to environmental change, identifying the time gaps of recordings, and changing the temporal resolution of data to different frequencies. Furthermore, the package calculates daily statistics of dendrometer data, including the daily amplitude of tree growth. Various approaches can be applied to separate radial growth from daily cyclic shrinkage and expansion due to uptake and loss of stem water. In addition, it identifies periods of consecutive days with user-defined climatic conditions in daily meteorological data, then check what trees are doing during that period.
Provides extra functions to manipulate dendrograms
    that build on the base functions provided by the 'stats' package. The main
    functionality it is designed to add is the ability to colour all the edges
    in an object of class 'dendrogram' according to cluster membership i.e. each
    subtree is coloured, not just the terminal leaves. In addition it provides
    some utility functions to cut 'dendrogram' and 'hclust' objects and to 
    set/get labels.
Computation of dendrometric and structural 
   parameters from forest inventory data. The objective is to provide an 
   user-friendly R package for researchers, ecologists, foresters, 
   statisticians, loggers and others persons who deal with forest inventory 
   data. Useful conversion of angle value from degree to radian, conversion from 
   angle to slope (in percentage) and their reciprocals as well as principal 
   angle determination are also included. Position and dispersion parameters 
   usually found in forest studies are implemented. The package contains 
   Fibonacci series, its extensions and the Golden Number computation.
   Useful references are Arcadius Y. J. Akossou, Soufianou Arzouma, 
   Eloi Y. Attakpa, Noël H. Fonton and Kouami Kokou (2013) <doi:10.3390/d5010099> 
   and W. Bonou, R. Glele Kakaï, A.E. Assogbadjo, H.N. Fonton, B. Sinsin (2009) 
   <doi:10.1016/j.foreco.2009.05.032> .
Provides functions for the calculation and plotting of synchrony in 
      tree growth from tree-ring width chronologies (TRW index). It combines
      variance-covariance (VCOV) mixed modelling with functions that quantify 
      the degree to which the TRW chronologies contain a common temporal 
      signal. It also implements temporal trends in spatial synchrony using a 
      moving window. These methods can also be used with other kind of ecological
      variables that have temporal autocorrelation corrected.
Provides novel dendroclimatological methods, primarily used by the
    Tree-ring research community. There are four core functions. The first one is 
    daily_response(), which finds the optimal sequence of days that are related 
    to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), 
    which implements partial correlations in the analysis of daily response functions.
    For the enthusiast of monthly data, there is monthly_response() function.
    The last core function is compare_methods(), which effectively compares several 
    linear and nonlinear regression algorithms on the task of climate reconstruction.
Re-arranges a dendrogram to optimize visualisation-based cost functions.
An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. This method is described in "dendsort: modular leaf ordering methods for dendrogram representations in R", F1000Research 2014, 3: 177 <doi:10.12688/f1000research.4784.1>.
Provides a weekly, monthly, yearly summary of dengue cases by state/ province/ country.
Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of  a bottom-up model for PCR amplification. This model is  developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
Estimate a low rank matrix from noisy data using singular values
    thresholding and shrinking functions. Impute missing values with matrix completion. The method is described in <arXiv:1602.01206>.
An integrated toolset for the analysis of de novo (sporadic)
    genetic sequence variants. denovolyzeR implements a mutational model that
    estimates the probability of a de novo genetic variant arising in each human
    gene, from which one can infer the expected number of de novo variants in a
    given population size. Observed variant frequencies can then be compared against
    expectation in a Poisson framework. denovolyzeR provides a suite of functions
    to implement these analyses for the interpretation of de novo variation in human
    disease.
Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.
Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020) <arXiv:2009.06182>.
Provides various tools for analysing density profiles
    obtained by resistance drilling. It can load individual or
    multiple files and trim the starting and ending part of each
    density profile. Tools are also provided to trim profiles
    manually, to remove the trend from measurements using several
    methods, to plot the profiles and to detect tree rings
    automatically. Written with a focus on forestry use of resistance
    drilling in standing trees.
With bivariate data, it is possible to calculate
    2-dimensional kernel density estimates that return polygons at given
    levels of probability. 'densityarea' returns these polygons for
    analysis, including for calculating their area.
An improved implementation (based on k-nearest neighbors) of
    the density peak clustering algorithm, originally described by Alex
    Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large 
    datasets (> 100,000 samples) very efficiently. It was initially implemented 
    by Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by 
    Xiaojie Qiu to handle large datasets with kNNs.
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.
Density ratio estimation.
    The estimated density ratio function can be used in many applications such as
    anomaly detection, change-point detection, covariate shift adaptation.
    The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>),
    RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>),
    and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds"
    in <doi:10.1098/rsif.2019.0234>.  Using the functions in the package,
    that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data
    using other functions in this package (as in the manuscript) or from some other source.
    The package provides a vignette which walks through the analysis in the publication, as well as a function
    to generate a project skeleton for such an analysis.
Implements the Differential Evolution algorithm for global
    optimization of a real-valued function of a real-valued parameter
    vector as described in Mullen et al. (2011) <doi:10.18637/jss.v040.i06>.
Differential Evolution (DE) stochastic heuristic algorithms for
    global optimization of problems with and without general constraints.
    The aim is to curate a collection of its variants that
    (1) do not sacrifice simplicity of design,
    (2) are essentially tuning-free, and
    (3) can be efficiently implemented directly in the R language.
    Currently, it provides implementations of the algorithms 'jDE' by
    Brest et al. (2006) <doi:10.1109/TEVC.2006.872133> for single-objective
    optimization and 'NCDE' by Qu et al. (2012) <doi:10.1109/TEVC.2011.2161873>
    for multimodal optimization (single-objective problems with
    multiple solutions).
Hash an expression with its dependencies and store its
	value, reloading it from a file as long as both the expression and
	its dependencies stay the same.
Dependent censoring regression models for survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2019) <doi:10.1002/bimj.201800391>.
Functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) <doi:10.2478/demo-2014-0004>; Liebscher (2017) <doi:10.1515/demo-2017-0012>; Liebscher (2019, submitted).
Estimation and testing methods for dependently truncated data.
 Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>,
 and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
 Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>.
 A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>.
 Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
Pigna [_pìn'n'a_] is the Italian word for pine cone.  In
    jargon, it is used to identify a task which is boring, banal,
    annoying, painful, frustrating and maybe even with a not so beautiful
    or rewarding result, just like the obstinate act of trying to
    challenge yourself in extracting pine nuts from a pine cone, provided
    that, in the end, you will find at least one inside it. Here you can
    find a backpack of functions to be used to solve small everyday
    problems of coding or analyzing (clinical) data, which would be
    normally solved using quick-and-dirty patches. You will be able to
    convert 'Hmisc' and 'rms' summary()es into data.frames ready to be
    rendered by 'pander' and 'knitr'. You can access easy-to-use wrappers
    to activate essential but useful progress bars (from 'progress') into
    your loops or functionals. Easy setup and control Telegram's bots
    (from 'telegram.bot') to send messages or to divert error messages to
    a Telegram's chat. You also have some utilities helping you in the
    development of packages, like the activation of the same user
    interface of 'usethis' into your package, or call polite functions to
    ask a user to install other packages. Finally, you find a set of
    thematic sets of packages you may use to set up new environments
    quickly, installing them in a single call.
Plots dependency logos from a set of aligned input sequences.
Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The 'Rdonlp2' package can optionally be used for optimization of the log-likelihood and is available from R-forge. See Visser et al. (2009, <DOI:10.1007/978-0-387-95922-1_13>) for examples and applications.
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).
Methods for analyzing population dynamics and movement tracks simulated using the DEPONS model <https://www.depons.eu> (v.3.0), for manipulating input raster files, shipping routes and for analyzing sound propagated from ships.
Manage your source code dependencies
  by decorating your existing R code with special,
  'roxygen'-style comments.
Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
Data depth concept offers a variety of powerful and user friendly
    tools for robust exploration and inference for multivariate data. The offered
    techniques may be successfully used in cases of lack of our knowledge on
    parametric models generating data due to their nature. The
    package consist of among others implementations of several data depth techniques
    involving multivariate quantile-quantile plots, multivariate scatter estimators,
    multivariate Wilcoxon tests and robust regressions.
Implementation of different statistical tools for the 
        description and analysis of gene expression data based on the 
        concept of data depth, namely, the scale curves for 
        visualizing the dispersion of one or various groups of samples 
        (e.g. types of tumors), a rank test to decide whether two groups 
        of samples come from a single distribution and two methods of
        supervised classification techniques, the DS and TAD methods. 
        All these techniques are based on the Modified Band Depth, which
        is a recent notion of depth with a low computational cost, what 
        renders it very appropriate for high dimensional data such as 
        gene expression data.
Modeling the zero coupon yield curve using the dynamic De Rezende and
             Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable
             or fixed decaying parameters. For explanatory purposes, the package also
             includes various short datasets of interest rates for the BRICS countries.
R-based solution for symbolic differentiation. It admits
    user-defined function as well as function substitution
    in arguments of functions to be differentiated. Some symbolic
    simplification is part of the work.
A set of pricing and expository functions that should
    be useful in teaching a course on financial derivatives.
Discrete event simulation (DES) involves modeling of systems
   having discrete, i.e. abrupt, state changes. For instance, when a job
   arrives to a queue, the queue length abruptly increases by 1.  This
   package is an R implementation of the event-oriented approach to DES;
   see the tutorial in Matloff (2008) 
   <http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf>.
Tools to read, write, create, and manipulate DESCRIPTION
    files.  It is intended for packages that create or manipulate other
    packages.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
Weighted frequency and contingency tables of categorical
       variables and of the comparison of the mean value of a numerical
       variable by the levels of a factor, and methods to produce xtable
       objects of the tables and to plot them. There are also functions to
       facilitate the character encoding conversion of objects, to quickly
       convert fixed width files into csv ones, and to export a data.frame to
       a text file with the necessary R and SPSS codes to reread the data.
Helper functions for descriptive tasks such as making print-friendly bivariate 
  tables, sample size flow counts, and visualizing sample distributions. Also 
  contains 'R' approximations of some common 'SAS' and 'Stata' functions such 
  as 'PROC MEANS' from 'SAS' and 'ladder', 'gladder', and 'pwcorr' from 'Stata'.
Helps to describe a data frame in hand. Has been developed during PhD work of the maintainer. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
Produce publication quality graphics from output of 'GGobi'
    describe display plugin.
Allows users to quickly and easily describe data using
    common descriptive statistics.
Description of statistical associations between two variables : measures of local and global association between variables (phi, Cramér V, correlations, eta-squared, Goodman and Kruskal tau, permutation tests, etc.), multiple graphical representations of the associations between two variables (using 'ggplot2') and weighted statistics.
A system for analyzing descriptive representation, especially for comparing the composition of a political body to the population it represents. Users can compute the expected degree of representation for a body under a random sampling model, the expected degree of representation variability, as well as representation scores from observed political bodies. The package is based on Gerring, Jerzak, and Oncel (2023) <doi:10.1017/S0003055423000680>.
Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface.
Exploratory analysis of a data base. Using the functions of this package is possible to filter the data set detecting atypical values (outliers) and to perform exploratory analysis through visual inspection or dispersion measures. With this package you can explore the structure of your data using several parameters at the same time joining statistical parameters with different graphics. Finally, this package aid to confirm or reject the hypothesis that your data structure presents a normal distribution. Therefore this package is useful to get a previous insight of your data before to carry out statistical analysis.
Generate descriptive statistics such as measures of location,
    dispersion, frequency tables, cross tables, group summaries and multiple
    one/two way tables.
Provides functions to create descriptive statistics tables for continuous and categorical variables.
    By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous
    variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to
    choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group
    differences of appropriated summary measures are automatically produces for two-group comparison.
    Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents.
    'DescrTab2' also allows printing tables to console and saving table objects for later use.
A toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.
It generates summary statistics on the input dataset using different descriptive univariate 
    statistical measures on entire data or at a group level. Though there are other packages which does 
    similar job but each of these are deficient in one form or other, in the measures generated, in
    treating numeric, character and date variables alike, no functionality to view these measures on a
    group level or the way the output is represented. Given the foremost role of the descriptive statistics
    in any of the exploratory data analysis or solution development, there is a need for a more constructive, 
    structured and refined version over these packages. This is the idea behind the package and it brings 
    together all the required descriptive measures to give an initial understanding of the data quality, 
    distribution in a faster,easier and elaborative way.The function brings an additional capability to be 
    able to generate these statistical measures on the entire dataset or at a group level. It calculates measures 
    of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, 
    standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on 
    the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures 
    are generated based on the data types as required by them,rather than applying numerical measures on character and 
    data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful 
    when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a 
    summary report for the data.
Easily create descriptive and comparative tables.
    It makes use and integrates directly with the tidyverse family of packages, and pipes.
    Tables are produced as (nested) dataframes for easy manipulation.
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.
'RStudio' as of recently offers the option to define addins and assign shortcuts to them. This package contains addins for a few most frequently used functions in a data scientist's (at least mine) daily work (like str(), example(), plot(), head(), view(), Desc()). Most of these functions will use the current selection in the editor window and send the specific command to the console while instantly executing it. Assigning shortcuts to these addins will save you quite a few keystrokes.
