These are miscellaneous functions for working with panel data, quantiles, and printing results.  For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance.  For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions.  Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.
Provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.
Easy estimation of Bayesian multilevel mediation models with Stan.
Reads Windows BMP format images. Currently limited to 8 bit
    greyscale images and 24,32 bit (A)RGB images. Pure R implementation without
    external dependencies.
The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.
Provides two Bayesian models for detecting the association between rare genetic variants and a trait that can be continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect and is dedicated to twin design while it can also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty information and can be applied to either independent or family samples. Furthermore, it deals with continuous, binary and ordinal traits.
Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.
Analyse single case analyses against a control group.
    Its purpose is to provide a flexible, with good power and
    low first type error
    approach that can manage at the same time controls' and patient's data.
    The use of Bayesian statistics allows to test both the alternative and
    null hypothesis.
    Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq>
    Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.
Fits, validates and compares a number of Bayesian models for
    spatial and space time point referenced and areal unit data. Model fitting
    is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer',
    'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using
    the DIC and WAIC,  and K-fold cross-validation where the user is free
    to select their own subset of data rows for validation. Sahu (2022)
    <doi:10.1201/9780429318443> describes the methods in detail.
Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) <arXiv:1709.05534>. Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included.
Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()),  identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) <doi:10.1080/03610926.2014.990758>.
State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.
Unsupervised Bayesian network-based clustering of multi-omics data. Both binary and continuous data types
    are allowed as inputs. The package serves a dual purpose: it clusters (patient) samples and learns the multi-omics networks that characterize discovered
    clusters. Prior network knowledge (e.g., public interaction databases) can be included via blacklisting and 
    penalization matrices. For clustering, the EM algorithm is employed. For structure search at the M-step,
    the Bayesian approach is used. The output includes membership assignments of samples, cluster-specific MAP networks, and posterior probabilities
    of all edges in the discovered networks. In addition to likelihood, AIC and BIC scores are returned. They can be used for choosing the number
    of clusters.
    References:
    P. Suter et al. (2021) <doi:10.1101/2021.12.16.473083>,
    J. Kuipers and P. Suter and G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>,
    J. Kuipers et al. (2018) <doi:10.1038/s41467-018-06867-x>.
Allows access to data on BNDES disbursements and contracts since 1995. The package makes it easy to import data from the bank into R.<https://www.bndes.gov.br/SiteBNDES/bndes/bndes_en>.
Functions to implement a Hwang(2021) <doi:10.2139/ssrn.3866876> estimator, which bounds an omitted variable bias using auxiliary data.
Bayesian network structure learning, parameter learning and inference.
  This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
  Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
  Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
  Gaussian and conditional Gaussian networks, along with many score functions and
  conditional independence tests.
  The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.
  Some utility functions (model comparison and manipulation, random data generation, arc
  orientation testing, simple and advanced plots) are included, as well as support for
  parameter estimation (maximum likelihood and Bayesian) and inference, conditional
  probability queries, cross-validation, bootstrap and model averaging.
  Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.
Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.
An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. References: H. Chan, A. Darwiche (2002) <doi:10.1613/jair.967>; C. Goergen, M. Leonelli (2020) <ArXiv:1809.10794>; M. Leonelli, R. Ramanathan, R.L. Wilkerson (2021) <ArXiv:2107.11785>.
Implements a bootstrap aggregated (bagged) version of
    the k-nearest neighbors survival probability prediction method (Lowsky et
    al. 2013). In addition to the bootstrapping of training samples, the
    features can be subsampled in each baselearner to break the correlation
    between them. The Rcpp package is used to speed up the computation.
This project aims to enable the method of Path Analysis to infer causalities 
             from data. For this we propose a hybrid approach, which uses Bayesian network 
             structure learning algorithms from data to create the input file for creation of a 
             PA model. The process is performed in a semi-automatic way by our intermediate 
             algorithm, allowing novice researchers to create and evaluate their own PA models
             from a data set. The references used for this project are: 
             Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. <doi:10.1017/S0269888910000275>. 
             Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>.
             Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. <doi:10.1201/b17065>. 
             Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.
Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.
Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.
The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions.  The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters.  The BN-PSD model can be used to yield complex population structures.  This simulation approach is now extended to subpopulations related by a tree.  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).
Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters.
From a given data frame, this package learns its Bayesian network structure based on a selected score.
MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) <doi: 10.1080/10618600.2020.1739534>, 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2022) <doi: 10.1002/sim.9376>, and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi: 10.1111/anzs.12273>.
Allows spatial implementation of Bayesian networks and mapping in geographical space. It makes maps of expected value (or most likely state) given known and unknown conditions, maps of uncertainty measured as coefficient of variation or Shannon index (entropy), maps of probability associated to any states of any node of the network. Some additional features are provided as well: parallel processing options, data discretization routines and function wrappers designed for users with minimal knowledge of the R language. Outputs can be exported to any common GIS format.
Bayesian Network Structure Learning from Data with Missing Values.
    The package implements the Silander-Myllymaki complete search,
    the Max-Min Parents-and-Children, the Hill-Climbing, the
    Max-Min Hill-climbing heuristic searches, and the Structural
    Expectation-Maximization algorithm. Available scoring functions are
    BDeu, AIC, BIC. The package also implements methods for generating and using
    bootstrap samples, imputed data, inference.
Bayesian networks provide an intuitive framework for probabilistic reasoning 
             and its graphical nature can be interpreted quite clearly. Graph based methods 
             of machine learning are becoming more popular because they offer a richer model 
             of knowledge that can be understood by a human in a graphical format. The 'bnviewer' 
             is an R Package that allows the interactive visualization of Bayesian Networks. 
             The aim of this package is to improve the Bayesian Networks visualization over 
             the basic and static views offered by existing packages.
A menu-driven program and library of functions for carrying out
    convergence diagnostics and statistical and graphical analysis of Markov
    chain Monte Carlo sampling output.
Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.
This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.
A gradient boosting-based algorithm by incorporating a diversity term to guide the gradient boosting iterations, see Bourel, Cugliari, Goude, Poggi (2021) <https://hal.archives-ouvertes.fr/hal-03041309/>.
A wrapper to allow users to download Bus Open Data Service 'BODS' transport information from the API (<https://www.bus-data.dft.gov.uk/>).
    This includes timetable and fare metadata (including links for full datasets), timetable data at line level,
    and real-time location data.
Skinfold measurements is one of the most popular and practical methods for estimating percent body fat. Body composition is a term that describes the relative proportions of fat, bone, and muscle mass in the human body. Following the collection of skinfold measurements, regression analysis (a statistical procedure used to predict a dependent variable based on one or more independent or predictor variables) is used to estimate total percent body fat in humans. <doi:10.4324/9780203868744>.
Generic Extraction of main text content from HTML files; removal
    of ads, sidebars and headers using the boilerpipe 
    <https://github.com/kohlschutter/boilerpipe> Java library. The
    extraction heuristics from boilerpipe show a robust performance for a wide
    range of web site templates.
The Bayesian optimal interval (BOIN) design is a novel phase I
    clinical trial design for finding the maximum tolerated dose (MTD). It can be
    used to design both single-agent and drug-combination trials. The BOIN design
    is motivated by the top priority and concern of clinicians when testing a new
    drug, which is to effectively treat patients and minimize the chance of exposing
    them to subtherapeutic or overly toxic doses. The prominent advantage of the
    BOIN design is that it achieves simplicity and superior performance at the same
    time. The BOIN design is algorithm-based and can be implemented in a simple
    way similar to the traditional 3+3 design. The BOIN design yields an average
    performance that is comparable to that of the continual reassessment method
    (CRM, one of the best model-based designs) in terms of selecting the MTD, but
    has a substantially lower risk of assigning patients to subtherapeutic or overly
    toxic doses. For tutorial, please check Yan et al. (2020) <doi:10.18637/jss.v094.i13>.
Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.
Provides an interface to Bank of Japan <https://www.boj.or.jp>
  statistics.
Implements the bolasso algorithm for consistent variable selection 
    and estimation accuracy. Includes support for many parallel backends via the 
    future package. For details see: Bach (2008),
    'Bolasso: model consistent Lasso estimation through the bootstrap', 
    <arXiv:0804.1302>.
A programmatic interface to the Web Service methods provided by
    Bold Systems (<http://www.boldsystems.org/>) for genetic 'barcode' data.
    Functions include methods for searching by sequences by taxonomic names,
    ids, collectors, and institutions; as well as a function for searching
    for specimens, and downloading trace files.
A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.
A set of R functions and data sets for the book "Understanding Computational Bayesian Statistics." This book was written by Bill (WM) Bolstad and published in 2009 by John Wiley & Sons (ISBN 978-0470046098).
Provides probability computation, data generation,
    and model estimation for fully-visible Boltzmann machines. It follows the methods
    described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> 
    and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.
Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return.
    CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577).
    Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132).
    Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143).
    Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211).
Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond.
Bone Profiler is a scientific method and a software used to model 
    bone section for paleontological and ecological studies. See Girondot and Laurin 
    (2003) <https://www.researchgate.net/publication/280021178_Bone_profiler_A_tool_to_quantify_model_and_statistically_compare_bone-section_compactness_profiles>
    and Gônet, Laurin and Girondot (2022) <https://palaeo-electronica.org/content/2022/3590-bone-section-compactness-model>.
An improved multiple testing procedure for controlling false discovery rates which is developed based on the Bonferroni procedure with integrated estimates from the Benjamini-Hochberg procedure and the Storey's q-value procedure. It controls false discovery rates through controlling the expected number of false discoveries.
Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.
Bindings for additional tree-based model engines for use with
    the 'parsnip' package. Models include gradient boosted decision trees
    with 'LightGBM' (Ke et al, 2017.) and
    conditional inference trees and conditional random forests with
    'partykit' (Hothorn and Zeileis, 2015. and
    Hothorn et al, 2006. <doi:10.1198/106186006X133933>).
Make some distributions from the 'C++' library 'Boost'
    available in 'R'. In addition, the normal-inverse Gaussian distribution
    and the generalized inverse Gaussian distribution are provided. The 
    distributions are represented by 'R6' classes. The method to sample from 
    the generalized inverse Gaussian distribution is the one given in 
    "Random variate generation for the generalized inverse Gaussian 
    distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.
Output formats and utilities for authoring books and technical documents with R Markdown.
A collection and selector of R 'bookdown' templates. 'bookdownplus' helps you write academic journal articles, guitar books, chemical equations, mails, calendars, and diaries. R 'bookdownplus' extends the features of 'bookdown', and simplifies the procedure. Users only have to choose a template, clarify the book title and author name, and then focus on writing the text. No need to struggle in 'YAML' and 'LaTeX'.
Tools for optimal and approximate state estimation as well as
    network inference of Partially-Observed Boolean Dynamical Systems.
Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.
A C++ library for Bayesian modeling, with an emphasis on Markov
   chain Monte Carlo.  Although boom contains a few R utilities (mainly plotting
   functions), its primary purpose is to install the BOOM C++ library on your
   system so that other packages can link against it.
Spike and slab regression with a variety of residual error
  distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
  few others.  Spike and slab regression is Bayesian regression with prior
  distributions containing a point mass at zero.  The posterior updates the
  amount of mass on this point, leading to a posterior distribution that is
  actually sparse, in the sense that if you sample from it many coefficients are
  actually zeros.  Sampling from this posterior distribution is an elegant way
  to handle Bayesian variable selection and model averaging.  See
  <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
Includes functions to estimate production frontiers 
    and make ideal output predictions in the Data Envelopment Analysis (DEA) 
    context using both standard models from DEA and Free Disposal Hull (FDH)
    and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 
    <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package 
    includes code for estimating several technical efficiency measures using 
    different models such as the input and output-oriented radial measures, the
    input and output-oriented Russell measures, the Directional Distance 
    Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based 
    Measure (SBM).
Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.
Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal.  A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.
Functions and datasets for bootstrapping from the
  book "Bootstrap Methods and Their Application" by A. C. Davison and 
  D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihoods, so installation of R package 'metafor' is required. This approach refers to the studies of Anscombe (1956) <doi:10.2307/2332926>, Haldane (1940) <doi:10.2307/2332614>, Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).
Computation of bootstrap p-values through inversion of confidence intervals, including convenience functions for regression models.
Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>.
Propagate uncertainty from several estimates when combining these estimates via a function.
    This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter.
    Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage.
    Gaussian copulas are used for when parameters are assumed to be dependent / correlated.
    References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.
The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - <doi:10.1002/jae.616>) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.
Calculate robust measures of effect sizes using the bootstrap.
Compare dissolution profiles with confidence interval of similarity
  factor f2 using bootstrap methodology as described in the literature, such as 
  Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997,
  ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>. 
  The package can also be used to simulate dissolution profiles based on 
  mathematical modelling and multivariate normal distribution.
Bootstrap based goodness-of-fit tests. It allows
    to perform rigorous statistical tests to check if a chosen
    model family is correct based on the marked empirical
    process. The implemented algorithms are described in
    (Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>)
    and can be applied
    to generalized linear models without any further implementation
    effort. As far as certain linearity conditions are fulfilled
    the resampling scheme are also applicable beyond generalized
    linear models. This is reflected in the software architecture
    which allows to reuse the resampling scheme by implementing
    only certain interfaces for models that are not supported
    natively by the package.
Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.
Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.
Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
Bootstrap methods to assess accuracy and stability of estimated network structures
              and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible 
              specification of any undirected network estimation procedure in R, and offers 
              default sets for various estimation routines.
Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', accepted.).
Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series.
We provide a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.
Model selection by bootstrapping the stepAIC() procedure.
Software (bootstrap, cross-validation, jackknife) and data
        for the book "An Introduction to the Bootstrap" by B. Efron and
        R. Tibshirani, 1993, Chapman and Hall. This package is
        primarily provided for projects already based on it, and for
        support of the book. New projects should preferentially use the
        recommended package "boot".
Finite Population bootstrap algorithms to estimate the variance
    of the Horvitz-Thompson estimator for single-stage sampling. 
    For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) <doi:10.1214/16-SS113>.
Identifies genome-related molecular traits with significant 
  evidence of genetic regulation and performs a bootstrap procedure to 
  correct estimated effect sizes for over-estimation present in cis-QTL
  mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 
  2018 <doi: 10.1093/nar/gky780>.
Implements fast, exact bootstrap Principal Component Analysis and
    Singular Value Decompositions for high dimensional data, as described in
    <doi:10.1080/01621459.2015.1062383> (see also <arXiv:1405.0922> ). For data matrices that are too large to operate
    on in memory, users can input objects with class 'ff' (see the 'ff'
    package), where the actual data is stored on disk. In response, this
    package will implement a block matrix algebra procedure for calculating the
    principal components (PCs) and bootstrap PCs. Depending on options set by
    the user, the 'parallel' package can be used to parallelize the calculation of
    the bootstrap PCs.
Set of functions to perform various bootstrap unit root tests for both individual time series
  (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see
  Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>,
  Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>,
  Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, 
  Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, 
  Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and 
  Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.
Provides significance tests for second-order stationarity
	for time series using bootstrap wavelet packet tests.
	Provides functionality to visualize the time series with
	the results of the hypothesis tests superimposed.
	The methodology is described in Cardinali, A and Nason, G P (2016)
	"Practical powerful wavelet packet tests for second-order stationarity."
	Applied and Computational Harmonic Analysis, 44, 558-585
	<doi:10.1016/j.acha.2016.06.006>.
Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings 
	using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> where the interest is in a 
	comparison of all possible elementary treatment contrasts. The package also provides an optional 
	method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Transforms focal observations' data, where different types of social interactions
  can be recorded by multiple observers, into asymmetric data matrices.
  Each cell in these matrices provides counts on the number of times
  a specific type of social interaction was initiated by the row subject and
  directed to the column subject.
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
Estimate population average treatment effects from a primary data source 
  with borrowing from supplemental sources. Causal estimation is done with either a 
  Bayesian linear model or with Bayesian additive regression trees (BART) to adjust 
  for confounding. Borrowing is done with multisource exchangeability models (MEMs). For 
  information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. 
  For information on MEMs, see Kaizer, Koopmeiners, & 
  Hobbs (2018) <doi:10.1093/biostatistics/kxx031>.
An all relevant feature selection wrapper algorithm.
 It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).
A novel feature selection algorithm for linear regression called 
    BOSO (Bilevel Optimization Selector Operator). The main contribution is the 
    use a bilevel optimization problem to select the variables in the training 
    problem that minimize the error in the validation set. Preprint available: 
    [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., 
    Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection 
    algorithm for linear regression with high-dimensional data." bioRxiv. 
    <doi:10.1101/2020.11.18.388579>]. 
    In order to run the vignette, it is recommended to install the 'bestsubset' package,
    using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset").
    If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset").
Classical Boson Sampling using the algorithm of
 Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for
 generating random unitary matrices, evaluation of matrix permanents (both
 real and complex) and evaluation of complex permanent minors.
Reads and plots phylogenetic placements.
Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset selection method, that performs best subset selection upon an orthogonalized basis of ordered predictors, with the computational effort of a single ordinary LS fit. This package provides a highly optimized implementation of BOSS and estimates a heuristic degrees of freedom for BOSS, which can be plugged into an information criterion (IC) such as AICc in order to select the subset from candidates. It provides various choices of IC, including AIC, BIC, AICc, Cp and GCV. It also implements the forward stepwise selection (FS) with no additional computational cost, where the subset of FS is selected via cross-validation (CV). CV is also an option for BOSS. For details see: Tian, Hurvich and Simonoff (2021), "On the Use of Information Criteria for Subset Selection in Least Squares Regression", <arXiv:1911.10191>.
Fork-safe, raw access to the 'Amazon Web Services' ('AWS') 'SDK' via the 'boto3' 'Python' module, and convenient helper functions to query the 'Simple Storage Service' ('S3') and 'Key Management Service' ('KMS'), partial support for 'IAM', the 'Systems Manager Parameter Store' and 'Secrets Manager'.
Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) <doi:10.2307/3545584>.
Computes uniform bounds on the distance between
  the cumulative distribution function of a standardized sum
  of random variables and its first-order Edgeworth expansion,
  following the article Derumigny, Girard, Guyonvarch (2021)
  <arXiv:2101.05780>.
Generate ground truth cases for object localization algorithms. 
    Cycle through a list of images, select points around which to generate bounding 
    boxes and assign classifiers. Output the coordinates, and images annotated with 
    boxes and labels. For an example study that uses bounding boxes for image 
    localization and classification see Ibrahim, Badr, Abdallah, and Eissa (2012)
    "Bounding Box Object Localization Based on Image Superpixelization"
    <doi:10.1016/j.procs.2012.09.119>.
A collection of R functions were implemented
        from published and available analytic solutions for the
        One-Dimensional Boussinesq Equation (ground-water). In
        particular, the function "beq.lin()" is the analytic solution of
        the linearized form of Boussinesq Equation between two
        different head-based boundary (Dirichlet) conditions;
        "beq.song" is the non-linear power-series analytic solution of
        the motion of a wetting front over a dry bedrock (Song at al,
        2007, see complete reference on function documentation).
        Bugs/comments/questions/collaboration of any kind are warmly
        welcomed.
Computational tools for outlier detection and influence diagnostics of meta-analysis. Bootstrap distributions of the influence statistics are calculated, and the thresholds to determine outliers are explicitly provided.
Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.
A modern module system for R. Organise code into hierarchical,
    composable, reusable modules, and use it effortlessly across projects via a
    flexible, declarative dependency loading syntax.
Noise filter based on determining the proportion of neighboring points. A false point will be rejected if it has only few neighbors, but accepted if the proportion of neighbors in a rectangular frame is high. The size of the rectangular frame as well as the cut-off value, i.e. of a minimum proportion of neighbor-points, may be supplied or can be calculated automatically. Originally designed for the cleaning of heart rates, but suitable for filtering any slowly-changing physiological variable.For more information see Signer (2010)<doi:10.1111/j.2041-210X.2009.00010.x>.
Interactive box plot using 'plotly' for clinical trial analysis.
Following Arroyo-Maté-Roque (2006), the function calculates the distance between rows or columns of the dataset using the generalized Minkowski metric as described by Ichino-Yaguchi (1994). The distance measure gives more weight to differences between quartiles than to differences between extremes, making it less sensitive to outliers. Further,the function calculates the silhouette width (Rousseeuw 1987) for different numbers of clusters and selects the number of clusters that maximizes the average silhouette width, unless a specific number of clusters is provided by the user. The approach implemented in this package is based on the following publications: Rousseeuw (1987) <doi:10.1016/0377-0427(87)90125-7>; Ichino-Yaguchi (1994) <doi:10.1109/21.286391>; Arroyo-Maté-Roque (2006) <doi:10.1007/3-540-34416-0_7>.
Correlation chart of two set (x and y) of data.  
  Using Quartiles with boxplot style. 
  Visualize the effect of factor.
An R interface for the remote file hosting service 'Box'
    (<https://www.box.com/>). In addition to uploading and downloading files,
    this package includes functions which mirror base R operations for local
    files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well
    as 'git' style functions for entire directories (e.g. box_fetch(),
    box_push()).
A comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.
Run basic pattern analyses on character sets, digits, or combined
    input containing both characters and numeric digits. Useful for data
    cleaning and for identifying columns containing multiple or nonstandard
    formats.
A comprehensive statistical analysis of the accuracy of blood
         pressure devices based on the method of AAMI/ANSI SP10 standards
         developed by the AAMI Sphygmomanometer Committee for indirect
         measurement of blood pressure, incorporated into IS0 81060-2. The 
         'bpAcc' package gives the exact probability 'of accepting a device D'
         derived from the join distribution of the sample standard deviation
         and a non-linear transformation of the sample mean for a specified
         sample size introduced by Chandel et al. (2022) and by the Association
         for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8).
Implementation of the nonparametric bounds for the average
    causal effect under an instrumental variable model by Balke and Pearl
    (Bounds on Treatment Effects from Studies with Imperfect Compliance,
    JASA, 1997, 92, 439, 1171-1176). The package can calculate bounds for
    a binary outcome, a binary treatment/phenotype, and an instrument with
    either 2 or 3 categories. The package implements bounds for situations
    where these 3 variables are measured in the same dataset (trivariate
    data) or where the outcome and instrument are measured in one study
    and the treatment/phenotype and instrument are measured in another
    study (bivariate data).
Implements biplot (2d and 3d) of multivariate data based
             on principal components analysis and diagnostic tools of the quality of the reduction.
Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <DOI:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.
Functions for drawing boxplots for data on (the boundary of) a unit circle (i.e., circular and axial data), from Buttarazzi D., Pandolfo G., Porzio G.C. (2018) <doi:10.1111/biom.12889>.
Model-based clustering using Bayesian parsimonious Gaussian mixture models.
  MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. 
  GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.
Bayesian purity model to estimate tumor purity using methylation 
    array data (DNA methylation Infinium 450K array data) without reference samples.
Creating, rendering and writing BPMN diagrams <https://www.bpmn.org/>. Functionalities can be used to visualize and export BPMN diagrams created using the 'pm4py' and 'bupaRminer' packages. Part of the 'bupaR' ecosystem.
To visualize the execution data of the processes on 'BPMN' (Business Process Model and Notation) diagrams, using overlays, style customization and interactions, with the 'bpmn-visualization' 'TypeScript' library.
Fitting Bayesian multiple and mixed-effect regression models for 
    circular data based on the projected normal distribution. Both continuous 
    and categorical predictors can be included. Sampling from the posterior is 
    performed via an MCMC algorithm. Posterior descriptives of all parameters, 
    model fit statistics and Bayes factors for hypothesis tests for inequality 
    constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) 
    <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) 
    <doi:10.1016/j.csda.2012.07.025>.
Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.
Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2022) <doi:10.1080/10618600.2022.2123337>.
Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models.
    Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>.
    Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>.
    Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>.
    Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>.
    Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.
QTL mapping toolkit for inbred crosses and recombinant
        inbred lines. Includes maximum likelihood and Bayesian tools.
Automated assessment of accuracy and geographical status of georeferenced biological data. The methods rely on reference regions, namely checklists and range maps. Includes functions to obtain data from the Global Biodiversity Information Facility <https://www.gbif.org/> and from the Global Inventory of Floras and Traits <https://gift.uni-goettingen.de/home>. Alternatively, the user can input their own data. Furthermore, provides easy visualisation of the data and the results through the plotting functions. Especially suited for large datasets. The reference for the methodology is: Arlé et al. (under review).
Adjusting the bias due to residual confounding (often called
    treatment selection bias) in estimating the treatment effect in a
    proportional hazard model, as described in Williamson et al.
    (2022) <doi:10.1158/1078-0432.ccr-21-2468>.
Performs brace expansions on strings.  Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string.  The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.
The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.
Provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.
Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
Forecasters predicting the chances of a future event may disagree due to
    differing evidence or noise. To harness the collective evidence of the crowd, 
    Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions"
    <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian
    aggregator that is regularized by analyzing the forecasters' disagreement and ascribing 
    over-dispersion to noise. This aggregator requires no user intervention and can be computed
    efficiently even for a large numbers of predictions. The author evaluates
    the aggregator on subjective probability predictions collected during
    a four-year forecasting tournament sponsored by the US intelligence community.
    The aggregator improves the accuracy of simple averaging by around 20% and
    other state-of-the-art aggregators by 10-25%. The advantage stems almost
    exclusively from improved calibration. This aggregator -- know as "the revealed
    aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event
    and b) the forecasters' common prior (p0) of the future event. In this R-package,
    the function sample_aggregator(p,p0,...) allows the user to calculate the revealed
    aggregator. Its use is illustrated with a simple example.
Provides functions to generate, format, and style surface plots for visualizing combined action data.  Also provides functions for reporting on a BRAID analysis, including plotting curve-shifts, calculating IAE values, and producing full BRAID analysis reports.
Contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Dose (BRAID) model of combined action.
Blind users do not have access to the graphical output from R
    without printing the content of graphics windows to an embosser of
    some kind. This is not as immediate as is required for efficient
    access to statistical output.  The functions here are created so that
    blind people can make even better use of R. This includes the text
    descriptions of graphs, convenience functions to replace the
    functionality offered in many GUI front ends, and experimental
    functionality for optimising graphical content to prepare it for
    embossing as tactile images.
A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using  the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.
A set of tools for performing graph theory analysis of brain MRI
    data. It works with data from a Freesurfer analysis (cortical thickness,
    volumes, local gyrification index, surface area), diffusion tensor
    tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from
    DPABI). It contains a graphical user interface for graph visualization and
    data exploration, along with several functions for generating useful
    figures.
It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.
This includes functions for creating 3D and 4D images using 
    'WebGL', 'rgl', and 'JavaScript' commands.  
    This package relies on the X toolkit ('XTK',
    <https://github.com/xtk/X#readme>).
Performs efficient and scalable glm best 
	subset selection using a novel implementation of a branch and bound algorithm.
	To speed up the model fitting process, a range of optimization
	methods are implemented in 'RcppArmadillo'. Parallel computation 
	is available using 'OpenMP'.
Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.
Interact with the 'Brandwatch' API <https://developers.brandwatch.com/docs>. 
  Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories.
  Also allows you to directly obtain mentions and aggregate data for a specified query or query group.
Tests the parallel regression assumption wit the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models generated with the function polr() from the package 'MASS'.
Test the robustness of a user's Qualitative Comparative Analysis
    solutions to randomness, using the bootstrapped assessment: baQCA(). This
    package also includes a function that provides recommendations for improving
    solutions to reach typical significance levels: brQCA(). Data included 
    come from McVeigh et al. (2014) <doi:10.1177/0003122414534065>.
Executes BASIC programs from the 1970s, for historical and
    educational purposes. This enables famous examples of early machine
    learning, artificial intelligence, natural language processing, cellular
    automata, and so on, to be run in their original form.
Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.
A compilation of functions to download and processing AWS data of INMET-Brazil, with the purpose of reference evapotranspiration (ETo) estimation. The package aims to make meteorological and agricultural data analysis more parsimonious.
Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects.
    Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames.
This is an implementation of design methods for binomial reliability demonstration tests (BRDTs) with failure count data. 
    The acceptance decision uncertainty of BRDT has been quantified and the impacts of the uncertainty on related reliability assurance activities such as reliability growth (RG) and warranty services (WS) are evaluated.
    This package is associated with the work from the published paper "Optimal Binomial Reliability Demonstration Tests Design under Acceptance Decision Uncertainty" by Suiyao Chen et al. (2020) <doi:10.1080/08982112.2020.1757703>.
A function to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models.
A simple set of wrapper functions for data.table::fread() that allows subsetting or
    filtering rows and selecting columns of table-formatted files too large for the available RAM.
    'b stands for 'big files'.
    bread makes heavy use of Unix commands like 'grep', 'sed', 'wc', 'awk' and 'cut'. They are available 
    by default in all Unix environments.
    For Windows, you need to install those commands externally in order to simulate a 
    Unix environment and make sure that the executables are in the Windows PATH variable.
    To my knowledge, the simplest ways are to install 'RTools', 'Git' or 'Cygwin'. If they have been 
    correctly installed (with the expected registry entries), they should be detected on loading
    the package and the correct directories will be added automatically to the PATH.
The goal of the package is to provide an easy-to-use method 
  for estimating degrees of relatedness (up to the second degree) 
  for extreme low-coverage data. The package also allows users to 
  quantify and visualise the level of confidence in the estimated 
  degrees of relatedness. A paper describing the method is available 
  at Rohrlach, A. B. et al (2023) <https://tinyurl.com/29t6gbbx>.
Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.
Model agnostic tool for decomposition of predictions from black boxes.
    Break Down Table shows contributions of every variable to a final prediction. 
    Break Down Plot presents variable contributions in a concise graphical way. 
    This package work for binary classifiers and general regression models.
A developing software suite for multiple change-point detection/estimation (data segmentation) in data sequences.
Implements the Cross-Entropy (CE) method, which is a model based stochastic optimization technique to estimate both the number and their corresponding locations of break-points in continuous and discrete measurements (Priyadarshana and Sofronov (2015), Priyadarshana and Sofronov (2012a), Priyadarshana and Sofronov (2012b)).
Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) <doi:10.1002/joc.3370060607>, Buishand, T. (1982) <doi:10.1016/0022-1694(82)90066-X>, Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) <doi:10.1016/j.atmosres.2020.104955>, Mann, H. B., Whitney, D. R. (1947) <doi:10.1214/aoms/1177730491>, Pettitt, A. N. (1979) <doi:10.2307/2346729>, Ruxton, G. D., jul (2006) <doi:10.1093/beheco/ark016>, Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) <doi:10.2151/jmsj1965.63.6_1157>.
Reads several formats of 13C data (IRIS/Wagner,
    BreathID) and CSV.  Creates artificial sample data for testing.  Fits
    Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'.
    Methods to fit breath test curves with Bayesian Stan methods are
    refactored to package 'breathteststan'. For a Shiny GUI, see package
    'dmenne/breathtestshiny' on github.
Stan-based curve-fitting function
  for use with package 'breathtestcore' by the same author.
  Stan functions are refactored here for easier testing.
A collection of functions to analyse, visualize and interpret wind data
         and to calculate the potential energy production of wind turbines.
Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.
Implements a templating framework for mixing text and R code
    for report generation. brew template syntax is similar to PHP, Ruby's
    erb module, Java Server Pages, and Python's psp module.
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).
Interface with the 'Brickset' API
    <https://brickset.com/article/52664/api-version-3-documentation> for
    getting data about LEGO sets. Data sets that
    can be used for teaching and learning without the need of a 'Brickset'
    account and API key are also included. Includes all LEGO since through
    the end of 2022.
