Query, download climate change projection data from the 'CMIP6' (Coupled
    Model Intercomparison Project Phase 6) project
    <https://pcmdi.llnl.gov/CMIP6/> in the 'ESGF' (Earth System Grid Federation)
    platform <https://esgf.llnl.gov>, and create future 'EnergyPlus'
    <https://energyplus.net> Weather ('EPW') files adjusted from climate changes
    using data from Global Climate Models ('GCM').
An ensemble method for the statistical detection of
    a rare class in two-class classification problems. The method uses an
    ensemble of classifiers where the constituent
    models of the ensemble use disjoint subsets (phalanxes) of explanatory
    variables. We provide an implementation of the phalanx-formation algorithm.
    Please see Tomal et al. (2015) <doi:10.1214/14-AOAS778>,
    Tomal et al. (2016) <doi:10.1021/acs.jcim.5b00663>, and
    Tomal et al. (2019) <arXiv:1706.06971> for more details.
Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
EQ-5D is a popular health related quality of life instrument used 
    in the clinical and economic evaluation of health care. Developed by the 
    EuroQol group <https://euroqol.org/>, the instrument consists of two 
    components: health state description and evaluation. For the description 
    component a subject self-rates their health in terms of five dimensions; 
    mobility, self-care, usual activities, pain/discomfort, and 
    anxiety/depression using either a three-level (EQ-5D-3L,
    <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/>) or a five-level
    (EQ-5D-5L, <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/>) 
    scale. Frequently the scores on these five dimensions are converted to a 
    single utility index using country specific value sets, which can be used
    in the clinical and economic evaluation of health care as well as in 
    population health surveys. The eq5d package provides methods to calculate 
    index scores from a subject's dimension scores. 26 TTO and 11 VAS EQ-5D-3L
    value sets including those for countries in Szende et al (2007) 
    <doi:10.1007/1-4020-5511-0> and Szende et al (2014) 
    <doi:10.1007/978-94-007-7596-1>, 26 EQ-5D-5L EQ-VT value sets from the 
    EuroQol website, and the EQ-5D-5L crosswalk value sets developed by 
    van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> are included. 
    Methods are also included for the analysis of EQ-5D profiles along with a 
    shiny web tool to enable the calculation, visualisation and automated 
    statistical analysis of EQ-5D data via a web browser using EQ-5D dimension 
    scores stored in CSV or Excel files.
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials).
Transitioning from EQS to R for structural equation modeling (SEM)
    is made easier with a set of functions to convert .out files into R code.
    The EQS output can be converted into lavaan syntax and run in the R
    environment.  Other functions parse descriptive statistics and the covariance matrix
    from an EQS .out file.  A heat map plot of a covariance matrix is also
    included.
Analysis of experimental crosses to identify genes (called
        quantitative trait loci, QTLs) contributing to variation in
        quantitative traits.
Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.
The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.
Functions for examining measurement invariance via equivalence testing are included in this package. The traditionally used RMSEA (Root Mean Square Error of Approximation) cutoff values are adjusted based on simulation results. In addition, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts. For more information, see Yuan, K. H., & Chan, W. (2016) <doi:10.1037/met0000080>, Deng, L., & Yuan, K. H. (2016) <doi:10.1007/s11336-015-9491-8>, and Jiang, G., Mai, Y., & Yuan, K. H. (2017) <doi:10.3389/fpsyg.2017.01823>.
Contains methods for observed-score linking
  and equating under the single-group, equivalent-groups,
  and nonequivalent-groups with anchor test(s) designs.
  Equating types include identity, mean, linear, general
  linear, equipercentile, circle-arc, and composites of
  these. Equating methods include synthetic, nominal
  weights, Tucker, Levine observed score, Levine true
  score, Braun/Holland, frequency estimation, and chained
  equating. Plotting and summary methods, and methods for
  multivariate presmoothing and bootstrap error estimation
  are also provided.