Various methods for the identification of trend and seasonal
  components in time series (TS) are provided. Among them is a data-driven locally
  weighted regression approach with automatically selected bandwidth for
  equidistant short-memory time series. The approach is a
  combination / extension of the algorithms by
  Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T.,
  and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief
  description of this new method is provided in the package documentation.
  Furthermore, the package allows its users to apply the base model of the
  Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>.
  Permission to include this procedure was kindly provided by the Federal
  Statistical Office of Germany.
Here, a function has been developed to generate parameters of the input designs, as well as incidence matrices. This is a general function that can be used to investigate the characterization properties of any block design.
Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.
A 'shiny' application that enables the user to create a
    prototype UI, being able to drag and drop UI components before being
    able to save or download the equivalent R code.
Intelligently assign samples to batches in order to reduce batch effects.
    Batch effects can have a significant impact on data analysis, especially
    when the assignment of samples to batches coincides with the contrast
    groups being studied. By defining a batch container and a scoring function
    that reflects the contrasts, this package allows users to assign samples in
    a way that minimizes the potential impact of batch effects on the
    comparison of interest. Among other functionality, we provide an
    implementation for OSAT score by Yan et al. (2012,
    <doi:10.1186/1471-2164-13-689>).
A simple interface to build designs using the package 'DeclareDesign'. 
    In one line of code, users can specify the parameters of individual 
    designs and diagnose their properties. The designers can also be used 
    to compare performance of a given design across a range of combinations 
    of parameters, such as effect size, sample size, and assignment probabilities.
Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'highs' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at <https://www.gurobi.com/documentation/quickstart.html> and <https://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>.  We have also included directions in the gurobi_installation file in the inst folder.
Generate balanced factorial designs with crossed and nested random and fixed effects <https://github.com/mmrabe/designr>.
Different sample size calculations with different study designs.
             These techniques are explained by Chow (2007)
             <doi:10.1201/9781584889830>.
Functions for (1) ranking, selecting, and prioritising  genes,
    proteins, and metabolites from high dimensional biology experiments, (2)
    multivariate hit calling in high content screens, and (3) combining data
    from diverse sources.
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
In-line functions for multivariate optimization via desirability 
    functions (Derringer and Suich, 1980, <doi:10.1080/00224065.1980.11980968>)
    with easy use within 'dplyr' pipelines.
Written to help undergraduate as well as graduate students to get started
    with R for basic econometrics without the need to import specific functions
    and datasets from many different sources. Primarily, the package is meant to 
    accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2023,
    ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook
    Auer, L.v. (2023, ISBN: 978-3-658-42699-6).
Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <arXiv:2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <arXiv:2209.03218>.
Functions that solve initial value problems of a system
        of first-order ordinary differential equations ('ODE'), of
        partial differential equations ('PDE'), of differential
        algebraic equations ('DAE'), and of delay differential
        equations.  The functions provide an interface to the FORTRAN
        functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK'
        collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk'
        and a C-implementation of solvers of the 'Runge-Kutta' family with
        fixed or variable time steps.  The package contains routines
        designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D
        partial differential equations ('PDE') that have been converted
        to 'ODEs' by numerical differencing.
A function for plotting maps of agricultural field experiments that are laid out in grids.  See Ryder (1981) <doi:10.1017/S0014479700011601>.
Create a details HTML tag around R objects to place
    in a Markdown, 'Rmarkdown' and 'roxygen2' documentation.
Models for analyzing site occupancy and count data models
  with detection error, including single-visit based models,
  conditional distance sampling and time-removal models.
  Package development was supported by the
  Alberta Biodiversity Monitoring Institute
  and the Boreal Avian Modelling Project.
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.
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>.
DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust
    Linear Regression.
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 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 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.
Conditional Maximum Likelihood Calibration and data management of multistage tests. 
  Supports polytomous items and incomplete designs with linear as well as multistage tests.
  Extended Nominal Response and Interaction models, DIF and profile analysis.
  See Robert J. Zwitser and Gunter Maris (2015)<doi:10.1007/s11336-013-9369-6>.
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>).
Implementation of automatically computing derivatives
    of functions (see Mailund Thomas (2017) <doi:10.1007/978-1-4842-2881-4>). Moreover, calculating gradients, Hessian and Jacobian matrices is possible.
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>).
Meta-analysis approaches for Phase I dose finding early phases clinical trials in order to better suit requirements in terms of maximum tolerated dose (MTD) and maximal dose regimen (MDR). This package has currently three different approaches: (a) an approach proposed by Zohar et al, 2011, <doi:10.1002/sim.4121> (denoted as ZKO), (b) the Variance Weighted pooling analysis (called VarWT) and (c) the Random Effects Model Based (REMB) algorithm, where user can input his/her own model based approach or use the existing random effect logistic regression model (named as glimem) through the 'dfmeta' package.
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. The estimation options follow advances in the 
  econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 
  2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated 
  Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) 
  <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, 
  allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and 
  the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports 
  interpretation and visualization of the model as well as forecasting. 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.
A unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults, is proposed in the 'dfped' package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through 'dfped' package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the 'dfped' package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial. These methods are proposed by Petit et al, (2016) <doi:10.1177/0962280216671348>.
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.
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.
Performs differential correlation analysis on input
    matrices, with multiple conditions specified by a design matrix. Contains
    functions to filter, process, save, visualize, and interpret differential
    correlations of identifier-pairs across the entire identifier space, or with
    respect to a particular set of identifiers (e.g., one). Also contains several
    functions to perform differential correlation analysis on clusters (i.e., modules)
    or genes. Finally, it contains functions to generate empirical p-values for the
    hypothesis tests and adjust them for multiple comparisons. Although the package
    was built with gene expression data in mind, it is applicable to other types of
    genomics data as well, in addition to being potentially applicable to data from
    other fields entirely. It is described more fully in the manuscript
    introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
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.
Dynamic graphical models for multivariate time series data to estimate directed
    dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et
    al. (2017) <doi:10.1016/j.neuroimage.2018.03.074>.
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.
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) <arXiv:2306.01212>. To get started with the package, 
             see <https://mingdeyu.github.io/dgpsi-R/>.
Gumbel distribution functions (De Haan L. (2007)
    <doi:10.1007/0-387-34471-3>) implemented with the techniques of automatic
    differentiation (Griewank A. (2008) <isbn:978-0-89871-659-7>).
    With this tool, a user should be able to quickly model extreme
    events for which the Gumbel distribution is the domain of attraction.
    The package makes available the density function, the distribution
    function the quantile function and a random generating function. In
    addition, it supports gradient functions. The package combines 'Adept'
    (C++ templated automatic differentiation) (Hogan R. (2017)
    <doi:10.5281/zenodo.1004730>) and 'Eigen' (templated matrix-vector
    library) for fast computations of both objective functions and exact
    gradients. It relies on 'RcppEigen' for easy access to 'Eigen' and
    bindings to 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', 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 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.
The density, cumulative distribution, quantiles, 
    and i.i.d random variables of a heavy-headed distribution.
    For more information, please see the vignette.
Connect and pull data from a 'DHIS2 (District Health Information Software 2)' instance into R.
Tools for accessing data from National Library of Norway's dhlab (digital humanities laboratory).
    Provides wrappers for accessing our API services at <https://api.nb.no/dhlab/>.
    To learn more about dhlab, visit out site <https://www.nb.no/dh-lab/>.
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>.
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>.
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 '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.
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.
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.
Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.
Provides a big-data-friendly and memory-efficient difference-in-differences estimator for staggered (and non-staggered) treatment contexts.
Estimates Two-way Fixed Effects difference-in-differences/event-study models using the imputation-based approach proposed by Borusyak, Jaravel, and Spiess (2021).
Estimate the effect of a treatment on an outcome in sharp Difference-in-Difference designs with multiple groups and periods. 
    It computes the DIDM estimator introduced in Section 4 of "Two-Way Fixed Effects Estimators with Heterogeneous
    Treatment Effects" (Chaisemartin, D'Haultfoeuille (2020)  <doi:10.1257/aer.20181169>), which
    generalizes the standard DID estimator with two groups, two periods and a binary treatment to situations
    with many groups,many periods and a potentially non-binary treatment. For each pair of consecutive time
    periods t-1 and t and for each value of the treatment d, the package computes a DID estimator comparing
    the outcome evolution among the switchers, the groups whose treatment changes from d to some other value
    between t-1 and t, to the same evolution among control groups whose treatment is equal to d both in t-1 and t.
    Then the DIDM estimator is equal to the average of those DIDs across all pairs of consecutive time periods and
    across all values of the treatment. Under a parallel trends assumption, DIDM is an unbiased and consistent estimator
    of the average treatment effect among switchers, at the time period when they switch.
    The package can also compute placebo estimators that can be used to test the parallel trends assumption.
    Finally, in staggered adoption designs where each group's treatment is weakly increasing over time,
    it can compute estimators of switchers' dynamic treatment effects, one time period or more after they have
    started receiving the treatment.
Estimation of event-study Difference-in-Difference (DID) estimators in designs with multiple groups and periods, and with a potentially non-binary treatment that may increase or decrease multiple times.
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.
Likelihood-based genome polarisation finds which alleles of genomic markers 
    belong to which side of the barrier. 
	Co-estimates which individuals belong to either side of the barrier and barrier strength.
	Uses expectation maximisation in likelihood framework. The method is described in 
	Baird et al. (2023) <doi:10.1111/2041-210X.14010>.
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. Additionally, approaches to estimating statistical power via Monte Carlo simulations are implemented.
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.
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.
Enables the evaluation of diffusion distances for complex single-layer networks.
    Given a network one can define different types of Laplacian (or transition)
    matrices corresponding to different continuous-time random walks dynamics on the network. 
    This package enables the evaluation of Laplacians, stochastic matrices, and the 
    corresponding diffusion distance matrices. The metric structure induced by the network-driven
    process is richer and more robust than the one given by shortest-paths and allows to study 
    the geometry induced by different types of diffusion-like communication mechanisms taking 
    place on complex networks. 
    For more details see: De Domenico, M. (2017) <doi:10.1103/physrevlett.118.168301> and 
    Bertagnolli, G. and De Domenico, M. (2021) <doi:10.1103/PhysRevE.103.042301>.
Various diffusion models to forecast new product growth. Currently
    the package contains Bass, Gompertz and Gamma/Shifted Gompertz 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.  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.
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>.
Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).
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.
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.
Deconvolution of bulk RNA-Seq data using context-specific deconvolution models based on Deep Neural Networks using scRNA-Seq data as input. These models are able to make accurate estimates of the cell composition of bulk RNA-Seq samples from the same context using the advances provided by Deep Learning and the meaningful information provided by scRNA-Seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> for more details.
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.
Import data from a digital image; it requires user input for
    calibration and to locate the data points. The end result is similar to
    'DataThief' and other other programs that 'digitize' published plots or
    graphs.
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.
Conducts a direction-projection-permutation test and displays diagnostic plots to facilitate the visual assessment of the test. See Wei et al. (2016) <doi:10.1080/10618600.2015.1027773> and Lam et al. (2018) <doi:10.1080/10618600.2017.1366915> for more details.
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'.
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>.
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>.
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) Phillip J. Paine, Simon P. Preston Michail Tsagris and Andrew T. A. Wood (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) P. J. Paine, S. P. Preston, M. Tsagris and Andrew T. A. Wood (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) Tsagris M. and Alzeley O. (2023). "Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling". <doi:10.48550/arXiv.2302.02468>.
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.
Implementation of Directional Metropolis Hastings Algorithm for
    MCMC.
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>.
Semiparametric distributional regression methods (expectile,
    quantile and mode regression) for time-to-event variables with
    right-censoring; uses inverse probability of censoring weights or
    accelerated failure time models with auxiliary likelihoods. Expectile
    regression using inverse probability of censoring weights has been
    introduced in Seipp et al. (2021) ``Weighted Expectile Regression for
    Right-Censored Data'' <doi:10.1002/sim.9137>, mode regression for
    time-to-event variables has been introduced in Seipp et al. (2022)
    ``Flexible Semiparametric Mode Regression for Time-to-Event Data''
    <doi:10.1177/09622802221122406>.
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>).
First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) <arXiv:2108.05783> 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>.
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). Currently, the package contains data restructuring functions and functions for generating biometrically informed data for kin pairs.
The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.
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.
Structure learning of Bayesian network using coordinate-descent
    algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
    and we use a multi-logit model to do the regression.
    The algorithm is described in Gu, Fu and Zhou (2016) <arXiv:1403.2310>.
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.
Multiple testing procedures 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 a set of observed p-values and their discrete support under the null. A function to compute such p-values and supports for Fisher's exact tests is also provided, along with a wrapper allowing to apply discrete procedures directly from contingency tables.
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>.
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.