An implementation of the bridge distribution with logit-link in
    R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate
    bridge distribution was derived as the distribution of the random intercept that
    'bridged' a marginal logistic regression and a conditional logistic regression.
    The conditional and marginal regression coefficients are a scalar multiple
    of each other. Such is not the case if the random intercept distribution was
    Gaussian.
Produce bridge hands, allowing parameters for hands to offer specific for bidding sequences.
BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells.
    This package provides a series of functions for performing quality check of your BRIC-seq data,
    calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.
Provides functions for estimating marginal likelihoods, Bayes
    factors, posterior model probabilities, and normalizing constants in general,
    via different versions of bridge sampling (Meng & Wong, 1996, 
    <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
    Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.
Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through 'OpenMP' <https://gcc.gnu.org/wiki/openmp>.
Implementation of the BRIk, FABRIk and FDEBRIk algorithms 
        to initialise k-means. These methods are intended for the 
        clustering of multivariate and functional data, respectively.
        They make use of the Modified Band Depth and bootstrap to 
        identify appropriate initial seeds for k-means, which are 
        proven to be better options than many techniques in the 
        literature. Torrente and Romo (2021) <doi:10.1007/s00357-020-09372-3>
        It makes use of the functions kma and kma.similarity, from the 
        archived package fdakma, by Alice Parodi et al.
An automated graphical exploratory data analysis (EDA) tool that introduces: 
  a.) wideplot graphics for exploring the structure of a dataset through a grid of variables 
      and graphic types. 
  b.) longplot graphics, which present the entire catalog of available graphics for representing 
      a particular variable using a grid of graphic types and variations on these types.
  c.) plotup function, which presents a particular graphic for a specific variable of a dataset. 
      The plotup() function also makes it possible to obtain the code used to generate the graphic, 
      meaning that the user can adjust its properties as needed.
  d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships
      between all pairs of variables of a certain(s) type(s) in a multivariate data set.
Functions to handle basic input output, these functions
    always read and write UTF-8 (8-bit Unicode Transformation Format)
    files and provide more explicit control over line endings.
An interface to the 'Briq' API <https://briq.github.io>. 'Briq' is
    a tool that aims to promote employee engagement by helping employees
    recognize and reward each other. Employees can praise and thank one another
    (for achieving a company goal, for example) by giving virtual credits (known
    as 'briqs' or 'bqs') that can be redeemed for various rewards. The 'Briq'
    API lets you create, read, update and delete users, user groups,
    transactions and messages. This package provides functions that simplify
    getting the users, user groups and transactions of your organization into R.
Fits bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest neighbor Gaussian processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.
Quantitative methods for benefit-risk analysis help to condense
    complex decisions into a univariate metric describing the overall benefit
    relative to risk.  One approach is to use the multi-criteria decision
    analysis framework (MCDA), as in Mussen, Salek, and Walker
    (2007) <doi:10.1002/pds.1435>.  Bayesian benefit-risk
    analysis incorporates uncertainty through posterior distributions which are
    inputs to the benefit-risk framework.  The brisk package provides functions
    to assist with Bayesian benefit-risk analyses, such as MCDA.
    Users input posterior samples, utility functions, weights, and the package
    outputs quantitative benefit-risk scores.  The posterior of the benefit-risk
    scores for each group can be compared.  Some plotting capabilities are also
    included.
A spatio-temporal exposure-hazard model for assessing biological
    risk and impact. The model is based on stochastic geometry for describing
    the landscape and the exposed individuals, a dispersal kernel for the
    dissemination of contaminants, a set of tools to handle spatio-temporal dataframe
    and ecotoxicological equations.
    Walker E, Leclerc M, Rey JF, Beaudouin R, Soubeyrand S, and Messean A, (2017),
    A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact,
    Risk Analysis, <doi:10.1111/risa.12941>.
    Leclerc M, Walker E, Messean A, Soubeyrand S (2018),
    Spatial exposure-hazard and landscape models for assessing the impact of GM crops on non-target organisms,
    Science of the Total Environment, 624, 470-479.
Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>.  It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.
Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068> to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) <https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>.
Fits novel models for the conditional relative risk, risk difference and odds ratio <doi:10.1080/01621459.2016.1192546>.
Fit Bayesian generalized (non-)linear multivariate multilevel models
    using 'Stan' for full Bayesian inference. A wide range of distributions
    and link functions are supported, allowing users to fit -- among others --
    linear, robust linear, count data, survival, response times, ordinal,
    zero-inflated, hurdle, and even self-defined mixture models all in a
    multilevel context. Further modeling options include both theory-driven and
    data-driven non-linear terms, auto-correlation structures, censoring and 
    truncation, meta-analytic standard errors, and quite a few more. 
    In addition, all parameters of the response distribution can be predicted 
    in order to perform distributional regression. Prior specifications are 
    flexible and explicitly encourage users to apply prior distributions that
    actually reflect their prior knowledge. Models can easily be evaluated and
    compared using several methods assessing posterior or prior predictions. 
    References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; 
    Bürkner (2018) <doi:10.32614/RJ-2018-017>;
    Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)
    <doi:10.18637/jss.v076.i01>.
Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).
Bayesian regularization for feed-forward neural networks.
Very large numbers in R.  Real numbers are held
        using their natural logarithms, plus a logical flag indicating
        sign.  Functionality for complex numbers is also provided.  The
        package includes a vignette that gives a step-by-step
        introduction to using S4 methods.
Approximates best-subset selection (L0) regression with
   an iteratively adaptive Ridge (L2) penalty for large-scale models.
   This package uses Cyclops for an efficient implementation and the
   iterative method is described in Kawaguchi et al (2020)
   <doi:10.1002/sim.8438> and Li et al (2021)
   <doi:10.1016/j.jspi.2020.12.001>.
Data on multiple individuals through time are often sampled at 
    times that differ between persons. Irregular observation times can severely 
    complicate the statistical analysis of the data. The broken stick model 
    approximates each subject’s trajectory by one or more connected line segments. 
    The times at which segments connect (breakpoints) are identical for all 
    subjects and under control of the user. A well-fitting broken stick model 
    effectively transforms individual measurements made at irregular times into 
    regular trajectories with common observation times. Specification of the 
    model requires three variables: time, measurement and subject. The 
    model is a special case of the linear mixed model, with time as a linear 
    B-spline and subject as the grouping factor. The main assumptions are: 
    subjects are exchangeable, trajectories between consecutive breakpoints are 
    straight, random effects follow a multivariate normal distribution, and 
    unobserved data are missing at random. The package contains functions for 
    fitting the broken stick model to data, for predicting curves in new data 
    and for plotting broken stick estimates. The package supports two 
    optimization methods, and includes options to structure the 
    variance-covariance matrix of the random effects. The analyst may use the 
    software to smooth growth curves by a series of connected straight lines, to 
    align irregularly observed curves to a common time grid, to create synthetic 
    curves at a user-specified set of breakpoints, to estimate the time-to-time 
    correlation matrix and to predict future observations. See 
    <doi:10.18637/jss.v106.i07> for additional documentation on background, 
    methodology and applications.
Miscellaneous R functions, including functions related to
    graphics (mostly for base graphics), permutation tests, running
    mean/median, and general utilities.
Summarizes key information about statistical
    objects in tidy tibbles. This makes it easy to report results, create
    plots and consistently work with large numbers of models at once.
    Broom provides three verbs that each provide different types of
    information about a model. tidy() summarizes information about model
    components such as coefficients of a regression. glance() reports
    information about an entire model, such as goodness of fit measures
    like AIC and BIC. augment() adds information about individual
    observations to a dataset, such as fitted values or influence
    measures.
Provides suite of functions to work with regression model
    'broom::tidy()' tibbles.  The suite includes functions to group
    regression model terms by variable, insert reference and header rows
    for categorical variables, add variable labels, and more.
Convert fitted objects from various R mixed-model packages
    into tidy data frames along the lines of the 'broom' package.
    The package provides three
    S3 generics for each model: tidy(), which summarizes a model's statistical findings such as
    coefficients of a regression; augment(), which adds columns to the original
    data such as predictions, residuals and cluster assignments; and glance(), which
    provides a one-row summary of model-level statistics.
A lossless compressed data format that uses a combination of the
    LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip)
    but offers more dense compression.
An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services 
    that provides modules to identify the conversion options for a file, to convert file to appropriate
    format, or to extract data from a file. See <http://browndog.ncsa.illinois.edu/> for more information.
Datasets with yearly (2000 to 2021) Brazilian population estimates from DataSUS/Brazilian Health Ministry, aggregated by state, municipality, sex, and age groups. The data in this package is manually downloaded from the DataSUS website (<http://tabnet.datasus.gov.br/cgi/deftohtm.exe?popsvs/cnv/popbr.def>) and converted to tibbles.
Bayesian estimation and variable selection for quantile regression models.
Implementation of the Bayesian inference for the two independent Poisson samples model, using the semi-conjugate family of prior distributions.
Provides the facility to calculate the Brainerd-Robinson similarity coefficient for the rows of an input table, and to calculate the significance of each coefficient based on a permutation approach; optionally, hierarchical agglomerative clustering can be performed and the silhouette method is used to identify an optimal number of clusters.
Broadly useful convenient and efficient R functions
    that bring users concise and elegant R data analyses.
    This package includes easy-to-use functions for
    (1) basic R programming
    (e.g., set working directory to the path of currently opened file;
    import/export data from/to files in any format;
    print tables to Microsoft Word);
    (2) multivariate computation
    (e.g., compute scale sums/means/... with reverse scoring);
    (3) reliability analyses and factor analyses;
    (4) descriptive statistics and correlation analyses;
    (5) t-test, multi-factor analysis of variance (ANOVA),
    simple-effect analysis, and post-hoc multiple comparison;
    (6) tidy report of statistical models
    (to R Console and Microsoft Word);
    (7) mediation and moderation analyses (PROCESS);
    and (8) additional toolbox for statistics and graphics.
Fully-interactive R interface to the 'OpenBUGS' software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on x86Linux and on 64-bit R under Windows are in "beta" status and less efficient.
Provides high-level modeling functions to define and train
    models using the 'torch' R package. Models include linear, logistic,
    and multinomial regression as well as multilayer perceptrons.
Inter-sample condition variability is a key challenge of normalising ChIP-seq data. This implementation uses either spike-in or a second factor as a control for normalisation. Input can either be from 'DiffBind' or a matrix formatted for 'DESeq2'. The output is either a 'DiffBind' object or the default 'DESeq2' output. Either can then be processed as normal. Supporting manuscript Guertin, Markowetz and Holding (2017) <doi:10.1101/182261>.
Provides the functions for Brunner-Munzel test and
    permuted Brunner-Munzel test,
    which enable to use formula, matrix, and table as argument.
    These functions are based on Brunner and Munzel (2000)
     <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>
    and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>,
    and are written with FORTRAN.
Provide real-time access to data from the Regional Securities Exchange SA, commonly known as the 'BRVM' stock exchange. The goal is to facilitate data access for users of the R programming language. The package includes a variety of data that can be accessed by calling functions.
When samples contain missing data, are small, or are suspected of bias,
    estimation of scale reliability may not be trustworthy.  A recommended solution
    for this common problem has been Bayesian model estimation.  Bayesian methods
    rely on user specified information from historical data or researcher intuition 
    to more accurately estimate the parameters.  This package provides a user friendly
    interface for estimating test reliability.  Here, reliability is modeled as a beta
    distributed random variable with shape parameters alpha=true score variance and
    beta=error variance (Tanzer & Harlow, 2020) <doi:10.1080/00273171.2020.1854082>.
Allows the user to generate bootstrap cards within
    R markdown documents. Intended for use in conjunction with
    R markdown HTML outputs and other formats that support the 
    bootstrap 4 library.
Make 'Bootstrap 4' Shiny dashboards. Use the full power
    of 'AdminLTE3', a dashboard template built on top of 'Bootstrap 4' 
    <https://github.com/ColorlibHQ/AdminLTE>.
Collection of functions, data sets and code examples 
 for evaluations of field trials with the objective of equivalence assessment.
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location 
    filtering, location filtering and behavioural state estimation, and their hierarchical versions. 
    The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit 
    to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. 
    Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged 
    to explore tools available in packages such as 'coda' and 'boa'.
Contains functions to perform Bayesian inference
    using a spectral analysis of Gaussian process priors.
    Gaussian processes are represented with a Fourier series 
    based on cosine basis functions. Currently the package
    includes parametric linear models, partial linear additive
    models with/without shape restrictions, generalized linear
    additive models with/without shape restrictions, and  
    density estimation model. To maximize computational 
    efficiency, the actual Markov chain Monte Carlo sampling 
    for each model is done using codes written in FORTRAN 90.
    This software has been developed using funding supported by
    Basic Science Research Program through the National Research
    Foundation of Korea (NRF) funded by the Ministry of Education
    (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).
An implementation of the Bayesian Spatial Bradley--Terry (BSBT) model. It can be used to investigate data sets where judges compared different spatial areas. It constructs a network to describe how the areas are connected, and then places a correlated prior distribution on the quality parameter for each area, based on the network. The package includes MCMC algorithms to estimate the quality parameters. The methodology is published in Seymour et. al. (2020) <arXiv:2010.14128>.
Data sets for book "Basic Statistics and Data Analysis" by
    Larry J. Kitchens.
Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
The function estimates the hazard function non parametrically 
	     from a survival object (possibly adjusted for covariates). 
	     The smoothed estimate is based on B-splines from the perspective 
	     of generalized linear mixed models. Left truncated 
	     and right censoring data are allowed.
Easily use 'Bootstrap' icons inside 'Shiny' apps and 'R Markdown' documents. More generally, icons can be inserted in any 'htmltools' document through inline 'SVG'.
A highly scientific and utterly addictive 
  bird point count simulator 
  to test statistical assumptions, aid survey design,
  and have fun while doing it.
  The simulations follow time-removal and distance sampling models 
  based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>,
  Solymos et al. (2013) <doi:10.1111/2041-210X.12106>,
  and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>,
  and sound attenuation experiments by 
  Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.
Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>)
    is an alternative to standard, non-parametric approximate Bayesian 
	computation (ABC). BSL assumes a multivariate normal distribution 
	for the summary statistic likelihood and it is suitable when the 
	distribution of the model summary statistics is sufficiently regular. 
	This package provides a Metropolis Hastings Markov chain Monte Carlo 
	implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two 
	shrinkage estimators (graphical lasso and Warton's estimator).
	uBSL (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses 
	an unbiased estimator to the normal density. A semi-parametric version 
	of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust 
	to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 
	<arXiv:1904.04551>) estimates the Gaussian synthetic likelihood whilst 
	acknowledging that there may be incompatibility between the model and the 
	observed summary statistic. Shrinkage estimation can help to decrease the
	number of model simulations when the dimension of the summary statistic is 
	high (e.g., BSLasso, An et al. (2019) <doi:10.1080/10618600.2018.1537928>). 
	Extensions to this package are planned. For a journal article describing how
	to use this package, see An et al. (2022) <doi:10.18637/jss.v101.i11>.
Simplifies custom 'CSS' styling of both 'shiny' and
    'rmarkdown' via 'Bootstrap' 'Sass'. Supports 'Bootstrap' 3, 4 and 5 as
    well as their various 'Bootswatch' themes. An interactive widget is
    also provided for previewing themes in real time.
Bayes screening and model discrimination follow-up designs.
Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) <arXiv:2205.15263> for technical details. Source code and more data sets are at <https://github.com/profyliu/bsnsing/>.
The purpose of this package is to fit the three Spatial Econometric Models proposed
  in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. The fit is made through MCMC algorithms
  and observational working variables approach.
Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu et al. (2022+) "Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process" <doi:10.1080/01621459.2022.2123336>.
Bayesian inference on the (discrete) power spectrum of time series.
Build and use B-splines for interpolation and regression.
  In case of regression, equality constraints as well as monotonicity
  and/or positivity of B-spline weights can be imposed. Moreover, 
  knot positions (not only spline weights) can be part of 
  optimized parameters too. For this end, 'bspline' is able to calculate
  Jacobian of basis vectors as function of knot positions. User is provided with 
  functions calculating spline values at arbitrary points. These 
  functions can be differentiated and integrated to obtain B-splines calculating 
  derivatives/integrals at any point. B-splines of this package can 
  simultaneously operate on a series of curves sharing the same set of 
  knots. 'bspline' is written with concern about computing 
  performance that's why the basis and Jacobian calculation is implemented in C++.
  The rest is implemented in R but without notable impact on computing speed.
Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series.  The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.
The Bootstrap framework lets you add some JavaScript functionality to your web site by
  adding attributes to your HTML tags - Bootstrap takes care of the JavaScript
  <https://getbootstrap.com/docs/3.3/javascript/>. If you are using R Markdown or Shiny, you can
  use these functions to create collapsible sections, accordion panels, modals, tooltips,
  popovers, and an accordion sidebar framework (not described at Bootstrap site).
  Please note this package was designed for Bootstrap 3.3.
Enables binary package installations on Linux distributions.
    Provides functions to manage packages via the distribution's package
    manager. Also provides transparent integration with R's install.packages()
    and a fallback mechanism. When installed as a system package, interacts
    with the system's package manager without requiring administrative
    privileges via an integrated D-Bus service; otherwise, uses sudo.
    Currently, the following backends are supported: DNF, APT, ALPM.
The main functions carry out Gibbs' sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis.
Fit Bayesian models with a focus on the spatial econometric models.
Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in 
    Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes
    to data, and functions to estimate the stochastic volatility process of a BSS process.
Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.
Efficient methods for Bayesian inference of state space models 
    via Markov chain Monte Carlo (MCMC) based on parallel 
    importance sampling type weighted estimators 
    (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), 
    particle MCMC, and its delayed acceptance version. 
    Gaussian, Poisson, binomial, negative binomial, and Gamma
    observation densities and basic stochastic volatility models 
    with linear-Gaussian state dynamics, as well as general non-linear Gaussian 
    models and discretised diffusion models are supported. 
    See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Also, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution. Additionally, we incorporate the EM algorithm based on the assumption that the error term follows a finite mixture of Sinh-normal distributions.
Provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. 'BSSoverSpace' is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.
Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods.
Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.
Functions are pre-configured to utilize Bootstrap 5 classes and 
  HTML structures to create Bootstrap-styled HTML quickly and easily. Includes 
  functions for creating common Bootstrap elements such as containers, rows, cols,
  navbars, etc. Intended to be used with the html5 package. Learn more at 
  <https://getbootstrap.com/>.
Perform record linkage on streaming files using recursive Bayesian updating.
Time series regression using dynamic linear models fit using
  MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many
  other sources.
It submits R code/R scripts/shell commands to 'LSF cluster' 
  (<https://en.wikipedia.org/wiki/Platform_LSF>, the 'bsub' system) without 
  leaving R. There is also an interactive 'shiny' app for monitoring the job status.
Efficient algorithms for Bayesian estimation of Structural Vector Autoregressive (SVAR) models via Markov chain Monte Carlo methods. A wide range of SVAR models is considered, including homo- and heteroskedastic specifications and those with non-normal structural shocks. The heteroskedastic SVAR model setup is similar as in Woźniak & Droumaguet (2015) <doi:10.13140/RG.2.2.19492.55687> and Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>. The sampler of the structural matrix follows Waggoner & Zha (2003) <doi:10.1016/S0165-1889(02)00168-9>, whereas that for autoregressive parameters follows Chan, Koop, Yu (2022) <https://www.joshuachan.org/papers/OISV.pdf>. The specification of Markov switching heteroskedasticity is inspired by Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>, and that of Stochastic Volatility model by Kastner & Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002>.
Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.
Performs (Adaptive) Boosting Trees for Poisson distributed response variables, using log-link function.
  The code approach is similar to the one used in 'gbm'/'gbm3'. Moreover, each tree in the expansion is built thanks to the 'rpart' package.
  This package is based on following books and articles
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25820-7>
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-57556-4>
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25827-6>
  Denuit, M., Hainaut, D., Trufin, J. (2022) <doi:10.1080/03461238.2022.2037016>
  Denuit, M., Huyghe, J., Trufin, J. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A244325/datastream/PDF_01/view>
  Denuit, M., Trufin, J., Verdebout, T. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A268577>.
The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. 
        The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid.
        The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid.
        The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid.
        The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid.
        Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, 
        Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.
We apply Bradley-Terry Model to estimate teams' ability in paired comparison data. Exponential Decayed Log-likelihood function is applied for dynamic approximation of current rankings and Lasso penalty is applied for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>. If the suggested speedglm cannot be found on CRAN, you can find an old version in the GitHub mirror of CRAN at <https://github.com/cran/speedglm>.
Performs 'BTLLasso' as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.
Biterm Topic Models find topics in collections of short texts. 
    It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms.
    This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis 
    which are word-document co-occurrence topic models.
    A biterm consists of two words co-occurring in the same short text window.  
    This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. 
    The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.
Provides advanced Bayesian methods to estimate
	     abundance and run-timing from temporally-stratified
	     Petersen mark-recapture experiments. Methods include
	     hierarchical modelling of the capture probabilities
  	     and spline smoothing of the daily run size. Theory
  	     described in Bonner and Schwarz (2011)
         <doi:10.1111/j.1541-0420.2011.01599.x>.
Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in 'FORTRAN', which translates into very fast algorithms. The main references are 
   Bayer et al. (2017)  <doi:10.1016/j.jhydrol.2017.10.006>, 
   Pumi et al. (2019) <doi:10.1016/j.jspi.2018.10.001>, 
   Pumi et al. (2021) <doi:10.1111/sjos.12439> and 
	 Pumi et al. (2022) <arXiv:2211.02097>.
Functions for data preparation, parameter estimation, scoring, and plotting for the 
    BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), 
    BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and 
    Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.
Provides advanced statistical methods to describe and predict customers'
  purchase behavior in a non-contractual setting. It uses historic transaction records to fit a
  probabilistic model, which then allows to compute quantities of managerial interest on a cohort-
  as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This
  package complements the BTYD package by providing several additional buy-till-you-die models, that
  have been published in the marketing literature, but whose implementation are complex and non-trivial.
  These models are: NBD [Ehrenberg (1959) <doi:10.2307/2985810>], MBG/NBD [Batislam et al (2007) 
  <doi:10.1016/j.ijresmar.2006.12.005>], (M)BG/CNBD-k [Reutterer et al (2020) 
  <doi:10.1016/j.ijresmar.2020.09.002>], Pareto/NBD (HB) [Abe (2009) <doi:10.1287/mksc.1090.0502>] 
  and Pareto/GGG [Platzer and Reutterer (2016) <doi:10.1287/mksc.2015.0963>].
Plotting package based on the grid system, combining elements of 
    a bubble plot and heatmap to conveniently display two numerical variables, 
    (represented by color and size) grouped by categorical variables on the x 
    and y axes. This is a useful alternative to a forest plot when the data can 
    be grouped in two dimensions, such as predictors x outcomes. It has 
    particular advantages for visualising the metabolic measures produced by 
    the 'Nightingale Health' metabolomics platform, and templates are included 
    for automatically generating figures from these datasets.
Creates bubbles within 'shiny' and 'rmarkdown' backgrounds using the 'bubbly-bg' 'JavaScript' library.
Provides functions for various statistical techniques commonly used in the social sciences, including functions to compute clustered robust standard errors, combine results across multiply-imputed data sets, and simplify the addition of robust and clustered robust standard errors.
Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and
    uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).
Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) <DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber (2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al. (1998) <DOI:10.1109/5.726791> for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data.
Finds the largest possible regression model that will still converge
    for various types of regression analyses (including mixed models and generalized
    additive models) and then optionally performs stepwise elimination similar to the
    forward and backward effect-selection methods in SAS, based on the change in
    log-likelihood or its significance, Akaike's Information Criterion, the Bayesian
    Information Criterion, the explained deviance, or the F-test of the change in R².
Working with reproducible reports or any other
    similar projects often require to run the script that builds the
    output file in a specified way. 'buildr' can help you organize, modify
    and comfortably run those scripts. The package provides a set of
    functions that interactively guides you through the process and that
    are available as 'RStudio' Addin, meaning you can set up the keyboard
    shortcuts, enabling you to choose and run the desired build script
    with one keystroke anywhere anytime.
A build system based on 'GNU make' that creates and
    maintains (simply) make files in an R session and provides
    GUI debugging support through 'Microsoft Visual Code'.
Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data. See Wang and Wang (2010) <doi:10.2202/1544-6115.1550>.
Given an expression matrix from a bulk sequencing experiment,
        pre-processes it and creates a shiny app for interactive data 
        analysis and visualisation. The app contains quality checks,
        differential expression analysis, volcano and cross plots,
        enrichment analysis and gene regulatory network inference,
        and can be customised to contain more panels by the user.
Provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step
    of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994) <doi:10.2307/2986119>. Bayesian changepoint detection will simply be an option
    in the function from the package 'bulletxtrctr' which identifies the groove locations.
Analyze bullet lands using nonparametric methods. We provide a
    reading routine for x3p files (see <http://www.openfmc.org> for more
    information) and a host of analysis functions designed to assess the
    probability that two bullets were fired from the same gun barrel.
Stock, Options and Futures Trading Strategies for Traders and Investors with Bullish Outlook are 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).
The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) <arXiv:1009.3977>.
Bumblebee colonies grow during worker production, then decline after switching to production of reproductive individuals (drones and gynes).  This package provides tools for modeling and visualizing this pattern by identifying a switchpoint with a growth rate before and a decline rate after the switchpoint. The mathematical models fit by bumbl are described in Crone and Williams (2016) <doi:10.1111/ele.12581>.
A simple tool to quantify the amount of transmission
   of an infectious disease of interest occurring within and between 
   population groups. 'bumblebee' uses counts of observed directed 
   transmission pairs, identified phylogenetically from deep-sequence data or 
   from epidemiological contacts, to quantify transmission flows within and 
   between population groups accounting for sampling heterogeneity. Population 
   groups might include: geographical areas (e.g. communities, regions), 
   demographic groups (e.g. age, gender) or arms of a randomized clinical 
   trial. See the 'bumblebee' website for statistical theory, documentation 
   and examples <https://magosil86.github.io/bumblebee/>.
Implementation of the bunching estimator for kinks and notches. 
        Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). 
        It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.
View and analyze data where bunching is expected. Estimate counter-
    factual distributions. For earnings data, estimate the compensated
    elasticity of earnings w.r.t. the net-of-tax rate.
Download data from the time-series
  databases of the Bundesbank, the German central
  bank. See the overview at the Bundesbank website
  (<https://www.bundesbank.de/en/statistics/time-series-databases>)
  for available series. The package provides only a
  single function, getSeries(), which supports both
  traditional and real-time datasets; it will also
  download meta data if available. Downloaded data
  can automatically be arranged in various formats,
  such as data frames or 'zoo' series. The data
  may optionally be cached, so as to avoid repeated
  downloads of the same series.
All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016.
Typically, models in 'R' exist in memory and can be saved via
    regular 'R' serialization. However, some models store information in 
    locations that cannot be saved using 'R' serialization alone. The goal of 
    'bundle' is to provide a common interface to capture this information, 
    situate it within a portable object, and restore it for use in new settings.
Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of 'Petri Net' objects and visualization of process maps. See also packages 'edeaR','processmapR', 'eventdataR' and 'processmonitR'.
The 'bupaverse' is an open-source, integrated suite of R-packages for handling and analysing business process data,
    developed by the Business Informatics research group at Hasselt University, Belgium. Profoundly inspired by the 'tidyverse' package,
    the 'bupaverse' package is designed to facilitate the installation and loading of multiple 'bupaverse' packages in a single step.
    Learn more about 'bupaverse' at the <https://bupar.net> homepage.
Tools to read, write, parse, and analyze forest fire history data (e.g. FHX). Described in Malevich et al. (2018) <doi:10.1016/j.dendro.2018.02.005>.
A suite of functions for finance, including the estimation
	of variance matrices via a statistical factor model or
	Ledoit-Wolf shrinkage.
Script search, corner, genetic optimization, permutation tests, write expect test.
An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003) <doi:10.1023/A:1024940629314>).  Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.
Get a current financial year, start of current
    month, End of current month, start of financial year and end of it.
    Allow for offset from the date.
Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours.
Provides a set of S3 generics to axe components of fitted
    model objects and help reduce the size of model objects saved to disk.
Trading of Butterfly 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).
Implementation of the Generalized Pairwise Comparisons (GPC)
             as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations,
             and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring.        
             GPC compare two groups of observations (intervention vs. control group)
			 regarding several prioritized endpoints to estimate the probability that a random observation drawn from
			 one group performs better than a random observation drawn from the other group (Mann-Whitney parameter).
			 The net benefit and win ratio statistics,
			 i.e. the difference and ratio between the probabilities relative to the intervention and control groups,
			 can then also be estimated. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>),
			 non-parametric bootstrap, or permutations.
			 The software enables the use of thresholds of minimal importance difference,
			 stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.
Calculates B-value and empirical equivalence bound. B-value is defined as the maximum magnitude of a confidence interval; and the empirical equivalence bound is the minimum B-value at a certain level. A new two-stage procedure for hypothesis testing is proposed, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using the introduced empirical equivalence bound. See Zhao et al. (2019) "B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing" <arXiv:1912.13084> for details.
Estimation of hierarchical Bayesian vector autoregressive models
    following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>.
    Implements hierarchical prior selection for conjugate priors in the fashion
    of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>.
    Functions to compute and identify impulse responses, calculate forecasts,
    forecast error variance decompositions and scenarios are available.
    Several methods to print, plot and summarise results facilitate analysis.
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Functions to prepare tidy objects from estimated models via 'BVAR'
    (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and
    visualisation thereof. Bridges the gap between estimating models with 'BVAR'
    and plotting the results in a more sophisticated way with 'ggplot2' as well
    as passing them on in a tidy format.
An R interface to the Stark-Parker implementation of an
        algorithm for bounded-variable least squares
Implements the EM algorithm with one-step Gradient Descent method to estimate
             the parameters of the Block-Basu bivariate Pareto distribution with location
             and scale. We also found parametric bootstrap and asymptotic confidence 
             intervals based on the observed Fisher information of scale and shape parameters,
             and exact confidence intervals for location parameters. Details are in 
             Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199>
             "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto
             distribution with location and scale"; 
             E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation";
             Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> 
             "An Introduction to the Bootstrap"; 
             A P Dempster, N M Laird and D B Rubin
             (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete
             Data via the EM Algorithm".
Functions that solve boundary value problems ('BVP') of systems of ordinary
             differential equations ('ODE') and differential algebraic equations ('DAE').
             The functions provide an interface to the FORTRAN functions
             'twpbvpC', 'colnew/colsys', and an R-implementation of the shooting method.
             Mazzia, F., J.R. Cash and K. Soetaert, 2014.
Variable/Feature selection in high or ultra-high dimensional
    settings has gained a lot of attention recently specially in cancer genomic
    studies. This package provides a Bayesian approach to tackle this problem,
    where it exploits mixture of point masses at zero and nonlocal priors to
    improve the performance of variable selection and coefficient estimation.
    product moment (pMOM) and product inverse moment (piMOM) nonlocal priors
    are implemented and can be used for the analyses. This package performs
    variable selection for binary response and survival time response datasets
    which are widely used in biostatistic and bioinformatics community.
    Benefiting from parallel computing ability, it reports necessary outcomes
    of Bayesian variable selection such as Highest Posterior Probability Model
    (HPPM), Median Probability Model (MPM) and posterior inclusion probability
    for each of the covariates in the model. The option to use Bayesian Model
    Averaging (BMA) is also part of this package that can be exploited for
    predictive power measurements in real datasets.