Computation of direct, chain and average (bisector) equating coefficients with 
  standard errors using Item Response Theory (IRT) methods for dichotomous items 
  (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, 
  Battauz (2015) <doi:10.18637/jss.v068.i07>). 
  Test scoring can be performed by true score equating and observed score equating methods. 
  DIF detection can be performed using a Wald-type test  
  (Battauz (2018) <doi:10.1007/s10260-018-00442-w>).
Equating of multiple forms using Item Response Theory (IRT) methods (Battauz M. (2017) <doi:10.1007/s11336-016-9517-x> and Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>).
The goal of 'equatiomatic' is to reduce the pain associated with
             writing 'LaTeX' formulas from fitted models. The primary function of
             the package, extract_eq(), takes a fitted model object as its input
             and returns the corresponding 'LaTeX' code for the model.
We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points.
    For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula.
    The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration.
    All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.
Provides statistical tests and graphics for assessing tests
        of equivalence.  Such tests have similarity as the alternative
	hypothesis instead of the null.  Sample data sets are included.
Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results.
Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph
             ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition''
             (Chapman&Hall/CRC).
Implementation of uniformly most powerful invariant equivalence
    tests for one- and two-sample problems (paired and unpaired) as described 
    in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives 
    (non-inferiority and non-superiority tests) are supported. Basically a variant 
    of a t-test with (relaxed) null and alternative hypotheses exchanged.
Multivariate modeling of data after deflation of interfering effects (EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>). The effects can be estimates from designed experiments, non-overlapping clusters or continuous effects.
Provides a consistent vector representation of years with an 
    associated calendar era or time scale. It includes built-in definitions of 
    many contemporary and historic calendars; time scales commonly used in
    archaeology, astronomy, geology, and other palaeosciences (e.g. Before 
    Present, SI-prefixed 'annus'); and support for arbitrary user-defined eras. 
    Functions for converting between eras and for type-stable arithmetic with 
    years are also provided.
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) <doi:10.1021/acs.analchem.6b02927>.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together
    with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks.
A template package to demonstrate the use of user-specified statistics for use in 'ergm' models as part of the Statnet suite of packages.
Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM).
    Includes functions to conduct mediation and moderation analyses and to diagnose
    multicollinearity.
    URL: <https://github.com/sduxbury/ergMargins>.
    BugReports: <https://github.com/sduxbury/ergMargins/issues>.
    Duxbury, Scott W (2021) <doi:10.1177/0049124120986178>.
    Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>.
    Mize, Trenton D. (2019) <doi:10.15195/v6.a4>.
    Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>.
    Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>.
Implements clustering and estimates parameters in Exponential-Family Random Graph Models (ERGMs) for static undirected and directed networks, developed in Vu et. al. (2013) <https://projecteuclid.org/euclid.aoas/1372338477>.
Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.
Simulation and estimation of Exponential Random Graph Models (ERGMs)
  for small networks using exact statistics as shown in Vega Yon et al. (2020)
  <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm'
  package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator
  (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs
  for small networks. As exhaustive enumeration is computationally feasible for
  small networks, this R package takes advantage of this and provides tools for
  calculating likelihood functions, and other relevant functions, directly,
  meaning that in many cases both estimation and simulation of ERGMs for
  small networks can be faster and more accurate than simulation-based
  algorithms.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).
A set of user-friendly functions to aid in organizing, plotting
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn
    method to explore ERP data. Should be useful to those without a background
    in computer programming, and to those who are new to ERPs (or new to the
    more advanced ERP software available). Emphasis has been placed on highly
    automated processes using functions with as few arguments as possible.
    Expects processed (cleaned) data.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Messages should provide users with readable information 
    about R objects without flooding their console. 
    'cc()' concatenates vector and data frame values 
    into a grammatically correct string using commas, an ellipsis and conjunction. 
    'cn()' allows the user to define a string which varies based on a count.
    'co()' combines the two to produce a customizable object aware string.