Create, manipulate, transform, and simulate from discrete random
    variables. The syntax is modeled after that which is used in mathematical
    statistics and probability courses, but with powerful support for more
    advanced probability calculations. This includes the creation of joint
    random variables, and the derivation and manipulation of their conditional
    and marginal distributions.
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>.
A disk-based data manipulation tool for working with 
  large-than-RAM datasets. Aims to lower the barrier-to-entry for 
  manipulating large datasets by adhering closely to popular and 
  familiar data manipulation paradigms like 'dplyr' verbs and 
  'data.table' syntax.
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 designed to be used with the 'mvp' class of
  packages that use the STL map class: its purpose is to trap
  plausible idiom that is ill-defined (implementation-specific) and
  return 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.
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.
Set of tools for molecular probes selection and design of a microarray, e.g. the assessment of physical and chemical properties, blast performance, selection according to sensitivity and selectivity. Methods used in package are described in: Lorenz R., Stephan H.B., Höner zu Siederdissen C. et al. (2011) <doi:10.1186/1748-7188-6-26>; Camacho C., Coulouris G., Avagyan V. et al. (2009) <doi:10.1186/1471-2105-10-421>.
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>.
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://examples.distancesampling.org/> for example analyses.
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.
Provides tools to assess the dissimilarity between multivariate time-series. It is based on the psi measure described  by Birks and Gordon (1985 <doi:10.1002/jqs.3390020110>), which computes dissimilarity between irregular time-series constrained by sample order. However, in this package the original idea has been extended to work with any kind of multivariate time-series, no matter whether they are regular, irregular, aligned or unaligned. Furthermore, the package allows to assess the significance of dissimilarity values by applying a restricted permutation test, allows to measure the contribution of individual variables to dissimilarity, and offers tools to transfer attributes (generally time or age, but other are possible) between sequences based on the similarity of their samples.
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>.
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>.
Provides several methods for model distillation and interpretability 
    for general black box machine learning models and treatment effect estimation
    methods. For details on the algorithms implemented, see <https://forestry-labs.github.io/distillML/index.html>
    Brian Cho, Theo F. Saarinen, Jasjeet S. Sekhon, Simon Walter.
Calculates Distinctiveness Centrality in social networks. 
	For formulas and descriptions, see Fronzetti Colladon and Naldi (2020) <doi:10.1371/journal.pone.0233276>.
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.
S4-classes and methods for distributions.
Provides documentation in form of a common vignette to packages 'distr',
            'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.
Functions for visualizing distributional regression models fitted using the 'gamlss', 'bamlss' or 'betareg' R package. The core of the package consists of a 'shiny' application, where the model results can be interactively explored and visualized.
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.
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, <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().
Facilitate the analysis of teams in a corporate setting:
    assess the diversity per grade and job, present the results,
    search for bias (in hiring and/or promoting processes).
    It also provides methods to simulate the effect of bias, random team-data, etc.
    White paper: 'Philippe J.S. De Brouwer' (2021) <http://www.de-brouwer.com/assets/div/div-white-paper.pdf>.
    Book (chapter 36): 'Philippe J.S. De Brouwer' (2020, ISBN:978-1-119-63272-6) and 'Philippe J.S. De Brouwer' (2020) <doi:10.1002/9781119632757>.
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.
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.
Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.
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.
Implements interaction forests [1], which are specific diversity forests and 
  the basic form of diversity forests that uses univariable, binary splitting [2].
  Interaction forests (IFs) are ensembles of decision trees that model quantitative and
  qualitative interaction effects using bivariable splitting. IFs come with the 
  Effect Importance Measure (EIM), which can be used to identify variable pairs that 
  feature quantitative and qualitative interaction effects with high predictive
  relevance. IFs and EIM focus on well interpretable forms of interactions.
  The package also offers plot functions for visualising the estimated forms of 
  interaction effects.
  Categorical, metric, and survival outcomes are supported.
  This is a fork of the R package 'ranger' (main author: Marvin N. Wright) that 
  implements random forests using an efficient C++  implementation.
  References:
  [1] Hornung, R. & Boulesteix, A.-L. (2022) Interaction Forests: Identifying and 
      exploiting interpretable quantitative and qualitative interaction effects.
	  Computational Statistics & Data Analysis 171:107460, <doi:10.1016/j.csda.2022.107460>.
  [2] Hornung, R. (2022) Diversity forests: Using split sampling to enable 
      innovative complex split procedures in random forests.
	  SN Computer Science 3(2):1, <doi:10.1007/s42979-021-00920-1>.
Provides tools to sort DICOM-format medical image files, and
    convert them to NIfTI-1 format.
A set of tools for empirical analysis of diversity (a number and frequency of different types in 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 (2023) <doi:10.31223/X5997Z>.
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>.
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.
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.
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.
Implementation of double machine learning (DML) algorithms in R, 
    based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning 
    in Partially Linear Endogenous Models" <arXiv:2101.12525> and Emmenegger and
    Buehlmann (2021) <arXiv:2108.13657> "Double Machine Learning for Partially 
    Linear Mixed-Effects Models with Repeated Measurements". 
    First part: our goal is to perform inference for the linear parameter in partially
    linear models with confounding variables.
    The standard DML estimator of the linear parameter has a two-stage least
    squares interpretation, which can lead to a large variance and overwide
    confidence intervals.
    We apply regularization to reduce the variance of the estimator,
    which produces narrower confidence intervals that are approximately valid.
    Nuisance terms can be flexibly estimated with machine learning algorithms.
    Second part: our goal is to estimate and perform inference for the linear 
    coefficient in a partially linear mixed-effects model
    with DML. Machine learning algorithms allows us to incorporate more
    complex interaction structures and high-dimensional variables.
Yang(2020,<doi:10.1016/j.jeconom.2020.01.018>) come up with Double Machine Learning model ,it is based on this model, using four machine learning methods-- bagging, Boosting, random forest and neural network, and then  based on the four models   for two different combinations of the integrated model -- linear model combination and random forest .
Dyadic mixed model analysis 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.
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>.
Perform estimation, prediction, and simulations using the Dynamic Multiple Quantile model of Catania and Luati (2023) <doi:10.1016/j.jeconom.2022.11.002>. Can be used to estimate a set of conditional time-varying quantiles of a time series that do not cross.
It provides functions to apply the deterministic tracking algorithm - DiST (Wong et al 2016) <doi:10.1214/15-AOAS880> and to plot tractography results.
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>.
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.
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
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/>.
Integrates pathway information into the differential network analysis of two gene expression datasets as described in Grimes, Potter, and Datta (2019) <doi:10.1038/s41598-019-41918-3>. Provides summary functions to break down the results at the pathway, gene, or individual connection level. The differential networks for each pathway of interest can be plotted, and the visualization will highlight any differentially expressed genes and all of the gene-gene associations that are significantly differentially connected.
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).
    All functions currently focus on Fifth Edition (a.k.a. "5e") but once the next edition is published functions will likely be expanded to include any rule changes.
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>).
Provides some utils to get Korean text sample from news articles
            in Daum which is popular news portal service in Korea.
Using Local Moran's I for detection of differential network local consistency.
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 tools to build deep neural network with flexible users define loss function and probability models. Several applications included in this package are, 1) The (deepAFT) model, a deep neural network model for accelerated failure time (AFT) model for survival data. 2) The (deepGLM) model, a deep neural network model for generalized linear model (glm) for continuous, categorical and Poisson data.
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().
Provides Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains.
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:
    1) Facilities for working with grouped data: 'do' something to data
    stratified 'by' some variables.
    2) LSmeans (least-squares means), general linear estimates.
    3) Restrict functions to a smaller domain.
    4) Miscellaneous other utilities.
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 <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 <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.
Gives you the ability to use arbitrary Docker images
    (including custom ones) to process Rmarkdown code chunks.
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.
Basic routines used in scientific coding, such as timing routines, vector/array handing functions and I/O support routines.
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.
A variety of sampling plans are able to be compared using evaluations of their operating characteristics (OC), average outgoing quality (OQ), average total inspection (ATI) etc.
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://www.gurobi.com/products/gurobi-optimizer/> and <https://www.gurobi.com/documentation/7.5/refman/r_api_overview.html> (or higher version). 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. 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) <arXiv:2209.07334>.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
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>) or 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.
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.
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 et al. (2021) <doi:10.18637/jss.v099.i05>. Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>), missing data (Mohan, K. and Pearl, J. and Tian., J. (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>).
Utilities for converting unstructured electronic prescribing instructions into structured medication data. Extracts drug dose, units, daily dosing frequency and intervals from English-language prescriptions. Based on Karystianis et al. (2015) <doi:10.1186/s12911-016-0255-x>.
Performs sensitivity analysis for the sharp null and attributable effects in matched studies with continuous exposures and binary outcomes as described in Zhang, Small, Heng (2024) <arXiv:2401.06909>. Two of the functions require installation of the 'Gurobi' optimizer. Please see <https://www.gurobi.com/documentation/9.0/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>.
Quick and easy dot-and-whisker plots of regression results.
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. (2020) <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>.
Overload utils::'?' to build unary and binary operators from existing functions, 
    piping operators of different precedence, and flexible syntaxes.
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.
Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.
Use dynamic programming method to solve l1 convex clustering with identical weights.
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.
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.
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.
Implements the dynamic panel models described by Allison, Williams,
 and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. 
 This class of models uses structural equation modeling to specify dynamic 
 (lagged dependent variable) models with fixed effects for panel data. 
 Additionally, models may have predictors that are only weakly exogenous, i.e.,
 are affected by prior values of the dependent variable. Options also allow for
 random effects, dropping the lagged dependent variable, and a number of other 
 specification choices.
Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.
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
    such as the Laplace mechanism (Dwork et al., 2006a)
    <doi:10.1007/11681878_14>, Gaussian mechanism (Dwork et al., 2006b)
    <doi:10.1007/11761679_29>, 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 threshold 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>.
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
  Xoroshiro128+ and Xoshiro256+ by Blackman and Vigna (2018
  <arXiv:1805.01407>). 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) <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>.
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. (2020) "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.
Fits (longitudinal) 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.  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 & Meinshausen & Buhlmann (2020) <arXiv:2005.14458>.
Fit restricted mean models for 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>.
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.
Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors' MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>.
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.
A collection of functions that perform jump regression
	     and image analysis such as denoising, deblurring and
	     jump detection.
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>.
Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings.
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.
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.
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.
Prepare prescription data (such as from the Clinical Practice Research Datalink) into an analysis-ready format, with start and stop dates for each patient's prescriptions. Based on Pye et al (2018) <doi:10.1002/pds.4440>.
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, 2022), available at <https://bookdown.org/hneth/ds4psy/>. The book and course 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.
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 is a quantitative measure of child development. 
    The D-score follows the Rasch model. See Jacobusse, van Buuren and 
    Verkerk (2006) <doi:10.1002/sim.2351>. The user can convert 
    milestone scores from many assessment instruments into the D-score 
    and the DAZ (D-score adjusted for age). Several tools assist in 
    mapping milestone names into the 9-position Global Scale of Early 
    Development (GSED) convention. Supports calculation of the D-score 
    using 'dutch' <doi:10.1177/0962280212473300>, 
    'gcdg' <doi:10.1136/bmjgh-2019-001724> and 'gsed' conversion keys.
    The user can calculate DAZ using 'phase1' (default), 'gcdg' and 'dutch' 
    age-conditional references.
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."
Framework to fit distributional stochastic frontier models. Casts the stochastic frontier model into the flexible framework of distributional regression or otherwise known as General Additive Models of Location, Scale and Shape (GAMLSS). Allows for linear, non-linear, random and spatial effects on all the parameters of the distribution of the output, e.g. effects on the production or cost function, heterogeneity of the noise and inefficiency. Available distributions are the normal-halfnormal and normal-exponential distribution. Estimation via the fast and reliable routines of the 'mgcv' package. For more details see <doi:10.1016/j.csda.2023.107796>.
'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 <http://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.
Tool collection for common and not so common data science use cases. This includes 
    custom made algorithms for data management as well as value calculations that are hard to find 
    elsewhere because of their specificity but would be a waste to get lost nonetheless. 
    Currently available functionality: find
    sub-graphs in an edge list data.frame, find mode or modes in a vector of values, extract 
    (a) specific regular expression group(s), generate ISO time stamps that play well with 
    file names, or generate URL parameter lists by expanding value combinations.
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 donwload 
    DSM (Digital Surface Model) and LiDAR (Light Detection and Ranging) data via APIs, 
    including 'OpenTopography' <https://portal.opentopography.org/apidocs/> and 
    'TNMAccess' <https://apps.nationalmap.gov/tnmaccess/#/>.
'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.
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 <http://distancesampling.org/>.
Draw samples from the direct sampling spatial prior model as
    described in G. White, D. Sun, P. Speckman (2019) <arXiv:1906.05575>. The basic model assumes a Gaussian
    likelihood and derives a spatial prior based on thin-plate splines.
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>.
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.
Provides fast methods to work with Merton's distance to default 
  model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. 
  The methods includes simulation and estimation of the parameters.
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>.
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 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.
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. (2020) <arXiv:2012.03294>.
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.
This package 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>.