Implements a backward procedure for single and multiple change point detection proposed by Shin et al. <arXiv:1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.
Whole-genome regression methods on Bayesian framework fitted via EM
    or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>),
    with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).
Package for Breed Wheat Genomic Selection Pipeline. 
    The R package 'BWGS' is developed by Louis Gautier Tran <louis.gautier.tran@gmail.com> and Gilles Charmet <gilles.charmet@inra.fr>.
    This repository is forked from original repository <https://forgemia.inra.fr/umr-gdec/bwgs>
    and modified as a R package.
A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.
Bandwidth selectors for local linear quantile regression, including cross-validation and plug-in methods. The local linear quantile regression estimate is also implemented.
An interface to the Bayesian Weighted Sums model implemented in 'RStan'.
    It estimates the summed effect of multiple, often moderately to highly correlated,
    continuous predictors. Its applications can be found in analysis of exposure mixtures.
    The model was proposed by Hamra, Maclehose, Croen, Kauffman, and
    Newschaffer (2021) <doi:10.3390/ijerph18041373>.
    This implementation includes an extension to model binary outcome.
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
   probability distributions, <doi:10.2307/2533862>. Also performs
   similar rank-based tests for equal probability distributions due to
   Neuhauser <doi:10.1080/10485250108832874> and
   Murakami <doi:10.1080/00949655.2010.551516>.
Tools to design best-worst scaling designs (i.e., balanced incomplete block designs) and
    to analyze data from these designs, using aggregate and individual methods such as: difference 
    scores, Louviere, Lings, Islam, Gudergan, & Flynn (2013) <doi:10.1016/j.ijresmar.2012.10.002>; 
    analytical estimation, Lipovetsky & Conklin (2014) <doi:10.1016/j.jocm.2014.02.001>; empirical 
    Bayes, Lipovetsky & Conklin (2015) <doi:10.1142/S1793536915500028>; Elo, Hollis (2018) 
    <doi:10.3758/s13428-017-0898-2>; and network-based measures.
Functions to get and download city bike data from 
    the website and API service of each city bike service in Norway. The
    package aims to reduce time spent on getting Norwegian city bike data,
    and lower barriers to start analyzing it. The data is retrieved from
    Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is 
    made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.
Shows statistics about bytes contained in a file 
  as a circle graph of deviations from mean in sigma increments. 
  The function can be useful for statistically analyze the content of files 
  in a glimpse: text files are shown as a green centered crown, compressed 
  and encrypted files should be shown as equally distributed variations with 
  a very low CV (sigma/mean), and other types of files can be classified between 
  these two categories depending on their text vs binary content, which can be 
  useful to quickly determine how information is stored inside them (databases, 
  multimedia files, etc).
Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.
The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.
Dataset containing cumulative COVID-19 deaths (absolute and per 100,000 pop) at the regional level (mostly NUTS 3) for 31 EU/EFTA countries.
Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.
Compare two classifications or clustering solutions that may or may
    not have the same number of classes, and that might have hard or soft
    (fuzzy, probabilistic) membership. Calculate various metrics to assess how
    the clusters compare to each other. The calculations are simple, but provide
    a handy tool for users unfamiliar with matrix multiplication. This package
    is not geared towards traditional accuracy assessment for classification/
    mapping applications - the motivating use case is for comparing a
    probabilistic clustering solution to a set of reference or existing class
    labels that could have any number of classes (that is, without having to
    degrade the probabilistic clustering to hard classes).
'Cristin' to 'Zotero' ('c2z') aims at obtaining total
    dominion over 'Cristin' ('Current Research Information SysTem in
    Norway') and 'Zotero'. The package enables manipulating 'Zotero'
    libraries using 'R'.  Import references from 'Cristin', 'Regjeringen',
    'CRAN', 'ISBN' ('Alma', 'LoC'), and 'DOI' ('CrossRef', 'DataCite') to
    a 'Zotero' library. Add, edit, copy, or delete items, including
    attachments and collections, and export references to 'BibLaTeX' (and
    other formats).
Create interactive charts with the 'C3.js' <http://c3js.org/> charting library. All plot 
    types in 'C3.js' are available and include line, bar, scatter, and mixed geometry plots. Plot 
    annotations, labels and axis are highly adjustable. Interactive web based charts can be embedded 
    in R Markdown documents or Shiny web applications.
Allows inferring gene regulatory networks
        with direct physical interactions from microarray expression
        data using C3NET.
Get insight into a forest of classification trees, by calculating similarities between the trees, and subsequently clustering them. Each cluster is represented by it's most central cluster member. The package implements the methodology described in Sies & Van Mechelen (2020) <doi:10.1007/s00357-019-09350-4>.
C5.0 decision trees and rule-based models for pattern
    recognition that extend the work of Quinlan (1993,
    ISBN:1-55860-238-0).
Computation and visualization of simple, multiple and joint correspondence analysis.
Provides four variants of three-way correspondence analysis (ca):
 three-way symmetrical ca, three-way non-symmetrical ca, three-way ordered symmetrical ca
 and three-way ordered non-symmetrical ca.
Creates project specific directory and file templates that are 
 written to a .Rprofile file. Upon starting a new R session, these templates 
 can be used to streamline the creation of new directories that are 
 standardized to the user's preferences and can include the initiation of a 
 git repository, an RStudio R project, and project-local dependency management 
 with the 'renv' package.
Performs simple correspondence analysis on a two-way contingency table, 
    or multiple correspondence analysis (homogeneity analysis) 
    on data with p categorical variables,
    and produces bootstrap-based elliptical confidence regions around the 
    projected coordinates for the category points. 
    Includes routines to plot the results in a variety of styles. 
    Also reports the standard numerical output for correspondence analysis.
A set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) as described in Miethe, Hart, and Regoeczi (2008) <doi:10.1007/s10940-008-9044-8>, and identify and quantify situational clustering in dominant case configurations as described in Hart (2019) <doi:10.1177/0011128719866123>. Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods.
Easily cache and retrieve computation results. The package works seamlessly across interactive R sessions, R scripts and Rmarkdown documents.
Key-value stores with automatic pruning. Caches can limit
    either their total size or the age of the oldest object (or both),
    automatically pruning objects to maintain the constraints.
A toolkit to perform cross-species analysis based on scRNA-seq data. This package contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotation (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.
Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.
Hansen's (1995) Covariate-Augmented
        Dickey-Fuller (CADF) test. The only required argument is y, the
        Tx1 time series to be tested. If no stationary covariate X is
        passed to the procedure, then an ordinary ADF test is
        performed. The p-values of the test are computed using the
        procedure illustrated in Lupi (2009).
Using Java GUI for R (JGR), CADStat provides a user
    interface for several statistical methods -
    scatterplot, boxplot, linear regression, generalized linear
    regression, quantile regression, conditional probability
    calculations, and regression trees.
Encrypts and decrypts strings using either the Caesar cipher or a
    pseudorandom number generation (using set.seed()) method.
Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.
Provides functions for the estimation of the conditional Akaike
    information in generalized mixed-effect models fitted with (g)lmer() 
    from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'.
    For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.
Allows to plot a number of information related to the interpretation of Correspondence Analysis' results. It provides the facility to plot the contribution of rows and columns categories to the principal dimensions, the quality of points display on selected dimensions, the correlation of row and column categories to selected dimensions, etc. It also allows to assess which dimension(s) is important for the data structure interpretation by means of different statistics and tests. The package also offers the facility to plot the permuted distribution of the table total inertia as well as of the inertia accounted for by pairs of selected dimensions. Different facilities are also provided that aim to produce interpretation-oriented scatterplots. Reference: Alberti 2015 <doi:10.1016/j.softx.2015.07.001>.
R graphics device using cairographics library that can be used to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.
Functions for performing experimental comparisons of algorithms 
             using adequate sample sizes for power and accuracy. Implements the 
             methodology originally presented in Campelo and Takahashi (2019) 
             <doi:10.1007/s10732-018-9396-7> 
             for the comparison of two algorithms, and later generalised in 
             Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.
Implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.
A first-principle, phylogeny-aware comparative genomics tool for 
             investigating associations between terms used to annotate genomic
             components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative 
             or rank variables such as number of cell types, genome size, or 
             density of specific genomic elements. See the project website for 
             more information, documentation and examples, and 
             <doi:10.1016/j.patter.2023.100728> for the full paper.
Arithmetic operations scalar multiplication, addition, subtraction, multiplication and division of LR fuzzy numbers (which are on the basis of extension principle) have a complicate form for using in fuzzy Statistics, fuzzy Mathematics, machine learning, fuzzy data analysis and etc. Calculator for LR Fuzzy Numbers package relieve and aid applied users to achieve a simple and closed form for some complicated operator based on LR fuzzy numbers and also the user can easily draw the membership function of the obtained result by this package.
Efficient C++ optimized functions for numerical and symbolic calculus as described in Guidotti (2022) <doi:10.18637/jss.v104.i05>. It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors.
This is a one-function package that will pass only unique values to a computationally-expensive function that returns an output of the same length as the input.
    In importing and working with tidy data, it is common to have index columns, often including time stamps that are far from unique. Some functions to work with these such as text conversion to other variable types (e.g. as.POSIXct()), various grep()-based functions, and often the cut() function are relatively slow when working with tens of millions of rows or more.
Calculates the wavelet-based organization index following Brune et al (2018) (<doi:10.1002/qj.3409>), the modified wavelet-based organization index and the local wavelet-based organization index of an arbitrary 2D array using Wavelet Transforms of the LS2W package by Eckley et al (2010) (<doi:10.1111/j.1467-9876.2009.00721.x>) and Eckley and Nason (2011) (<doi:10.18637/jss.v043.i03>). In Version 1.0.3 the calculation of LW is added.
Provides function to create, read, write, and work with 'iCalander' files
  (which typically have '.ics' or '.ical' extensions), and the scheduling data,
  calendars and timelines of people, organisations and other entities that they represent.
  'iCalendar' is an open standard for
  exchanging calendar and scheduling information between users and computers, 
  described at <https://icalendar.org/>.
Contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.
Fork of 'calendR' R package to generate ready to print
  calendars with 'ggplot2' (see <https://r-coder.com/calendar-plot-r/>)
  with additional features (backwards compatible).
  'calendRio' provides a 'calendR()' function that serves as a drop-in
  replacement for the upstream version but allows for additional
  parameters unlocking extra functionality.
Contains greedy algorithms for coarse approximation linear functions.
Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) <doi:10.1093/aje/kwt312>.
Automated parameter estimation for complex (ecological) models in R. 
  This package allows the parameter estimation or calibration of complex models, 
  including stochastic ones. It is a generic tool that can be used for fitting 
  any type of models, especially those with non-differentiable objective functions. 
  It supports multiple phases and constrained optimization. 
  It implements maximum likelihood estimation methods and automated construction 
  of the objective function from simulated model outputs. 
  See <http://roliveros-ramos.github.io/calibrar> for more details.
Package for drawing calibrated scales with tick marks on (non-orthogonal) 
             variable vectors in scatterplots and biplots. Also provides some functions for biplot creation and
	     for multivariate analysis such as principal coordinate analysis.
Performs the calibration procedure proposed by Sung et al. (2018+) <arXiv:1806.01453>. This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest.
Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the survey package is used for this purpose. It is also possible to use ReGenesees, which can be installed from <https://github.com/DiegoZardetto/ReGenesees>. Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) <http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A021.pdf>.
Package to assess the calibration of probabilistic classifiers using confidence bands for monotonic functions. Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests.  For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) <arXiv:2203.04065>.
Plots calibration curves and computes statistics for assessing calibration performance. See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>.
Performs Bayesian calibration of computer models as per
 Kennedy and O'Hagan 2001.  The package includes routines to find the
 hyperparameters and parameters; see the help page for stage1() for a
 worked example using the toy dataset.  A tutorial is provided in the
 calex.Rnw vignette; and a suite of especially simple one dimensional
 examples appears in inst/doc/one.dim/.
Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.
Assesses the quality of estimates made by complex sample designs,
  following the methodology developed by the National Institute of Statistics Chile (2020, <https://www.ine.cl/docs/default-source/institucionalidad/buenas-pr%C3%A1cticas/clasificaciones-y-estandares/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-publicaci%C3%B3n-27022020.pdf>)
  and by Economic Commission  for Latin America and
  Caribbean (2020, <https://repositorio.cepal.org/bitstream/handle/11362/45681/1/S2000293_es.pdf>).
It is sometimes useful to perform a computation in a separate
    R process, without affecting the current R process at all.  This
    packages does exactly that.
Intended to analyse recordings from multiple microphones (e.g., backpack
  microphones in captive setting). It allows users to align recordings even if there is non-linear 
  drift of several minutes between them. A call detection and assignment pipeline can be used 
  to find vocalisations and assign them to the vocalising individuals (even if the vocalisation
  is picked up on multiple microphones). The tracing and measurement functions allow for detailed
  analysis of the vocalisations and filtering of noise. Finally, the package includes a function
  to run spectrographic cross correlation, which can be used to compare vocalisations. It also 
  includes multiple other functions related to analysis of vocal behaviour.
The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.
Implements methods for querying data from CalPASS using its API.
 CalPASS Plus.  MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.
Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.
Record and generate a 'gif' of your 'R' sessions plots. When creating 
    a visualization, there is inevitably iteration and refinement that occurs. 
    Automatically save the plots made to a specified directory, previewing them 
    as they would be saved. Then combine all plots generated into a 'gif'
    to show the plot refinement over time.
Implements the board game 'CamelUp' for use in introductory statistics classes using a Shiny app.
Quick and easy access to datasets that let you replicate the
    empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and
    Applications" (ISBN: 9780521848053).The data are available as soon as you install
    and load the package (lazy-loading) as data frames. The documentation includes
    reference to chapter sections and page numbers where the datasets are used.
Creates multi-label cell-types for single-cell RNA-sequencing data based on weighted VAM scoring of cell-type specific gene sets. Schiebout, Frost (2022) <https://psb.stanford.edu/psb-online/proceedings/psb22/schiebout.pdf>.
Collect  marketing data from Campaign Manager using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Explore and normalize American campaign finance
    data. Created by the Investigative Reporting Workshop to facilitate
    work on The Accountability Project, an effort to collect public data
    into a central, standard database that is more easily searched:
    <https://publicaccountability.org/>.
A generic, easy-to-use and intuitive
    pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R
    packages 'rxode2', 'RxODE' and 'mrgsolve'. CAMPSIS provides an
    abstraction layer over the underlying processes of writing a PK/PD
    model, assembling a custom dataset and running a simulation. CAMPSIS
    has a strong dependency to the R package 'campsismod', which allows to
    read/write a model from/to files and adapt it further on the fly in
    the R environment. Package 'campsis' allows the user to assemble a
    dataset in an intuitive manner. Once the user’s dataset is ready, the
    package is in charge of preparing the simulation, calling 'rxode2',
    'RxODE' or 'mrgsolve' (at the user's choice) and returning the
    results, for the given model, dataset and desired simulation settings.
A generic, easy-to-use and expandable implementation of a
    pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4
    class system. This package allows the user to read/write a
    pharmacometric model from/to files and adapt it further on the fly in
    the R environment. For this purpose, this package provides an
    intuitive API to add, modify or delete equations, ordinary
    differential equations (ODE's), model parameters or compartment
    properties (like infusion duration or rate, bioavailability and
    initial values). Finally, this package also provides a useful export
    of the model for use with simulation packages 'rxode2' and 'mrgsolve'.
    This package is designed and intended to be used with package
    'campsis', a PK/PD simulation platform built on top of 'rxode2' and
    'mrgsolve'.
Copernicus Atmosphere Monitoring Service (CAMS) radiations service 
    provides time series of global, direct, and diffuse irradiations on horizontal
    surface, and direct irradiation on normal plane for the actual weather 
    conditions as well as for clear-sky conditions.
    The geographical coverage is the field-of-view of the Meteosat satellite,
    roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage
    of data is from 2004-02-01 up to 2 days ago. Data are available with a time step
    ranging from 15 min to 1 month. For license terms and to create an account,
    please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>.
Management of and data extraction from camera trap data in wildlife studies. The package provides a workflow for storing and sorting camera trap photos (and videos), tabulates records of species and individuals, and creates detection/non-detection matrices for occupancy and spatial capture-recapture analyses with great flexibility. In addition, it can visualise species activity data and provides simple mapping functions with GIS export.
Terrestrial maps with simplified topologies for Census Divisions,
    Agricultural Regions, Economic Regions, Federal Electoral Divisions and
    Provinces.
Simple and seamless access to a variety of 'StatCan' shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular 'ggplot2' package.
Provides functions to analyze the spatial distribution of
    biodiversity, in particular categorical analysis of neo- and paleo-endemism
    (CANAPE) as described in Mishler et al (2014) <doi:10.1038/ncomms5473>.
    'canaper' conducts statistical tests to determine the types of endemism that
    occur in a study area while accounting for the evolutionary relationships of
    species.
Integrated, convenient, and uniform access to Canadian
    Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready 
    tidy data frames and spatial data in multiple formats, as well as convenience functions
    for working with Census variables, variable hierarchies, and region selection. API
    keys are freely available with free registration at <https://censusmapper.ca/api>.
    Census data and boundary geometries are reproduced and distributed on an "as
    is" basis with the permission of Statistics Canada (Statistics Canada 2001; 2006;
    2011; 2016; 2021).
Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.
Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network).  Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.
Predicts anticancer peptides using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI. The CancerGram model is too large 
    for CRAN and it has to be downloaded separately from the repository:
    <https://github.com/BioGenies/CancerGramModel>. For more information see: 
    Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.
Timing copy number changes using estimates of mutational allele frequency from resequencing of tumor samples.
Functions for computing and visualizing 
	generalized canonical discriminant analyses and canonical correlation analysis
	for a multivariate linear model.
	Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA'
	design and is equivalent to canonical correlation analysis between a set of quantitative
	response variables and a set of dummy variables coded from the factor variable.
	The 'candisc' package generalizes this to higher-way 'MANOVA' designs
	for all factors in a multivariate linear model,
	computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) 
	visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are
	now provided for canonical correlation analysis when all predictors are quantitative.
Non-linear/linear hybrid method for batch-effect correction
    that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between 
    datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.
A statistical framework and computational procedure for identifying
  the sub-populations within a tumor, determining the mutation profiles of each 
  subpopulation, and inferring the tumor's phylogenetic history. The input are 
  variant allele frequencies (VAFs) of somatic single nucleotide alterations 
  (SNAs) along with allele-specific coverage ratios between the tumor and matched
  normal sample for somatic copy number alterations (CNAs). These quantities can
  be directly taken from the output of existing software. Canopy provides a 
  general mathematical framework for pooling data across samples and sites to 
  infer the underlying parameters. For SNAs that fall within CNA regions, Canopy
  infers their temporal ordering and resolves their phase.  When there are 
  multiple evolutionary configurations consistent with the data, Canopy outputs 
  all configurations along with their confidence assessment.
Chemical metrics of differentially expressed proteins in cancer
  and cell culture proteomics experiments. Data files in the package have amino
  acid compositions of proteins obtained from UniProt and >250 published lists of
  up- and down-regulated proteins in different cancer types and laboratory
  experiments. Functions are provided to calculate chemical metrics including
  protein length, grand average of hydropathicity (GRAVY), isoelectric point
  (pI), carbon oxidation state, and stoichiometric hydration state; the latter
  two are described in Dick et al. (2020) <doi:10.5194/bg-17-6145-2020>. The
  vignettes visualize differences of chemical metrics between up- and
  down-regulated proteins and list literature references for all datasets.
Searches for, accesses, and retrieves new-format and old-format Statistics Canada data 
    tables, as well as individual vectors, as tidy data frames. This package deals with encoding issues, allows for 
    bilingual English or French language data retrieval, and bundles convenience functions 
    to make it easier to work with retrieved table data. Optional caching features are provided.
Extract CANSIM (Statistics Canada) tables and transform them into readily usable data in panel (wide) format. It can also extract more than one table at a time and produce the resulting merge by time period and geographical region.
Enables creation of visualizations using the CanvasXpress framework
    in R. CanvasXpress is a standalone JavaScript library for reproducible research
    with complete tracking of data and end-user modifications stored in a single
    PNG image that can be played back. See <https://www.canvasxpress.org> for more
    information.
Contains the prepared data that is needed for the 'shiny' application examples in the 
    'canvasXpress' package.  This package also includes datasets used for automated 'testthat' tests.
    Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>.
    Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>.
Performs Covariate Assisted Principal (CAP) Regression for covariance matrix outcomes. The method identifies the optimal projection direction which maximizes the log-likelihood function of the log-linear heteroscedastic regression model in the projection space. See Zhao et al. (2018), Covariate Assisted Principal Regression for Covariance Matrix Outcomes, <doi:10.1101/425033> for details.
Combined Analysis of Pleiotropy and Epistasis infers predictive 
    networks between genetic variants and phenotypes. It can be used with 
    standard two-parent populations as well as multi-parent populations, such 
    as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the 
    multi-parent advanced generation intercross (MAGIC) population of Arabidopsis
    thaliana. It uses complementary information of pleiotropic gene variants across
    different phenotypes to resolve models of epistatic interactions between alleles. 
    To do this, cape reparametrizes main effect and interaction coefficients from
    pairwise variant regressions into directed influence parameters. These
    parameters describe how alleles influence each other, in terms of suppression 
    and enhancement, as well as how gene variants influence phenotypes. All of the 
    final interactions are reported as directed interactions between pairs of 
    parental alleles. For detailed descriptions of the methods used in this package 
    please see the following references.
    Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>.
    Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.
Functions for performing phylogenetic comparative analyses.
Provides Capital Budgeting Analysis functionality and the essential Annuity loan functions.
    Also computes Loan Amortization Schedules including schedules with irregular payments.
A toolkit for computing and visualizing CAPL-2
    (Canadian Assessment of Physical Literacy, Second Edition;
    <https://www.capl-eclp.ca>) scores and interpretations from raw data.
Performs Correspondence Analysis on the given dataframe and plots the results in a scatterplot that emphasizes the geometric interpretation aspect of the analysis, following Borg-Groenen (2005) and Yelland (2010). It is particularly useful for highlighting the relationships between a selected row (or column) category and the column (or row) categories. See Borg-Groenen (2005, ISBN:978-0-387-28981-6); Yelland (2010) <doi:10.3888/tmj.12-4>.
Quantitative analysis to support companion animal population
    management. Some functions assist survey sampling tasks (calculate sample 
    size for simple and complex designs, select sampling units and estimate 
    population parameters) while others assist the modelling of population 
    dynamics. For demographic characterizations and population management 
    evaluations see: "Baquero, et al." (2018), 
    <doi:10.1016/j.prevetmed.2018.07.006>. For modelling of 
    population dynamics see: "Baquero et al." (2016),
    <doi:10.1016/j.prevetmed.2015.11.009>. For sampling methods
    see: "Levy PS & Lemeshow S" (2013), "ISBN-10: 0470040076"; 
    "Lumley" (2010), "ISBN: 978-0-470-28430-8".
Implements approximation methods for natural capital asset prices suggested by Fenichel and Abbott (2014) <doi:10.1086/676034> in Journal of the Associations of Environmental and Resource Economists (JAERE), Fenichel et al. (2016) <doi:10.1073/pnas.1513779113> in Proceedings of the National Academy of Sciences (PNAS), and Yun et al. (2017) in PNAS (accepted), and their extensions: creating Chebyshev polynomial nodes and grids, calculating basis of Chebyshev polynomials, approximation and their simulations for: V-approximation (single and multiple stocks, PNAS), P-approximation (single stock, PNAS), and Pdot-approximation (single stock, JAERE). Development of this package was generously supported by the Knobloch Family Foundation.
Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel 'Shiny' apps, 
    building upon on a previously published mathematical model written in C, 
    to ensure efficient computations. The underlying model is accessible
    here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>.
    The first application explores the fundamentals of Ca-Pi homeostasis, 
    while the second provides interactive case studies for in-depth exploration of the topic, 
    thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation. 
    These applications are hosted at <https://rinterface.com/AppsPhysiol.html>.
Provides a method for automatically numbering figures,
    tables, or other objects.  Captions can be displayed in full, or as citations.
    This is especially useful for adding figures and tables to R markdown
    documents without having to numbering them manually.
Get text from images of text using Captricity Optical Character
    Recognition (OCR) API. Captricity allows you to get text from handwritten
    forms --- think surveys --- and other structured paper documents. And it can
    output data in form a delimited file keeping field information intact. For more
    information, read <https://shreddr.captricity.com/developer/overview/>.
Calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.
Functions to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage, 2019.
Computer algebra via the 'SymPy' library (<https://www.sympy.org/>). 
  This makes it possible to solve equations symbolically, 
  find symbolic integrals, symbolic sums and other important quantities.
Multi-objective optimizer initially developed for the calibration of hydrological models.
     The algorithm is a hybrid of the MEAS algorithm (Efstratiadis and Koutsoyiannis (2005) <doi:10.13140/RG.2.2.32963.81446>) by using the directional search method based on the simplexes of the objective space
     and the epsilon-NGSA-II algorithm with the method of classification of the parameter vectors archiving management by epsilon-dominance (Reed and Devireddy <doi:10.1142/9789812567796_0004>).
Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria.
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.
Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ)  that make up the 2001 definition of the  Greater Glasgow and Clyde health board.
Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by  random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council  (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.
Create beautiful images of source code using
    'carbon.js'<https://carbon.now.sh/about>.
The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
Tools that can aid in the assessment of the autonomic regulation 
		of cardiovascular physiology. The aims of this package are to: 1) study 
		electrocardiography (both intervals and morphology) as extensions 
		of signal processing, 2) study circadian rhythms and how it effects 
		autonomic physiology, 3) assess clinical risk of autonomic dysfunction on 
		cardiovascular health through the perspective of epidemiology and causality.
		The analysis of circadian rhythms through cosinor analysis are built upon 
		the methods by Cornelissen (2014) <doi:10.1186/1742-4682-11-16> and 
		Refinetti, Cornelissen, Halberg (2014) <doi:10.1080/09291010600903692>.
Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage (2019).
Identification of cardinal dates
             (begin, time of maximum, end of mass developments)
             in ecological time series using fitted Weibull functions.
Implements the regression approach 
  of Zuber and Strimmer (2011) "High-dimensional regression and variable 
  selection using CAR scores" SAGMB 10: 34, <DOI:10.2202/1544-6115.1730>.
  CAR scores measure the correlation between the response and the 
  Mahalanobis-decorrelated  predictors.  The squared CAR score is a 
  natural measure of variable importance and provides a canonical 
  ordering of variables. This package provides functions for estimating 
  CAR scores, for variable selection using CAR scores, and for estimating 
  corresponding regression coefficients. Both shrinkage as well as 
  empirical estimators are available.
Provides API access to the Government of Canada Vehicle Recalls 
    Database <https://tc.api.canada.ca/en/detail?api=VRDB> used by the Defect 
    Investigations and Recalls Division for vehicles, tires, and child car 
    seats. The API wrapper provides access to recall summary information 
    searched using make, model, and year range, as well as detailed recall 
    information searched using recall number.
When taking online surveys, participants sometimes respond to items without regard to their content.
    These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing, such as spurious correlations. The 'R' package 'careless' provides solutions designed to detect such careless / insufficient effort responses by allowing easy calculation of indices proposed in the literature. It currently supports the calculation of longstring, even-odd consistency, psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual response variability (also termed inter-item standard deviation). For a review of these methods, see Curran (2016) <doi:10.1016/j.jesp.2015.07.006>.
Performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. Optionally, the function can add segments to the plot to visualize significant associations between row and column categories on the basis of positive (unadjusted) standardized residuals larger than a given threshold.
Misc functions for training and plotting classification and
    regression models.
Functions for creating ensembles of caret models: caretList()
    and caretStack().  caretList() is a convenience function for fitting multiple
    caret::train() models to the same dataset. caretStack() will make linear or
    non-linear combinations of these models, using a caret::train() model as a
    meta-model, and caretEnsemble() will make a robust linear combination of
    models using a GLM.
Conformal time series forecasting using the caret infrastructure. 
  It provides access to state-of-the-art machine learning models for forecasting
  applications. The hyperparameter of each model is selected based on time 
  series cross-validation, and forecasting is done recursively.
We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via both frequentist and Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) <doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
A framework is provided to develop R packages using 'Rust' <https://www.rust-lang.org/> with
 minimal overhead, and more wrappers are easily added. Help is provided to use 'Cargo' <https://doc.rust-lang.org/cargo/> in a manner
 consistent with CRAN policies. 'Rust' code can also be embedded directly in an R script. The package is not official, affiliated with,
 nor endorsed by the Rust project.
Estimation of population size of migratory caribou herds based on large scale 
             aggregations monitored by radio telemetry. It implements the methodology found 
             in the article by Rivest et al. (1998) about caribou abundance estimation. It 
             also includes a function based on the Lincoln-Petersen Index as applied to 
             radio telemetry data by White and Garrott (1990).
Algorithms to fit Bayesian Conditional Autoregressive LASSO with automatic and adaptive shrinkage described in Shen and Solis-Lemus (2020) <arXiv:2012.08397>.
Includes commands for bootstrapping and permutation tests, a
    command for created grouped bar plots, and a demo of the
    quantile-normal plot for data drawn from different distributions.
Evaluation of the Carlson elliptic integrals and the
    incomplete elliptic integrals with complex arguments. The
    implementations use Carlson's algorithms <doi:10.1007/BF02198293>.
    Applications of elliptic integrals include probability distributions,
    geometry, physics, mechanics, electrodynamics, statistical mechanics,
    astronomy, geodesy, geodesics on conics, and magnetic field
    calculations.
In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2022). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. <doi:10.5705/ss.202020.0440>. In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.<doi:10.1016/j.csda.2022.107642>.
'Stan' based functions to estimate CAR-MM models. These models allow to estimate Generalised Linear Models with CAR (conditional autoregressive) spatial random effects for spatially and temporally misaligned data, provided a suitable Multiple Membership matrix. The main references are Gramatica, Liverani and Congdon (2023) <doi:10.1214/23-BA1370>, Petrof, Neyens, Nuyts, Nackaerts, Nemery and Faes (2020) <doi:10.1002/sim.8697> and Gramatica, Congdon and Liverani <doi:10.1111/rssc.12480>.
Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment".
The caroline R library contains dozens of functions useful
    for: database migration (dbWriteTable2), database style joins &
    aggregation (nerge, groupBy & bestBy), data structure
    conversion (nv, tab2df), legend table making (sstable &
    leghead), plot annotation (labsegs & mvlabs), data
    visualization (pies, distro dots, & raPlot), character string
    manipulation (m & pad), file I/O (write.delim), batch scripting
    and more.  The package's greatest contributions lie in the 
    database style merge, aggregation and interface functions 
    as well as in it's extensive use and propagation of 
    row, column and vector names in most functions.
Mainly used to build tables that are commonly presented for
    bio-medical/health research, such as basic characteristic tables or
    descriptive statistics.
CRISPR-Analyzer for pooled CRISPR screens (caRpools) provides an end-to-end analysis of CRISPR screens including quality control, hit candidate analysis, visualization and automated report generation using R markdown. Needs MAGeCK (http://sourceforge.net/p/mageck/wiki/Home/), bowtie2 for all functions. CRISPR (clustered regularly interspaced short palindromic repeats) is a method to perform genome editing. See <https://en.wikipedia.org/wiki/CRISPR> for more information on
  CRISPR.