    The package further facilitates this process by providing five 'sprintf'-like 
    types such as '%n' for the length of an object and '%o' for its name as
    well as wrappers for pasting objects and issuing errors, warnings and messages.
Elegantly handle error and warning messages.
Builds and analyzes error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.
Provides a function to convert existing R functions into "errorized" versions 
    with added logging and handling functionality when encountering errors or warnings. 
    The errorize function accepts an existing R function as its first argument and 
    returns a R function with the exact same arguments and functionality. However, 
    if an error or warning occurs when running that "errorized" R function, it will save a 
    .Rds file to the current working directory with the relevant objects and information 
    required to immediately recreate the error.
Errors in data can be located and removed using validation rules from package 'validate'.
Support for measurement errors in R vectors, matrices and arrays:
    automatic uncertainty propagation and reporting.
    Documentation about 'errors' is provided in the paper by Ucar, Pebesma &
    Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a
    vignette; see 'citation("errors")' for details.
Perform a Bayesian estimation of the exploratory reduced
    reparameterized unified model (ErRUM) described by Culpepper and Chen (2018)
    <doi:10.3102/1076998618791306>.
Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.
Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories.
    Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>.
    Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.
Implementation of the Edge Selection Algorithm
Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges' d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality).
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).
Tools for fitting the Extended Empirical Saddlepoint (EES) density of Fasiolo et al. (2018) <doi:10.1214/18-EJS1433>.
It allows to construct two types of indicators used in the study of
    Electoral Systems and Party Systems starting from electoral results data.
    The Effective Number of Parties (Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>)
    and Electoral Volatility in its three versions (Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>,
    Powell and Tucker (2014) <doi:10.1017/S0007123412000531> and Torcal and Lago (2015, ISBN:9788415260356)).
Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> 
    as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the 
    expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk 
    of investments. A special feature of the backtests of  Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> 
    is that they only require forecasts of  the expected shortfall, which is in striking contrast to all other existing 
    backtests, making them particularly attractive for practitioners.
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>)
    in R. Based on the input, the effect size can be returned as standardized mean 
    difference, Cohen's f, Hedges' g, Pearson's r or Fisher's
    transformation z, odds ratio or log odds, or eta squared effect size.
Methods for working with dose-finding clinical trials. We provide 
    implementations of many dose-finding clinical trial designs, including the 
    continual reassessment method (CRM) by O'Quigley et al. (1990) 
    <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji
    et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design
    by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal 
    interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox
    by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of
    Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 
    described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs
    are implemented with a common interface. We also offer optional additional 
    classes to tailor the behaviour of all designs, including avoiding skipping 
    doses, stopping after n patients have been treated at the recommended dose,  
    stopping when a toxicity condition is met, or demanding that n patients are 
    treated before stopping is allowed. By daisy-chaining together these classes
    using the pipe operator from 'magrittr', it is simple to tailor the 
    behaviour of a dose-finding design so it behaves how the trialist wants.
    Having provided a flexible interface for specifying designs, we then provide
    functions to run simulations and calculate dose-paths for future cohorts of 
    patients.
Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.
A tool which allows users to create and evaluate ensembles 
    of species distribution model (SDM) predictions. 
    Functionality is offered through R functions or a GUI (R Shiny app). 
    This tool can assist users in identifying spatial uncertainties and 
    making informed conservation and management decisions. The package is 
    further described in Woodman et al (2019) <doi:10.1111/2041-210X.13283>.
The package can identify the dysregulated canonical pathways by investigating the changes of biological relationships of pathways in the context of gene expression data. (1) The ESEA package constructs a background set of edges by extracting pathway structure (e.g. interaction, regulation, modification, and binding etc.) from the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther) and the edge sets of pathways for each of the above databases. (2) The ESEA package can can quantify the change of correlation between genes for each edge based on gene expression data with cases and controls. (3) The ESEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an edge enrichment score (EES), which reflects the degree to which a given pathway is associated the specific phenotype. (4) The ESEA package can provide the visualization of the results.