Provides a series of functions for fitting a dual sparse partial least squares (Dual-SPLS) regression. These functions differ by the choice of the underlying norm.
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 a 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.
    Also defines a set of generics that provide a low-level
    implementer's interface for the high-level user interface of 'dplyr'.
Generates visualizations with Duke’s official suite of colors in a color blind friendly way.
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 stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. '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. '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>.
Creates a new graph from an existing one, duplicating nodes
    with self-loops. This can be used for a computation of betweenness
    centrality that 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>.
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>.
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) <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.
Infers networks with hubs using degree weighted Lasso method.
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.
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.
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.
An application of Dynamic Mode Decomposition for prediction of time features. Automatic search for the best model across the space of all possible feature combinations and ranks of Singular Value Decomposition.
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.
A comprehensive and dynamic configuration driven logging package for R. While there 
    are several excellent logging solutions already in the R ecosystem, I always feel constrained 
    in some way by each of them. Every project is designed differently to solve it's domain specific problem, 
    and ultimately the utility of a logging solution is its ability to adapt to this design. This is the 
    raison d'être for 'dyn.log': to provide a modular design, template mechanics and a configuration-based 
    integration model, so that the logger can integrate deeply into your design, even though it knows 
    nothing about it.
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>.
Returns dynamic fit index (DFI) cutoffs for latent variable models 
    that are tailored to the user's model statement, model type, and sample size.  
    This is the counterpart of the Shiny Application, <https://dynamicfit.app>.
Emulating and solving inverse problems for dynamic computer experiments.
	     It contains two major functionalities: (1) localized GP model for large-scale
	     dynamic computer experiments using the algorithm proposed by
	     Zhang et al. (2018) <arXiv:1611.09488>; (2) solving inverse problems
	     in dynamic computer experiments. The current version only supports 64-bit
	     version of R.
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 (2022) 
  <doi:10.31235/osf.io/mdwu5>. 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, 2023) 
  <arxiv:2302.01607>.
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.
A multiple testing procedure aims to find the rare-variant association regions. When variants are rare, the single variant association test approach suffers from low power. To improve testing power, the procedure dynamically and hierarchically aggregates smaller genome regions to larger ones and performs multiple testing for disease associations with a controlled node-level false discovery rate. This method are members of the family of ancillary information assisted recursive testing introduced in Pura, Li, Chan and Xie (2021) <arXiv:1906.07757v2> and Li, Sung and Xie (2021) <arXiv:2103.11085v2>.
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.
While there are many well-established measures for identifying critical fluctuations and phase transitions, these measures only work with many points of measurement and thus are unreliable when studying short and coarse-grained time series. This package provides a measure for complexity in a time series that does not rely on long time series (Kaiser (2017), <doi:10.17605/OSF.IO/GWTKX>).
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.
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, mgcv::gam and gam::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 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.  Current models
    supported include Dyadic Confirmatory Factor Analysis, the Actor–Partner 
    Interdependence Model (observed and latent), the Common Fate Model
    (observed and latent), Mutual Influence Model (latent), and the Bifactor
    Dyadic Model (latent).
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 ...
Process and manage the data from the Empatica E4. All functions operate on the EDA data stream, but other streams will be added soon. The Empatica E4 is a wearable physiological monitor made by Empatica (Empatica is not associated with any of this code). You can find more information about the E4 at Empatica's website <https://www.empatica.com/research/e4/>.
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.
Estimator augmentation methods for statistical inference on high-dimensional data, 
    as described in Zhou, Q. (2014) <arXiv:1401.4425v2>
    and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>.
    It provides several simulation-based inference methods: (a) Gaussian and 
    wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
    lasso and their de-biased estimators, (b) importance sampler for approximating
    p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with 
    applications in post-selection inference.
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>.
The Early-Warning-Signals Toolbox provides methods for estimating
    statistical changes in time series that can be used for identifying nearby
    critical transitions.
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/eosdis/science-system-description/eosdis-components/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 Google Maps/Earth (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, data manipulation or color palettes. 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'. 
    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.
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.
Allows users to easily read multiple comma separated tables and create a data frame under the same name.
    Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory.
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.
A function that reads in the GEO code of a list of probesets or gene symbols, a gene expression dataset GEO accession code, the name of the dataset feature discriminating the two conditions for the differential coexpression, and the values of the two different conditions for the differential coexpression, and returns the significant pairs of genes/probesets with highest differential coexpression (p-value < 0.005). If the input gene list is made of gene symbols, this package associates the probesets to these gene symbols, if found.  Platforms available: GPL80, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293,  GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>.
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.
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.
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.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models.
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.
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.
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 and generalized linear models) in complex survey designs comprising multiple
  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>.
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>.
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>.
Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.
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.
Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects. Please reference its use as A Huang and D Liu (2016) <doi: 10.1093/bioinformatics/btw143>.
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.
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(). A detailed introduction to the package is provided
    by Willwerscheid and Stephens (2021) <arXiv:2110.00152>.
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.
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.
Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.
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) <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 Statistical
    Data Warehouse' API <https://sdw.ecb.europa.eu/>, allowing for programmatic 
    retrieval of a vast quantity of statistical data.
If translate English words into Chinese, there is a faster 
    way for R user. 'RYoudaoTranslate' package provides interface to 
    'Youdao' <http://youdao.com/> translation open API for R user. 'entcn' 
    package also provides similar features. But it does not support Chinese 
    words translation into English, I have made some improvements on the basis 
    of this software. You can pass in an English or Chinese word, 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, so that 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.
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.
A goodness-of-fit test for elliptical distributions with diagnostic
  capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2019) <arXiv:1902.03622>.
A consistent tool for downloading ECH data, processing them and generating new indicators: poverty, education, employment, etc. All data are downloaded from the official site of the National Institute of Statistics at <https://www.gub.uy/instituto-nacional-estadistica/datos-y-estadisticas/encuestas/encuesta-continua-hogares>.
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 <http://biblio.uqar.ca/archives/30005500.pdf> 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/>.
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>.
Companion package to the paper: An analytic approach for 
    interpretable predictive models in high dimensional data, in the presence of 
    interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. 
    This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.
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 Climate Data Store (CDS; 
    <https://cds.climate.copernicus.eu>). Allows for easy downloads of weather 
    forecasts and climate reanalysis data in R.
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, 
    and the optimization follows the following procedure of four functions (1) 
    sim_beta() estimates statistical power and type 2 error by using Permutational 
    Multivariate Analysis of Variance, (2) plot_power() represents the results 
    of the previous function, (3) scompvar() calculates the variation components 
    necessary for (4) sim_cbo() to calculate the optimal combination of number 
    of sites and samples depending on either an economical budget or on a desired 
    statistical accuracy.
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>.
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>.
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>.
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. (2017) <doi:10.13140/RG.2.2.31737.26728>. 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).
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>.
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.
Tools for using 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.
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 500 habitat suitability models developed by the U.S. Fish and 
    Wildlife Service (USFWS 1980) <https://www.fws.gov/policy/ESMindex.html>. 
    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)
    <https://www.iwr.usace.army.mil/Portals/70/docs/iwrreports/95-R-1.pdf>.
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.
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.
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.
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.
Assists ecologists in the analysis of temporal changes of 
    ecosystems, 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) ].
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.
Computer simulations of classical ecological models as a
    learning resource.
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.
Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties.
  The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources.
  Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage.
  Various types of hyper shrinkage may be used for various co-data.
  Co-data may be continuous or categorical. 
  The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types.
  The model fit is used to predict for new samples.
  The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection.
  See Van Nee et al. (2020) <arXiv:2005.04010>.
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.
Energy decomposition analysis for measuring contributions of components
             and factors on energy or carbon emission changes, as described in 
             B.W. Ang (2005) <doi:10.1016/j.enpol.2003.10.010>. Includes Log Mean 
             Devisia Index method and multi-year energy decomposition analysis using 
             five methods. Please refer P. Wu (2019) <doi:10.1016/j.jclepro.2019.02.200> 
             to use the package.
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.
Import US Census Bureau, Education Demographic and Geographic Estimates Program,
  Composite School District Boundaries Files for 2013-2019 with the option to attach the 'EdBuild'
  master dataset of school district finance, student demographics, and community economic
  indicators for every school district in the United States. The master dataset is built
  from the US Census, Annual Survey of School System Finances (F33) and joins data from the
  National Center for Education Statistics, Common Core of Data; the US Census, Small Area
  Income and Poverty Estimates; and the US Census, Education Demographic and Geographic
  Estimates. Additional functions in the package create a dataset of all pairs of school
  district neighbors as either a dataframe or a shapefile and create formatted maps of
  selected districts at the state or neighbor level, symbolized by a selected variable
  in the 'EdBuild' master dataset. For full details about 'EdBuild' data processing please
  see 'EdBuild' (2020) <http://data.edbuild.org/>.
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.
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).
This repository contains software for the calculation of goodness-of-fit test statistics and their P-values. The three statistics computed are the Empirical Distribution function statistics called Cramer-von Mises, Anderson-Darling, and Watson statistics. The statistics and their P-values can be used to assess an assumed distribution.The following distributions are available: Uniform, Normal, Gamma, Logistic, Laplace, Weibull, Extreme Value, and Exponential.
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>.
A system to facilitate designing 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.
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.
Implements various general algorithms to estimate missing elements
   of a Euclidean (squared) distance matrix.  
   Includes optimization methods based on semi-definite programming found in
   Alfakih, Khadani, and Wolkowicz (1999)<doi:10.1023/A:1008655427845>, 
   a non-convex position formulation by Fang and O'Leary (2012)<doi:10.1080/10556788.2011.643888>, and 
   a dissimilarity parameterization formulation by Trosset (2000)<doi:10.1023/A:1008722907820>.
   When the only non-missing
   distances are those on the minimal spanning tree, the guided random search
   algorithm will complete the matrix while preserving the minimal spanning tree following
   Rahman and Oldford (2018)<doi:10.1137/16M1092350>.
   Point configurations in specified dimensions can be determined from the completions. 
   Special problems such as the sensor localization problem, 
   as for example in Krislock and Wolkowicz (2010)<doi:10.1137/090759392>,
   as well as reconstructing
   the geometry of a molecular structure, as for example in 
   Hendrickson (1995)<doi:10.1137/0805040>, can also be solved.
   These and other methods are described in the thesis of Adam Rahman(2018)<https://hdl.handle.net/10012/13365>.
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:
    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>.
Implementations of (1) mutual dependence measures and mutual independence tests in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>;
    (2) independent component analysis methods based on mutual dependence measures in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532> 
    and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>;
    (3) conditional mean dependence measures and conditional mean independence tests in 
    Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012>,
    Park, T., et al. (2015) <doi:10.1214/15-EJS1047>,
    and Lee, C. E., and Shao, X. (2017) <doi:10.1080/01621459.2016.1240083>.
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.
Provides convenience functions and pre-programmed Stan models
    related to item response theory. Its purpose is to make fitting
    common item response theory models using Stan easy.
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.
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 at the European level from the 
  European Environmental Agency (EEA) dataflows (<https://www.eea.europa.eu/data-and-maps/data/aqereporting-9>). 
	The package allows dynamically mapping the stations, summarising and time aggregating the measurements and building 
	spatial interpolation maps.
	See the webpage <https://www.eea.europa.eu/en> for further information on EEA's activities and history.
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>.
Forecasting univariate time series with ensemble empirical mode decomposition (EEMD) with long short-term memory (LSTM). For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
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>.
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 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.
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.
Predicts cytoplasmic effector proteins using genomic data by searching for motifs of interest using regular expression searches and hidden Markov models (HMM) based in Haas et al. (2009) <doi:10.1038/nature08358>.
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.
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.
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 European Eddy Fluxes Database Cluster distributes fluxes of different Green House Gases measured mainly using the eddy covariance technique acquired in sites involved in EU projects but also single sites in Europe, Africa and others continents that decided to share their measurements in the database <http://gaia.agraria.unitus.it>. The package provides two functions to load and row-wise bind CSV files distributed by the database. Currently only L2, L3, and L4 (L=Level), half-hourly and daily (aggregation) files are supported.
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.
Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().
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.
An easy-to-use implementation of the Engle-Granger
  two-step procedure for identifying pairs of cointegrated series.  It is
  geared towards the analysis of pairs of securities.  Summary and plot
  functions are provided, and the package is able to fetch closing prices of
  securities from Yahoo.  A variety of unit root tests are supported, and 
  an improved unit root test is included.
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.
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.
Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman et al. 1993 Journal of Vegetation Science <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
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>.
Functions to delineate temporal dataset shifts in Electronic Health 
             Records through the projection and visualization of dissimilarities 
             among data temporal batches. This is done through the estimation of 
             data statistical distributions over time and their projection in 
             non-parametric statistical manifolds, uncovering the patterns of the 
             data latent temporal variability. 'EHRtemporalVariability' is 
             particularly suitable for multi-modal data and categorical variables 
             with a high number of values, common features of biomedical data where 
             traditional statistical process control or time-series methods may not 
             be appropriate. 'EHRtemporalVariability' allows you to explore and 
             identify dataset shifts through visual analytics formats such as 
             Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. 