Sending functions to remote processes can be wasteful of
    resources because they carry their environments with them. With the
    carrier package, it is easy to create functions that are isolated from
    their environment. These isolated functions, also called crates, print
    at the console with their total size and can be easily tested locally
    before being sent to a remote.
Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.
Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.
Functions to prepare and filter an origin-destination matrix for thematic flow mapping purposes.   
             This comes after Bahoken, Francoise (2016), Mapping flow matrix a contribution, PhD in Geography - Territorial sciences. See Bahoken (2017) <doi:10.4000/netcom.2565>.
Construct continuous and non-contiguous area cartograms.
Procedures for making continuous cartogram. Procedures available are:
     flow based cartogram (Gastner & Newman (2004) <doi:10.1073/pnas.0400280101>),
     fast flow based cartogram (Gastner, Seguy & More (2018) <doi:10.1073/pnas.1712674115>),
     rubber band based cartogram (Dougenik et al. (1985)
                        <doi:10.1111/j.0033-0124.1985.00075.x>).
A tool for easily matching spatial data when you have a list of
    place/region names. You might have a data frame that came from a
    spreadsheet tracking some data by suburb or state. This package can
    convert it into a spatial data frame ready for plotting. The actual map
    data is provided by other packages (or your own code).
Create and integrate maps in your R workflow. This package helps 
    to design cartographic representations such as proportional symbols, 
    choropleth, typology, flows or discontinuities maps. It also offers several 
    features that improve the graphic presentation of maps, for instance, map 
    palettes, layout elements (scale, north arrow, title...), labels or legends. 
    See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.
It provides real-time data from the Casablanca Stock Exchange. The objective is to facilitate access to data for all users of the R programming language. 
             It includes a variety of data accessible just by function call.
A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
Provides a cascade select widget for usage in 'Shiny'
    applications. This is useful for selection of hierarchical choices
    (e.g. continent, country, city). It is taken from the 'JavaScript'
    library 'PrimeReact'.
Functions, data sets and exercises solutions for the book 'Séries Temporelles Avec R' (Yves Aragon, edp sciences, 2016). For all chapters, a vignette is available with some additional material and exercises solutions.
Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) <arXiv:2306.15616>. 
    In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.
It is an open source insurance claim simulation engine sponsored
    by the Casualty Actuarial Society. It generates individual insurance claims 
	including open claims, reopened claims, incurred but not reported claims 
	and future claims. It also includes claim data fitting functions to help set 
	simulation assumptions. It is useful for claim level reserving analysis.
	Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.
Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple 
    competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms 
    of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the 
    fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival 
    curves for a given covariate profile. This approach accommodates any log-linear hazard function of 
    prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide 
    a plot method for visualizing incidence density via population time plots. Based on the case-base sampling 
    approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, 
    and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.
Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. (2002) <doi:10.1016/S0167-9473(02)00058-0>.
Cox model inference for relative hazard and covariate-specific pure risk estimated 
             from stratified and unstratified case-cohort data as described in 
             Etievant, L., Gail, M.H. (2023) <arXiv:2304.03396>.
Allows users to identify similar cases for qualitative case studies using statistical matching methods.
Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity. (Westphal, Max, and Antonia Zapf. (2021). "Statistical Inference for Diagnostic Test Accuracy Studies with Multiple Comparisons." <arXiv:2105.13469>.)
Play casino games in the R console,
  including poker, blackjack, and a slot machine.
  Try to build your fortune before you succumb to the gambler's ruin!
A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>.
Contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. 'CASMI' is a fundamental concept of a series of methods. For more information about 'CASMI' and 'CASMI'-related methods, please refer to the corresponding publications (for example, a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author.
Provides methods to deal with under sampling in ecological bipartite networks. Includes 
     tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely 
     missing links. Also includes simple functions to resample from observed networks to generate confidence 
     intervals for common ecological network metrics.
Computes a range of scatterplot diagnostics (scagnostics) on pairs
  of numerical variables in a data set. A range of scagnostics, including graph
  and association-based scagnostics described by Leland Wilkinson and Graham
  Wills (2008) <doi:10.1198/106186008X320465> and association-based
  scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be 
  computed. Summary and plotting functions are provided.
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>.
Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.
Performs analysis of categorical-variable with missing values. Implements methods from Schafer, JL, Analysis of Incomplete Multivariate Data, Chapman and Hall.
Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. 
    The main rule is to replicate the observation if it could be assigned to a few categories. 
    Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. 
    This procedure was invented and implemented in the paper by Nasinski, Majchrowska, and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>.
Functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis provided, as described in for CATA data analysis by Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis. The quality of cluster analysis solutions can be evaluated using various measures. The methods related to b-cluster analysis are described in a manuscript by Castura, Meyners, Varela & Naes (2022) <doi:10.1016/j.foodqual.2022.104564>.
Gain access to the 'Spark Catalog' API making use of the 'sparklyr' API. 'Catalog' <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).
Access public spatial data available under the 'INSPIRE'
    directive. Tools for downloading references and addresses of
    properties, as well as map images.
Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <arXiv:1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.
Methods and utilities for testing, identifying, selecting and  
    mutating objects as categorical or continous types. These functions work on both 
    atomic vectors as well as recursive objects: data.frames, data.tables, 
    tibbles, lists, etc..
Categorical data analysis by AIC. The methodology is described in
 Sakamoto (1992) <ISBN 978-0-7923-1429-5>.
This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Datasets used in the book "Categorical Data Analysis"
   by Agresti (2012, ISBN:978-0-470-46363-5) but not printed in the book.
   Datasets and help pages were automatically produced from the source
   <https://users.stat.ufl.edu/~aa/cda/data.html> by the R script foo.R,
   which can be found in the GitHub repository.
Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.
Provides several methods for factor analysis in high dimension (both n,p >> 1) and methods to adjust for possible confounders in multiple hypothesis testing.
Simple, fast, and automatic encodings for category data using 
             a data.table backend. Most of the methods are an implementation 
             of "Sufficient Representation for Categorical Variables" by
             Johannemann, Hadad, Athey, Wager (2019) <arXiv:1908.09874>,
             particularly their mean, sparse principal component analysis, 
             low rank representation, and multinomial logit encodings.
Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).
Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) <doi:10.1093/jnci/22.4.719> and Alan Agresti (2002) <ISBN:0-471-36093-7>.
Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models.
Functions designed to simulate data that conform to basic
          unidimensional IRT models (for now 3-parameter binary response models
          and graded response models) along with Post-Hoc CAT simulations of
          those models given various item selection methods, ability estimation
          methods, and termination criteria. See
          Wainer (2000) <doi:10.4324/9781410605931>,
          van der Linden & Pashley (2010) <doi:10.1007/978-0-387-85461-8_1>,
          and Eggen (1999) <doi:10.1177/01466219922031365> for more details.
Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.
Although many software tools can perform meta-analyses on genetic case-control data,
    none of these apply to combined case-control and family-based (TDT) studies. This package conducts
    fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986)
    <DOI:10.1016/0197-2456(86)90046-2>] meta-analyses on combined genetic data. Specifically, this package
    implements a fixed-effects model [Kazeem and Farrall (2005) <DOI:10.1046/j.1529-8817.2005.00156.x>]
    and a random-effects model [Nicodemus (2008) <DOI:10.1186/1471-2105-9-130>] for combined studies.
Methods and plotting functions for displaying categorical data on an 
             interactive heatmap using 'plotly'. Provides functionality for strictly 
             categorical heatmaps, heatmaps illustrating categorized continuous data 
             and annotated heatmaps. Also, there are various options to interact with the x-axis
             to prevent overlapping axis labels, e.g. via simple sliders or range sliders. 
             Besides the viewer pane, resulting plots can be saved as a standalone HTML file, 
             embedded in 'R Markdown' documents or in a 'Shiny' app.
Contains several basic utility functions including: moving
        (rolling, running) window statistic functions, read/write for
        GIF and ENVI binary files, fast calculation of AUC, LogitBoost
        classifier, base64 encoder/decoder, round-off-error-free sum
        and cumsum, etc.
Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio,  MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf>.
Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).
Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.
Cohort plAtform Trial Simulation whereby every cohort consists of two arms, 
             control and experimental treatment. Endpoints are co-primary binary endpoints and
             decisions are made using either Bayesian or frequentist decision rules. 
             Realistic trial trajectories are simulated and the operating characteristics of the 
             designs are calculated.
Provides the tools to produce catseye plots, principally
    by catseyesplot() function which calls R's standard plot() function internally, or alternatively
    by the catseyes() function to overlay the catseye plot onto an existing
    R plot window. Catseye plots illustrate the normal distribution of the mean (picture a 
    normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence
    interval; they are an intuitive way of illustrating and comparing normally distributed estimates,
    and are arguably a superior alternative to standard confidence intervals, since they show the full
    distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require
    pre-calculated means and standard errors (or standard deviations), provided as numeric vectors;
    this allows the flexibility of obtaining this information from a variety of sources, such as
    direct calculation or prediction from a model.  Catseye plots, as illustrations of the
    normal distribution of the means, are described in Cumming (2013 & 2014).
    Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.
Computes a structural similarity metric (after the style of
    MS-SSIM for images) for binary and categorical 2D and 3D images. Can be
    based on accuracy (simple matching), Cohen's kappa, Rand index, adjusted
    Rand index, Jaccard index, Dice index, normalized mutual information, or
    adjusted mutual information. In addition, has fast computation
    of Cohen's kappa, the Rand indices, and the two mutual informations.
    Implements the methods of Thompson and Maitra (2020) <arXiv:2004.09073>.
Provides methods of computerized adaptive testing for survey researchers.  See Montgomery and Rossiter (2020) <doi:10.1093/jssam/smz027>. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model.  Additionally, includes several ability parameter estimation and item selection routines.  During item selection, all calculations are done in compiled C++ code.
This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.
Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test
  statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test
  is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.
An omnibus test of change-point Cox regression models to improve the statistical power of detecting signals of non-proportional hazards patterns. The technical details can be found in Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen (2021) <arXiv:2101.00059>. Extensive simulation studies demonstrate that, compared to existing tests under non-proportional hazards, the proposed CauchyCP test 1) controls the type I error better at small alpha levels; 2) increases the power of detecting time-varying effects; and 3) is more computationally efficient.
A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2022). Cauchy robust principal component analysis with applications to high-dimensional data sets. <arXiv:2211.03181>.
Accelerate Bayesian analytics workflows in 'R' through interactive modelling,
    visualization, and inference. Define probabilistic graphical models using directed
    acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, 
    and programmers. This package relies on the sleek and elegant 'greta' package for 
    Bayesian inference. 'greta', in turn, is an interface into 'TensorFlow' from 'R'. 
    Install 'greta' using instructions available here: <https://www.causact.com/install-tensorflow-greta-and-causact.html>.
    See <https://github.com/flyaflya/causact> or <https://www.causact.com/> for more documentation.
We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) and Park, Kang, and Lee (2021+) <arXiv:2109.06940>. This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates. We also implement sensitivity analysis for the causal decomposition analysis using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023).
Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.
Example data sets to run the example
    problems from causal inference textbooks. Currently, contains data
    sets for Huntington-Klein, Nick (2021) "The Effect" <https://theeffectbook.net>,
    Cunningham, Scott (2021, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", 
    and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" 
    <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.
Various tools for inferring causal models from observational data. The package 
    includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler 
    and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools
    for evaluating differences in adjacency matrices, which can be used for evaluating
    performance of causal discovery procedures.
Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments.  A description of the methods is given in Galagate (2016) <https://drum.lib.umd.edu/handle/1903/18170>.
Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
Implements various estimators for average
  treatment effects - an inverse probability weighted (IPW) estimator, 
  an augmented inverse probability weighted (AIPW) estimator, and a standard
  regression estimator - that make use of generalized additive models for
  the treatment assignment model and/or outcome model. See: Glynn, Adam N.
  and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse
  Propensity Weighted Estimator." Political Analysis. 18: 36-56.
Provides a framework for estimating causal effects of a continuous 
    exposure using observational data, and implementing matching and weighting 
    on the generalized propensity score.
    Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. 
    Matching on generalized propensity scores with continuous exposures. Journal 
    of the American Statistical Association, pp.1-29.
Implements a Bayesian approach to causal impact estimation in time
  series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
  See the package documentation on GitHub
  <https://google.github.io/CausalImpact/> to get started.
Infers the causal effect of an intervention on a multivariate response through the use of Multivariate 
    Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) <arXiv:2006.12269>. 
    The package also includes functions for model building and forecasting.
Provides an array of statistical models common in causal inference such as 
  standardization, IP weighting, propensity matching, outcome regression, and doubly-robust 
  estimators. Estimates of the average treatment effects from each model are given with the 
  standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>).
When causal quantities are not identifiable from the observed data, it still may be possible 
            to bound these quantities using the observed data. We outline a class of problems for which the 
            derivation of tight bounds is always a linear programming problem and can therefore, at least 
            theoretically, be solved using a symbolic linear optimizer. We extend and generalize the 
            approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide 
            a user friendly graphical interface for setting up such problems via directed acyclic 
            graphs (DAG), which only allow for problems within this class to be depicted. The user can 
            then define linear constraints to further refine their assumptions to meet their specific 
            problem, and then specify a causal query using a text interface. The program converts this 
            user defined DAG, query, and constraints, and returns tight bounds. The bounds can be 
            converted to R functions to evaluate them for specific datasets, and to latex code for 
            publication. The methods and proofs of tightness and validity of the bounds are described in
            a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) 
            <doi:10.1080/10618600.2022.2071905>.
Uses optimal transport distances to find probabilistic 
    matching estimators for causal inference.
    These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>.
    The package will build the weights, estimate treatment effects, and
    calculate confidence intervals via the methods described in the paper.
    The package also supports several other methods as described in the help 
    files.
Calculates population attributable fraction causal effects.
    The 'causalPAF' package contains a suite of functions for causal
    analysis calculations of population attributable fractions (PAF) given
    a causal diagram which apply both: Pathway-specific population
    attributable fractions (PS-PAFs) O’Connell and Ferguson (2022)
    <doi:10.1093/ije/dyac079> and Sequential population attributable
    fractions Ferguson, O’Connell, and O’Donnell (2020)
    <doi:10.1186/s13690-020-00442-x>.  Results are presentable in both
    table and plot format.
Users can declare binary causal models, update beliefs about causal types given data and calculate arbitrary estimands.  Model definition makes use of 'dagitty' functionality. Updating is implemented in 'stan'. The approach used in 'CausalQueries' is a generalization of the 'biqq' models described in "Mixing Methods: A Bayesian Approach" (Humphreys and Jacobs, 2015, <DOI:10.1017/S0003055415000453>). The conceptual extension makes use of work on probabilistic causal models described in Pearl's Causality (Pearl, 2009, <DOI:10.1017/CBO9780511803161>).
The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020)  <doi:10.1002/jae.2765>, and others.
This tool performs pairwise correlation analysis and estimate causality.
    Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.
Design and use of control charts for detecting mean changes 
 based on a delayed updating of the in-control parameter estimates.
 See Capizzi and Masarotto (2019) <doi:10.1080/00224065.2019.1640096> for the
 description of the method.
Provides six variants of two-way correspondence analysis (ca):
   simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca,
   singly ordered non symmetrical ca, and doubly ordered non symmetrical
   ca.
Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is
             implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates.
             An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since 
             pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids
             clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.
Provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. 
  Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.
Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.
A set of functions that helps you to generate descriptive statistics based on the variable types.
Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review.
Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.
Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, full factorial designs, orthogonal designs, D-optimal designs, and Bayesian D-efficient designs as well as designs with "no choice" options and "labeled" (also known as "alternative specific") designs. Conveniently inspect the design balance and overlap, and simulate choice data for a survey design either randomly or according to a multinomial or mixed logit utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Full factorial and orthogonal designs are obtained using the 'DoE.base' package (Grömping, 2018) <doi:10.18637/jss.v085.i05>. D-optimal designs are obtained using the 'AlgDesign' package (Wheeler, 2022) <https://CRAN.R-project.org/package=AlgDesign>. Bayesian D-efficient designs are obtained using the 'idefix' package (Traets et al, 2020) <doi:10.18637/jss.v096.i03>. Choice simulation and model estimation in power analyses are handled using the 'logitr' package (Helveston, 2023) <doi:10.18637/jss.v105.i10>.
Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size + 1], following Ilienko (2013) <arXiv:1303.5990>.
Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly
    functions that wrap 'cBioPortal's' existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations,
    copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'),
    microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). 
    See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.
Methods for learning causal relationships among a set of
    foreground variables X based on signals from a (potentially much
    larger) set of background variables Z, which are known non-descendants
    of X. The confounder blanket learner (CBL) uses sparse regression
    techniques to simultaneously perform many conditional independence
    tests, with complementary pairs stability selection to guarantee
    finite sample error control. CBL is sound and complete with respect to
    a so-called "lazy oracle", and works with both linear and nonlinear
    systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.
Implements the covariate balancing propensity score (CBPS) proposed
    by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is
    estimated such that it maximizes the resulting covariate balance as well as the
    prediction of treatment assignment. The method, therefore, avoids an iteration
    between model fitting and balance checking.  The package also implements optimal
    CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>,  
    several extensions of the CBPS beyond the cross-sectional, binary treatment setting.
    They include the CBPS for longitudinal settings so that it can be used in 
    conjunction with marginal structural models from Imai and Ratkovic (2015) 
    <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment 
    variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) 
    <DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of 
    covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation 
    with multiple distinct binary treatments administered simultaneously. In the future 
    it will be extended to other settings including the generalization of experimental 
    and instrumental variable estimates.
Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.
The data and meta data from Statistics
    Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses
    the open data API of Statistics Netherlands.
Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable <doi:10.1007/s11336-020-09723-4>.
The Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) <arXiv:1805.11793>.
Tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing.  The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals.  Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology.
Provides a set of functions that extend the
        'cancor' function with new numerical and graphical outputs. It
        also include a regularized extension of the canonical
        correlation analysis to deal with datasets with more variables
        than observations.
Loads and creates spatial data, including layers and tools that are relevant
    to the activities of the Commission for the Conservation of Antarctic Marine Living
    Resources. Provides two categories of functions: load functions and create functions.
    Load functions are used to import existing spatial layers from the online CCAMLR GIS
    such as the ASD boundaries. Create functions are used to create layers from user data
    such as polygons and grids.
Canonical correlation analysis and maximum correlation via
    projection pursuit, as well as fast implementations of correlation
    estimators, with a focus on robust and nonparametric methods.
Class Cover Catch Digraphs, neighborhood graphs, and
        relatives.
Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.
Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <http://cvxopt.org> for more information).
Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) <doi:10.1002/9781118032923> and Carrasco et al. (2013) <doi:10.1016/j.cmpb.2012.09.002>.
Implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. <doi:10.1016/j.envsoft.2014.01.010>.
Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
Supporting the use of the Canadian Community Health Survey 
             (CCHS) by transforming variables from each cycle into harmonized, 
             consistent versions that span survey cycles (currently, 2001 to 
             2018). CCHS data used in this library is accessed and adapted in 
             accordance to the Statistics Canada Open Licence Agreement. This 
             package uses rec_with_table(), which was developed from 'sjmisc' 
             rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation 
             Functions". Journal of Open Source Software, 3(26), 754. 
             <doi:10.21105/joss.00754>.
Provides efficient implementation of the Cross-Covariance
    Isolate Detect (CCID) methodology for the estimation of the number
    and location of multiple change-points in the second-order
    (cross-covariance or network) structure of multivariate, possibly
    high-dimensional time series. The method is motivated by the detection
    of change points in functional connectivity networks for functional
    magnetic resonance imaging (fMRI), electroencephalography (EEG),
    magentoencephalography (MEG) and electrocorticography (ECoG) data. The
    main routines in the package have been extensively tested on fMRI data. 
    For details on the CCID methodology, please see Anastasiou et
    al (2022), Cross-covariance isolate detect: A new change-point method for
    estimating dynamic functional connectivity. Medical Image Analysis, Volume
    75.
Convex Clustering methods, including K-means algorithm,
  On-line Update algorithm (Hard Competitive Learning) and Neural Gas
  algorithm (Soft Competitive Learning), and calculation of several
  indexes for finding the number of clusters in a data set.
Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes.
Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.
Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).
Implements the convex clustering through majorization-minimization (CCMM) algorithm described in Touw, Groenen, and Terada (2022) <arXiv:2211.01877> to perform minimization of the convex clustering loss function.
Provides a set of functions to perform queries against the 'CCM' API <https://mohcontacttracing.my.salesforce.com>.
Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses 
             the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The 
             user can select between replacement and without replacement, the number of controls, and several covariates 
             to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.
Implementation of estimators for inferring 
    the mean of censored cost data. Including the estimators
    BT from Bang and Tsiatis (2000) <doi:10.1093/biomet/87.2.329> and 
    ZT from Zhao and Tian (2001) <doi:10.1111/j.0006-341X.2001.01002.x>.
Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method.
With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods.
Implements a method for identifying and removing
				the cell-cycle effect from scRNA-Seq data. The description of the 
				method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing 
				the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. 
				Different from previous methods, ccRemover implements a mechanism that
				formally tests whether a component is cell-cycle related or not, and thus
				while it often thoroughly removes the cell-cycle effect, it preserves
				other features/signals of interest in the data.
Functions for performing Correcting and Clustering response-style-biased preference data (CCRS). The main functions are correct.RS() for correcting for response styles, and ccrs() for simultaneously correcting and content-based clustering. The procedure begin with making rank-ordered boundary data from the given preference matrix using a function called create.ccrsdata(). Then in correct.RS(), the response style is corrected as follows: the rank-ordered boundary data are smoothed by I-spline functions, the given preference data are transformed by the smoothed functions. The resulting data matrix, which is considered as bias-corrected data, can be used for any data analysis methods. If one wants to cluster respondents based on their indicated preferences (content-based clustering), ccrs() can be applied to the given (response-style-biased) preference data, which simultaneously corrects for response styles and clusters respondents based on the contents. Also, the correction result can be checked by plot.crs() function.
A set of radiative transfer models to quantitatively describe the absorption, reflectance and transmission of solar energy in vegetation, and model remotely sensed spectral signatures of vegetation at distinct spatial scales (leaf,canopy and stand). The main principle behind ccrtm is that many radiative transfer models can form a coupled chain, basically models that feed into each other in a linked chain (from leaf, to canopy, to stand, to atmosphere). It allows the simulation of spectral datasets in the solar spectrum (400-2500nm) using leaf models as PROSPECT5, 5b, and D which can be coupled with canopy models as 'FLIM', 'SAIL' and 'SAIL2'. Currently, only a simple atmospheric model ('skyl') is implemented. Jacquemoud et al 2008 provide the most comprehensive overview of these models <doi:10.1016/j.rse.2008.01.026>.
Draws systematic samples from a population that follows linear trend. The function returns a matrix comprising of the required samples as its column vectors. The samples produced are highly efficient and the inter sampling variance is minimum. The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance.
CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.
Implements the uniform scaled beta distribution and
  the continuous convolution kernel density estimator.
Consensus analysis, model-based clustering, and cultural consensus theory applications to response data (e.g. questionnaires). The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.
Automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. 
 In addition, a 'shiny' App is provided, allowing users to analyze their data using an interactive graphical user interface, without any programming requirements.
Duplicated music data (pre-processed and formatted) for entity resolution. The total size of the data set is 9763. There are respective gold standard records that are labeled and can be considered as a unique identifier.
Supplies higher-order coordinatized data specification and fluid transform operators that include pivot and anti-pivot as special cases. 
    The methodology is describe in 'Zumel', 2018, "Fluid data reshaping with 'cdata'", <https://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , <DOI:10.5281/zenodo.1173299> .
    This package introduces the idea of explicit control table specification of data transforms.
    Works on in-memory data or on remote data using 'rquery' and 'SQL' database interfaces.
Likelihood-based estimation and data generation from a class of models used to estimate peer effects on count data by controlling for the network endogeneity. This class includes count data models with social interactions (Houndetoungan 2023; <doi:10.2139/ssrn.3721250>), spatial tobit models (Xu and Lee 2015; <doi:10.1016/j.jeconom.2015.05.004>), and spatial linear-in-means models (Lee 2004; <doi:10.1111/j.1468-0262.2004.00558.x>).
Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen, 2009) <DOI:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <DOI:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <DOI:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <DOI:10.1027/1614-2241/a000131>.
The 'U.S.' Centers for Disease Control and Prevention (CDC) maintain
    a portal <https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for
    accessing state, regional and national influenza statistics as well as
    mortality surveillance data. The web interface makes it difficult and
    time-consuming to select and retrieve influenza data. Tools are provided 
    to access the data provided by the portal's underlying 'API'.
Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <http://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.
Connect to the California Data Exchange Center (CDEC) 
    Web Service <http://cdec.water.ca.gov/>. 'CDEC' provides a centralized 
    database to store, process, and exchange real-time hydrologic information 
    gathered by various cooperators throughout California. The 'CDEC' Web Service 
    <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download 
    service for accessing historical records.
Employs a two-parameter family of
    distributions for modelling random variables on the (0, 1) interval by
    applying the cumulative distribution function (cdf) of one parent
    distribution to the quantile function of another.
Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.
The framework of causal decomposition of group disparities developed by
    Yu and Elwert (2023)
    <arXiv:2306.16591>.
    This package implements the decomposition estimators
    that are based on efficient influence functions. For the
    nuisance functions of the estimators, both parametric and
    nonparametric options are provided, as well as manual options in case
    the default models are not satisfying.
Coordinate Descent Algorithms for Lasso Penalized L1, L2,
        and Logistic Regression
The concept of cause-deleted life expectancy improvement is statistic designed to quantify the increase in life expectancy if a certain cause of death is removed. See Adamic, P. (2015) (<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689352>).
Downloads USDA National Agricultural Statistics Service (NASS) 
    cropscape data for a specified state. Utilities for fips, abbreviation, 
    and name conversion are also provided. Full functionality requires an 
    internet connection, but data sets can be cached for later off-line use.
Functions for cognitive diagnosis modeling and multidimensional item response modeling 
    for dichotomous and polytomous item responses. This package enables the estimation of 
    the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>),
    the multiple group (polytomous) GDINA model (de la Torre, 2011, 
    <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, 
    <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, 
    <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, 
    <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis 
    (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). 
    See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> 
    or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>)     
    for further details on estimation and the package structure.
    For tutorials on how to use the CDM package see 
    George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as
    Ravand and Robitzsch (2015).
Provides tools for working with observational health data in the 
  Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax.
  Common data model database table references are stored in a single compound object along with metadata.
Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.
Components of Canadian Credit Aggregates and Monetary Aggregates with continuity adjustments.
Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).
Parallel coordinate plotting with resolutions for large data sets
 and missing values.
A wrapper for the 'CDRC' 'API' that returns data frames or 'sf' of 'CDRC' data. The 'API' web reference is:<https://api.cdrc.ac.uk/swagger/index.html>.
Classification is based on the recently developed phylogenetic
    approach by Assis and Bachtrog (2013). The method classifies the
    evolutionary mechanisms retaining pairs of duplicate genes (conservation,
    neofunctionalization, subfunctionalization, or specialization) by comparing gene
    expression profiles of duplicate genes in one species to those of their single-
    copy ancestral genes in a sister species.
This is an implementation of constrained dual scaling for
    detecting response styles in categorical data, including utility functions. The
    procedure involves adding additional columns to the data matrix representing the
    boundaries between the rating categories. The resulting matrix is then doubled
    and analyzed by dual scaling. One-dimensional solutions are sought which provide
    optimal scores for the rating categories. These optimal scores are constrained
    to follow monotone quadratic splines. Clusters are introduced within which the
    response styles can vary. The type of response style present in a cluster can
    be diagnosed from the optimal scores for said cluster, and this can be used to
    construct an imputed version of the data set which adjusts for response styles.
Deriving skill structures from skill assignment 
    data for courses (sets of learning objects).
Provides tools for sampling from a conditional copula density decomposed via 
     Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a 
     sampling are those which sample as first the conditioning variables (when following the 
     sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). 
     The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) 
     <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) 
     <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine 
     (based on information criteria) among those which allow for such a conditional sampling. 
     The package includes a function to compare scatterplot matrices and pair-dependencies of 
     two multivariate datasets.
Splits data into Gaussian type clusters using the Cross-Entropy 
    Clustering ('CEC') method. This method allows for the simultaneous use of 
    various types of Gaussian mixture models, for performing the reduction of 
    unnecessary clusters, and for discovering new clusters by splitting them. 
    'CEC' is based on the work of Spurek, P. and Tabor, J. (2014) 
    <doi:10.1016/j.patcog.2014.03.006>.
Provides analytical methods for analyzing CRISPR screen data
    at different levels of gene expression. Multi-component normal mixture models
    and EM algorithms are used for modeling.
Streamlined annotation pipeline for collection and aggregation of time-to-event data in retrospective clinical studies. 'CEDARS' aims to systematize and accelerate the review of electronic health record (EHR) corpora. It accomplishes those goals by deploying natural language processing as a tool to assist detection and characterization of clinical events by human abstractors. The online user manual presents the necessary steps to install 'CEDARS', process EHR corpora and obtain clinical event dates: <https://cedars.io>.
Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
Create and learn Chain Event Graph (CEG) models using a Bayesian 
    framework. It provides us with a Hierarchical Agglomerative algorithm to 
    search the CEG model space.
    The package also includes several facilities for visualisations of the
    objects associated with a CEG. The CEG class can represent a range of
    relational data types, and supports arbitrary vertex, edge and graph
    attributes. A Chain Event Graph is a tree-based graphical model that
    provides a powerful graphical interface through which domain experts can
    easily translate a process into sequences of observed events using plain
    language. CEGs have been a useful class of graphical model especially to
    capture context-specific conditional independences. References: Collazo R, 
    Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018
    (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. 
    The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169
    <doi:10.1214/15-EJS1068>.
Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.
Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.
Data from statistical agencies and other institutions often
    need to be protected before they can be published. This package 
    can be used to perturb statistical tables in a consistent way. The
    main idea is to add - at the micro data level - a record key for each
    unit. Based on these keys, for any cell in a statistical table a
    cell key is computed as a function on the record keys contributing to
    a specific cell. Values that are added to the cell in order to perturb
    it are derived from a lookup-table that maps values of cell keys to 
    specific perturbation values. The theoretical basis for the methods 
    implemented can be found in Thompson, Broadfoot and Elazar 
    (2013) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf>
    which was extended and enhanced by Giessing and Tent 
    (2019) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S2_Germany_Giessing_Tent_AD.pdf>.
Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products.
Annotate single-cell RNA sequencing data manually based on
    marker gene thresholds.
    Find cell type rules (gene+threshold) through exploration,
    use the popular piping operator '%>%' to reconstruct complex
    cell type hierarchies.
    'cellpypes' models technical noise to find positive and negative cells for
    a given expression threshold and returns cell type labels or pseudobulks.
    Cite this package as Frauhammer (2022) <doi:10.5281/zenodo.6555728> and
    visit <https://github.com/FelixTheStudent/cellpypes> for tutorials and newest
    features.
Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.
Provides methodology to analyze cells that move in a two- or three-dimensional
	space. Available measures include displacement, confinement ratio, autocorrelation,
	straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks,
	steps, or subtracks with varying length. While the methodology has been developed for
	cell trajectory analysis, it is applicable to anything that moves including animals,
	people, or vehicles.