Environmental seismology is a scientific field that studies the  
    seismic signals, emitted by Earth surface processes. This package 
    provides all relevant functions to read/write seismic data files, prepare, 
    analyse and visualise seismic data, and generate reports of the processing 
    history.
Presents a "Scenarios" class containing
        general parameters, risk parameters and projection results.
        Risk parameters are gathered together into a ParamsScenarios
        sub-object. The general process for using this package is to
        set all needed parameters in a Scenarios object, use the
        customPathsGeneration method to proceed to the projection, then
        use xxx_PriceDistribution() methods to get asset prices.
Computes shrinkage estimators for regression problems. Selects
    penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) <doi:10.1093/aje/kwx225> for more details.
Misc functions programmed by Eduard Szöcs. 
    Provides read_regnie() to read gridded precipitation data from German Weather 
    Service  (DWD, see <http://www.dwd.de/> for more information).
Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.
A 'shiny' gadget to create 'ggplot2' charts interactively with drag-and-drop to map your variables.
    You can quickly visualize your data accordingly to their type, export to 'PNG' or 'PowerPoint',
    and retrieve the code to reproduce the chart.
Simultaneous modeling of the quantile and the expected shortfall of a response variable given 
    a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.
An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis,
	     Michael I Jordan (2013) <arXiv:1301.2267>. The ESS algorithm
	     is used for model selection in decomposable graphical models.
Functions for converting objects to scalars (vectors of length 1) 
    and a more inclusive definition of data that can be interpreted as numbers 
    (numeric and complex alike).
Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
An implementation of sequential testing that uses evidence ratios
    computed from the weights of a set of models. These weights correspond either
    to Akaike weights computed from the Akaike Information Criterion (AIC) or the
    Bayesian Information Criterion (BIC) and following Burnham & Anderson
    (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA
    weights computed from the WAIC or the LOO-IC of models fitted
    with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
Provides tools for determining estimability of linear functions 
  of regression coefficients, and 'epredict' methods that handle 
  non-estimable cases correctly. Estimability theory is discussed in
  many linear-models textbooks including Chapter 3 of Monahan, JF (2008), 
  "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4).
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation.
Routines for parameter estimation for any probability density or
    mass function implemented in R via maximum likelihood (ML) given a data set. 
    The main routines 'maxlogL' and 'maxlogLreg' are  wrapper functions specifically 
    developed for ML estimation. There are included optimization procedures such as
    'nlminb' and 'optim' from base package, and 'DEoptim' Mullen (2011) 
    <doi: 10.18637/jss.v040.i06>. Standard errors are estimated with 'numDeriv' 
    Gilbert (2011) <https://CRAN.R-project.org/package=numDeriv>
    or the option 'Hessian = TRUE' of 'optim' function.
Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance.
Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.
Implements the methods of McGrath et al. (2020) 
    <doi:10.1177/0962280219889080> for estimating the sample mean and standard 
    deviation from commonly reported quantiles in meta-analysis. These methods 
    can be applied to studies that report the sample median, sample size, and 
    one or both of (i) the sample minimum and maximum values and (ii) the first 
    and third quartiles.
Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, <doi:10.1214/17-AOAS1026> for more details.
This package is intended to speedup the process of
        creating model-comparing tables common in Macroeconomics. The
        function collection stores the estimates of several models and
        formats it to a table of the form estimate starred and std.err.
        below. The default output is LaTeX but output to CSV for later
        editing in a spreadsheet tool is possible as well. It works for
        linear models (lm) and panel models from the "plm"-package
        (plm). Two further implemented functions "descsto" and
        "desctab" enable you to export descriptive statistics of
        data-frames and single variables to LaTeX and CSV.
Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. 
    The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. 
    The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>.
    The second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>.
	And the third by Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>.