             An additional 'EHRtemporalVariability' Shiny app can be used to load 
             and explore the package results and even to allow the use of these 
             functions to those users non-experienced in R coding. (Sáez et al. 2020)
             <doi:10.1093/gigascience/giaa079>.
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 'US Energy Information Administration' ('EIA') <https://www.eia.gov/>.
    Use of the API requires a free API key obtainable at <https://www.eia.gov/opendata/register.php>.
    The package includes functions for searching 'EIA' data categories and importing time series and geoset time series datasets.
    Datasets returned by these functions are provided in a tidy format or alternatively in more raw form.
    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.
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) <arXiv:0711.1146> 
        for details on the model.
Matrix algebra using the 'Eigen' C++ library: determinant, rank, inverse, pseudo-inverse, kernel and image, QR decomposition, Cholesky decomposition, linear least-squares problems. Also provides matrix functions such as exponential, logarithm, power, sine and cosine. Complex matrices are supported.
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.
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/>.
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.
This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression.  It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.
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:10.48550/arXiv.2203.01686>.
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.
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', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' API
    (<https://www.elastic.co/elasticsearch/>), including functions for
    setting connection details to 'Elasticsearch' instances, loading bulk data,
    searching for documents with both 'HTTP' query variables and 'JSON' based body
    requests. In addition, 'elastic' provides functions for interacting with API's
    for 'indices', documents, nodes, clusters, an interface to the cat API, and
    more.
Provides functions for fitting the entire
        solution path of the Elastic-Net and also provides functions
        for doing sparse PCA.
We developed a consistent and robust information criterion to conduct model selection for semiparametric models. It is free of distribution specification and powerful to locate the true model given large sample size. This package provides several usage of ELCIC with applications in generalized linear model (GLM), generalized estimating equation (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanism of missing at random and drop-out. Chixaing Chen, Ming Wang, Rongling Wu, Runze Li (2020) <doi:10.5705/ss.202020.0254>.
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.
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>.
Perform ballot-polling Bayesian audits for ranked voting elections
    using Dirichlet-tree prior distributions. Everest et al. (2022)
    <arXiv:2206.14605>, <arXiv:2209.03881>.
Offers a set of functions to easily download and clean 
    Brazilian electoral data from the Superior Electoral Court website. 
    Among others, the package retrieves data on local and
    federal elections for all positions (city councilor, mayor, state deputy,
    federal deputy, governor, and president) aggregated by
    state, city, and electoral zones.
Provides all electivity algorithms (including Vanderploeg and Scavia 
    electivity) that were examined in Lechowicz (1982) <doi:10.1007/BF00349007>, 
    plus the example data that were provided for moth resource utilisation.
Highest averages & largest remainders allocating seats methods and
    several party system scores.
    Implemented highest averages allocating seats methods are D'Hondt, Webster,
    Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague,
    equal proportions and Adams.
    Implemented largest remainders allocating seats methods are Hare, Droop,
    Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders.
    The main advantage of this package is that ties are always reported
    and not incorrectly allocated.
    Party system scores provided are competitiveness, concentration,
    effective number of parties, party nationalization score,
    party system nationalization score and volatility.
    References:
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>.
    Norris (2004, ISBN:0-521-82977-1).
    Consejo Nacional Electoral del Ecuador (2014)<http://cne.gob.ec/documents/Estadisticas/Atlas/ATLAS/CAPITULO%206%20web.pdf>.
    Laakso & Taagepera (1979) <https://journals.sagepub.com/doi/pdf/10.1177/001041407901200101>.
    Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>.
    Pedersen (1979) <https://janda.org/c24/Readings/Pedersen/Pedersen.htm>.
    Golosov (2010) <https://ppq.sagepub.com/content/16/2/171.abstract>.
    Golosov (2014) <https://ppq.sagepub.com/content/early/2014/09/08/1354068814549342.abstract>.
Several web services are available that provide access to elevation
             data. This package provides access to many of those services and 
             returns elevation data either as an 'sf' simple features object 
             from point elevation services or as a 'raster' object from raster 
             elevation services. In future versions, 'elevatr' will drop 
             support for 'raster' and will instead return 'terra' objects. 
             Currently, the package supports access to the Amazon Web Services 
             Terrain Tiles <https://registry.opendata.aws/terrain-tiles/>, 
             the Open Topography Global Datasets 
             API <https://opentopography.org/developers/>, and the USGS 
             Elevation Point Query Service <https://apps.nationalmap.gov/epqs/>.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
Density, distribution function, quantile function
    and random generation for the Kumaraswamy Complementary Weibull
    Geometric (Kw-CWG) lifetime probability distribution proposed
    in Afify, A.Z. et al (2017) <doi:10.1214/16-BJPS322>.
Performs hypothesis testing for general block designs with
    empirical likelihood. The core computational routines are implemented
    using the 'Eigen' 'C++' library and 'RcppEigen' interface, with
    'OpenMP' for parallel computation. Details of the methods are given in
    Kim, MacEachern, and Peruggia (2023)
    <doi:10.1080/10485252.2023.2206919>. This work was supported by the
    U.S. National Science Foundation under Grants No.  SES-1921523 and
    DMS-2015552.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
To run data analysis for enzyme-link immunosorbent assays (ELISAs). 
	Either the five- or four-parameter logistic model will be fitted for data of single ELISA. 
	Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. 
	Feng (2018) <doi:10.1101/483800>.
An alternative to Exploratory Factor Analysis (EFA) for
    metrical data in R. Drawing on characteristics of classical test
    theory, Exploratory Likert Scaling (ELiS) supports the user exploring
    multiple one-dimensional data structures. In common research practice,
    however, EFA remains the go-to method to uncover the (underlying)
    structure of a data set. Orthogonal dimensions and the potential of
    overextraction are often accepted as side effects. As described in
    Müller-Schneider (2001) <doi:10.1515/zfsoz-2001-0404>), ELiS confronts
    these problems. As a result, 'elisr' provides the platform to fully
    exploit the exploratory potential of the multiple scaling approach
    itself.
Create list comprehensions (and other types of comprehension) similar to those in
    'python', 'haskell', and other languages. List comprehension in 'R' converts a 
    regular for() loop into a vectorized lapply() function. Support for looping 
    with multiple variables, parallelization, and across non-standard objects included. Package 
    also contains a variety of functions to help with list comprehension.
Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name "elitism" is an acronym of "e"quipment for "l"ogarithmic and l"i"near "ti"me "s"tepwise "m"ultiple hypothesis testing.
    See Gou, J. (2022), "Quick multiple test procedures and p-value adjustments", Statistics in Biopharmaceutical Research 14(4), 636-650.
Tool for Environment-Wide Association Studies (EnvWAS / EWAS) 
    which are repeated analysis. It includes three functions. One function for
    linear regression, a second for logistic regression and a last one for
    generalized linear models.
Correlation chart of two set (x and y) of data.  
  Using Quantiles.  Visualize the effect of factor.
Contains various routines for drawing
    ellipses and ellipse-like confidence regions, implementing the plots
    described in Murdoch and Chow (1996, <doi:10.2307/2684435>). There are
    also routines implementing the profile plots described in Bates and
    Watts (1988, <doi:10.1002/9780470316757>).
The ellipsis is a powerful tool for extending functions. Unfortunately 
    this power comes at a cost: misspelled arguments will be silently ignored. 
    The ellipsis package provides a collection of functions to catch problems
    and alert the user.
Provides functions for the simulation and
  the nonparametric estimation of elliptical distributions,
  meta-elliptical copulas and trans-elliptical distributions,
  following the article Derumigny and Fermanian (2022) <doi:10.1016/j.jmva.2022.104962>.
A suite of elliptic and related functions including Weierstrass and
 Jacobi forms.  Also includes various tools for manipulating and
 visualizing complex functions.
Given the omnipresence of the assumption of elliptical symmetry, it is essential to be able to test whether that assumption actually holds true or not for the data
            at hand. This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2021) <arXiv:2011.12560v2>.
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 functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries' I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.
Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) <doi:10.1111/2041-210X.14021>). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores.
Implements a Markov Chain Monte Carlo algorithm to approximate 
	exact conditional inference for logistic regression models. Exact 
	conditional inference is based on the distribution of the sufficient 
	statistics for the parameters of interest given the sufficient statistics 
	for the remaining nuisance parameters. Using model formula notation, users 
	specify a logistic model and model terms of interest for exact inference.
	See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.
A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.
Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
A generic function for running the Expectation-Maximization (EM) algorithm
  within a maximum likelihood framework, based on Dempster, Laird, and Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>
  is implemented. It can be applied after a model fitting using R's existing functions and packages.
The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) <doi:10.1016/j.fss.2011.05.022>.
Use 'emailjs' API easily in 'R'. This package is not official. <https://www.emailjs.com/docs/rest-api/send/>. You can send e-mail with 'emailjs' with function, based on 'httr'. You can also make a 'shiny' ui and server function. It can be used for making feedback form, inquiry, and so on.
An R client for the 'emailvalidation.io' e-mail verification API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://emailvalidation.io/docs> .
A tool that allows users to generate various indices for evaluating statistical models. The fitstat() function computes indices based on the fitting data. The valstat() function computes indices based on the validation data set. Both fitstat() and valstat() will return 16 indices SSR: residual sum of squares, TRE: total relative error, Bias: mean bias, MRB: mean relative bias, MAB: mean absolute bias, MAPE: mean absolute percentage error, MSE: mean squared	error, RMSE: root mean square error, Percent.RMSE: percentage root mean squared error, R2: coefficient of determination, R2adj: adjusted coefficient of determination, APC: Amemiya's prediction criterion, logL: Log-likelihood, AIC: Akaike information criterion, AICc: corrected Akaike information criterion, BIC: Bayesian information criterion, HQC: Hannan-Quin information criterion. The	lower the better for the SSR, TRE, Bias, MRB, MAB, MAPE, MSE, RMSE, Percent.RMSE, APC, AIC, AICc, BIC and HQC indices. The higher the better for R2 and R2adj indices. Petre Stoica, P., Selén, Y. (2004) <doi:10.1109/MSP.2004.1311138>\n Zhou et al. (2023) <doi:10.3389/fpls.2023.1186250>\n Ogana, F.N., Ercanli, I. (2021) <doi:10.1007/s11676-021-01373-1>\n Musabbikhah et al. (2019) <doi:10.1088/1742-6596/1175/1/012270>.
DNA methylation is essential for human, and environment can change the DNA methylation
    and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find
    potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide.
    For more information on the methods we used, please see the following references:
    Tingley, D. (2014) <doi:10.18637/jss.v059.i05>,
    Turner, S. D. (2018) <doi:10.21105/joss.00731>,
    Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.
Do data management functions common in real-time monitoring (also called: ecological momentary assessment, experience sampling, micro-longitudinal) data, including creating power curves for multilevel data, centering on participant means and merging event-level data into momentary data sets where you need the events to correspond to the nearest data point in the momentary data. This is VERY early release software, and more features will be added over time.
A light, simple tool for sending emails with minimal dependencies.
Variable selection methods have been extensively developed for analyzing high-dimensional omics data within both the frequentist and Bayesian frameworks. This package implemented the spike-and-slab quantile LASSO which has been developed along the line of Bayesian hierarchical model but deeply rooted in the frequentist regularization methods by utilizing the Expectation–Maximization (EM) algorithm. Therefore, the proposed method borrows strength from both the frequentist and Bayesian frameworks while overcoming their respective limitations. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in the disease trait, compared to its nonrobust alternative, the spike-and-slab LASSO, which has also been implemented in the package. 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 <arXiv:1611.09477> or nonlinear
    models <arXiv:1604.06737> can be used.  There are also functions for
    dimension reduction and other approaches.
Provides a smooth mapping of multidimensional points into
    low-dimensional space defined by a self-organizing map. Designed to work
    with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019)
    <doi:10.12688/f1000research.21642.1>.
Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.
EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.
For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Application of empirical mode decomposition based artificial neural network model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=55&issue=1&article=013>; (ii) Das (2020) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=56&issue=2&article=002>.
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Functions that support estimating, assessing and mapping regional
    disaggregated indicators. So far, estimation methods comprise direct estimation,
    the model-based unit-level approach Empirical Best Prediction (see "Small area
    estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), 
    the area-level model (see "Estimates of income for small places: An 
    application of James-Stein procedures to Census Data" by Fay and Herriot (1979) 
    <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance 
    estimation methods, log and arcsin transformation, spatial, robust and measurement 
    error models), as well as their precision estimates. The assessment of the used model
    is supported by a summary and diagnostic plots. For a suitable presentation of
    estimates, map plots can be easily created. Furthermore, results can easily be
    exported to excel. For a detailed description of the package and the methods used
    see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" 
    by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette 
    "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".
Package providing calculation of Earth Mover's Distance (EMD).
Description: Application of empirical mode decomposition based support vector regression model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <http://krishi.icar.gov.in/jspui/handle/123456789/44873>; (ii) Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/43174>; (iii) Das (2023) <http://krishi.icar.gov.in/jspui/handle/123456789/77772>.