	Some of the methodology implemented in this packages was described by: 
	Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, 
	Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>,
	Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>,
	Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>,
	Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>,
	Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>,
  Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>,
  Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.
Implements a methodology for using cell volume
        distributions to estimate cell growth rates and division times
        that is described in the paper entitled, "Cell Volume
        Distributions Reveal Cell Growth Rates and Division Times", by
        Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro
        Tona and Anne L. Plant, which is in press in the Journal of
        Theoretical Biology.  In order to reproduce the analysis used
        to obtain Table 1 in the paper, execute the command
        "example(fitVolDist)".
Tools for detecting cellwise outliers and robust methods to analyze
    data which may contain them. Contains the implementation of the algorithms described in
    Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access)
    Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access),
    Raymaekers and Rousseeuw (2022) <arXiv:2207.13493> (open access)
    Rousseeuw (2022) <arXiv:2209.12697> (open access).
    Examples can be found in the vignettes:
    "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples",
    "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples",
    and "cellwise_weights_examples".
Implementation of the Coarsened Exact Matching algorithm discussed 
	along with its properties in
  Iacus, King, Porro (2011) <DOI:10.1198/jasa.2011.tm09599>;
	Iacus, King, Porro (2012) <DOI:10.1093/pan/mpr013> and
	Iacus, King, Porro (2019) <DOI:10.1017/pan.2018.29>.
'CemCO' algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).
Broken adaptive ridge estimator for censored data is used to
  select variables and estimate their coefficients in the semi-parametric 
  accelerated failure time model for right-censored survival data.
The network analysis plays an important role in numerous application domains including biomedicine. 
             Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. 
	           The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. 
	           Reference: 
             Ren, M., Zhang S. and Wang J. (2022). "Consistent Estimation of the Number of Communities via Regularized Network Embedding". Biometrics, <doi:10.1111/biom.13815>.
Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.
Contains functions to estimate a smoothed and a non-smoothed (empirical) time-dependent 
             receiver operating characteristic curve and the corresponding area under the receiver 
             operating characteristic curve and the optimal cutoff point for the right and interval 
             censored survival data. See Beyene and El Ghouch (2020)<doi:10.1002/sim.8671> and Beyene 
             and El Ghouch (2022) <doi:10.1002/bimj.202000382>.
Creates a common framework for organizing, naming, and gathering 
    population, age, race, and ethnicity data from the Census Bureau. Accesses 
    the API <https://www.census.gov/data/developers/data-sets.html>. Provides 
    tools for adding information to existing data to line up with Census data.
Implementations of threshold regression approaches for linear
	     regression models with a covariate subject to random censoring,
	     including deletion threshold regression and completion threshold regression.
	     Reverse survival regression, which flip the role of response variable and the
	     covariate, is also considered.
It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) <doi:10.1016/j.jmva.2017.05.005> for the multivariate skew-normal case.
Provee un acceso conveniente a mas de 17 millones de registros
    de la base de datos del Censo 2017. Los datos fueron importados desde
    el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De
    Grande. Esta paquete esta documentado intencionalmente en castellano
    asciificado para que funcione sin problema en diferentes plataformas.
    (Provides convenient access to more than 17 million records from the
    Chilean Census 2017 database. The datasets were imported from the official
    DVD provided by the Chilean National Bureau of Statistics by using the
    REDATAM converter created by Pablo De Grande and in addition it includes the
    maps accompanying these datasets.)
Implement an interval censor method 
 to break ties when using data with ties to fitting a 
 bivariate copula.
Engines for survival models from the 'parsnip' package. These
    include parametric models (e.g., Jackson (2016)
    <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al
    (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g.,
    Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).
Maximum Likelihood estimation of censored regression (Tobit) models
   with cross-sectional and panel data.
It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.
Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis.
A wrapper for the U.S. Census Bureau APIs that returns data frames of 
	Census data and metadata. Available datasets include the 
	Decennial Census, American Community Survey, Small Area Health Insurance Estimates,
	Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.
Use the US Census API to collect summary data tables
    for SF1 and ACS datasets at arbitrary geographies.
The 'Censys' public search engine enables researchers to quickly ask 
    questions about the hosts and networks that compose the Internet. Details on how 
    'Censys' was designed and how it is operated are available at <https://www.censys.io/about>. 
    Both basic and extended research access queries are made available. More information 
    on the SQL dialect used by the 'Censys' engine can be found at 
    <https://cloud.google.com/bigquery/docs/reference/legacy-sql>.
Calculates centrality indices additional to the 'igraph' package centrality functions.
The degree of correlation between centre and peripheral items are shown by the length of the line between them. You can self-define the length by inputing the "distance" parameter. For example, you can input (1 - Pearson's correlation coefficient) as "distance" so that the stronger the correlation between centre and peripheral item, the nearer they will be in this plot. Also, If you do a hypothesis test and the null hypothesis is centre and peripheral items are the same, you can input -log(P) as distance. To sum up, the stronger the correlation between centre and peripheral is, the smaller the "distance" parameter should be. Due to its high degree of freedom, it can be applied to many different circumstance.
Find the numbers of test tubes that can be balanced in centrifuge
    rotors and show various ways to load them. Refer to Pham (2020)
    <doi:10.31224/osf.io/4xs38> for more information on package functionality.
Easy and convenient access to the datasets of the
  "Centre d'Estudis d'Opinio", the Catalan institution for polling and public
  opinion. The package uses the data stored in the servers of the CEO and returns
  it in a tidy format.
Optimization solver based on the Cross-Entropy method.
It aims to find significant pathways through 
    network topology information. It has several advantages compared 
    with current pathway enrichment tools. First, pathway node instead 
    of single gene is taken as the basic unit when analysing networks 
    to meet the fact that genes must be constructed into complexes to 
    hold normal functions. Second, multiple network centrality measures are 
    applied simultaneously to measure importance of nodes from different 
    aspects to make a full view on the biological system. CePa extends 
    standard pathway enrichment methods, which include both over-representation 
    analysis procedure and gene-set analysis procedure. 
    <doi:10.1093/bioinformatics/btt008>.
Provides data on countries and their main city or agglomeration and
    the different distance measures and dummy variables indicating whether two
    countries are contiguous, share a common language or a colonial
    relationship. The reference article for these datasets is Mayer and Zignago
    (2011) <http://www.cepii.fr/CEPII/en/publications/wp/abstract.asp?NoDoc=3877>.
Functions and Data to support Context Driven Exploratory Projection Pursuit.
Retorna detalhes de dados de CEPs brasileiros, bairros, logradouros 
    e tal. (Returns info of Brazilian postal codes, city names, addresses 
    and so on.)
Read Condensed Cornell Ecology Program ('CEP') and legacy
    'CANOCO' files into R data frames.
Perform censored quantile regression of Huang (2010) <doi:10.1214/09-AOS771>, and restore monotonicity respecting via adaptive interpolation for dynamic regression of Huang (2017) <doi:10.1080/01621459.2016.1149070>. The monotonicity-respecting restoration applies to general dynamic regression models including (uncensored or censored) quantile regression model, additive hazards model, and dynamic survival models of Peng and Huang (2007) <doi:10.1093/biomet/asm058>, among others.
Download imagery tiles to a standard cache and load the data into raster objects. 
 Facilities for 'AWS' terrain <https://registry.opendata.aws/terrain-tiles/> terrain and 'Mapbox' 
 <https://www.mapbox.com/> servers are provided.
The 'vctrs' package provides a concept of vector prototype
    that can be especially useful when deploying models and code.  Serialize 
    these object prototypes to 'JSON' so they can be used to check and coerce 
    data in production systems, and deserialize 'JSON' back to the correct 
    object prototypes.
Fits a Causal Effect Random Forest of Interaction Tress (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see X. Su, A. T. Peña, L. Liu, and R. A. Levine (2018) <doi:10.48550/arXiv.1709.04862>.
Implements the iterated RMCD method of Cerioli (2010)
	for multivariate outlier detection via robust Mahalanobis distances. Also
	provides the finite-sample RMCD method discussed in the paper, as well as 
	the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> 
	and Green and Martin (2017).
Provides several functions to identify and analyse miRNA sponge, including
             popular methods for identifying miRNA sponge interactions, two types 
             of global ceRNA regulation prediction methods and four types of context-specific 
             prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based 
             on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, 
             respectively. In addition, For predictive ceRNA relationship pairs, this package 
             provides several downstream analysis algorithms, including regulatory network 
             analysis and functional annotation analysis, as well as survival prognosis analysis
             based on expression of ceRNA ternary pair.
Compute the certainty equivalents and premium risks
              as tools for risk-efficiency analysis. For more technical information, please refer to: Hardaker, Richardson, Lien, & Schumann (2004) <doi:10.1111/j.1467-8489.2004.00239.x>, and Richardson, & Outlaw (2008) <doi:10.2495/RISK080231>.
Perform Nonlinear Mixed-Effects (NLME) Modeling using Certara's
    NLME-Engine. Access the same Maximum Likelihood engines used in the Phoenix
    platform, including algorithms for parametric methods, individual, and pooled
    data analysis <https://www.certara.com/app/uploads/2020/06/BR_PhoenixNLME-v4.pdf>.
    The Quasi-Random Parametric Expectation-Maximization Method (QRPEM) is also
    supported <https://www.page-meeting.org/default.asp?abstract=2338>. Execution
    is supported both locally or on remote machines. Remote execution includes
    support for Linux Sun Grid Engine (SGE), Terascale Open-source Resource and
    Queue Manager (TORQUE) grids, Linux and Windows multicore, and individual runs.
A convenient set of wrapper functions to install
    pharmacometric packages and Shiny applications developed by Certara
    PMX and Integrated Drug Development (iDD). The functions ensure the
    successful installation of packages from non-standard repositories.
Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.
Makes accessing and loading the Canadian Election Study (<http://www.ces-eec.ca/>, <https://ces-eec.arts.ubc.ca/>, <https://search1.odesi.ca/#/>) surveys into the R workspace more efficient by 
  downloading a requested survey and loading it as a data object. This removes the need to locate, download, load, 
  and change working directories when working with the Canadian Election Study surveys.
Collection of perceptually uniform colour maps made by Peter Kovesi
    (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> 
    at the Centre for Exploration Targeting (CET).
Ceteris Paribus Profiles (What-If Plots) are designed to present model 
    responses around selected points in a feature space. 
    For example around a single prediction for an interesting observation. 
    Plots are designed to work in a model-agnostic fashion, they are working 
    for any predictive Machine Learning model and allow for model comparisons.
    Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.
Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.
Cellular cooperation compromises the plating efficiency-based 
    analysis of clonogenic survival data. This tool provides functions that 
    enable a robust analysis of colony formation assay (CFA) data in presence 
    or absence of cellular cooperation. 
    The implemented method has been described 
    in Brix et al. (2020). (Brix, N., Samaga, D., Hennel, R. et al. 
    "The clonogenic assay: robustness of plating efficiency-based analysis is 
    strongly compromised by cellular cooperation." Radiat Oncol 15, 248 (2020).
    <doi:10.1186/s13014-020-01697-y>)
        Power regression for parameter estimation, calculation of survival
    fractions, uncertainty analysis and plotting functions are provided.
A utility to quickly obtain clean and tidy college football
    data. Serves as a wrapper around the
    <https://collegefootballdata.com/> API and provides functions to
    access live play by play and box score data from ESPN
    <https://www.espn.com> when available. It provides users the
    capability to access a plethora of endpoints, and supplement that data
    with additional information (Expected Points Added/Win Probability
    added).
Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
  with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
  CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++. For 
  mathematical details and software tutorial, see Mahani and Sharabiani (2019) <DOI:10.18637/jss.v089.i09>.
Package for the analysis of categorical functional data. 
  The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>. 
  It also provides functions to perform basic statistical analysis on categorical functional data.
Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.
A simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems,
             to break free from the complex computation of existing similarity formulas and the ability to solve big data.
This project provides a group of new functions to calculate the
    outputs of the two main components of the Canadian Forest Fire Danger Rating
    System (CFFDRS) Van Wagner and Pickett (1985) 
    <https://cfs.nrcan.gc.ca/publications?id=19973>)
    at various time scales: the Fire Weather Index (FWI) System Wan Wagner 
    (1985) <https://cfs.nrcan.gc.ca/publications?id=19927> and the Fire 
    Behaviour Prediction (FBP) System Forestry Canada Fire Danger Group (1992) 
    <https://cfs.nrcan.gc.ca/pubwarehouse/pdfs/10068.pdf>. Some functions 
    have two versions, table and raster based.
The Citation File Format version 1.2.0
    <doi:10.5281/zenodo.5171937> is a human and machine readable file
    format which provides citation metadata for software. This package
    provides core utilities to generate and validate this metadata.
Facilitates the identification of counterfactual queries in
 structural causal models via the ID* and IDC* algorithms
 by Shpitser, I. and Pearl, J. (2007, 2008) 
 <arXiv:1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>. 
 Provides a simple interface for defining causal diagrams and counterfactual 
 conjunctions. Construction of parallel worlds graphs and counterfactual graphs 
 is carried out automatically based on the counterfactual query and the causal 
 diagram.
The collaborative Filtering methodology has been widely used in recommendation systems, which
    uses similarities between users or items to make recommendations. A class called CF was implemented,
    where it is structured by matrices and composed of recommendation and database manipulation functions.
    See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.
Performs causal functional mediation analysis (CFMA) for functional treatment, functional mediator, and functional outcome. This package includes two functional mediation model types: (1) a concurrent mediation model and (2) a historical influence mediation model. See Zhao et al. (2018), Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data, <arXiv:1805.06923> for details.
Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) <doi:10.1183/13993003.00224-2019>.
Performs forward model selection, using the C-index/concordance
  in survival analysis models.
Developed as a collaboration between Earth lab and the North Central Climate Adaptation Science Center to help users gain insights from available climate data. Includes tools and instructions for downloading climate data via a 'USGS' API and then organizing those data for visualization and analysis that drive insight. Web interface for 'USGS' API can be found at <http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_CMIP5_aggregated_macav2_catalog.html>.
Support for all calendars as specified in the Climate and Forecast 
    (CF) Metadata Conventions for climate and forecasting data. The CF Metadata 
    Conventions is widely used for distributing files with climate observations 
    or projections, including the Coupled Model Intercomparison Project (CMIP) 
    data used by climate change scientists and the Intergovernmental Panel on
    Climate Change (IPCC). This package specifically allows the user to work 
    with any of the CF-compliant calendars (many of which are not compliant with 
    POSIXt). The CF time coordinate is formally defined in the CF Metadata 
    Conventions document available at <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.html#time-coordinate>.
Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.
Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.
'CGAL' is a C++ library that aims to provide easy access to efficient and
  reliable algorithms in computational geometry. Since its version 4, 'CGAL' can be used
  as standalone header-only library and is available under a double GPL-3|LGPL license.
  <https://www.cgal.org/>.
Provides some utilities for 3D meshes: clipping of a mesh to
    the volume bounded by another mesh, decomposition into convex parts,
    distance between a mesh and a point, Hausdorff distance between two
    meshes, triangulation, geodesic distance, Boolean operations
    (intersection, union, difference), Minkowski sum, subdivision
    algorithms, random sampling on a mesh, volume, area, and centroid.
    Also provides two algorithms for surface reconstruction from a cloud
    of points. Meshes are represented by R6 classes. All algorithms are
    performed by the 'C++' library 'CGAL' (<https://www.cgal.org/>).
Provides some utilities for polygons: area calculation,
    decomposition into convex parts, Minkowski addition, Boolean operations, 
    and more. Polygons with holes are supported. The algorithms are performed 
    by the 'C++' library' CGAL' (<https://www.cgal.org/>).
A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions.  It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019)<doi:10.18637/jss.v089.i05> for more details.
The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.
Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package.
Run computer experiments using the adaptive composite grid
    algorithm with a Gaussian process model.
    The algorithm works best when running an experiment that can evaluate thousands
    of points from a deterministic computer simulation.
    This package is an implementation of a forthcoming paper by Plumlee,
    Erickson, Ankenman, et al. For a preprint of the paper,
    contact the maintainer of this package.
Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) <doi: 10.18637/jss.v105.i01>, Augugliaro et al. (2020b) <doi: 10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi: 10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi: 10.1214/11-AOAS494> and Stadler et al. (2012) <doi: 10.1007/s11222-010-9219-7>.
Estimates the ratio of the regression coefficients and the dispersion parameter in conditional generalized linear models for clustered data.
This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit <https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.
Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)<doi:10.1093/bioinformatics/btx826>'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'.
Continuous glucose monitoring (CGM) systems provide real-time, 
  dynamic glucose information by tracking interstitial glucose values 
  throughout the day. Glycemic variability, also known as glucose variability, 
  is an established risk factor for hypoglycemia (Kovatchev) and 
  has been shown to be a risk factor in diabetes complications. 
  Over 20 metrics of glycemic variability have been identified. 
  Here, we provide functions to calculate glucose summary metrics, 
  glucose variability metrics (as defined in clinical publications), 
  and visualizations to visualize trends in CGM data.
  Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract>
  American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc>
  Kovatchev B (2019) <doi:10.1177/1932296819826111>
  Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3>
  Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017>
  Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.
Find multiple solutions of a nonlinear least squares problem.  Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute approximate multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gauss–Newton method. Optimization and Engineering, 1-31.
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not new methods but rather wrappers around either base R or other packages.
Allows users to construct the Continuous Time Generalized Rapid 
            Response CUSUM (CGR-CUSUM), Biswas & Kalbfleisch (2008) 
            <doi:10.1002/sim.3296> CUSUM, Binary CUSUM and risk-adjusted funnel 
            plot for survival data. These procedures can be used to monitor
            survival processes and detect problems in their quality.
Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc.  The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' .  Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min.  In addition, functions which mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'.
The solution to some common problems is proposed, 
     as well as a summary of some small functions. In particular, 
     it provides a useful function for some problems in chemistry.
     For example, monoa(), monob() and mono() function can be used 
     to calculate The pH of weak acid/base. 
     The ggpng() function can save the PNG format with transparent background. 
     The period_table() function will show the periodic table. Also the
     show_ruler() function will show the ruler.
     The show_color() function is funny and easier to show colors.
      I also provide the symb() function to generate multiple symbols at once.
     The csv2vcf() function provides an easy method to generate a file.
     The sym2poly() and sym2coef() function can extract coefficients from 
     polynomials.
Various statistical methods and models which are
    typically used for the estimation of outstanding claims reserves
    in general insurance, including those to estimate the claims
    development result as required under Solvency II.
Assign distinct colors to arbitrary multi-dimensional data, considering its structure.
Performs adjustments of a user-supplied independence loglikelihood 
    function using a robust sandwich estimator of the parameter covariance 
    matrix, based on the methodology in Chandler and Bate (2007) 
    <doi:10.1093/biomet/asm015>.  This can be used for cluster correlated data 
    when interest lies in the parameters of the marginal distributions or for 
    performing inferences that are robust to certain types of model 
    misspecification.  Functions for profiling the adjusted loglikelihoods are 
    also provided, as are functions for calculating and plotting confidence 
    intervals, for single model parameters, and confidence regions, for pairs 
    of model parameters.  Nested models can be compared using an adjusted 
    likelihood ratio test.
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) <doi:10.1007/s11222-020-09940-y>.
Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2021) <arXiv:2107.10572>.  Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this.  There are options for different types of graphics to assess the influence.
Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) <doi:10.18637/jss.v058.i03> ).
Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.
Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.
A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.
A multiple testing procedure for clustered alternative hypotheses. 
    It is assumed that the p-values under the null hypotheses follow U(0,1) and 
    that the distributions of p-values from the alternative hypotheses are 
    stochastically smaller than U(0,1). By aggregating information, this 
    method is more sensitive to detecting signals of low magnitude than 
    standard methods. Additionally, sporadic small p-values appearing 
    within a null hypotheses sequence are avoided by averaging on the 
    neighboring p-values.
Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers <doi:10.1016/j.jmva.2014.07.012> and Nasri & Remillard <doi:10.1016/j.jmva.2019.03.002>.
Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.
Facilitates workflows to reproducibly transform estimates of species’ distributions into metrics relevant for conservation. For example, combining predictions from species distribution models with other maps of environmental data to characterize the proportion of a species’ range that is under protection, calculating metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and calculating more general metrics such as taxonomic and phylogenetic diversity, as well as endemism. Also facilitates temporal comparisons among biodiversity metrics to inform efforts towards complementarity and consideration of future scenarios in conservation decisions. 'changeRangeR' also provides tools to determine the effects of modeling decisions through sensitivity tests.
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package 'ChannelAttribution'.
Adjusts the loglikelihood of common econometric models for
    clustered data based on the estimation process suggested in 
    Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, using the 'chandwich' 
    package <https://cran.r-project.org/package=chandwich>, and provides 
    convenience functions for inference on the adjusted models.
Computes and visualize the results of the 0-1 test for chaos proposed
    by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. The algorithm is
    available in parallel for the independent values of parameter c. Additionally,
    fast RQA is added to distinguish chaos from noise.
The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals.
Make fake data, supporting addresses, person names, dates,
    times, colors, coordinates, currencies, digital object identifiers
    ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers
    from distributions and within a range.
Provides a very simple syntax for the user to generate custom plot(s) without having to remember complicated 'ggplot2' syntax. The 'chartql' package uses 'ggplot2' and manages all the syntax complexities internally. As an example, to generate a bar chart of company sales faceted by product category further faceted by season of the year, we simply write: "CHART bar X category, season Y sales".
The Large Language Model (LLM) represents a groundbreaking advancement 
    in data science and programming, and also allows us to extend the world of R. 
    A seamless interface for integrating the 'OpenAI' API 
    (see <https://openai.com/blog/openai-api>), 'Stable Diffusion' and 
    other Web APIs into R is provided in this package. This package leverages LLM-based 
    AI techniques, enabling efficient knowledge discovery and data analysis.
    In addition, this package includes functionality for performing 
    image generation such as txt2img (t2i) and img2img (i2i) using the 'DALL-E 2' and 
    'Stable Diffusion' APIs.
'OpenAI's 'ChatGPT' <https://chat.openai.com/> coding assistant for 'RStudio'. A set 
  of functions and 'RStudio' addins that aim to help the R developer in tedious coding tasks.
A simple package to grab cheat sheets and save them to your local computer.
Utility functions that allow checking the basic validity of a function argument or any other value, 
             including generating an error and assigning a default in a single line of code. The main purpose of
             the package is to provide simple and easily readable argument checking to improve code robustness.
Check digits are used like file hashes to verify that a
    number has been transcribed accurately. The functions provided by this
    package help to calculate and verify check digits according to various
    algorithms.
Creates auto checking check-fields and check-boxes for 'rmarkdown' html. It could be used in class, when teacher share materials and tasks, so student can solve some problems and check themselves. In contrast with the 'learnr' package the 'checkdown' package works without 'shiny'.
Checks that students have the correct version of 'R', 'R' packages,
    'RStudio' and other dependencies installed, and that the recommended 'RStudio' 
    configuration has been applied.
A minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis.
Deal with packages 'check' outputs and reduce the risk of
    rejection by 'CRAN' by following policies.
Confirms if the number is Luhn compliant.
    Can check if credit card, IMEI number or any other Luhn based number is correct. 
    For more info see: <https://en.wikipedia.org/wiki/Luhn_algorithm>.
Tests and assertions to perform frequent argument checks. A
    substantial part of the package was written in C to minimize any worries
    about execution time overhead.
The goal of checkpoint is to solve the problem of package
    reproducibility in R. Specifically, checkpoint allows you to install packages
    as they existed on CRAN on a specific snapshot date as if you had a CRAN time
    machine. To achieve reproducibility, the checkpoint() function installs the
    packages required or called by your project and scripts to a local library
    exactly as they existed at the specified point in time. Only those packages
    are available to your project, thereby avoiding any package updates that came
    later and may have altered your results. In this way, anyone using checkpoint's
    checkpoint() can ensure the reproducibility of your scripts or projects at any
    time. To create the snapshot archives, once a day (at midnight UTC) Microsoft
    refreshes the Austria CRAN mirror on the "Microsoft R Archived Network"
    server (<https://mran.microsoft.com/>). Immediately after completion
    of the rsync mirror process, the process takes a snapshot, thus creating the
    archive. Snapshot archives exist starting from 2014-09-17.
Expressive, assertive, pipe-friendly functions 
  to check the properties of common R objects.
  In the case of failure the functions issue informative error messages.
Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
Given a tree-based machine learning model, calculate the tree SHAP 
    <arXiv:1802.03888>; <https://github.com/ModelOriented/treeshap> 
    local explanation of every observation. View the data space, explanation 
    space, and model residuals as ensemble graphic interactive on a shiny 
    application. After an observation of interest is identified, the normalized 
    variable importance of the local explanation is used as a 1D projection
    basis. The support of the local explanation is then explored by changing
    the basis with the use of the radial tour <doi:10.32614/RJ-2020-027>; 
    <doi:10.1080/10618600.1997.10474754>.
Contains tools for working with data during statistical analysis, promoting flexible, intuitive, and reproducible workflows. There are functions designated for specific statistical tasks such building a custom univariate descriptive table, computing pairwise association statistics, etc. These are built on a collection of data manipulation tools designed for general use that are motivated by the functional programming concept.
Combines taxonomic classifications of high-throughput 16S rRNA
  gene sequences with reference proteomes of archaeal and bacterial taxa to
  generate amino acid compositions of community reference proteomes. Calculates
  chemical metrics including carbon oxidation state ('Zc'), stoichiometric
  oxidation and hydration state ('nO2' and 'nH2O'), H/C, N/C, O/C, and S/C
  ratios, grand average of hydropathicity ('GRAVY'), isoelectric point ('pI'),
  protein length, and average molecular weight of amino acid residues. Uses
  precomputed reference proteomes for archaea and bacteria derived from the
  Genome Taxonomy Database ('GTDB'). Also includes reference proteomes derived
  from the NCBI Reference Sequence ('RefSeq') database and manual mapping from
  the 'RDP Classifier' training set to 'RefSeq' taxonomy as described by Dick and
  Tan (2023) <doi:10.1007/s00248-022-01988-9>. Processes taxonomic
  classifications in 'RDP Classifier' format or OTU tables in 'phyloseq-class'
  objects from the Bioconductor package 'phyloseq'.
Simple functions for plotting linear
	calibration functions and estimating standard errors for measurements
	according to the Handbook of Chemometrics and Qualimetrics: Part A
	by Massart et al. (1997) There are also functions estimating the limit
	of detection (LOD) and limit of quantification (LOQ).
	The functions work on model objects from - optionally weighted - linear
	regression (lm) or robust linear regression ('rlm' from the 'MASS' package).
A collection of functions that have been developed to assist
    experimenter in modeling chemical degradation kinetic data. The
    selection of the appropriate degradation model and parameter
    estimation is carried out automatically as far as possible and is
    driven by a rigorous statistical interpretation of the results.  The
    package integrates already available goodness-of-fit statistics for
    nonlinear models. In addition it allows data fitting with the
    nonlinear first-order multi-target (FOMT) model.
We aim to deal with the average treatment effect (ATE), where the data are
    subject to high-dimensionality and measurement error. This package primarily contains two 
    functions, which are used to generate artificial data and estimate ATE with high-dimensional 
    and error-prone data accommodated.
Contains a set of methods for fitting models and methods for
    validating the resulting models. The statistical methodologies comprise
    a comprehensive collection of approaches whose validity and utility have
    been accepted by experts in the Cheminformatics field. As promising new
    methodologies emerge from the statistical and data-mining communities, they
    will be incorporated into the laboratory. These methods are aimed at discovering
    quantitative structure-activity relationships (QSARs). However, the user can
    directly input their own choices of descriptors and responses, so the capability
    for comparing models is effectively unlimited.
Quantify and visualise various measures of chemical diversity and
    dissimilarity, for phytochemical compounds and other sets of chemical
    composition data. Importantly, these measures can incorporate biosynthetic
    and/or structural properties of the chemical compounds, resulting in a more
    comprehensive quantification of diversity and dissimilarity. For details,
    see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.
R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).
A collection of functions for top-down exploratory data analysis
    of spectral data including nuclear magnetic resonance (NMR), infrared (IR),
    Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy.
    Includes functions for plotting and inspecting spectra, peak alignment,
    hierarchical cluster analysis (HCA), principal components analysis (PCA) and
    model-based clustering. Robust methods appropriate for this type of
    high-dimensional data are available. ChemoSpec is designed for structured
    experiments, such as metabolomics investigations, where the samples fall into
    treatment and control groups. Graphical output is formatted consistently for
    publication quality plots. ChemoSpec is intended to be very user friendly and
    to help you get usable results quickly. A vignette covering typical operations
    is available.
A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the  sample from a statistical perspective.  In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly.
Functions supporting the common needs of packages 'ChemoSpec' and 'ChemoSpec2D'.
Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, <doi:10.1198/10618600152627997>.    Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2).
Provides an alternative approach to multiple testing
        by calculating a simultaneous upper confidence bounds for the
        number of true null hypotheses among any subset of the hypotheses of interest, 
        using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>.
Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC.
This is an opinionated wrapper around the
    python-chess package. It allows users to read and write PGN files as
    well as create and explore game trees such as the ones seen in chess
    books.
Applies the change-in-effect estimate method to assess confounding 
    effects in medical and epidemiological research (Greenland & Pearce (2016) 
    <doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model 
    including only the outcome and exposure variables. At each of the subsequent 
    steps, one variable which creates the largest change among the remaining variables 
    is selected. This process is repeated until all variables have been entered into 
    the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' 
    package has functions for linear regression, logistic regression, negative 
    binomial regression, Cox proportional hazards model and conditional logistic 
    regression.
The software matches the current history to the closest history in a time series to build a forecast.
Light weight implementation of the standard distribution 
  functions for the chi distribution, wrapping those for the chi-squared 
  distribution in the stats package.
Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.
Measuring child development starts by collecting responses to 
    developmental milestones, such as "able to sit" or "says two words". 
    There are many ways to combine such responses into summaries. 
    The package bundles publicly available datasets with individual milestone 
    data for children aged 0-5 years, with the aim of supporting the construction, 
    evaluation, validation and interpretation of methodologies that aggregate 
    milestone data into informative measures of child development.
Tools for connecting to 'CHILDES', an open repository for
    transcripts of parent-child interaction. For more information on the 
    underlying data, see <https://langcog.github.io/childes-db-website/>.
Calculation of standard deviation scores and percentiles adduced from different
    standards (WHO, UK, Germany, Italy, China, etc). Also, references for laboratory values in
    children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. See package documentation for full list.
Mapas terrestres con topologias simplificadas. Estos mapas no 
  tienen precision geodesica, por lo que aplica el DFL-83 de 1979 de la Republica
  de Chile y se consideran referenciales sin validez legal.
  No se incluyen los territorios antarticos y bajo ningun evento estos mapas
  significan que exista una cesion u ocupacion de territorios soberanos en
  contra del Derecho Internacional por parte de Chile. Esta paquete esta 
  documentado intencionalmente en castellano asciificado para que funcione sin 
  problema en diferentes plataformas.
  (Terrestrial maps with simplified toplogies. These maps lack geodesic
  precision, therefore DFL-83 1979 of the Republic of Chile applies and are
  considered to have no legal validity.
  Antartic territories are excluded and under no event these maps mean
  there is a cession or occupation of sovereign territories against International
  Laws from Chile. This package was intentionally documented in asciified
  spanish to make it work without problem on different platforms.)
Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 <DOI:10.1111/j.1438-8677.2012.00590.x>), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Model for apple "Gala" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Taiwan Model (Lu et al., 2012 <DOI:10.17660/ActaHortic.2012.962.35>), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the 'chillR' package (Luedeling, 2018), Unified Model (Chuine et al., 2016 <DOI:10.1111/gcb.13383>) and Heat Restriction model.
The phenology of plants (i.e. the timing of their annual life
    phases) depends on climatic cues. For temperate trees and many other plants,
    spring phases, such as leaf emergence and flowering, have been found to result
    from the effects of both cool (chilling) conditions and heat. Fruit tree
    scientists (pomologists) have developed some metrics to quantify chilling
    and heat (e.g. see Luedeling (2012) <doi:10.1016/j.scienta.2012.07.011>).
    'chillR' contains functions for processing temperature records into
    chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units
    (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often
    considered the most promising, but they are difficult to calculate. This package
    makes it easy. 'chillR' also contains procedures for conducting a PLS analysis
    relating phenological dates (e.g. bloom dates) to either mean temperatures or
    mean chill and heat accumulation rates, based on long-term weather and phenology
    records (Luedeling and Gassner (2012) <doi:10.1016/j.agrformet.2011.10.020>).
    As of version 0.65, it also includes functions for generating weather
    scenarios with a weather generator, for conducting climate change analyses
    for temperature-based climatic metrics and for plotting results from such
    analyses. Since version 0.70, 'chillR' contains a function for interpolating
    hourly temperature records.
Efforts are made to make Chinese text mining easier, faster, and robust to errors. 
    Document term matrix can be generated by only one line of code; detecting encoding, 
    segmenting and removing stop words are done automatically. 
	Some convenient tools are also supplied.
A database of Chinese surnames and Chinese given names (1930-2008).
    This database contains nationwide frequency statistics of
    1,806 Chinese surnames and 2,614 Chinese characters used in given names,
    covering about 1.2 billion Han Chinese population
    (96.8% of the Han Chinese household-registered population
    born from 1930 to 2008 and still alive in 2008).
    This package also contains a function for computing multiple features of
    Chinese surnames and Chinese given names for scientific research (e.g.,
    name uniqueness, name gender, name valence, and name warmth/competence).
A collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) <doi:10.1093/bioinformatics/btv205>.
Nonparametric Tests to identify the differential enrichment region for two conditions or time-course ChIP-seq data. It includes: data preprocessing function, estimation of a small constant used in hypothesis testing, a kernel-based two sample nonparametric test, two assumption-free  two sample nonparametric test.
API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. 
  The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution 
  (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery 
  and in-situ station data to create gridded rainfall time series for trend 
  analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global 
  (60°S – 70°N), high-resolution data set of daily maximum and minimum
  temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit 
  its official home page <https://www.chc.ucsb.edu/data>.
Perform post hoc analysis based on residuals of Pearson's Chi-squared Test for Count Data based on T. Mark Beasley & Randall E. Schumacker (1995) <doi: 10.1080/00220973.1995.9943797>.
Provides the facility to perform the chi-square and G-square test of independence, calculates permutation-based p value, and provides measures of association such as Phi, odds ratio with 95 percent CI and p value, adjusted contingency coefficient, Cramer's V and 95 percent CI, bias-corrected Cramer's V, Cohen's w, Goodman-Kruskal's lambda, gamma and its p value, and tau, Cohen's k and its 95 percent CI. It also calculates standardized, moment-corrected standardized, and adjusted standardized residuals, and their significance. Different outputs are returned in nicely formatted tables.
For developers to check user-supplied function arguments.  It
    is designed to be simple, fast and customizable.  Error messages
    follow the tidyverse style guide.
Fit Bayesian models in Stan <doi: 10.18637/jss.v076.i01> 
  with checkpointing, that is, the ability to stop the MCMC sampler at 
  will, and then pick right back up where the MCMC sampler left off. 
  Custom 'Stan' models can be fitted, or the popular package 'brms' 
  <doi: 10.18637/jss.v080.i01> can be used to generate the 'Stan' code. This 
  package is fully compatible with the R packages 'brms', 'posterior', 'cmdstanr', 
  and 'bayesplot'.
As different antipsychotic medications have different potencies,
    the doses of different medications cannot be directly compared. Various
    strategies are used to convert doses into a common reference so that
    comparison is meaningful. Chlorpromazine (CPZ) has historically been used
    as a reference medication into which other antipsychotic doses can be
    converted, as "chlorpromazine-equivalent doses". Using conversion keys
    generated from widely-cited scientific papers, e.g. Gardner et. al 2010
    <doi:10.1176/appi.ajp.2009.09060802> and Leucht et al. 2016
    <doi:10.1093/schbul/sbv167>, antipsychotic doses are converted
    to CPZ (or any specified antipsychotic) equivalents. The use of the package
    is described in the included vignette. Not for clinical use.
An exact and a variational inference for
    coupled Hidden Markov Models applied to the joint detection of copy number variations.
Compute Chinese capital stocks in provinces level, based on Zhang (2008) <DOI:10.1080/14765280802028302>.
Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) <DOI:10.1186/s12859-017-1863-x> and the package vignette.
An integrated set of tools for thermodynamic calculations in
  aqueous geochemistry and geobiochemistry. Functions are provided for writing
  balanced reactions to form species from user-selected basis species and for
  calculating the standard molal properties of species and reactions, including
  the standard Gibbs energy and equilibrium constant. Calculations of the
  non-equilibrium chemical affinity and equilibrium chemical activity of species
  can be portrayed on diagrams as a function of temperature, pressure, or
  activity of basis species; in two dimensions, this gives a maximum affinity or
  predominance diagram. The diagrams have formatted chemical formulas and axis
  labels, and water stability limits can be added to Eh-pH, oxygen fugacity-
  temperature, and other diagrams with a redox variable. The package has been
  developed to handle common calculations in aqueous geochemistry, such as
  solubility due to complexation of metal ions, mineral buffers of redox or pH,
  and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ
  also implements a group additivity algorithm for the standard thermodynamic
  properties of proteins.
Design functions for DCMs and other types of choice studies (including MaxDiff and other tradeoffs).
Implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.
Collection of utility functions for visualizing
    body map data collected with the Collaborative Health Outcomes
    Information Registry.
Amends errors, augments data and aids analysis of John Snow's map
  of the 1854 London cholera outbreak.
Sampling from the Cholesky factorization of a Wishart random 
    variable, sampling from the inverse Wishart distribution, sampling from 
    the Cholesky factorization of an inverse Wishart random variable, sampling
    from the pseudo Wishart distribution, sampling from the generalized
    inverse Wishart distribution, computing densities for the Wishart 
    and inverse Wishart distributions, and computing the multivariate gamma 
    and digamma functions. Provides a header file so the C functions can be
    called directly from other programs.
Calculates permutation tests that can be powerful for comparing two groups with some positive but many zero responses (see Follmann, Fay, and Proschan <DOI:10.1111/j.1541-0420.2008.01131.x>).
Resampling is a standard step in particle filtering and in
    sequential Monte Carlo. This package implements the chopthin resampler, which
    keeps a bound on the ratio between the largest and the smallest weights after
    resampling.
Learning the structure of graphical models from datasets with thousands of variables.
    More information about the research papers detailing the theory behind Chordalysis is available at
    <http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013).
    The R package development site is <https://github.com/HerrmannM/Monash-ChoR>.
Maximum likelihood estimation in respondent driven samples.
Choropleths are thematic maps where geographic regions, such as
    states, are colored according to some metric, such as the number of people
    who live in that state. This package simplifies this process by 1.
    Providing ready-made functions for creating choropleths of common maps. 2.
    Providing data and API connections to interesting data sources for making
    choropleths. 3. Providing a framework for creating choropleths from
    arbitrary shapefiles. 4. Overlaying those maps over reference maps from
    Google Maps.
Contains an administrative-level-1 map of the world.
    Administrative-level-1 is the generic term for the largest sub-national
    subdivision of a country. This package was created for use with the
    choroplethr package.
Contains 3 maps. 1) US States 2) US Counties 3) Countries of the
    world.
Generation of different Christmas cards, most of them being
    animated. Most of the cards can be generated in three languages (English,
    Catalan and Spanish). The collection started in 2009.
Reads chromatograms from binary formats into R objects. Currently supports conversion of 'Agilent ChemStation', 'Agilent MassHunter', and 'ThermoRaw' files as well as various text-based formats. Utilizes file parsers from external libraries, such as 'Aston' <https://github.com/bovee/aston>, 'Entab' <https://github.com/bovee/entab>, and 'ThermoRawFileParser' <https://github.com/compomics/ThermoRawFileParser>.
A programmatic interface to the Chromosome Counts Database
    (<http://ccdb.tau.ac.il/>), Rice et al. (2014) <doi:10.1111/nph.13191>.
    This package is part of the 'ROpenSci' suite (<https://ropensci.org>).
Provides interactive, configurable and elegant graphics visualization of the chromosomes or chromosome regions
    of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces
    a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data
    associated with chromosome elements (like gene expression) in the form of heat colors which can be highly
    advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes,
    it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each
    of chromosome locus to render additional information and visualization specific for that location. You can map
    thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings
    (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the
    chromosome at a location showing each mapped element in sequential order. The package provide multiple features
    like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling.
    The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications.
An implementation of the 'Chrome DevTools Protocol', for
    controlling a headless Chrome web browser.
Chromosome files in the 'Fasta' format usually contain large sequences like human genome.
  Sometimes users have to split these chromosomes into different files according to their 
  chromosome number. The 'chromseq' can help to handle this. So the selected chromosome sequence can be
  used for downstream analysis like motif finding. Howard Y. Chang(2019)
  <doi:10.1038/s41587-019-0206-z>.
Provides chronological objects which can handle dates and times.
A system for creating R Markdown reports with a sequential syntax.
Decorate functions to make them return enhanced output. The enhanced output consists in an object of type
  'chronicle' containing the result of the function applied to its arguments, as well as a log detailing when the function
  was run, what were its inputs, what were the errors (if the function failed to run) and other useful information.
  Tools to handle decorated functions are included, such as a forward pipe operator that makes chaining decorated functions possible.
Easy way to draw chronological charts from tables, aiming to include an intuitive environment for anyone new to R. Includes 'ggplot2' geoms and theme for chronological charts.
The implemented functions allow the query, download, and import of remotely-stored and version-controlled data items. The inherent meta-database maps data files and import code to programming classes and allows access to these items via files deposited in public repositories. The purpose of the project is to increase reproducibility and establish version tracking of results from (paleo)environmental/ecological research.
Functions for use in perturbing data prior to use of nonparametric smoothers
             and clustering.
Data stored in text file can be processed chunkwise using 'dplyr' commands. These
    are recorded and executed per data chunk, so large files can be processed with
    limited memory using the 'LaF' package.
Set chunk hooks for 'R Markdown' documents <https://rmarkdown.rstudio.com/>,
    and improve user experience.
    For example, change units of figure sizes, benchmark chunks, and number
    lines on code blocks.
Read tables chunk by chunk using a C++ backend and a simple R interface.
An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size.
Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.
Identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space <https://github.com/ScialdoneLab>.
Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.
Provides functionality for computing support intervals for univariate parameters based on confidence intervals or parameter estimates with standard errors (Pawel et al., 2022) <doi:10.48550/arXiv.2206.12290>.
Estimation and inference methods for causal relative and attributable risk in case-control and case-population studies.
    Semiparametrically efficient estimation of the aggregated (log) odds ratio and causal inference procedures for relative and attributable risk. 
    For more details, see the paper by Jun and Lee (2020), "Causal Inference in Case-Control Studies," <arXiv:2004.08318 [econ.EM]>.
Provide step by step guided tours of 'Shiny' applications.
A workflow of (a) meta-clustering based on 
    inter-group similarity measures and (b) a ground-truth-free test metric to 
    assess the biological correctness of integration in real datasets. See Hu Z,
    Ahmed A, Yau C (2021) <doi:10.1101/2021.03.29.437525> for
    more details.
In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, <DOI:10.1002/gepi.22107>) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison.
Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in  Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, <https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf>.
Computes confidence intervals for the positive predictive value (PPV) and negative predictive value (NPV) based on varied scenarios. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) three methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), and MOVER-J (Laud, 2017); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. See the Details and References sections in the corresponding functions.
Functions for the input/output and visualization of
    medical imaging data in the form of 'CIFTI' files 
    <https://www.nitrc.org/projects/cifti/>.
CIFTI files contain brain imaging data in "grayordinates," which 
    represent the gray matter as cortical surface vertices (left and right) and
    subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). 
    'ciftiTools' provides a unified environment for reading, writing, 
    visualizing and manipulating CIFTI-format data. It supports the "dscalar," 
    "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" 
    object, which is structured for convenient access to the data and metadata,
    and includes support for surface geometry files to enable
    spatially-dependent functionality such as static or interactive 
    visualizations and smoothing.
Produces statistical indicators of the impact of migration on the socio-demographic
	composition of an area. Three measures can be used: ratios, percentages and the Duncan index
	of dissimilarity. The input data files are assumed to be in an  origin-destination matrix format,
	with each cell representing a flow count between an origin and a destination area. Columns are expected
	to represent origins, and rows are expected to represent destinations. The first row and column are
	assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018)
	<doi:10.1080/00324728.2017.1416155> for technical details.
Connect to the California Irrigation Management 
    Information System (CIMIS) Web API. See the CIMIS main page 
    <https://cimis.water.ca.gov> and web API documentation
    <https://et.water.ca.gov> for more information.
Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. <doi:10.1177/0962280220921909>.
Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data
  analyses. This package combines different packages to have an ultimate package
  for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 
  'Karakaslar et al.' (2021) <doi:10.1101/2021.03.05.434143>.
Immune related gene sets provided along with the 'cinaR' package.
Function using lemmatization to classify educational programs according to the CINE(Classification International Normalized of Education) for Peru.
Method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.
Implement various chromosomal instability metrics. 'CINmetrics' (Chromosomal INstability metrics) provides functions to 
    calculate various chromosomal instability metrics on masked Copy Number Variation(CNV) data at individual sample level. The 
    chromosomal instability metrics have been implemented as described in the following studies: 
    Baumbusch LO et al. 2013 <doi:10.1371/journal.pone.0054356>, 
    Davidson JM et al. 2014 <doi:10.1371/journal.pone.0079079>, 
    Chin SF et al. 2007 <doi:10.1186/gb-2007-8-10-r215>.
Computing, comparing, and demonstrating top informative centrality measures within a network. "CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis" provides a comprehensive overview of the package functionality Ashtiani et al. (2018) <doi:10.1093/bioinformatics/bty819>.
Simple interpolation methods designed to be used from C
    code. Supports constant, linear and spline interpolation. An R
    wrapper is included but this package is primarily designed to be
    used from C code using 'LinkingTo'.  The spline calculations are
    classical cubic interpolation, e.g., Forsythe, Malcolm and Moler
    (1977) <ISBN: 9780131653320>.
Implements computationally-efficient construction of
    confidence intervals from permutation or randomization tests
    for simple differences in means,
    based on Nguyen (2009) <doi:10.15760/etd.7798>.
Encrypts and decrypts using basic ciphers. None of 
  these should be used in place of real encryption using state of the art tools. 
  The ciphers included use methods described in the ciphers's Wikipedia and 
  cryptography hobby websites.
Plot confidence interval from the objects of statistical tests such as
  t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest' class),
  Tukey test [TukeyHSD()], Dunnett test [glht() in 'multcomp' package],
  logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in
  'userfriendlyscience' package].
  Users are able to set the styles of lines and points.
  This package contains the function to calculate odds ratios and their confidence
  intervals from the result of logistic regression.
Isotonic regression (IR) and its improvement: centered isotonic regression (CIR). CIR is recommended in particular with small samples. Also, interval estimates for both, and additional utilities such as plotting dose-response data. For dev version and change history, see GitHub assaforon/cir.
Uses non-linear regression to statistically compare two circadian rhythms.
    Groups are only compared if both are rhythmic (amplitude is non-zero).
    Performs analyses regarding mesor, phase, and amplitude, reporting on estimates and statistical differences, for each, between groups.
    Details can be found in Parsons et al (2020) <doi:10.1093/bioinformatics/btz730>.
Tools for interacting with the 'Circle CI' API
    (<https://circleci.com/docs/api/v2/>). Besides executing common tasks
    such as querying build logs and restarting builds, this package also
    helps setting up permissions to deploy from builds.
Implementation of Librino, Levorato, and Zorzi (2014) <doi:10.1002/wcm.2305>
    algorithm for computation of the intersection areas of an arbitrary number of circles.
Enables curving text elements in 'Shiny' apps.
Circular layout is an efficient way for the visualization of huge 
    amounts of information. Here this package provides an implementation 
    of circular layout generation in R as well as an enhancement of available 
    software. The flexibility of the package is based on the usage of low-level 
    graphics functions such that self-defined high-level graphics can be easily 
    implemented by users for specific purposes. Together with the seamless 
    connection between the powerful computational and visual environment in R, 
    it gives users more convenience and freedom to design figures for 
    better understanding complex patterns behind multiple dimensional data. 
    The package is described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.
A series of wrapper functions to
    implement the 10 maximum likelihood models of animal orientation
    described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The
    functions also include the ability to use different optimizer
    methods and calculate various model selection metrics (i.e., AIC,
    AICc, BIC).  The ability to perform variants of the Hermans-Rasson test and
    Pycke test is also included as described in Landler et al. (2019) 
    <DOI:10.1186/s12898-019-0246-8>. The latest version also includes a new
    method to calculate circular-circular and circular-linear distance correlations.
Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.
Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.
Implementation of Bayesian models for spatial and spatio-temporal
             interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions.
             We developed the methods described in Jona Lasinio G. et al. (2012) <doi: 10.1214/12-aoas576>, 
             Wang F. et al. (2014) <doi: 10.1080/01621459.2014.934454> and 
             Mastrantonio G. et al. (2016) <doi: 10.1007/s11749-015-0458-y>.
Circular Statistics, from "Topics in Circular Statistics"
        (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular drift-diffusion model for continuous reports.
General functions for performing extreme value analysis on a circular 
    domain as part of the statistical methodology in the paper by Konzen, E., 
    Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.
Calculating silhouette information for clusters on
 circular or linear data using fast algorithms. These algorithms run in
 linear time on sorted data, in contrast to quadratic time by the
 definition of silhouette. When used together with the fast and optimal
 circular clustering method FOCC (Debnath & Song 2021)
 <doi:10.1109/TCBB.2021.3077573> implemented in R package 'OptCirClust',
 circular silhouette can be maximized to find the optimal number of
 circular clusters; it can also be used to estimate the period of noisy
 periodical data.
Circumplex models, which organize constructs in a circle around two 
    underlying dimensions, are popular for studying interpersonal functioning, 
    mood/affect, and vocational preferences/environments. This package provides 
    tools for analyzing and visualizing circular data, including scoring 
    functions for relevant instruments and a generalization of the bootstrapped 
    structural summary method from Zimmermann & Wright (2017) 
    <doi:10.1177/1073191115621795> and functions for creating publication-ready 
    tables and figures from the results.
Jointly model the accuracy of cognitive responses and item choices
    within a Bayesian hierarchical framework as described by Culpepper and
    Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package
    contains the datasets used within the analysis of the paper.
An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set.
A likelihood-based hypothesis testing approach is implemented for
  assessing causal mediation. Described in Millstein, Chen, and Breton (2016),
  <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation
  of a known causal association between a DNA variant, the 'instrumental variable',
  and a clinical outcome or phenotype by gene expression or DNA methylation, the
  potential mediator. Another example would be testing mediation of the effect
  of a drug on a clinical outcome by the molecular target. The hypothesis test
  generates a p-value or permutation-based FDR value with confidence intervals
  to quantify uncertainty in the causal inference. The outcome can be represented
  by either a continuous or binary variable, the potential mediator is continuous,
  and the instrumental variable can be continuous or binary and is not limited to
  a single variable but may be a design matrix representing multiple variables.
Supports quantitative
    research in scientometrics and bibliometrics. Provides
    various tools for preprocessing bibliographic
    data retrieved, e.g., from Elsevier's SciVerse Scopus,
    computing bibliometric impact of individuals,
    or modelling phenomena encountered in the social sciences.
    This package is deprecated, see 'agop' instead.
A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (<https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the 'utils' package.
In searching for research articles, we often want to 
  obtain lists of references from across studies, and also obtain lists 
  of articles that cite a particular study. In systematic reviews, this 
  supplementary search technique is known as 'citation chasing': forward 
  citation chasing looks for all records citing one or more articles of 
  known relevance; backward citation chasing looks for all records 
  referenced in one or more articles. Traditionally, this process would 
  be done manually, and the resulting records would need to be checked 
  one-by-one against included studies in a review to identify potentially 
  relevant records that should be included in a review. This package 
  contains functions to automate this process by making use of the 
  Lens.org API. An input article list can be used to return a list of 
  all referenced records, and/or all citing records in the Lens.org 
  database (consisting of PubMed, PubMed Central, CrossRef, Microsoft 
  Academic Graph and CORE; <https://www.lens.org>).
Client for the Open Citations Corpus (<http://opencitations.net/>).
    Includes a set of functions for getting one identifier type from another,
    as well as getting references and citations for a given identifier.
Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.
Building and training custom neural networks in the typical R syntax. The 'torch' package is used for numerical calculations, which allows for training on CPU as well as on a graphics card.
Functions to append confidence intervals, prediction intervals,
    and other quantities of interest to data frames. All appended quantities
    are for the response variable, after conditioning on the model and covariates.
    This package has a data frame first syntax that allows for easy piping.
    Currently supported models include (log-) linear, (log-) linear mixed,
    generalized linear models, generalized linear mixed models, and
    accelerated failure time models.
A tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. 
    It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting.
    Users can either run the pipeline as a whole, or choose to run any one of the three individual steps.
    It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models.
Implementation of the Contextual Importance and Utility 
    (CIU) concepts for Explainable AI (XAI). A recent description of CIU
    can be found in e.g. Främling (2020) <arXiv:2009.13996>.
Computes a confidence interval for a specified linear combination of 
    the regression parameters in a linear regression model with iid normal 
    errors with unknown variance when there is uncertain prior information 
    that a distinct specified linear combination of the regression 
    parameters takes a specified number. This confidence interval, found by 
    numerical nonlinear constrained optimization, has the required minimum coverage 
    and utilizes this uncertain prior information through desirable 
    expected length properties. This confidence interval is proposed by 
    Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.
A convenient interface for making
  requests directly to the 'Civis Platform API' <https://www.civisanalytics.com/platform/>.
  Full documentation available 'here' <https://civisanalytics.github.io/civis-r/>.
We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.
Connect to the 'CJA' API, which powers 'CJA Workspace' <https://github.com/AdobeDocs/cja-apis>. The package 
             was developed with the analyst in mind and will continue to be 
             developed with the guiding principles of iterative, repeatable, 
             timely analysis. New features are actively being developed and we 
             value your feedback and contribution to the process.
A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.
Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into
		shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative
		approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be
		challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component
		scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between
		corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which
		provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations.
		Please cite the following article when utilizing this package: 
		Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) <doi:10.3389/fnins.2022.969510>.
An R implementation of the Average Marginal Component-specific
    Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto
    T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional
    Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Client for 'CKAN' API (<https://ckan.org/>). Includes interface
    to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
    resources. In addition, provides an interface to the 'datastore' API.
Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials.
Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).)
Fast, optimal, and reproducible weighted univariate
 clustering by dynamic programming. Four problems are solved, including
 univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015>
 (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median,
 k-segments, and multi-channel weighted k-means. Dynamic programming
 is used to minimize the sum of (weighted) within-cluster distances
 using respective metrics. Its advantage over heuristic clustering in
 efficiency and accuracy is pronounced at a large number of clusters.
 Multi-channel weighted k-means groups multiple univariate
 signals into k clusters. An auxiliary function generates histograms
 adaptive to patterns in data. This package provides a powerful set
 of tools for univariate data analysis with guaranteed optimality,
 efficiency, and reproducibility, useful for peak calling on temporal,
 spatial, and spectral data.
Provides an R wrapper around the program 'spip'
    (<https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1471-8286.2005.00884.x>), a C
    program for the simulation of pedigrees within age-structured populations
    with user-specified life histories.  Also includes a variety
    of functions to parse 'spip' output to compile information about
    related pairs amongst simulated, sampled individuals, to assess
    the feasibility and potential accuracy of close-kin mark-recapture (CKMR).
    Full documentation
    and vignettes are mirrored at <https://eriqande.github.io/CKMRpop/index.html>
    and can be read online there.
Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical
  mean-variance portfolio optimization, see Markowitz (1952) <doi:10.2307/2975974>.
  Care has been taken for correctness in light of previous buggy implementations.
Measures morphological diversity from discrete character data and
    estimates evolutionary tempo on phylogenetic trees. Imports morphological
    data from #NEXUS (Maddison et al. (1997) <doi:10.1093/sysbio/46.4.590>)
    format with read_nexus_matrix(), and writes to both #NEXUS and TNT format 
    (Goloboff et al. (2008) <doi:10.1111/j.1096-0031.2008.00217.x>). Main
    functions are test_rates(), which implements AIC and likelihood
    ratio tests for discrete character rates introduced across Lloyd et al.
    (2012) <doi:10.1111/j.1558-5646.2011.01460.x>, Brusatte et al. (2014)
    <doi:10.1016/j.cub.2014.08.034>, Close et al. (2015)
    <doi:10.1016/j.cub.2015.06.047>, and Lloyd (2016) <doi:10.1111/bij.12746>,
    and MatrixDistances(), which implements multiple discrete character
    distance metrics from Gower (1971) <doi:10.2307/2528823>, Wills (1998)
    <doi:10.1006/bijl.1998.0255>, Lloyd (2016) <doi:10.1111/bij.12746>, and
    Hopkins and St John (2018) <doi:10.1098/rspb.2018.1784>. This also includes
    the GED correction from Lehmann et al. (2019) <doi:10.1111/pala.12430>.
    Multiple functions implement morphospace plots:
    plot_chronophylomorphospace() implements Sakamoto and Ruta (2012)
    <doi:10.1371/journal.pone.0039752>, plot_morphospace() implements Wills et
    al. (1994) <doi:10.1017/S009483730001263X>, plot_changes_on_tree()
    implements Wang and Lloyd (2016) <doi:10.1098/rspb.2016.0214>, and
    plot_morphospace_stack() implements Foote (1993)
    <doi:10.1017/S0094837300015864>. Other functions include
    safe_taxonomic_reduction(), which implements Wilkinson (1995)
    <doi:10.1093/sysbio/44.4.501>, map_dollo_changes() implements
    the Dollo stochastic character mapping of Tarver et al. (2018)
    <doi:10.1093/gbe/evy096>, and estimate_ancestral_states() implements
    the ancestral state options of Lloyd (2018) <doi:10.1111/pala.12380>.
Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events.  Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state).  This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue.  C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations.  CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it!  Type: citation(package="cladoRcpp") to get the citation information.
The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a  division among the claimants. This package computes the main rules introduced in the literature from the old times until nowadays. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the  constrained egalitarian, the Piniles’ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average of awards rules are also included. All of them can be found in the book of W. Thomson (2019), 'How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', with the exception of the average of awards rule (Mirás Calvo et al. (2022), <doi:10.1007/s00355-022-01414-6>). In addition,  graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, and the schedules of awards of a rule, and some indexes. A good understanding of the similarities and the differences of the rules is useful for a better decision making. Therefore this package could be helpful to students, researchers and managers alike.
Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010). <doi:10.1016/j.quageo.2010.01.002>.
Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.
A versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (<https://oxfordcontrol.github.io/ClarabelDocs/stable/>). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, 'Clarabel' handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique.
Get description of images from Clarifai API. For more information,
    see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come
    up with descriptive labels of the things in an image. It also provides how
    confident it is about each of the labels.
Performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.
Implementation of Clarke's distribution-free test of
  non-nested models.  Currently supported model functions are: 
  lm(), glm() ('binomial', 'poisson', 'negative binomial' links), 
  polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet').  
  For more information on the test, see Clarke (2007) 
  <doi:10.1093/pan/mpm004>.
Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.
Defines the classes used for "class comparison" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  comparison includes tests for differential expression; see Simon's
  book for details on typical problem types.
Defines the classes used for "class discovery" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  discovery primarily consists of unsupervised clustering methods with
  attempts to assess their statistical significance.
Construct directed graphs of S4 class hierarchies and
  visualize them.  In general, these graphs typically are DAGs (directed
  acyclic graphs), often simple trees in practice.
Generates a visualization of binary classifier performance as a grid of
  diagnostic plots with just one function call. Includes ROC curves,
  prediction density, accuracy, precision, recall and calibration plots, all using
  ggplot2 for easy modification.
  Debug your binary classifiers faster and easier!
Given $p$-dimensional training data containing $d$ groups
    (the design space), a classification algorithm (classifier) predicts
    which group new data belongs to.  Generally the input to these
    algorithms is high dimensional, and the boundaries between groups will
    be high dimensional and perhaps curvilinear or multi-faceted. This
    package implements methods for understanding the division of space
    between the groups.
Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.
Tools to visualize the results of a classification of cases.
    The graphical displays include stacked plots, silhouette plots, quasi residual plots, and class maps.
    Implements the techniques described and illustrated in Raymaekers, Rousseeuw and Hubert (2021), Class maps for visualizing classification results, Technometrics, appeared online.
    <doi:10.1080/00401706.2021.1927849> (open access) and Raymaekers and Rousseeuw (2021),
    Silhouettes and quasi residual plots for neural nets and tree-based classifiers,
    <arXiv:2106.08814>. Examples can be found in the vignettes:
    "Discriminant_analysis_examples","K_nearest_neighbors_examples",
    "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples",
    and "Neural_net_examples".
The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, <doi:10.1002/sim.8909>.
Bindings to Google's C++ library Compact Language Detector 2
    (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically
    detects over 80 languages in plain text or HTML. For mixed-language input it returns the
    top three detected languages and their approximate proportion of the total classified 
    text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3'
    package on CRAN which uses a neural network model instead.
Google's Compact Language Detector 3 is a neural network model for language 
    identification and the successor of 'cld2' (available from CRAN). The algorithm is still
    experimental and takes a novel approach to language detection with different properties
    and outcomes. It can be useful to combine this with the Bayesian classifier results 
    from 'cld2'. See <https://github.com/google/cld3#readme> for more information.
Contains a time series classification method that obtains a set of filters that maximize the between-class and minimize the within-class distances.
A wrapper around the new 'cleaner' package, that allows
  data cleaning functions for classes 'logical', 'factor', 'numeric', 
  'character', 'currency' and 'Date' to make data cleaning fast and
  easy. Relying on very few dependencies, it provides smart guessing,
  but with user options to override anything if needed.
Curates biological sequences massively, quickly, without errors and 
 without internet connection.  Biological sequences curing is performed by 
 aligning the forward and / or revers primers or ends of cloning vectors with the sequences 
 to be cleaned. After the alignment, new subsequences are generated without biological fragment 
 not desired by the user.
 Pozzi et al (2020) <doi:10.1007/s00438-020-01671-z>.
Wrapper of .Call() that runs exit handlers to clean up C
    resources. Helps managing C (non-R) resources while using the R API.
Functions to work with data frames to prepare data for further analysis.
    The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process.