Produce maximum likelihood estimates of common accuracy statistics
 for multiple measurement methods when a gold standard is not available. An R 
 implementation of the expectation maximization algorithms described in Zhou et 
 al. (2011) <doi:10.1002/9780470906514> with additional functions for creating 
 simulated data and visualizing results. Supports binary, ordinal, and 
 continuous measurement methods.
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
Initially designed to distribute code for estimating the Gaussian
  graphical model with Lasso regularization, also known as the graphical lasso
  (glasso), using an Expectation-Maximization (EM) algorithm based on work by
  Städler and Bühlmann (2012) <doi:10.1007/s11222-010-9219-7>. As a byproduct,
  code for estimating means and covariances (or the precision matrix) under a
  multivariate normal (Gaussian) distribution is also available.
Simulate and fitting exponential multivariate Hawkes model. 
    This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.2307/2334319>, with an exponential kernel and fits the parameters from the data. 
    Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. 
    The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with 'maxLik' package.
A C++ implementation of the following evolutionary
    algorithms: Bat Algorithm (Yang, 2010 <doi:10.1007/978-3-642-12538-6_6>),
    Cuckoo Search (Yang, 2009 <doi:10.1109/nabic.2009.5393690>),
    Genetic Algorithms (Holland, 1992, ISBN:978-0262581110),
    Gravitational Search Algorithm (Rashedi et al., 2009 <doi:10.1016/j.ins.2009.03.004>),
    Grey Wolf Optimization (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>),
    Harmony Search (Geem et al., 2001 <doi:10.1177/003754970107600201>),
    Improved Harmony Search (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>),
    Moth-flame Optimization (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>),
    Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959),
    Simulated Annealing (Kirkpatrick et al., 1983 <doi:10.1126/science.220.4598.671>),
    Whale Optimization Algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>).
    'EmiR' can be used not only for unconstrained optimization problems, but also
    in presence of inequality constrains, and variables restricted to be integers.
Processing tools to create emissions for use in numerical air 
  quality models. Emissions can be calculated both using emission factors 
  and activity data (Schuch et al 2018) <doi:10.21105/joss.00662> or using 
  pollutant inventories (Schuch et al., 2018) <doi:10.30564/jasr.v1i1.347>. 
  Functions to process individual point emissions, line emissions and 
  area emissions of pollutants are available as well as methods to 
  incorporate alternative data for Spatial distribution of emissions 
  such as satellite images (Gavidia Calderon et. al, 2018) 
  <doi:10.1016/j.atmosenv.2018.09.026> or openstreetmap data 
  (Andrade et al, 2015) <doi:10.3389/fenvs.2015.00009>.
Provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The 'EmiStatR' package implements modular R methods. This enables 
    to add new functionalities through the R framework.
Work with Ecological Metadata Language ('EML') files. 
    'EML' is a widely used metadata standard in the ecological and
    environmental sciences, described in Jones et al. (2006),
    <doi:10.1146/annurev.ecolsys.37.091305.110031>.
This is a utility for transforming Ecological Metadata Language
        ('EML') files into 'JSON-LD' and back into 'EML.'  Doing so creates a
        list-based representation of 'EML' in R, so that 'EML' data can easily
        be manipulated using standard 'R' tools. This makes this package an
        effective backend for other 'R'-based tools  working with 'EML.' By
        abstracting away the complexity of 'XML' Schema, developers can
        build around native 'R' list objects and not have to worry about satisfying
        many of the additional constraints of set by the schema (such as element
        ordering, which is handled automatically). Additionally, the 'JSON-LD' 
        representation enables the use of developer-friendly 'JSON' parsing and
        serialization that may facilitate the use of 'EML' in contexts outside of 'R,'
        as well as the informatics-friendly serializations such as 'RDF' and
        'SPARQL' queries.
Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models that represent linear dynamical systems. Currently, one such algorithm is implemented for the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence. The corresponding scientific paper is yet to be published, therefore the relevant reference will be provided later.
End-member modelling analysis of grain-size data is an approach 
    to unmix a data set's underlying distributions and their contribution to 
    the data set. EMMAgeo provides deterministic and robust protocols for 
    that purpose.
Obtain estimated marginal means (EMMs) for many linear, generalized 
  linear, and mixed models. Compute contrasts or linear functions of EMMs,
  trends, and comparisons of slopes. Plots and other displays.
  Least-squares means are discussed, and the term "estimated marginal means"
  is suggested, in Searle, Speed, and Milliken (1980) Population marginal means 
  in the linear model: An alternative to least squares means, The American 
  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Provides unsupervised selection and clustering of microarray data
    using mixture models. Following the methods described in McLachlan, Bean and
    Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected
    based one the likelihood ratio statistic for the test of one versus two
    components when fitting mixtures of t-distributions to the expression data
    for each gene. The dimensionality of this gene subset is further reduced through
    the use of mixtures of factor analyzers, allowing the tissue samples to be
    clustered by fitting mixtures of normal distributions.
We provide functions to fit finite mixtures of multivariate normal or t-distributions to 
    data with various factor analytic structures adopted for the covariance/scale matrices. The 
    factor analytic structures available include mixtures of factor analyzers and mixtures of common 
    factor analyzers. The latter approach is so termed because the matrix of factor loadings is
    common to components before the component-specific rotation of the component factors to 
    make them white noise. Note that the component-factor loadings are not common after 
    this rotation. Maximum likelihood estimators of model parameters are obtained via the 
    Expectation-Maximization algorithm. See descriptions of the algorithms used in
    McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8>
    McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> 
    McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> 
    McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> 
    Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> 
    Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> 
    McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.
The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayes’ rule.
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
        of evolutionary multiobjective optimization algorithms.
Contains data about emojis with relevant metadata, and functions
    to work with emojis when they are in strings.
An implementation of using emoji and fontawesome for using in both
    base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.
The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps    (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) <doi:10.1080/14686996.2019.1620123>, <doi:10.1080/27660400.2021.1899449> <doi:10.1080/27660400.2022.2159753>. The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.
Evaluates the empirical characteristic function of univariate and multivariate samples.
    This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level.
Routines for performing empirical calibration of observational
  study estimates. By using a set of negative control hypotheses we can
  estimate the empirical null distribution of a particular observational
  study setup. This empirical null distribution can be used to compute a
  calibrated p-value, which reflects the probability of observing an
  estimated effect size when the null hypothesis is true taking both random
  and systematic error into account. A similar approach can be used to
  calibrate confidence intervals, using both negative and positive controls. 
  For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and
  Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.
Empirical likelihood ratio tests for means/quantiles/hazards
 	from possibly censored and/or truncated data. Now does regression too.
	This version contains some C code.
Calculates the 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, as described in
			 <https://www.ms.uky.edu/~mai/research/eAUC1.pdf>.
Collection of functions related to benchmark with prediction models
	 for data analysis and editing of clinical and epidemiological data.
Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.
Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM’s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.
Fast and easily computes an Euclidean Minimum Spanning Tree (EMST) from data,
    relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013).
    'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), 
    which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides 
    functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the 
    style of the 'base', 'scatterplot3d', or 'ggplot2' libraries; and functions to export the MST output to shapefiles.
Goodness-of-fit tests for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. Monte Carlo methods are provided to
        make the package capable of solving high-dimensional problems.
Allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a training set
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  The
 package includes functionality to evaluate quadratic forms 
 efficiently.
Provide the EMU Speech Database Management System (EMU-SDMS) with
    database management, data extraction, data preparation and data
    visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/>
    for more details.
We describe fifteen different splice site sequence encoding schemes that have been used in earlier studies for mapping of splice site sequences into numeric feature vectors. These encoding schemes will also be helpful for transforming other nucleotide sequences into numeric forms, provided they are of equal length. These encoding schemes will help the computational biologist working in the field of classification (binary or multiclass) or prediction involving nucleic acid sequences of equal length.
Implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2020) <arXiv:2003.03649>.
Interconverts between ordered lists and compact string notation.  
 Useful for capturing code lists, and pair-wise codes and decodes, for text storage.
 Analogous to factor levels and labels. Generics encode() and decode()
 perform interconversion, while codes() and decodes() extract components of an encoding.
 The function encoded() checks whether something is interpretable as an encoding.
 If a vector has an encoded 'guide' attribute, as_factor() uses it to coerce to factor.
The encompassing test is developed based on multi-step-ahead predictions of two nested models as in Pitarakis, J. (2023) <doi:10.48550/arXiv.2312.16099>. The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output.
Create encrypted html files that are fully self contained and do
  not require any additional software. Using the package you can encrypt
  arbitrary html files and also directly create encrypted 'rmarkdown' html reports.
It is important to ensure that sensitive data is protected. 
    This straightforward package is aimed at the end-user. 
    Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns.
    A public key can be shared to allow others to encrypt data to be sent to you. 
    This is particularly aimed a healthcare settings so patient data can be pseudonymised.
Various recursive two-stage models to address the endogeneity issue of treatment variables in observational study or mediators in experiments. The details of the models are discussed in Peng (2023) <doi:10.1287/isre.2022.1113>.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Maximum likelihood estimation of endogenous switching regression models from Heckman (1979) <doi:10.2307/1912352> and estimation of treatment effects.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy 
             distance, testing for multivariate normality, distance components (disco) for non-parametric 
             analysis of structured data, and other energy statistics/methods are implemented.
Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) <https://github.com/YafeiXu/working_paper/raw/master/CPM102.pdf>.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
Compute energy landscapes using a digital elevation model raster and body mass of animals.
Fully robust versions of the elastic net estimator are introduced for linear and binary and multinomial regression, in particular high dimensional data. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied. A reweighting step is added to improve the statistical efficiency of the proposed estimators. Selecting appropriate tuning parameters for elastic net penalties are done via cross-validation.
Allow numbers to be presented in an English language
        version, one, two, three, ...  Ordinals are also available,
        first, second, third, ... and indefinite article choice, "a" or "an".
Computing economic analysis in civil infrastructure and  
    ecosystem restoration projects is a typical activity. This package contains Standard 
    cost engineering and engineering economics methods that are applied to convert between 
    present, future, and annualized costs. Newnan D. (2020) <ISBN 9780190931919> “Engineering Economic Analysis”.
Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation for nonlinear regression?" by Xinwei Shen and Nicolai Meinshausen (2023). Also supports classification (experimental). 
 <arxiv:2307.00835>.
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate niche overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.
A set of tools to perform Ecological Niche Modeling with
    presence-absence data. It includes algorithms for data partitioning, 
    model fitting, calibration, evaluation, selection, and prediction. 
    Other functions help to explore signals of ecological niche using univariate
    and multivariate analyses, and model features such as variable response 
    curves and variable importance. Unique characteristics of this package are
    the ability to exclude models with concave quadratic responses, and the
    option to clamp model predictions to specific variables. These tools are
    implemented following principles proposed in
    Cobos et al., (2022) <doi:10.17161/bi.v17i.15985>, 
    Cobos et al., (2019) <doi:10.7717/peerj.6281>, 
    and Peterson et al., (2008) <doi:10.1016/j.ecolmodel.2007.11.008>.
Implements species distribution modeling and ecological niche
	modeling, including: bias correction, spatial cross-validation, model
	evaluation, raster interpolation, biotic "velocity" (speed and
	direction of movement of a "mass" represented by a raster), interpolating
	across a time series of rasters, and use of spatially imprecise records.
	The heart of the package is a set of "training" functions which
	automatically optimize model complexity based number of available
	occurrences. These algorithms include MaxEnt, MaxNet, boosted regression
	trees/gradient boosting machines, generalized additive models,
	generalized linear models, natural splines, and random forests. To enhance
	interoperability with other modeling packages, no new classes are created.
	The package works with 'PROJ6' geodetic objects and coordinate reference
	systems.
Constructing niche models and analyzing patterns of niche evolution.  Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography.  Warren, D.L., R.E. Glor, and M. Turelli (2008) <doi:10.1111/j.1558-5646.2008.00482.x> Glor, R.E., and D.L. Warren (2011) <doi:10.1111/j.1558-5646.2010.01177.x>  Warren, D.L., R.E. Glor, and M. Turelli (2010) <doi:10.1111/j.1600-0587.2009.06142.x> Cardillo, M., and D.L. Warren (2016) <doi:10.1111/geb.12455> D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) <doi:10.1111/ecog.03900>.
An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.
A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 <doi:10.1016/j.isci.2022.104767>) or similar methods.
Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See <https://maayanlab.cloud/Enrichr/> for further details.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.
An implementation of the clustering methods of categorical data
    discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical 
    data via ensembling dissimilarity matrices.  Preprint <arXiv:1506.07930>.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
        from ensemble forecasts and weather observations
 <https://stat.uw.edu/sites/default/files/files/reports/2007/tr516.pdf>.
Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.
Functions to use ensembles of several machine learning models
    specified in caret package.
Creates ensemble taxonomic assignments of amplicon sequencing data 
  in R using outputs of multiple taxonomic assignment algorithms and/or
	reference databases. Includes flexible algorithms for mapping taxonomic 
	nomenclatures onto one another and for computing ensemble taxonomic 
	assignments.
Gene information from 'Ensembl' genome builds 'GRCh38.p14' and 'GRCh37.p13' to use with the 'topr' package. The datasets were originally downloaded from <https://ftp.ensembl.org/pub/current/gtf/homo_sapiens/Homo_sapiens.GRCh38.111.gtf.gz> and <https://ftp.ensembl.org/pub/grch37/current/gtf/homo_sapiens/Homo_sapiens.GRCh37.87.gtf.gz> and converted into the format required by the 'topr' package. See <https://github.com/totajuliusd/topr?tab=readme-ov-file#how-to-use-topr-with-other-species-than-human> to see the required format.
Elastic net regression models are controlled by two parameters,
  lambda, a measure of shrinkage, and alpha, a metric defining the model's
  location on the spectrum between ridge and lasso regression.  
  glmnet provides tools for selecting lambda via cross
  validation but no automated methods for selection of alpha.  Elastic Net
  SearcheR automates the simultaneous selection of both lambda and alpha.
  Developed, in part, with support by NICHD R03 HD094912.
Add simple runtime contracts to R values. These ensure that values
    fulfil certain conditions and will raise appropriate errors if they do not.
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>.
  'entropart' provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
  Estimation-bias corrections are available.
Contains methods for data analysis in entropic perspective. These entropic perspective methods are nonparametric, and perform better on non-ordinal data. Currently, the package has a function HeatMap() for visualizing distributional characteristics among multiple populations (groups).
Implements various estimators of entropy for discrete random 
  variables, including the shrinkage estimator by Hausser and Strimmer (2009),
  the maximum likelihood and the Millow-Madow estimator, various Bayesian
  estimators, and the Chao-Shen estimator.  It also offers an R interface to the
  NSB estimator.  Furthermore, the package provides functions for estimating the
  Kullback-Leibler divergence, the chi-squared divergence, mutual information,
  and the chi-squared divergence of independence.  It also computes the
  G statistic and the chi-squared statistic and corresponding p-values.
  Furthermore, there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) <doi:10.1051/ps/2012004> and next articles.
Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991) <https://www.jstor.org/stable/2937655>.
Small toolbox for data analyses in environmental chemistry and
    ecotoxicology. Provides, for example, calibration() to calculate calibration
    curves and corresponding limits of detection (LODs) and  limits of 
    quantification (LOQs) according to German DIN 32645 (2008). texture() makes
    it easy to estimate soil particle size distributions from hydrometer
    measurements (ASTM D422-63, 2007).
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
Prints out information about the R working environment
    (system, R version,loaded and attached packages and versions) from a single
    function "env_doc()".  Optionally adds information on git repository,
    tags, commits and remotes (if available).
A collection of functions for microbial ecology and other
   applications of genomics and metagenomics. Companion package for the
   Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016
   <DOI:10.7287/peerj.preprints.1900v1>).
Estimates an ecological niche using occurrence data, covariates, and kernel
        density-based estimation methods. For a single species with presence and absence data,
        the 'envi' package uses the spatial relative risk function that is estimated using the
        'sparr' package. Details about the 'sparr' package methods can be found in the tutorial:
        Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can
        be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about
        relative risk functions using kernel density estimation can be found in J. F. Bithell
        (1991) <doi:10.1002/sim.4780101112>.
Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.
Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing.
	Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) 
	<doi:10.1021/acs.analchem.5b00941>.
Provides a small set of functions for managing R environments, with defaults designed to encourage usage patterns that scale well to larger code bases. It provides: import_from(), a flexible way to assign bindings that defaults to the current environment; include(), a vectorized alternative to base::source() that also default to the current environment; and attach_eval() and attach_source(), a way to evaluate expressions in attached environments. Together, these (and other) functions pair to provide a robust alternative to base::library() and base::source().
Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.
It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" <doi:10.1016/j.landurbplan.2019.02.005>.
Set of functions to keep track and find objects in user-defined environments
    by identifying environments by name --which cannot be retrieved with the built-in function environmentName().
    The package also provides functionality to obtain simplified information about function calling chains
    and to get an object's memory address.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
Contains utilities for the analysis of protein sequences in a phylogenetic context. 
    Allows   the generation of phylogenetic trees base on protein sequences in an alignment-independent way. 
    Two different methods have been implemented. One approach is based on the frequency analysis of n-grams, 
    previously described in Stuart et al. (2002) <doi:10.1093/bioinformatics/18.1.100>. The other approach is based on the species-specific neighborhood preference around amino acids. Features include the conversion of a protein set into a vector 
    reflecting these neighborhood preferences, pairwise distances (dissimilarity) between these vectors, 
    and the generation of trees based on these distance matrices.
Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).
Runs a series of configurable tests against a user's compute 
  environment. This can be used for checking that things like a specific 
  directory or an environment variable is available before you start an analysis.
  Alternatively, you can use the package's situation report when filing error
  reports with your compute infrastructure.
Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, 
  <https://link.springer.com/book/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>.
Official 'eodhd' API R Library. It helps to get and work with financial data,
              historical data and etc. API reference available at <https://eodhd.com/financial-apis/>.
Provides wrap functions to export and import graphics and
             data frames in R to 'microsoft' office. And This package also 
             provide write out figures with lots of different formats.
             Since people may work on the platform without GUI support, the package 
             also provide function to easily write out figures
             to lots of different type of formats. Now this package provide function 
             to extract colors from all types of figures and pdf files.
This data management package provides some helper
    classes for publicly available data sources (HMD, DESTATIS) in
    Demography. Similar to ideas developed in the Bioconductor
    project <https://bioconductor.org> we strive to encapsulate data in easy
    to use S4 objects.
    If original data is provided in a text file, the resulting S4
    object contains all information from that text file. But the
    information is somehow structured (header, footer, etc).
    Further the classes provide methods to make a subset for selected
    calendar years or selected regions. The resulting subset
    objects still contain the original header and footer information.
A collection of nice plotting functions directly from a data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Enables simulation of water piping networks using 'EPANET'.
    The package provides functions from the 'EPANET' programmer's toolkit as R
    functions so that basic or customized simulations can be carried out from R.
    The package uses 'EPANET' version 2.2 from Open Water Analytics
    <https://github.com/OpenWaterAnalytics/EPANET/releases/tag/v2.2>.
Reads water network simulation data in 'Epanet' text-based
    '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'.
    Provides basic summary information and plots.  The README file has a 
    quick introduction. See <http://www2.epa.gov/water-research/epanet> 
    for more information on the Epanet software for modeling
    hydraulic and water quality behavior of water piping systems.
Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation.
The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.
EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuída - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. <https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-689/topico-639/NT_Metodologia_4MD_PDE_2032_VF.pdf>.
Tools to download and manipulate the Permanent Household Survey from Argentina
    (EPH is the Spanish acronym for Permanent Household Survey).
    e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets,
    calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology.
    organize_panels() is used to concatenate observations from different periods, and organize_labels()
    adds the official labels to the data. The implemented methods are based on INDEC (2016) <http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf>.
    As this package works with the argentinian Permanent Household Survey and its main audience is from this country,
    the documentation was written in Spanish.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data. In
  particular representation, manipulation, rate estimation and
  simulation for multistate data - the Lexis suite of functions, which
  includes interfaces to 'mstate', 'etm' and 'cmprsk' packages.
  Contains functions for Age-Period-Cohort and Lee-Carter modeling and
  a function for interval censored data and some useful functions for
  tabulation and plotting, as well as a number of epidemiological data
  sets.
Contains elementary tools for analysis of
        common epidemiological problems, ranging from sample size
        estimation, through 2x2 contingency table analysis and basic
        measures of agreement (kappa, sensitivity/specificity).
        Appropriate print and summary statements are also written to
        facilitate interpretation wherever possible.  Source code is 	commented throughout to facilitate modification.  The target audience includes advanced undergraduate and graduate students
        in epidemiology or biostatistics courses, and clinical researchers.
Method and tool for generating time series forecasts using
        an ensemble wavelet-based auto-regressive neural network architecture. This method provides
        additional support of exogenous variables and also generates confidence interval. This 
        package provides EWNet model for time series forecasting based on the algorithm by
        Panja, et al. (2022) and Panja, et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.
Offers a tidy solution for epidemiological data. It houses a range of functions for epidemiologists and public health data wizards for data management and cleaning.
A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.
Routines for epidemiological contact tracing
    and visualisation of network of contacts.
Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.
The Economic Policy Institute (<https://www.epi.org/>) provides
    researchers, media, and the public with easily accessible, up-to-date, and
    comprehensive historical data on the American labor force. It is compiled
    from Economic Policy Institute analysis of government data sources. Use
    it to research wages, inequality, and other economic indicators over time
    and among demographic groups. Data is usually updated monthly.
The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. <https://github.com/cmu-delphi/delphi-epidata>.
Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<https://medipe.psu.ac.th/epicalc/>'.
Contains utilities and functions for the cleaning, processing and
    management of patient level public health data for surveillance
    and analysis held by the UK Health Security Agency, UKHSA.
Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.
Tools to quantify transmissibility throughout
    an epidemic from the analysis of time series of incidence as described in
    Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) 
    <doi:10.1093/aje/kwh255>.
Analysis and visualization of plant disease progress curve data. Functions for
    fitting two-parameter population dynamics models (exponential, monomolecular, logistic
    and Gompertz) to proportion data for single or multiple epidemics using either linear
    or no-linear regression.  Statistical and visual outputs are provided to aid in model
    selection. Synthetic curves can be simulated for any of the models given the parameters.
    See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>
    for further information on the methods.
Provides functions and classes designed to handle and visualise
    epidemiological flows between locations. Also contains a statistical method
    for predicting disease spread from flow data initially described in
    Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>.
    This package is part of the RECON (<https://www.repidemicsconsortium.org/>)
    toolkit for outbreak analysis.
This is a collection of assorted functions and examples collected 
    from various projects. Currently we have functionalities for simplifying 
    overlapping time intervals, Charlson comorbidity score constructors for 
    Danish data, getting frequency for multiple variables, getting standardized
    output from logistic and log-linear regressions, sibling design linear 
    regression functionalities a method for calculating the confidence intervals 
    for functions of parameters from a GLM, Bayes equivalent for hypothesis 
    testing with asymptotic Bayes factor, and several help functions for 
    generalized random forest analysis using 'grf'.
The interface package to access data from the
    'EpiGraphDB' <https://epigraphdb.org> platform.
    It provides easy access to the 'EpiGraphDB' platform with functions that
    query the corresponding REST endpoints on the API <https://api.epigraphdb.org>
    and return the response data in the 'tibble' data frame format.
Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.
Using variational techniques we address some epidemiological
  problems as the incidence curve decomposition by inverting the renewal 
  equation as described in Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> 
  and Alvarez et al. (2022) <doi:10.3390/biology11040540> or the estimation of 
  the functional relationship between epidemiological indicators. We also 
  propose a learning method for the short time forecast of the trend 
  incidence curve  as described in 
  Morel et al. (2022) <doi:10.1101/2022.11.05.22281904>.
Contains tools for formatting inline code, renaming redundant
  columns, aggregating age categories, adding survey weights, finding the earliest
  date of an event, plotting z-curves, generating population counts and 
  calculating proportions with confidence intervals. This is part of the 
  'R4Epis' project <https://r4epis.netlify.app/>.
The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics. <doi:10.1002/sim.4780120902>.
Estimation of epidemiological parameters with 
 Laplacian-P-splines following the methodology of Gressani et al. (2022)
 <doi:10.1371/journal.pcbi.1010618>.
Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.
Functions, data sets and shiny apps for "Epidemics: Models and Data in R (2nd edition)" by Ottar N. Bjornstad (2022, ISBN: 978-3-031-12055-8) <https://link.springer.com/book/10.1007/978-3-319-97487-3>. The package contains functions to study the Susceptible-Exposed-Infected-Removed SEIR model, spatial and age-structured Susceptible-Infected-Removed SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the 'Coursera' Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.
Tools for simulating mathematical models of infectious disease dynamics.
    Epidemic model classes include deterministic compartmental models, stochastic
    individual-contact models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs)
    from the Statnet suite of software packages in R. Standard templates for epidemic
    modeling include SI, SIR, and SIS disease types. EpiModel features an API for
    extending these templates to address novel scientific research aims. Full
    methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
Allows for forward-in-time simulation of epistatic networks with associated
    phenotypic output.
Estimates the time-varying reproduction number,
    rate of spread, and doubling time using a range of open-source tools
    (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and
    current best practices (Gostic et al. (2020)
    <doi:10.1101/2020.06.18.20134858>).  It aims to help users avoid some
    of the limitations of naive implementations in a framework that is
    informed by community feedback and is actively supported.