Data cleaning functions for classes logical,
  factor, numeric, character, currency and Date to make
  data cleaning fast and easy. Relying on very few dependencies, it 
  provides smart guessing, but with user options to override 
  anything if needed.
Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issue with sp objects.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.
Provides a set of fast tools for converting a textual corpus into
  a set of normalized tables. Users may make use of the 'udpipe' back end with
  no external dependencies, or two Python back ends with 'spaCy'
  <https://spacy.io> or 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/>.
  Exposed annotation tasks include tokenization, part of speech tagging, named
  entity recognition, and dependency parsing.
Check your R code for some of the most common
    layout flaws.  Many tried to teach us how to write code less dreadful,
    be it implicitly as B. W. Kernighan and D. M. Ritchie (1988)
    <ISBN:0-13-110362-8> in 'The C Programming Language' did, be it
    explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in 'Clean Code:
    A Handbook of Agile Software Craftsmanship' did.  So we should check
    our code for files too long or wide, functions with too many lines,
    too wide lines, too many arguments or too many levels of nesting.
    Note: This is not a static code analyzer like pylint or the like.
    Checkout <https://cran.r-project.org/package=lintr> instead.
A collection of clean 'R Markdown' HTML document templates
    using classy-looking classless CSS styles. These documents use a
    minimal set of dependencies but still look great, making them suitable
    for use a package vignettes or for sharing results via email.
A reliable and efficient tool for cleaning univariate time 
    series data. It implements reliable and efficient procedures for 
    automating the process of cleaning univariate time series data. 
    The package provides integration with already developed and deployed 
    tools for missing value imputation and outlier detection. It also 
    provides a way of visualizing large time-series data in different 
    resolutions.
Small package to clean the R console and the R environment
    with the call of just one function.
Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.
Tools for evaluating link prediction and clustering algorithms 
    with respect to ground truth. Includes efficient implementations of 
    common performance measures such as pairwise precision/recall, 
    cluster homogeneity/completeness, variation of information, 
    Rand index etc.
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728>).
A suite of tools to build attractive command line interfaces
    ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs,
    etc. Supports custom themes via a 'CSS'-like language. It also
    contains a number of lower level 'CLI' elements: rules, boxes, trees,
    and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI
    colors and text styles as well.
Create rich command line applications, with colors, headings,
    lists, alerts, progress bars, etc. It uses CSS for custom themes.
Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. Sequences' clusters are identified by using a model-based approach, specifically mixture of discrete time first-order Markov models for categorical web sequences. A Bayesian approach is used to estimate model parameters and identify sequences classification as proposed by Fruehwirth-Schnatter and Pamminger (2010) <doi:10.1214/10-BA606>.
Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.
Provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) <arXiv:1802.04849>.
'ClickHouse' (<https://clickhouse.com/>)
   is an open-source, high performance columnar
   OLAP (online analytical processing of queries) database management system
   for real-time analytics using SQL. This 'DBI' backend
   relies on the 'ClickHouse' HTTP interface and support HTTPS protocol.
Tools for assessing data quality, performing exploratory analysis, and 
    semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.
A set of tools to read, analyze and write lists of click sequences
    on websites (i.e., clickstream). A click can be represented by a number,
    character or string. Clickstreams can be modeled as zero- (only computes
    occurrence probabilities), first- or higher-order Markov chains.
This data package contains monthly climate data for Germany, it can be used for heating and 
    cooling calculations (external temperature, heating / cooling days, solar radiation).
Includes climate data from Japan Meteorological Agency ('JMA') <https://www.jma.go.jp/jma/indexe.html>.
    Can download climate data from 'JMA'.
Execute command line programs and format results for interactive use. It is based on the package 'processx' so it does not use shell to start up the process like system() and system2(). It also provides a simpler and cleaner interface than processx::run().
A suite of routines for Clifford algebras, using the
   'Map' class of the Standard Template Library.  Canonical
   reference: Hestenes (1987, ISBN 90-277-1673-0, "Clifford algebra
   to geometric calculus").  Special cases including Lorentz transforms,
   quaternion multiplication, and Grassman algebra, are discussed.
   Conformal geometric algebra theory is implemented.  Uses 'disordR'
   discipline.
CliFlo is a web portal to the New Zealand National Climate
    Database and provides public access (via subscription) to around 6,500
    various climate stations (see <https://cliflo.niwa.co.nz/> for more
    information). Collating and manipulating data from CliFlo
    (hence clifro) and importing into R for further analysis, exploration and
    visualisation is now straightforward and coherent. The user is required to
    have an internet connection, and a current CliFlo subscription (free) if
    data from stations, other than the public Reefton electronic weather
    station, is sought.
Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) <doi:10.1007/s11222-018-9820-8>.
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Tools to download the climatic data of the Spanish
    Meteorological Agency (AEMET) directly from R using their API and
    create scientific graphs (climate charts, trend analysis of climate
    time series, temperature and precipitation anomalies maps, warming
    stripes graphics, climatograms, etc.).
Climate stability measures are not formalized in the literature and
  tools for generating stability metrics from existing data are nascent.
  This package provides tools for calculating climate stability from raster data
  encapsulating climate change as a series of time slices. The methods follow
  Owens and Guralnick <doi:10.17161/bi.v14i0.9786> Biodiversity Informatics.
Functions for the quality control, homogenization and missing data filling of climatological series and to obtain climatological summaries and grids from the results. Also functions to display wind-roses, meteograms, Walter&Lieth diagrams, and more.
Supports analysis of trends in climate change,
        ecological and crop modelling.
Classification of climate according to Koeppen - Geiger, of aridity
    indices, of continentality indices, of water balance after Thornthwaite, of
    viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy,
    Bagnouls-Gaussen.
A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models.  The computation uses linear programming.  The method was published in TT Cai, W Liu, X Luo (2011) <doi:10.1198/jasa.2011.tm10155>.
A framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time.
Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from 'extRemes', including for fitting the
    statistical models. Details are given in Paciorek, Stone, and Wehner (2018)
    <doi:10.1016/j.wace.2018.01.002>.
Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).
API client for 'ClimMob', an open source software for decentralized 
    large-N trials with the 'tricot' approach <https://climmob.net/>. 
    Developed by van Etten et al. (2016) <doi:10.1017/S0014479716000739>, it turns the 
    research paradigm on its head; instead of a few researchers designing complicated 
    trials to compare several technologies in search of the best solutions for the 
    target environment, it enables many participants to carry out reasonably simple 
    experiments that taken together can offer even more information. 'ClimMobTools' 
    enables project managers to deep explore and analyse their 'ClimMob' data in R.
Set of tools to compute metrics and indices for climate analysis.
    The package provides functions to compute extreme indices, evaluate the
    agreement between models and combine theses models into an ensemble. Multi-model
    time series of climate indices can be computed either after averaging the 2-D
    fields from different models provided they share a common grid or by combining
    time series computed on the model native grid. Indices can be assigned weights
    and/or combined to construct new indices.
Contains functions to detect and visualise periods of climate
    sensitivity (climate windows) for a given biological response. 
    Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> 
    and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details.
Creation of interactive tables, listings and figures ('TLFs') 
  and associated report for exploratory analysis of data in a clinical trial, 
  e.g. for clinical oversight activities.
  Interactive figures include sunburst, treemap, scatterplot, line plot and
  barplot of counts data. 
  Interactive tables include table of summary statistics
  (as counts of adverse events, enrollment table) and listings.
  Possibility to compare data (summary table or listing) across two data batches/sets.
  A clinical data review report is created via study-specific configuration 
  files and template 'R Markdown' reports contained in the package.
Bayesian and ML Emax model fitting, graphics and simulation for clinical dose
    response.  The summary data from the dose response meta-analyses in  
    Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and
    Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> 
    Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> 
    are included 
    in the package.  The prior distributions for the Bayesian analyses default to
    the posterior predictive distributions derived from these references.
Utilities to make your clinical collaborations easier if not
          fun. It contains functions for designing studies such as Simon
          2-stage and group sequential designs and for data analysis such
          as Jonckheere-Terpstra test and estimating survival quantiles.
A clinical significance analysis can be used to determine if an 
    intervention has a meaningful or practical effect for patients. You provide 
    a tidy data set plus a few more metrics and this package will take care of 
    it to make your results publication ready as proposed by Jacobson et al., 
    (1984) <doi:10.1016/S0005-7894(84)80002-7>.
Provides estimates of several summary measures for clinical trials including
    the average hazard ratio, the weighted average hazard ratio, the restricted 
    superiority probability ratio, the restricted mean survival difference and 
    the ratio of restricted mean times lost, based on the short-term and 
    long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) 
    which accommodates various non-proportional hazards scenarios. The inference
    procedures and the asymptotic results for the summary measures are discussed 
    in Yang (2018, <doi:10.1002/sim.7676>).
Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" <http://hdl.handle.net/1773/44068>.
Every research team have their own script for calculation of hemodynamic indexes. 
   This package makes it possible  to insert a long-format dataframe, and add both 
   periods of interest (trigger-periods), and delete artifacts with deleter-files.
Calculates equations commonly used in clinical
        pharmacokinetics and clinical pharmacology, such as equations
        for dose individualization, compartmental pharmacokinetics,
        drug exposure, anthropomorphic calculations, clinical
        chemistry, and conversion of common clinical parameters. Where
        possible and relevant, it provides multiple published and
        peer-reviewed equations within the respective R function.
Functions for calculating clinical significance.
Provides measures of effect sizes from summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, and Youden index.
Performs biomedical named entity recognition,
    Unified Medical Language System (UMLS) concept mapping, and negation
    detection using the Python 'spaCy', 'scispaCy', and 'medspaCy' packages, and 
    transforms extracted data into a wide format for inclusion in machine
    learning models. The development of the 'scispaCy' package is described by
    Neumann (2019) <doi:10.18653/v1/W19-5034>. The 'medspacy' package uses
    'ConText', an algorithm for determining the context of clinical statements
    described by Harkema (2009) <doi:10.1016/j.jbi.2009.05.002>. Clinspacy
    also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept
    embeddings developed by Beam (2018) <arXiv:1804.01486>.
Every research team have their own script for data management, statistics and 
   most importantly hemodynamic indices. The purpose is to standardize scripts 
   utilized in clinical research. The hemodynamic indices can be used in a long-format dataframe, 
   and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. 
   Transfer function analysis (Claassen et al. (2016) <doi:10.1177/0271678X15626425>) and
   Mx (Czosnyka et al. (1996) <doi:10.1161/01.str.27.10.1829>) can be calculated using this package.
Utility functions to facilitate the import, 
 the reporting and analysis of clinical data.
 Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains
 from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate
 the package functionalities.
A fast and general implementation of the Elston-Stewart algorithm
 that can calculate the likelihoods of large and complex pedigrees. 
 References for the Elston-Stewart algorithm are
 Elston & Stewart (1971) <doi:10.1159/000152448>,
 Lange & Elston (1975) <doi:10.1159/000152714> and
 Cannings et al. (1978) <doi:10.2307/1426718>.
Simple utility functions to read from and write to
    the Windows, OS X, and X11 clipboards.
Clique percolation community detection for weighted and
    unweighted networks as well as threshold and plotting functions.
    For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180>
    and Palla et al. (2005) <doi:10.1038/nature03607>.
A small subset of Unicode symbols, that are useful
    when building command line applications. They fall back to
    alternatives on terminals that do not support Unicode.
    Many symbols were taken from the 'figures' 'npm' package
    (see <https://github.com/sindresorhus/figures>).
Estimation and inference for linear models where some or all of the
    fixed-effects coefficients are subject to order restrictions. This package uses
    the robust residual bootstrap methodology for inference, and can handle some
    structure in the residual variance matrix.
Implementation of the chain ladder model under the reverse time framework
    introduced in Hiabu (2017) <doi:10.1080/03461238.2016.1240709>.
    It also implements extensions that add flexibility to the individual development factors modeling
    by allowing practitioners to set their own hazard rate model.
Provides a comprehensive library for date-time manipulations
    using a new family of orthogonal date-time classes (durations, time
    points, zoned-times, and calendars) that partition responsibilities so
    that the complexities of time zones are only considered when they are
    really needed. Capabilities include: date-time parsing, formatting,
    arithmetic, extraction and updating of components, and rounding.
A wrapper for the Clockify API <https://clockify.me/developers-api>, making it possible to query, insert and update time keeping data.
A set of functions to fit a boosting conditional logit model.
Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples. Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015) <doi:10.1186/1471-2105-16-S6-S1>.
Defines the classes and functions used to simulate and
  to analyze data sets describing copy number variants and,
  optionally, sequencing mutations in order to detect clonal subsets.
  See Zucker et al. (2019) <doi:10.1093/bioinformatics/btz057>.
Analyze data from next-generation sequencing experiments on genomic samples. 'CLONETv2' offers a set of functions to compute allele specific copy number and clonality from segmented data and SNPs position pileup. The package has also calculated the clonality of single nucleotide variants given read counts at mutated positions. The package has been developed at the laboratory of Computational and Functional Oncology, Department of CIBIO, University of Trento (Italy), under the supervision of prof Francesca Demichelis. References: Prandi et al. (2014) <doi:10.1186/s13059-014-0439-6>; Carreira et al. (2014) <doi:10.1126/scitranslmed.3009448>; Romanel et al. (2015) <doi:10.1126/scitranslmed.aac9511>.
Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.
Interface to the Google Cloud Machine Learning Platform
  <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine
  learning models.
The 'CloudOS' client library for R makes it easy to interact with 
    CloudOS in the R environment for analysis.
Access Cloudstor via their WebDAV API. This package can read, write, and navigate Cloudstor from R.
Provides means of plots for comparing utilization data of compute systems.
Estimation of linear model with predictions inside the (0,1) interval. Standard least squares criterion is minimized subjected to a penalty term that enforces the constraints. The estimator is suitable for binary responses, or any response between 0 and 1.
Computes the uniform rate of profit, the vector of price of 
    production and the vector of labor values; and also compute measures of deviation 
    between relative prices of production and relative values.  
    <https://scholarworks.umass.edu/econ_workingpaper/347/>. You provide the  
    input-output data and 'clptheory' does the calculations for you.
A new methodology for linear regression with both curve response 
    and curve regressors, which is described in Cho, Goude, Brossat and Yao 
    (2013) <doi:10.1080/01621459.2012.722900> and (2015) 
    <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is 
    dimension reduction based on a singular value decomposition in a Hilbert 
    space, which reduces the curve regression problem to several scalar linear 
    regression problems.
Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.
Provides several cluster-robust variance estimators (i.e.,
    sandwich estimators) for ordinary and weighted least squares linear regression
    models, including the bias-reduced linearization estimator introduced by Bell
    and McCaffrey (2002) 
    <https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and 
    developed further by Pustejovsky and Tipton (2017) 
    <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating
    the variance- covariance matrix and for testing single- and multiple-
    contrast hypotheses based on Wald test statistics. Tests of single regression
    coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-
    contrast hypotheses use an approximation to Hotelling's T-squared distribution.
    Methods are provided for a variety of fitted models, including lm() and mlm
    objects, glm(), geeglm() (from package 'geepack'), ivreg() (from package 'AER'), ivreg() (from package 'ivreg' when 
    estimated by ordinary least squares), plm() (from package 'plm'), gls() and 
    lme() (from 'nlme'), lmer() (from `lme4`), robu() (from 'robumeta'), and rma.uni() 
    and rma.mv() (from 'metafor').
An implementation of the clugen algorithm for generating
    multidimensional clusters with arbitrary distributions. Each cluster is
    supported by a line segment, the position, orientation and length of which
    guide where the respective points are placed. This package is described in
    Fachada & de Andrade (2023) <doi:10.1016/j.knosys.2023.110836>.
Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.
Providing a cluster allocation for n samples, either with an $n \times p$ data matrix or an $n \times n$ distance
                matrix, a bootstrap procedure is performed. The proportion of bootstrap replicates where a pair of samples
                cluster in the same cluster indicates who tightly the samples in a particular cluster clusters together.
Clustered covariate regression enables estimation and inference in 
 both linear and non-linear models with linear predictor functions even when the
 design matrix is column rank deficient. Routines in this package implement algorithms
 in Soale and Tsyawo (2019) <doi:10.13140/RG.2.2.32355.81441>.
Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) <arXiv:2012.14159> are implemented.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test)
       for clustered data documented in
       Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.
Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for:
  (i) true class fractions (TCFs) at fixed pairs of thresholds;
  (ii) the ROC surface;
  (iii) the volume under ROC surface (VUS);
  (iv) the optimal pairs of thresholds.
  Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) <doi:10.1177/09622802221089029>. Referring to point (iv), three  different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) <doi:10.1177/0962280217742539>. Visualization tools are also provided. We refer readers to the articles cited above for all details.
Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).
	Simultaneously perform clustering analysis and signature 	gene selection on high-dimensional transcriptome data sets. 	To do so, 'ClussCluster' incorporates a Lasso-type 	regularization penalty term to the objective function of K-	means so that cell-type-specific signature genes can be 	identified while clustering the cells.
Tests, utilities, and case studies for analyzing significance in
  clustered binary matched-pair data. The central function clust.bin.pair uses
  one of several tests to calculate a Chi-square statistic. Implemented are the
  tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998)
  <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>,
  Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010)
  <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996>
  included for comparison. The utility functions nested.to.contingency and
  paired.to.contingency convert data between various useful formats. Thyroids
  and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989)
  <doi:10.1016/0165-1781(89)90196-0> respectively.
Evaluates the stability and significance of clusters on 'igraph' graphs.
    Supports weighted and unweighted graphs. Implements the cluster evaluation methods
    defined by Arratia A, Renedo M (2021) <doi:10.7717/peerj-cs.600>. Also includes an
    implementation of the Reduced Mutual Information introduced by Newman et al. (2020) 
    <doi:10.1103/PhysRevE.101.042304>.
A set of tools for evaluating clustering robustness using 
    proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) 
    <doi:10.1038/srep06207>), as well as similarity across methods 
    and method stability using element-centric clustering comparison (Gates et 
    al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package 
    enables stability-based parameter assessment for graph-based clustering 
    pipelines typical in single-cell data analysis.
Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available.
Functions to identify the pattern of a dose-response
		curve. Then fit a set of appropriate models to it according to the identified pattern, 
		followed by model averaging to estimate the effective dose.
Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.
Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
A collection of data sets for teaching cluster analysis.
Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.
Test for cluster tendency (clusterability) of a data set.
    The methods implemented - reducing the data set to a single dimension using principal component analysis or computing
    pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - 
    are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms
    are appropriate for a data set.
Provides functionality for the analysis of clustered data using the cluster bootstrap.
Functions for calculation of robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take. See Simpson, T. I., Armstrong, J. D. & Jarman, A. P. (2010) <doi:10.1186/1471-2105-11-590> and Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003) <doi:10.1023/a:1023949509487>.
Estimating causal effects from observational studies assuming 
    clustered (or partial) interference. These inverse probability-weighted
    estimators target new estimands arising from population-level treatment 
    policies. The estimands and estimators are introduced in Barkley et al. 
    (2017) <arXiv:1711.04834>.
Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.
We developed the clusterGeneration package to provide functions 
        for generating random clusters, generating random 
        covariance/correlation matrices,
        calculating a separation index (data and population version)
        for pairs of clusters or cluster distributions, and 1-D and 2-D
        projection plots to visualize clusters.  The package also
        contains a function to generate random clusters based on
        factorial designs with factors such as degree of separation,
        number of clusters, number of variables, number of noisy
        variables.
One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
  clusterhap groups together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, clusterhap groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, clusterhap calculates such
  probability from relative frequencies.
Tools for clustering high-dimensional data.
  In particular, it contains the methods described in
   <doi:10.1093/bioinformatics/btaa243>,
   <arXiv:2010.00950>.
The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided.
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of 'Ckmeans.1d.dp' to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp() can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp() and backtracking.sc.dp() can be used. See Szkaliczki, T. (2016) "clustering.sc.dp: Optimal Clustering with Sequential Constraint by Using Dynamic Programming" <doi: 10.32614/RJ-2016-022> for more information.
Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.
Evaluate arbitrary function calls using workers on HPC schedulers
    in single line of code. All processing is done on the network without
    accessing the file system. Remote schedulers are supported via SSH.
Integrative context-dependent clustering for heterogeneous
    biomedical datasets. Identifies local clustering structures in related
    datasets, and a global clusters that exist across the datasets.
Calculate power for cluster randomized trials (CRTs) including multi-arm trials, individually randomized group treatment trials (IGRTTs), stepped wedge trials (SWTs) and others using closed-form (analytic) solutions, and estimates power using Monte Carlo methods.
Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.
This is a function for validating microarray clusters via reproducibility,
 based on the paper referenced below.
Calculate p-values and confidence intervals using cluster-adjusted
    t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). 
 (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, 
 HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, 
 RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, 
 JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, 
 MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, 
 JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, 
 DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, 
 CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>,
 HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, 
 TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, 
 BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, 
 WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).
Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.
Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins' statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.
Implements a Ward-like hierarchical clustering
    algorithm including soft spatial/geographical constraints.
This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.
Model-based clustering of mixed data (i.e. data which consist of
    continuous, binary, ordinal or nominal variables) using a parsimonious
    mixture of latent Gaussian variable models.
Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304.
Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.
Provides various tools of for clustering multivariate angular 
  data on the torus. The package provides angular 
  adaptations of usual clustering methods such as the k-means 
  clustering, pairwise angular distances, which can be used as an 
  input for distance-based clustering algorithms, and implements
  clustering based on the conformal prediction framework. Options 
  for the conformal scores include scores based on a kernel density 
  estimate, multivariate von Mises mixtures, and naive k-means clusters. 
  Moreover, the package provides some basic data handling tools for 
  angular data.
Clusters longitudinal trajectories over time (can be unequally 
    spaced, unequal length time series and/or partially overlapping series) on
    a common time axis. Performs k-means clustering on a single continuous 
    variable measured over time, where each mean is defined by a thin plate 
    spline fit to all points in a cluster. Distance is MSE across trajectory 
    points to cluster spline. Provides graphs of derived cluster splines, 
    silhouette plots, and Adjusted Rand Index evaluations of the number
    of clusters. Scales well to large data with multicore parallelism available
    to speed computation.
Monitor and trace changes in clustering solutions of accumulating datasets 
    at successive time points. The clusters can adopt External and Internal transition at 
    succeeding time points. The External transitions comprise of Survived, Merged, Split, 
    Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes 
    in location and cohesion of the survived clusters. The package uses MONIC framework developed by 
    Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006)<doi:10.1145/1150402.1150491> .
A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <DOI:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <DOI:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.
Deciding what resolution to use can be a difficult question when
    approaching a clustering analysis. One way to approach this problem is to
    look at how samples move as the number of clusters increases. This package
    allows you to produce clustering trees, a visualisation for interrogating
    clusterings as resolution increases.
Returns an edit-distance based clusterization of an input vector of strings.
    Each cluster will contain a set of strings w/ small mutual edit-distance
    (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by
    stringdist::stringdist(). The set of all mutual edit-distances is then used by
    graph algorithms (from package 'igraph') to single out subsets of high connectivity.
An interactive document on  the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.
Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.
Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.
Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification.
Package contains most of the popular internal and external
        cluster validation methods ready to use for the most of the
        outputs produced by functions coming from package "cluster".
        Package contains also functions and examples of usage for
        cluster stability approach that might be applied to algorithms
        implemented in "cluster" package as well as user defined
        clustering algorithms.
Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008) <doi: 10.18637/jss.v025.i04>.
A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV).
    Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. 
    Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. 
    They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. 
    Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or 
    posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in 
    lifetime values estimates for individual customers.
    This package includes fast and accurate implementations of various probabilistic models for non-contractual settings 
    (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., 
    socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying 
    covariates to control for e.g., seasonal patterns. 
    Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: 
    [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), 
    [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), 
    [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the 
    [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). 
    Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.
Single objective optimization using a CMA-ES.
Pure R implementation of the Covariance Matrix Adaptation -
    Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).
Contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.
Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>. 
  It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.
An implementation of 'Conic Multivariate Adaptive Regression Splines (CMARS)' in R.
    See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with 
    multivariate adaptive regression splines supported by continuous optimization, 
    <DOI:10.1080/17415977.2011.624770>. It constructs models by using the terms
    obtained from the forward step of MARS and then estimates parameters by using 
    'Tikhonov' regularization and conic quadratic optimization. It is possible to 
    construct models for prediction and binary classification. It provides performance 
    measures for the model developed. The package needs the optimisation software 'MOSEK' 
    <https://www.mosek.com/> to construct the models. Please follow the instructions in 
    'Rmosek' for the installation.
Conditional mixture model fitted via EM (Expectation Maximization) algorithm for model-based clustering, including parsimonious procedure, optimal conditional order exploration, and visualization.
Implements the Bayesian calibration model described
    in Pratola and Chkrebtii (2018) <DOI:10.5705/ss.202016.0403> for stochastic 
    and deterministic simulators.  Additive and multiplicative discrepancy models 
    are currently supported. See <http://www.matthewpratola.com/software> for 
    more information and examples.
An open-source implementation of the 'Congruent Matching Cells' 
    method for cartridge case identification as proposed by Song (2013) <https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=911193> as well 
    as an extension of the method proposed by Tong et al. (2015) <doi:10.6028/jres.120.008>.
    Provides a wide range of pre, inter, and post-processing options when
    working with cartridge case scan data and their associated comparisons. See
    the cmcR package website for more details and examples.
Writing interfaces to command line software is cumbersome. 
    'cmdfun' provides a framework for building function calls to seamlessly 
    interface with shell commands by allowing lazy evaluation of command line arguments. 
    'cmdfun' also provides methods for handling user-specific paths to tool installs or secrets like API keys. 
    Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside 
    R when they might usually leave to execute non-R software.
Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) <doi:10.1080/01621459.2018.1448828>). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs can quantify more interpretable interaction effects in many problems. The current implementation performs variable selection on only binary CMEs; we are working on an extension for the continuous setting.
Collective matrix factorization (CMF) finds joint low-rank
	representations for a collection of matrices with shared row or column
	entities. This code learns a variational Bayesian approximation for CMF,
	supporting multiple likelihood potentials and missing data, while
	identifying both factors shared by multiple matrices and factors private
	for each matrix. For further details on the method see
	Klami et al. (2014) <arXiv:1312.5921>.
	The package can also be used to learn Bayesian canonical correlation
	analysis (CCA) and group factor analysis (GFA) models, both of which are
	special cases of CMF. This is likely to be useful for people looking for
	CCA and GFA solutions supporting missing data and non-Gaussian likelihoods.
	See Klami et al. (2013) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf>
	and	Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html>
	for details on Bayesian CCA and GFA, respectively.
Collective matrix factorization (a.k.a. multi-view or multi-way factorization,
	Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a (potentially very sparse
	or having many missing values) matrix 'X' as the product of two low-dimensional matrices,
	optionally aided with secondary information matrices about rows and/or columns of 'X',
	which are also factorized using the same latent components.
	The intended usage is for recommender systems, dimensionality reduction, and missing value imputation.
	Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce
	different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky,
	(2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model,
	(Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>),
	or the enhanced model with 'implicit features' (Rendle, Zhang,
	Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based
	procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009)
	<doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver
	(Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative
	coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>),
	providing efficient methods for sparse and dense data, and mixtures thereof.
	Supports L1 and L2 regularization in the main models,
	offers alternative most-popular and content-based models, and implements functionality
	for cold-start recommendations and imputation of 2D data.
Uses a calibrated model fusion approach to optimally combine multiple surrogate markers. Specifically, two initial estimates of optimal composite scores of the markers are obtained; the optimal calibrated combination of the two estimated scores is then constructed which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained (PTE) by the final combined score. The primary function, pte.estimate.multiple(), estimates the PTE of the identified combination of multiple surrogate markers. Details are described in Wang et al (2022) <doi:10.1111/biom.13677>.
Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC.
Cochran-Mantel-Haenszel methods (Cochran (1954) <doi:10.2307/3001616>; Mantel and Haenszel (1959) <doi:10.1093/jnci/22.4.719>; Landis et al. (1978) <doi:10.2307/1402373>) are a suite of tests applicable to categorical data. A competitor to those tests is the procedure of Nonparametric ANOVA which was initially introduced in Rayner and Best (2013) <doi:10.1111/anzs.12041>. The methodology was then extended in Rayner et al. (2015) <doi:10.1111/anzs.12113>. This package employs functions related to both methodologies and serves as an accompaniment to the book: An Introduction to Cochran–Mantel–Haenszel and Non-Parametric ANOVA. The package also contains the data sets used in that text.
Finds a low-dimensional embedding of high-dimensional data, conditioning on available manifold information. The current version supports conditional MDS (based on either conditional SMACOF in Bui (2021) <arXiv:2111.13646> or closed-form solution in Bui (2022) <doi:10.1016/j.patrec.2022.11.007>) and conditional ISOMAP in Bui (2021) <arXiv:2111.13646>.
Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm.
Quite extensive package for maximum likelihood estimation and
    weighted least squares estimation of categorical marginal models (CMMs;
    e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?;
    Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.
CEU (CEU San Pablo University) Mass Mediator is an on-line tool for aiding researchers in 
  performing metabolite annotation. 'cmmr' (CEU Mass Mediator RESTful API) allows 
  for programmatic access in R: batch search, batch advanced search, MS/MS (tandem mass spectrometry) search, etc.
  For more information about the API Endpoint please go to <https://github.com/lzyacht/cmmr>.
A compositional mediation model for continuous outcome and binary outcomes to deal with mediators that are compositional data. Lin, Ziqiang et al. (2022) <doi:10.1016/j.jad.2021.12.019>.
Provides the source and examples for James P. Howard, II, 
             "Computational Methods for Numerical Analysis with R," 
             <https://jameshoward.us/cmna/>, a book on numerical
             methods in R.
Perceptually uniform palettes for commonly used
	variables in oceanography as functions taking an integer
	and producing character vectors of colours.
	See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M.
	and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for
	the guidelines adhered to when creating the palettes.
Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out. Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull's eye style plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals. Additional functions include: highlight signals, a group of SNPs, chromosome visualization and candidate genes around SNPs.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks, as described in Gray
 (1988), A class of K-sample tests for comparing the cumulative
 incidence of a competing risk, Ann. Stat. 16:1141-1154
 <DOI:10.1214/aos/1176350951>, and Fine JP and
 Gray RJ (1999), A proportional hazards model for the subdistribution
 of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>.
Uses inverse probability weighting methods to estimate treatment effect under marginal structure model for the cause-specific hazard of competing risk events. Estimates also the cumulative incidence function (i.e. risk) of the potential outcomes, and provides inference on risk difference and risk ratio. Reference: Kalbfleisch & Prentice (2002)<doi:10.1002/9781118032985>; Hernan et al (2001)<doi:10.1198/016214501753168154>.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks using quantile regressions,
 as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.
This is an open-source implementation of the Congruent Matching Profile 
    Segments (CMPS) method (Chen et al. 2019)<doi:10.1016/j.forsciint.2019.109964>. 
    In general, it can be used for objective comparison of striated tool marks, and in our 
    examples, we specifically use it for bullet signatures comparisons. The CMPS score is 
    expected to be large if two signatures are similar. So it can also be considered as a 
    feature that measures the similarity of two bullet signatures.
Computes maximum response from Cardiac Magnetic Resonance Images using spatial and voxel wise spline based Bayesian model. This is an implementation of the methods described in Schmid (2011) <doi:10.1109/TMI.2011.2109733> "Voxel-Based Adaptive Spatio-Temporal Modelling of Perfusion Cardiovascular MRI". IEEE TMI 30(7) p. 1305 - 1313.