A collection of fast and flexible functions for analyzing
    omics data in observational studies. Multiple different approaches for
    integrating multiple environmental/genetic factors, omics data, and/or
    phenotype data are implemented. This includes functions for performing
    omics wide association studies with one or more variables of interest
    as the exposure or outcome; a function for performing a meet in the
    middle analysis for linking exposures, omics, and outcomes (as
    described by Chadeau-Hyam et al., (2010)
    <doi:10.3109/1354750X.2010.533285>); and a function for performing a
    mixtures analysis across all omics features using quantile-based
    g-Computation (as described by Keil et al., (2019)
    <doi:10.1289/EHP5838>).
A toolbox to make it easy to analyze plant disease epidemics. It
    provides a common framework for plant disease intensity data recorded over
    time and/or space. Implemented statistical methods are currently mainly
    focused on spatial pattern analysis (e.g., aggregation indices, Taylor and
    binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See
    Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007)
    <doi:10.1094/9780890545058> for further information on these methods.
    Several data sets that were mainly published in plant disease epidemiology
    literature are also included in this package.
Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.
Drafting an epidemiological report in 'Microsoft Word' format for a given disease,
  similar to the Annual Epidemiological Reports published by the European Centre 
  for Disease Prevention and Control. Through standalone functions, it is specifically 
  designed to generate each disease specific output presented in these reports and includes:
  - Table with the distribution of cases by Member State over the last five years;
  - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Trend plot with the trend and number of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level.
  Two types of datasets can be used:
  - The default dataset of dengue 2015-2019 data;
  - Any dataset specified as described in the vignette.
Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). <doi:10.1038/ejhg.2010.196>. 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011).  <doi:10.1093/bioinformatics/btr218>.)
DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.
Basic sensitivity analysis of the observed relative risks adjusting
    for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Lash T.L, Fox M.P, and Fink A.K.
    "Applying Quantitative Bias Analysis to Epidemiologic Data",
    ('Springer', 2021).
Exploring time series for signal detection. It is specifically designed
    to detect possible outbreaks using infectious disease surveillance data
    at the European Union / European Economic Area or country level.
    Automatic detection tools used are presented in the paper
    "Monitoring count time series in R: aberration detection in public health surveillance",
    by Salmon (2016) <doi:10.18637/jss.v070.i10>.
    The package includes:
    - Signal Detection tool, an interactive 'shiny' application
      in which the user can import external data and perform basic signal detection analyses;
    - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs.
      This report can also be stratified by population characteristics (see 'Population' variable).
    This project was funded by the European Centre for Disease Prevention and Control.
Provides set of functions aimed at epidemiologists. 
 The package includes commands for measures of association and impact for case control studies and cohort studies. 
 It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis.
 The functions for cohort studies include the CS(), CSTable() and CSInter() commands. 
 The functions for case control studies include the CC(), CCTable() and CCInter() commands. 
 References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press.
 Woolf, B. 1955. On estimating the relation between blood group disease. 
 Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. 
 Newton Lower Falls, MA Epidemiology Resources.
 Gilles Desve & Peter Makary, 2007. 'CSTABLE Stata module to calculate summary table for cohort study' Statistical Software Components S456879, Boston College Department of Economics.
 Gilles Desve & Peter Makary, 2007. 'CCTABLE Stata module to calculate summary table for case-control study' Statistical Software Components S456878, Boston College Department of Economics.
Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.
Builds contingency tables that cross-tabulate multiple 
    categorical variables and also calculates various summary measures.
    Export to a variety of formats is supported, including: 'HTML', 
    'LaTeX', and 'Excel'.
Provides functions to test for gene x gene interactions in a
    bi-parental population of inbred lines. The data are fitted with the
    mixed linear model described in Rio et al. (2022) <doi:10.1101/2022.12.18.520958>, 
    that accounts for gene x gene interactions
    at both the fixed effect and variance levels. The package also
    provides graphical tools to display the gene x gene interaction trend at
    the mean level and the variance component analysis.
Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.
It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information is available in the 'epitweetr' peer-review publication (doi:10.2807/1560-7917.ES.2022.27.39.2200177).
A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations.
Collection of ancillary functions and utilities for Partial Linear Single Index Models for Environmental mixture analyses, which currently provides functions for scalar outcomes. The outputs of these functions include the single index function, single index coefficients, partial linear coefficients, mixture overall effect, exposure main and interaction effects, and differences of quartile effects. In the future, we will add functions for binary, ordinal, Poisson, survival, and longitudinal outcomes, as well as models for time-dependent exposures. See Wang et al (2020) <doi:10.1186/s12940-020-00644-4> for an overview.
A rich toolkit of using the whole building
    simulation program 'EnergyPlus'(<https://energyplus.net>), which
    enables programmatic navigation, modification of 'EnergyPlus' models
    and makes it less painful to do parametric simulations and analysis.
Evaluate a function over a data frame of expressions.
Facilitates the aggregation of species' geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) <doi:10.1111/2041-210X.13914>.
Package provides a set of tools for robust estimation and inference for probit model with endogenous covariates. The current version contains a robust two-step estimator. For technical details, see Naghi, Varadi and Zhelonkin (2022), <doi:10.1016/j.ecosta.2022.05.001>.
Implements the Enhanced Portfolio Optimization (EPO) method as 
    described in Pedersen, Babu and Levine (2021)
    <doi:10.2139/ssrn.3530390>.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
      AY + U + R = 0
  and EPoC G where
      Y = GU + E,
  the matrices R and E are so far treated as noise. For
  details see the manual page of 'lassoshooting' and the
  article Rebecka Jörnsten, Tobias Abenius, Teresia Kling, 
  Linnéa Schmidt, Erik Johansson, Torbjörn E M Nordling, 
  Bodil Nordlander, Chris Sander, Peter Gennemark, 
  Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander
  (2011) <doi:10.1038/msb.2011.17>.
Clinical coding and diagnosis of patients with kidney using
    clinical practice guidelines. The guidelines used are the evidence-based
    KDIGO guidelines, see <https://kdigo.org/guidelines/> for more information.
    This package covers acute kidney injury (AKI), anemia, and
    chronic kidney disease (CKD).
Provides a set of functions, which facilitates removing objects from an environment. 
             It allows to delete objects specified with regular expression or with other conditions (e.g. if object is numeric),
             using one function call.
Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>.
Extra strength 'glue' for data-driven templates. String
    interpolation for 'Shiny' apps or 'R Markdown' and 'knitr'-powered
    'Quarto' documents, built on the 'glue' and 'whisker' packages.
Performs analysis of polynomial regression in simple designs with quantitative treatments.
Implements the conditional estimation procedure of
  Lee, Sun, Sun and Taylor (2016) <doi:10.1214/15-AOS1371>.
  This procedure allows hypothesis testing on the mean of
  a normal random vector subject to linear constraints.
For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Kim et al. (2020), and Oh and Kim (2020). D. Kim, G. Choi, H.-S. Oh, Ensemble patch transformation: a flexible framework for decomposition and filtering of signal, EURASIP Journal on Advances in Signal Processing 30 (2020) 1-27 <doi:10.1186/s13634-020-00690-7>. H.-S. Oh, D. Kim, Image decomposition by bidimensional ensemble patch transform, Pattern Recognition Letters 135 (2020) 173-179 <doi:10.1016/j.patrec.2020.03.029>.
Provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    The 'epubr' package provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. 
    E-book formatting is not completely standardized across all literature. 
    It can be challenging to curate parsed e-book content across an arbitrary collection of e-books 
    perfectly and in completely general form, to yield a singular, consistently formatted output. 
    Many EPUB files do not even contain all the same pieces of information in their respective metadata. 
    EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. 
    However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. 
    There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with 'epubr'. 
    Text is read 'as is' for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. 
    Substantive changes are expected to be performed subsequently by the user as part of their text analysis. 
    Additional text cleaning can be performed at the user's discretion, such as with functions from packages like 'tm' or 'qdap'.
Query, download climate change projection data from the 'CMIP6' (Coupled
    Model Intercomparison Project Phase 6) project
    <https://pcmdi.llnl.gov/CMIP6/> in the 'ESGF' (Earth System Grid Federation)
    platform <https://esgf.llnl.gov>, and create future 'EnergyPlus'
    <https://energyplus.net> Weather ('EPW') files adjusted from climate changes
    using data from Global Climate Models ('GCM').
An ensemble method for the statistical detection of
    a rare class in two-class classification problems. The method uses an
    ensemble of classifiers where the constituent
    models of the ensemble use disjoint subsets (phalanxes) of explanatory
    variables. We provide an implementation of the phalanx-formation algorithm.
    Please see Tomal et al. (2015) <doi:10.1214/14-AOAS778>,
    Tomal et al. (2016) <doi:10.1021/acs.jcim.5b00663>, and
    Tomal et al. (2019) <arXiv:1706.06971> for more details.
Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
EQ-5D is a popular health related quality of life instrument used 
    in the clinical and economic evaluation of health care. Developed by the 
    EuroQol group <https://euroqol.org/>, the instrument consists of two 
    components: health state description and evaluation. For the description 
    component a subject self-rates their health in terms of five dimensions; 
    mobility, self-care, usual activities, pain/discomfort, and 
    anxiety/depression using either a three-level (EQ-5D-3L,
    <https://euroqol.org/information-and-support/euroqol-instruments/eq-5d-3l/>) or a five-level
    (EQ-5D-5L, <https://euroqol.org/information-and-support/euroqol-instruments/eq-5d-5l/>) 
    scale. Frequently the scores on these five dimensions are converted to a 
    single utility index using country specific value sets, which can be used
    in the clinical and economic evaluation of health care as well as in 
    population health surveys. The eq5d package provides methods to calculate 
    index scores from a subject's dimension scores. 30 TTO and 11 VAS EQ-5D-3L
    value sets including those for countries in Szende et al (2007) 
    <doi:10.1007/1-4020-5511-0> and Szende et al (2014) 
    <doi:10.1007/978-94-007-7596-1>, 40 EQ-5D-5L EQ-VT value sets, the 
    EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) 
    <doi:10.1016/j.jval.2012.02.008>, the crosswalk value set for Russia and 
    reverse crosswalk value sets. Nine EQ-5D-Y value sets are also included 
    as are the NICE 'DSU' age-sex based EQ-5D-3L to EQ-5D-5L and EQ-5D-5L to 
    EQ-5D-3L mappings. Methods are also included for the analysis of EQ-5D 
    profiles along with a shiny web tool to enable the calculation, 
    visualisation and automated statistical analysis of EQ-5D data via a web 
    browser using EQ-5D dimension scores stored in CSV or Excel files.
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials).
Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.
The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.
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 access the stability of the equating transformations
  (Battauz(2022) <doi:10.1111/stan.12277>).
Equating of multiple forms using Item Response Theory (IRT) methods (Battauz M. (2017) <doi:10.1007/s11336-016-9517-x> and Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>).
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>.
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>.
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.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
Fits the Expectile Regression for Fixed Effect (ERFE)
    estimator. The ERFE model extends the within-transformation strategy
    to solve the incidental parameter problem within the expectile
    regression framework. The ERFE model estimates the regressor effects
    on the expectiles of the response distribution.  The ERFE estimate
    corresponds to the classical fixed-effect within-estimator when the
    asymmetric point is 0.5. The paper by Barry, Oualkacha, and
    Charpentier (2021, <arXiv:2108.04737>) gives more details about the
    ERFE model.
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 (2021) <arXiv:2106.04997>.
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 (2021) <arXiv:2106.04997>.
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>.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Estimate the effective reproduction number from wastewater
    and clinical data sources.
Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).
A set of user-friendly functions to aid in organizing, plotting
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn
    method to explore ERP data. Should be useful to those without a background
    in computer programming, and to those who are new to ERPs (or new to the
    more advanced ERP software available). Emphasis has been placed on highly
    automated processes using functions with as few arguments as possible.
    Expects processed (cleaned) data.
Computes the probability density and cumulative distribution functions of fourteen distributions used for the probabilistic hazard assessment. Estimates the model parameters of the distributions using the maximum likelihood and reports the goodness-of-fit statistics. The recurrence interval estimations of earthquakes are computed for each distribution.
Messages should provide users with readable information 
    about R objects without flooding their console. 
    'cc()' concatenates vector and data frame values 
    into a grammatically correct string using commas, an ellipsis and conjunction. 
    'cn()' allows the user to define a string which varies based on a count.
    'co()' combines the two to produce a customizable object aware string.
    The package further facilitates this process by providing five 'sprintf'-like 
    types such as '%n' for the length of an object and '%o' for its name as
    well as wrappers for pasting objects and issuing errors, warnings and messages.
Builds and analyzes error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.
Errors in data can be located and removed using validation rules from package 
   'validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>,
   chapter 7.
Support for measurement errors in R vectors, matrices and arrays:
    automatic uncertainty propagation and reporting.
    Documentation about 'errors' is provided in the paper by Ucar, Pebesma &
    Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a
    vignette; see 'citation("errors")' for details.
Perform a Bayesian estimation of the exploratory reduced
    reparameterized unified model (ErRUM) described by Culpepper and Chen (2018)
    <doi:10.3102/1076998618791306>.
Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.
Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories.
    Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>.
    Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.
Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges' d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality).
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (<arXiv:1503.03515>).
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., (2021) <https://ideas.repec.org/p/pdn/ciepap/145.html>.
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/>).