A collection of useful helper routines developed by
  students of the Center for Mathematical Research, Stankin,
  Moscow.
Uses the 'CMS' application programming interface <https://dnav.cms.gov/api/healthdata>
    to provide users databases containing yearly
    Medicare reimbursement rates in the United States. Data can be acquired
    for the entire United States or only for specific localities. Currently,
    support is only provided for the Medicare Physician Fee Schedule,
    but support will be expanded for other 'CMS' databases in future versions.
The Satellite Application Facility on Climate Monitoring (CM SAF) 
    is a ground segment of the European Organization for the Exploitation of 
    Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
    Facilities. The CM SAF contributes to the sustainable monitoring of the climate 
    system by providing essential climate variables related to the energy and water 
    cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight 
    National Meteorological and Hydrological Services.
    The 'cmsaf' R-package includes a 'shiny' based interface for an easy application of 
    the 'cmsafops' and 'cmsafvis' packages - the CM SAF R Toolbox. The Toolbox offers an 
    easy way to prepare, manipulate, analyse and visualize CM SAF NetCDF formatted data. 
    Other CF conform NetCDF data with time, longitude and latitude dimension should be 
    applicable, but there is no guarantee for an error-free application.
    CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>).
    Detailed information and test data are provided on the CM SAF webpage 
    (<http://www.cmsaf.eu/R_toolbox>).
The Satellite Application Facility on Climate Monitoring (CM SAF) 
    is a ground segment of the European Organization for the Exploitation of 
    Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
    Facilities. The CM SAF contributes to the sustainable monitoring of the climate 
    system by providing essential climate variables related to the energy and water 
    cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight 
    National Meteorological and Hydrological Services.
    The 'cmsafops' R-package provides a collection of R-operators for the analysis and 
    manipulation of CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, 
	longitude and latitude dimension should be applicable, but there is no guarantee for 
	an error-free application.
    CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>).
    Detailed information and test data are provided on the CM SAF webpage 
    (<http://www.cmsaf.eu/R_toolbox>).
The Satellite Application Facility on Climate Monitoring (CM SAF) 
    is a ground segment of the European Organization for the Exploitation of 
    Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
    Facilities. The CM SAF contributes to the sustainable monitoring of the climate 
    system by providing essential climate variables related to the energy and water 
    cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight 
    National Meteorological and Hydrological Services.
    The 'cmsafvis' R-package provides a collection of R-operators for the analysis and
    visualization of CM SAF NetCDF data.
	CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>).
    Detailed information and test data are provided on the CM SAF webpage 
    (<http://www.cmsaf.eu/R_toolbox>).
An interactive document on  the topic of confusion matrix analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ConfusionMatrixShiny/>.
An implementation of the statistical methods commonly
  used for advanced composite materials in aerospace applications.
  This package focuses on calculating basis values (lower tolerance
  bounds) for material strength properties, as well as performing the
  associated diagnostic tests. This package provides functions for
  calculating basis values assuming several different distributions,
  as well as providing functions for non-parametric methods of computing
  basis values. Functions are also provided for testing the hypothesis
  that there is no difference between strength and modulus data from an
  alternate sample and that from a "qualification" or "baseline" sample.
  For a discussion of these statistical methods and their use, see the
  Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4).
  Additional details about this package are available in the paper by
  Kloppenborg (2020, <doi:10.21105/joss.02265>).
Conditional moments test, as proposed by Newey (1985) <doi:10.2307/1911011 > and Tauchen (1985) <doi:10.1016/0304-4076(85)90149-6>, useful to detect specification violations for models estimated by maximum likelihood. Methods for probit and tobit models are provided.
Various utilities for the complex multivariate Gaussian distribution and complex Gaussian processes.
Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures.
Implementation of the CNAIM standard in R. Contains a series of
    algorithms which determine the probability of failure, consequences of
    failure and monetary risk associated with electricity distribution
    companies' assets such as transformers and cables. Results are visualized
    in an easy-to-understand risk matrix.
This is an add-on to the 'cna' package <https://CRAN.R-project.org/package=cna> comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see Baumgartner and Ambuehl (2021) <doi:10.1177/0049124121995554>.
Provided R functions for working with the Conditional Negative Binomial distribution.
A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.
Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.
Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.
Conventional methods for producing standard scores or percentiles 
    in psychometrics or biometrics are often plagued with 'jumps' or 'gaps' 
    (i.e., discontinuities) in norm tables and low confidence for assessing 
    extreme scores. The continuous norming method introduced by A. Lenhard et al.
    (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>;
    2021 <doi: 10.1177/0013164420928457>) estimates percentile development 
    (e. g. over age) and generates continuous test norm scores on the basis of 
    the raw data from standardization samples, without requiring assumptions 
    about the distribution of the raw data: Norm scores are directly established 
    from raw data by modeling the latter ones as a function of both percentile 
    scores and an explanatory variable (e.g., age). The method minimizes bias 
    arising from sampling and measurement error, while handling marked deviations 
    from normality, addressing bottom or ceiling effects and capturing almost 
    all of the variance in the original norm data sample. It includes procedures 
    for post stratification of norm samples to overcome bias in data collection 
    and to mitigate violations of representativeness. An online demonstration is 
    available via <https://cnorm.shinyapps.io/cNORM/>.
DNA copy number data evaluation using both their initial form 
    (copy number as a noisy function of genomic position) and their 
    approximation by a piecewise-constant function (segmentation), 
    for the purpose of identifying genomic regions where the copy number 
    differs from the norm.
We unify various nonparametric hypothesis testing problems in a framework of permutation testing, enabling hypothesis testing on multi-sample, multidimensional data and contingency tables. Most of the functions available in the R environment to implement permutation tests are single functions constructed for specific test problems; to facilitate the use of the package, the package encapsulates similar tests in a categorized manner, greatly improving ease of use.
    We will all provide functions for self-selected permutation scoring methods and self-selected p-value calculation methods (asymptotic, exact, and sampling). For two-sample tests, we will provide mean tests and estimate drift sizes; we will provide tests on variance; we will provide paired-sample tests; we will provide correlation coefficient tests under three measures. For multi-sample problems, we will provide both ordinary and ordered alternative test problems. For multidimensional data, we will implement multivariate means (including ordered alternatives) and multivariate pairwise tests based on four statistics; the components with significant differences are also calculated. For contingency tables, we will perform permutation chi-square test or ordered alternative.
Chinese numerals processing in R, such as conversion between 
    Chinese numerals and Arabic numerals as well as detection and extraction of
    Chinese numerals in character objects and string. This package supports 
    the casual scale naming system and the respective SI prefix systems used 
    in mainland China and Taiwan: 
    "China Statutory Measurement Units"
    State Administration for Market Regulation (2019) <http://gkml.samr.gov.cn/nsjg/jls/201902/t20190225_291134.html>
    "Names, Definitions and Symbols of the Legal Units of Measurement and the Decimal Multiples and Submultiples" 
    Ministry of Economic Affairs (2019) <https://gazette.nat.gov.tw/egFront/detail.do?metaid=108965>.
Implements Dirichlet multinomial modeling of relative abundance data using functionality provided by the 'Stan' software. The purpose of this package is to provide a user friendly way to interface with 'Stan' that is suitable for those new to modeling. For more regarding the modeling mathematics and computational techniques we use see our publication in Molecular Ecology Resources titled 'Dirichlet multinomial modeling outperforms alternatives for analysis of ecological count data' (Harrison et al. 2020 <doi:10.1111/1755-0998.13128>).
Provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps.
Coalescent simulators can rapidly simulate biological sequences
    evolving according to a given model of evolution.
    You can use this package to specify such models, to conduct the simulations
    and to calculate additional statistics from the results (Staab, Metzler,
    2016 <doi:10.1093/bioinformatics/btw098>).
    It relies on existing simulators for doing the simulation, and currently
    supports the programs 'ms', 'msms' and 'scrm'. It also supports finite-sites
    mutation models by combining the simulators with the program 'seq-gen'.
    Coala provides functions for calculating certain summary statistics, which
    can also be applied to actual biological data.
    One possibility to import data is through the 'PopGenome' package
    (<https://github.com/pievos101/PopGenome>).
Flexible framework for coalescent analyses in R. It includes a main function running the  MCMC algorithm, auxiliary functions for tree rearrangement, and some functions to compute population genetic parameters. Extended description can be found in Paradis (2020) <doi:10.1201/9780429466700>. For details on the MCMC algorithm, see Kuhner et al. (1995) <doi:10.1093/genetics/140.4.1421> and Drummond et al. (2002) <doi:10.1093/genetics/161.3.1307>.
An implementation of a Bayesian framework for the opinion poll
    based estimation of event probabilities in multi-party electoral systems
    (Bender and Bauer (2018) <doi:10.21105/joss.00606>).
Functions to analyze coarse data.
    Specifically, it contains functions to (1) fit parametric accelerated
    failure time models to interval-censored survival time data, and (2)
    estimate the case-fatality ratio in scenarios with under-reporting.
    This package's development was motivated by applications to infectious
    disease: in particular, problems with estimating the incubation period and
    the case fatality ratio of a given disease.  Sample data files are included
    in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>, 
    Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and 
    Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.
Agreement of continuously scaled measurements made by two techniques, devices or methods is usually
             evaluated by the well-established Bland-Altman analysis or plot. Conditional method agreement trees (COAT),
             proposed by Karapetyan, Zeileis, Henriksen, and Hapfelmeier (2023) <doi:10.48550/arXiv.2306.04456>,
             embed the Bland-Altman analysis in the framework of recursive partitioning to explore heterogeneous method
             agreement in dependence of covariates. COAT can also be used to perform a Bland-Altman test for differences
             in method agreement.
Generate balance tables and plots for covariates of groups preprocessed through 
             matching, weighting or subclassification, for example, using propensity scores. Includes 
             integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', 
             'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing 
             functions. Users can also specify data for balance assessment not generated through 
             the above packages. Also included are methods for assessing balance in clustered or 
             multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.
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.
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.
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.
Tools for analyzing R expressions
  or blocks of code and determining the dependencies between them.
  It focuses on R scripts, but can be used on the bodies of functions.
  There are many facilities including the ability to summarize  or get a high-level
  view of code, determining dependencies between variables,  code improvement
  suggestions.
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>.
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).
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.
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.
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,
    and programmer friendly through a flexible and robust API. 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 basic R, 'dplyr'/'tibble', 'data.table', 'sf', 'plm' 
    (panel-series and data frames), and accommodates, by virtue of smart internal 
    attribute handling, many other classes (like 'ts', 'xts'/'zoo' or 'tsibble').
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>.
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.
The website <https://www.color-hex.com> is a great resource of hex colour
  codes and palettes. This package allows you to retrieve palettes
  and colour information from the website directly from R. There are 
  also custom scale-functions for 'ggplot2'.
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>.
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.
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.
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)".
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>.
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.ml>.
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.
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).
Functions to
    produce nicely formatted comments to use in R-scripts (or
    Latex/HTML/markdown etc). A comment with formatting is printed to the
    console and can then be copied to a script.
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.
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.
Regression methodologies with compositional covariates, including (1) sparse log-contrast regression with compositional covariates proposed by Lin et al. (2014) <doi:10.1093/biomet/asu031>, and (2) sparse log-contrast regression with functional compositional predictors proposed by Sun et al. (2020) <arXiv:1808.02403>.
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. This package also includes functions to calculate the probability of observing the composite endpoint and the expected effect on the composite endpoint, among others. The methods implemented 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. 
        The package includes the two-level functions to calculate the LR assuming multivariate normality, and another with drops 
        this assumption and uses a multivariate kernel density estimate. The package also contains code to perform empirical 
        cross entropy (ECE) calibration of likelihood ratios. The LR functions are based primarily on 
        Aitken, C.G.G. and Lucy, D. (2004) <doi:10.1046/j.0035-9254.2003.05271.x>, "Evaluation of trace evidence in the form of multivariate data," 
        Journal of the Royal Statistical Society: Series C (Applied Statistics), 53: 109-122. The ECE functions are based primarily 
        on  D. Ramos and J. Gonzalez-Rodrigues, (2008) "Cross-entropy analysis of the information in forensic speaker recognition," 
        in Proc. IEEE Odyssey, Speaker Lang. Recognit. Workshop.
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'.
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
Contains functions to calculate the confidence interval for
    the difference between the intraclass correlation coefficients (ICCs) of
    two datasets. The package contains two functions: one for two dependent
    datasets and one for two independent datasets. The method for calculating
    the confidence intervals is found in Ramasundarahettige et al. (2009)
    <DOI: 10.1002/sim.3523>.
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.org/els/soc/handbookonconstructingcompositeindicatorsmethodologyanduserguide.htm>, '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.
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>.
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).
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.
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.
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 Monge (2020) <arXiv:1912.12880v2>.
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.
Implements concordance regression which can be used to estimate generalized odds of concordance.
	Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and 
    for continuous outcome data. The method was introduced by Dunkler, Schemper and Heinze (2010) <doi:10.1093/bioinformatics/btq035>.
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 (2020) <arXiv:2008.09498>).
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.
Neural network conditional mixtures are mixture models whose parameters are predicted by a neural network. The mixture model can thus change its parameters in response to changes in predictive covariates. Mixtures included are gaussian, log-normal and hybrid Pareto mixtures. The latter relies on the generalized Pareto distribution to account for the presence of large extreme events. The unconditional mixtures are also available.
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.
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.
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>.
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.
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 5.29.5 or later. 
    A demonstration version can be downloaded from <https://shop.acer.org/acer-conquest-5.html>.
Multi-species estimation of geographical range parameters
	for preliminary assessment of conservation status following Criterion B of the 
	International Union for Conservation of Nature (IUCN, 
	see <http://www.iucnredlist.org>).
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.
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.
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 (2022) <arXiv:2208.04644v1>.
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.
Converts graphical file formats (SVG,
    PNG, JPEG, BMP, GIF, PDF, etc) to one another. The exceptions are the
    SVG file format that can only be converted to other formats and in contrast,
    PDF format, which can only be created from others graphical formats.
    The main purpose of the package was to provide a solution for converting SVG
    file format to PNG which is often needed for exporting graphical files
    produced by R widgets.
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.
Provides a simple high-level wrapper for
    'Julia' package 'Convex.jl' (see <https://github.com/JuliaOpt/Convex.jl> for
    more information),
    which makes it easy to describe and solve convex optimization problems in R.
    The problems can be dealt with include:
    linear programs,
    second-order cone programs,
    semidefinite programs,
    exponential cone programs.
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>.
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.
Collection of routines for efficient scientific computations in physics and astrophysics. These routines include utility functions, numerical computation tools, as well as visualisation tools. They can be used, for example, for generating random numbers from spherical and custom distributions, information and entropy analysis, special Fourier transforms, two-point correlation estimation (e.g. as in Landy & Szalay (1993) <doi:10.1086/172900>), binning & gridding of point sets, 2D interpolation, Monte Carlo integration, vector arithmetic and coordinate transformations. Also included is a non-exhaustive list of important constants and cosmological conversion functions. The graphics routines can be used to produce and export publication-ready scientific plots and movies, e.g. as used in Obreschkow et al. (2020, MNRAS Vol 493, Issue 3, Pages 4551–4569). These routines include special color scales, projection functions, and bitmap handling routines.
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 Twitter 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).
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.
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 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, and Sun et.al (2022) Biometrics.
Data sets used for copula modeling in addition to those in
 the 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.
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.
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>.
A collection of tools for correspondence regression, i.e. the
    correspondence analysis of the crosstabulation of a categorical variable Y
    in function of another one X, where X can in turn be made up of the
    combination of various categorical variables. Consequently, correspondence
    regression can be used to analyze the effects for a polytomous or
    multinomial outcome variable.
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.
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.
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 4 mutation
     types:
     * single base substitutions in the context of preceding and following bases,
     * Strand bias single base substitutions: single base substitutions
        from transcribed regions, that take into consideration the 
        the transcribed versus non-transcribed strand,
     * 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.
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.
Density, distribution function, quantile function, random
    generation and survival function for the Cosine Weibull Distribution as defined
    by SOUZA, L. New Trigonometric Class of Probabilistic Distributions. 219 p.
    Thesis (Doctorate in Biometry and Applied Statistics) - Department of Statistics
    and Information, Federal Rural University of Pernambuco, Recife, Pernambuco,
    2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
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>.
Interface to the couchDB document database <http://couchdb.apache.org>.
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.
Calculates the difference in average change over time for
 variables in given dataset. Generates a randomization matrix to resample data
 for permutation testing. Creates and plots null distributions and calculates 
 P-Values. Identifies potential counternull values by generating and plotting 
 counternull distributions.
 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.
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).
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 (EPPM) as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .
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>.
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>.
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>.
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 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. (2022)
    <arXiv:2209.08173>. '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 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. Reference: 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.
Fast algorithms for fitting a Cox mixed-effects model for e.g., genome-wide association studies. See Liang He and Alexander Kulminski (2020) <doi: 10.1534/genetics.119.302940>.
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.
Sparse estimation for Cox PH models is done via
    Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, 
    Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. MIC mimics the best 
    subset selection using a penalized likelihood approach yet with no need 
    of a tuning parameter. The problem is further reformulated with a 
    re-parameterization step so that it reduces to one unconstrained non-convex
    yet smooth programming problem, which can be solved efficiently. Furthermore,
    the re-parameterization tactic yields an additional advantage in terms of
    circumventing post-selection inference.
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>.
This package contains methods to perform Common Principal
    Component Analysis (CPCA). The stepwise method by Trendafilov is published
    in the current version. Please see Trendafilov (2010). Stepwise estimation
    of common principal components. Computational Statistics & Data Analysis,
    54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
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 (Parafac) model-1 fit to a three-way or four-way data tensor/array. See Harshman and Lundy (1994): <doi:10.1016/0167-9473(94)90132-5>. Uses Parafac factor weights from one mode of this model as predictors to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Supports penalized logistic regression, support vector machine, random forest, and feed-forward neural network. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Parallel computing is implemented 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.
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug's maximum concentration, (13) dlar.fn for calculating loading dose based on drug's accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <https://link.springer.com/article/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.
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.
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. (2021) <arXiv:2107.10017>.
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)
  <https://CRAN.R-project.org/package=mirai> 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.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'.
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.
Provides a general toolkit for drug target identification. We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008755>.
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 the monotonicity direction classification procedure establishes it. 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 it -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 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>.
Provides wrapper functions for running classification and
    regression tests using different machine learning techniques, such as Random
    Forests and decision trees. The package provides standardized methods for
    preparing data to suit the algorithm's needs, training a model, making
    predictions, and evaluating results. Also, some functions are provided to run
    multiple instances of a test.
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).
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>.
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>).
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.
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.
Provides accurate cell type proportion estimation by incorporating covariance structure in both single-cell and bulk RNA-seq datasets into the analysis. For more detail, see Karimnezhad, A. (2022) <doi:10.1101/2022.05.13.491858>.
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 2020.
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 was developed in the context of H2020 MED-GOLD (776467) and 
  S2S4E (776787) projects. Lledó et al. (2019) <doi:10.1016/j.renene.2019.04.135>.
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.
Construct confidence sets for positions of populations in a ranking 
    based on values of a certain feature and their estimation errors. 
    Both simultaneous and marginal confidence sets are available, as well as 
    confidence sets with populations occupying top-n positions in the ranking.
    Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006>.
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.
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 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.
This is a package for constructing hypothesis trees for treatment comparisons based 
  on the closure principle and analysing the corresponding Closed Testing Procedures (CTP)
  using adjusted p-values. For reference, see
  Marcus, R., Peritz, E, and Gabriel, K.R. (1976) <doi:10.2307/2335748> and
  Bauer, P (1991) <doi:10.1002/sim.4780100609>.
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/>), 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'). 
        All 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.
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.
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>.
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. (2019) <arXiv:1909.03017> 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>.
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>.
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.
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.
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>.
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>.
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://rdcu.be/bHhJ4>.
Provides functions to manage databases: select, update, insert,
    and delete records, list tables, backup tables as CSV files, and import
    CSV files as tables.
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.
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).
Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.
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 
    <http://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).
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.
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>.
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..
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 (1995) <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>.
An easy-to-use, dependency-free set of functions for iterating over
    elements of various input objects. Functions are wrappers around base
    apply()/lapply()/vapply() functions but designed to have similar
    functionality to the mapping functions in the 'purrr' package
    <https://purrr.tidyverse.org/>. Specifically, function names more explicitly
    communicate the expected class of the output and functions also allow for
    the convenient shortcut of '~ .x' instead of the more verbose
    'function(.x) .x'.
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.
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.
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.
'Dash' ships with supercharged components for interactive user interfaces. A core set of components, written and maintained by the 'Dash' team, is available in the 'dashCoreComponents' package. The source for this package is on GitHub: plotly/dash-core-components.
'Dash' is a web application framework that provides pure Python and R abstraction around HTML, CSS, and JavaScript. Instead of writing HTML or using an HTML templating engine, you compose your layout using R functions within the 'dashHtmlComponents' package. The source for this package is on GitHub: plotly/dash-html-components.
Pivot tables are useful for interactive presentation of  summary statistics computed for data contained in another table. The 'dashPivottable' package wraps 'react-pivottable', making it easy to add drag-and-drop tables into your Dash for R applications.
An interactive table component designed for editing and exploring large datasets, 'dashDataTable' is rendered with standard, semantic HTML <table/> markup, which makes it accessible, responsive, and easy to style. This component was written from scratch in 'React.js' specifically for the 'dash' community. Its API was designed to be ergonomic and its behaviour is completely customizable through its  properties.
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>.
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.
Writes data frames to xls files. It supports 
        multiple sheets and basic formatting.
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.
'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) 
    deemed too large.
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.
The aim of the 'dataset' package is to make tidy datasets easier to release, 
    exchange and reuse. It organizes and formats data frame 'R' objects into well-referenced, 
    well-described, interoperable datasets into release and reuse ready form. A subjective 
    interpretation of the  W3C  DataSet recommendation and the datacube model  <https://www.w3.org/TR/vocab-data-cube/>, 
    which is also used in the global Statistical Data and Metadata eXchange standards, 
    the application of the connected Dublin Core <https://www.dublincore.org/specifications/dublin-core/dcmi-terms/> 
    and DataCite <https://support.datacite.org/docs/datacite-metadata-schema-44/> standards 
    preferred by European open science repositories to improve the findability, accessibility,
    interoperability and reusability of the datasets.
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.refinitiv.com/en>.
    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>.
Create, update, read and delete data frames in time versioned, parquet-backed datasets. Inspect how your data frame has changed over time. Run your analysis against multiple versions of your data frame.
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.
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.
Discipline-Based Education Research scientists repeatedly analyze assessment data to ensure question items’ reliability and examine the efficacy of a new educational intervention. Analyzing assessment data comprises multiple steps and statistical techniques that consume much of researchers’ time and are error-prone. While education research continues to grow across many disciplines of science, technology, engineering, and mathematics (STEM), the discipline-based education research community lacks tools to streamline education research data analysis. ‘DBERlibR’—an ‘R’ package to streamline and automate assessment data processing and analysis—fills this gap. The package reads user-provided assessment data, cleans them, merges multiple datasets (as necessary), checks assumption(s) for specific statistical techniques (as necessary), applies various statistical tests (e.g., one-way analysis of covariance, one-way repeated-measures analysis of variance), and presents and interprets the results all at once. By providing the most frequently used analytic techniques, this package will contribute to education research by facilitating the creation and widespread use of evidence-based knowledge and practices. The outputs contain a sample interpretation of the results for users’ convenience. User inputs are minimal; they only need to prepare the data files as instructed and type a function in the 'R' console to conduct a specific data analysis.\n For descriptions of the statistical methods employed in package, refer to the following Encyclopedia of Research Design, edited by Salkind, N. (2010) <doi:10.4135/9781412961288>.
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>.
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. 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>).
There lacks a package for analysing domain-centric ontologies and annotations, particularly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a comprehensive domain-centric database for annotating protein domains using a panel of ontologies including Gene Ontology. With the package, users are expected to analyse and visualise domain-centric ontologies and annotations. Supported analyses include but are not limited to: easy access to a wide range of ontologies and their domain-centric annotations; able to build customised ontologies and annotations; domain-based enrichment analysis and visualisation; construction of a domain (semantic similarity) network according to ontology annotations; significance analysis for estimating a contact (statistical significance) network via Random Walk with Restart; and high-performance parallel computing. The new functionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input protein sequences (precisely domain content in the form of architectures) plus to assess the predictions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony.
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.
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.
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.
Allows to build Data-Driven Sparse Partial Least Squares models 
    with high-dimensional settings. 
    Number of components and regularization coefficients are automatically set. 
    It comes with visualization functions and uses 'Rcpp' functions for fast 
    computations and 'doParallel' to parallelize bootstrap operations. 
    An applet has been developed to apply this procedure.
    This is based on H Lorenzo, O Cloarec, R Thiebaut, J Saracco (2021) 
    <doi:10.1002/sam.11558>.
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.
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.
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.
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 and declared missing 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.
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).
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 <http://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.
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." bioRxiv (2022).
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.
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.
Multi-binary response models are a class of models that allow for the estimation of multiple binary outcomes simultaneously. This package provides functions to estimate and simulate these models using the Discrete Exponential-Family Models [DEFM] framework. In it, we implement the models described in Vega Yon, Valente, and Pugh (2023) <doi:10.48550/arXiv.2211.00627>. DEFMs include Exponential-Family Random Graph Models [ERGMs], which characterize graphs using sufficient statistics, which is also the core of DEFMs. Using sufficient statistics, we can describe the data through meaningful motifs, for example, transitions between different states, joint distribution of the outcomes, etc.
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.
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 used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.
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.
Construction and visualization of 2d Delaunay triangulations,
    possibly constrained, 2.5d (i.e. elevated) Delaunay triangulations,
    3d Delaunay triangulations, and 2D Voronoï tessellations.
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.
An open-source toolkit developed by ISciences, LLC and the DANTE 
    Project that is intended for learning, accessing, pre-processing, and 
    visualizing popular political, institutional, and constitutional datasets. 
    ‘demcon' aims to reduce barriers to entry in political science research by 
    automating common acquisition and pre-processing procedures. This package 
    particularly focuses on the V-Dem dataset (<https://www.v-dem.net/vdemds.html>),
    and provides adaptations of methods presented in Fjelde, H., Knutsen, C. H. 
    & Nygård, H. M. 2021, <doi:10.1093/isq/sqaa076>.
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.
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) <DOI: 10.1126/science.1242072>. 
    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.
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.
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.
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.
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.
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.
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.
Solvers and test set for stiff and non-stiff differential equations, and 
  differential algebraic equations. 
  See Mazzia, F., Cash, J.R., and K. Soetaert (2012) <DOI:10.1016/j.cam.2012.03.014>.
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.
Contains 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), two DFA automatic methods for identification of crossover points and a Deltalog automatic method for identification of 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.
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.
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.
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.
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.
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>.
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 Gaussian process emulations of computer models and systems of computer
             models. 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>).
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.
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).
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.
An implementation of the metrics dief@t and dief@k to measure the diefficiency (or continuous efficiency) of incremental approaches, see Acosta, M., Vidal, M. E., & Sure-Vetter, Y. (2017) <doi:10.1007/978-3-319-68204-4_1>. The metrics dief@t and dief@k allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. dief@t and dief@k rely on the computation of the area under the curve of answer traces, and thus capturing the answer rate concentration over a time interval.
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 differences between correlations. diffcor.one() tests whether an expected value differs from an observed value, for example, in construct validation. diffcor.two() tests whether the correlation between two constructs differed across two studies/samples. diffcor.dep() checks whether the correlation between two constructs (r12) is significantly different from the correlation between the first construct with a third one (r13), given the intercorrelation of the compared constructs (r23). All outputs provide the compared correlations, test statistic in z-units, and p-values. For diffcor.one() and diffcor.two(), the output further provides confidence intervals of the empirical correlations and the effect size Cohens q. According to Cohen (1988), q = |.10|, |.30| and |.50| are considered small, moderate, and large differences, respectively.
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.
  'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information,
  see Rackauckas and Nie (2017) <doi:10.5334/jors.151>.
Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.
Computes the ATM (Attractor Transition Matrix) structure and
    the tree-like structure describing the cell differentiation process
    (based on the Threshold Ergodic Set concept introduced by Serra and
    Villani), starting from the Boolean networks with synchronous updating
    scheme of the 'BoolNet' R package. TESs (Threshold Ergodic Sets) are
    the mathematical abstractions that represent the different cell types
    arising during ontogenesis. TESs and the powerful model of biological
    differentiation based on Boolean networks to which it belongs have
    been firstly described in "A Dynamical Model of Genetic Networks for
    Cell Differentiation" Villani M, Barbieri A, Serra R (2011) A
    Dynamical Model of Genetic Networks for Cell Differentiation. PLOS ONE
    6(3): e17703.
Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit using marginal maximum 
	likelihood. Parameter restrictions (fixed value and equality 
	constraints) are possible. In addition, factor scores (person drift 
	rate and person boundary separation) can be estimated. Model fit 
	assessment tools are also available. The traditional diffusion model 
	can be estimated as well.
A wrapper for Google's 'diff-match-patch' library. It provides basic tools
    for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.
This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution (i.e., the conceptually highest fidelity).
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>.
Implementation of network diffusion algorithms such as
  heat diffusion or Markov random walks. Network diffusion algorithms generally
  spread information in the form of node weights along the edges of a graph to other nodes.
  These weights can for example be interpreted as temperature, an initial amount
  of water, the activation of neurons in the brain, or the location of a random
  surfer in the internet. The information (node weights) is iteratively propagated
  to other nodes until a equilibrium state or stop criterion occurs.
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' and 'crc32c' 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>.
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
    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>.
Performs Rayleigh's method of dimensional analysis. 
 This form of dimensional analysis expresses a functional relationship 
 of some variables in the form of an exponential equation 
 (Buckingham, (1914), <doi:10.1103/PhysRev.4.345>).
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).
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>.
This function 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. (2022). An investigation of hypothesis testing procedures for circular and spherical mean vectors. Communications in Statistics-Simulation and Computation (Accepted for publication). <doi:10.1080/03610918.2022.2045499>. e) Tsagris M. and Alzeley O. (2023). Circular and spherical projected Cauchy distributions. <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. (2021) <arXiv:2111.14100>.
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>.
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.
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>.
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.
Estimate, summarize, and perform predictions with the market in 
  disequilibrium model, as found in Gourieroux, C. (2000) <doi:10.1017/CBO9780511805608> and 
  Maddala, G. (1983) <doi:10.1017/CBO9780511810176>. The parameters are estimated with maximum likelihood.
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 pipeline to analyze photographs of disk diffusion plates. This removes the need to analyze the plates themselves, and thus analysis can be done separate from the assay. Furthermore, diskImageR removes potential researcher bias, by quantitative assessment of drug resistance as the zone diameter at multiple cutoff values of growth inhibition. This method also extends the disk diffusion assay by measuring drug tolerance (in addition to drug resistance) as the fraction of the subpopulation that is able to grow above the resistance point ("FoG"), and drug sensitivity as the rate of change from no growth to full growth ("slope").
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.
Calculates the study size (either number of detections, or
    proportion of region that should be covered) to achieve a target precision for
    the estimated abundance. The calculation allows for the penalty due to unknown
    detection function, and for overdispersion. The user must specify a guess at the
    true detection function.
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.
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.
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.
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.
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>.