This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
An implementation of a most commonly used event study methodology,
    including both parametric and nonparametric tests. It contains variety
    aspects of the rate of return estimation (the core calculation is done in
    C++), as well as three classical for event study market models: mean
    adjusted returns, market adjusted returns and single-index market models.
    There are 6 parametric and 6 nonparametric tests provided, which examine
    cross-sectional daily abnormal return (see the documentation of the
    functions for more information). Parametric tests include tests proposed by 
    Brown and Warner (1980) <DOI:10.1016/0304-405X(80)90002-1>, Brown and Warner
    (1985) <DOI:10.1016/0304-405X(85)90042-X>, Boehmer et al. (1991)
    <DOI:10.1016/0304-405X(91)90032-F>, Patell (1976) <DOI:10.2307/2490543>, and
    Lamb (1995) <DOI:10.2307/253695>. Nonparametric tests covered in estudy2 are
    tests described in Corrado and Zivney (1992) <DOI:10.2307/2331331>,
    McConnell and Muscarella (1985) <DOI:10.1016/0304-405X(85)90006-6>,
    Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Cowan (1992)
    <DOI:10.1007/BF00939016>, Corrado (1989) <DOI:10.1016/0304-405X(89)90064-0>,
    Campbell and Wasley (1993) <DOI:10.1016/0304-405X(93)90025-7>, Savickas (2003)
    <DOI:10.1111/1475-6803.00052>, Kolari and Pynnonen (2010)
    <DOI:10.1093/rfs/hhq072>. Furthermore, tests for the cumulative
    abnormal returns proposed by Brown and Warner (1985)
    <DOI:10.1016/0304-405X(85)90042-X> and Lamb (1995) <DOI:10.2307/253695>
    are included.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
Fits the space-time Epidemic Type Aftershock Sequence
    ('ETAS') model to earthquake catalogs using a stochastic 'declustering' 
    approach. The 'ETAS' model is a 'spatio-temporal' marked point process
    model and a special case of the 'Hawkes' process. The package is based 
    on a Fortran program by 'Jiancang Zhuang'
    (available at <http://bemlar.ism.ac.jp/zhuang/software.html>),
    which is modified and translated into C++ and C such that it 
    can be called from R. Parallel computing with 'OpenMP' is possible 
    on supported platforms.
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017)<doi:10.18637/jss.v076.i03>.
The package allows selecting those treatments of a one-way layout
 being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
 (TOST) and related simultaneous confidence intervals are given for both
 differences or ratios of means of normally distributed data. For the case of
 equal variances and balanced sample sizes for the treatment groups, the
 single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
 non-normal data, the Wilcoxon test is applied.
Interacts with the open-source, public 'Ethereum' <https://www.ethereum.org/> blockchain.
    It provides a distributed computing platform via smart contracts. This package provides
    functions which interrogate blocks and transactions in the 'Ethereum' blockchain.
An implementation of the quantitative ethnobotany indices in R. The goal is to provide an easy-to-use platform for ethnobotanists to assess the cultural significance of plant species based on informant consensus. The package closely follows the paper by Tardio and Pardo-de-Santayana (2008). Tardio, J., and M. Pardo-de-Santayana, 2008. Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain) 1. Economic Botany, 62(1), 24-39. <doi:10.1007/s12231-007-9004-5>.
Reliable and rapid ethnicity annotation from whole exome sequencing data.
A predictable and pipeable framework for performing ETL 
    (extract-transform-load) operations on publicly-accessible medium-sized data 
    set. This package sets up the method structure and implements generic 
    functions. Packages that depend on this package download specific data sets 
    from the Internet, clean them up, and import them into a local or remote 
    relational database management system.
Provides functions to facilitate the use of the 'ff' package
    in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL',
    'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects
    using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to
    do fast left outer join merging based on 'match', factorisation of data and a
    basic function for re-coding vectors.
The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 <doi:10.18637/jss.v038.i04>). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 <doi:10.1007/s00180-010-0200-x>).
Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).
Use R to interface with the 'ETRADE' API <https://developer.etrade.com/home>.
    Functions include authentication, trading, quote requests, account information, and option 
    chains. A user will need an ETRADE brokerage account and 'ETRADE' API approval. See README 
    for authentication process and examples.
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
The remit of the European Clinical Trials Data Base (EudraCT <https://eudract.ema.europa.eu/> ) is to provide open access to summaries of all registered clinical trial results; thus aiming to prevent non-reporting of negative results and provide open-access to results to inform future research. The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by EudraCT and formats them into the precise requirements to directly upload an XML file into the web portal, with no further data entry by hand.
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
Generate area-proportional Euler diagrams
    using numerical optimization. An Euler diagram is a generalization of a Venn
    diagram, relaxing the criterion that all interactions need to be
    represented. Diagrams may be fit with ellipses and circles via
    a wide range of inputs and can be visualized in numerous ways.
A sample dataset in the OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM) format. The CDM enables uniform storage of observational health care data, and is widely used for health care analytics. 'Eunomia' contains simulated data as well as a subset of the OMOP Vocabulary, and enables testing of additional packages and is used for educational and demonstration purposes.
Access to data on European Union laws and court decisions made easy with pre-defined 'SPARQL' queries and 'GET' requests.
An R Client for the Europe PubMed Central RESTful Web Service
    (see <https://europepmc.org/RestfulWebService> for more information). It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
This dataset contains population estimates of all European cities 
    with at least 10,000 inhabitants during the period 1500-1800. These data are
    adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).
Bayesian sample size calculation software and examples for EuroSARC
    clinical trials which utilise Bayesian methodology. These trials rely on
    binomial based endpoints so the majority of programs found here relate to this
    sort of endpoint. Developed as part of the EuroSARC FP7 grant.
Tools to download data from the Eurostat database
    <http://ec.europa.eu/eurostat> together with search and
    manipulation utilities.
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
    Bader, B., Yan, J. & Zhang, X. (2016) <doi:10.1007/s11222-016-9697-3>.
    Bader, B., Yan, J. & Zhang, X. (2018) <doi:10.1214/17-AOAS1092>.
Evaluates the performance of binary classifiers.
    Computes confusion measures (TP, TN, FP, FN), derived measures (TPR,
    FDR, accuracy, F1, DOR, ..), and area under the curve.  Outputs are
    well suited for nested dataframes.
Provides functions for evaluating (time series) model
	estimation methods. These facilitate Monte Carlo experiments of repeated
	simulations and estimations. Also provides methods for
	looking at the distribution of the results from these experiments,
	including model roots (which are an equivalence class invariant).
Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) <arXiv:1905.05389>.
Parsing and evaluation tools that make it easy to recreate the
    command line behaviour of R.
Implements various quality evaluation statistics to assess the
    value of plant germplasm core collections using qualitative and 
    quantitative phenotypic trait data according to Odong et al. (2015) 
    <doi:10.1007/s00122-012-1971-y>.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
An open source risk analysis toolkit based on the OpenFAIR ontology 
  <https://www2.opengroup.org/ogsys/catalog/C13K> and risk assessment standard 
  <https://www2.opengroup.org/ogsys/catalog/C13G>. Empowers an organization to 
  perform a quantifiable, repeatable, and data-driven risk review.
Conducts sensitivity analyses for unmeasured confounding,selection bias, and measurement error (individually or in combination) in individual studies. Also conducts sensitivity analyses for unmeasured confounding in meta-analyses.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Different evidential distance-based classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: the
    evidential K-nearest neighbor rule and the evidential neural network.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the     
    objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means, 
    Constrained Evidential c-Means, Evidential Clustering, Constrained Evidential 
    Clustering, Evidential K-nearest-neighbor-based Clustering, Bootstrap Model-Based
    Evidential Clustering, Belief Peak Evidential Clustering, Neural-Network-based
    Evidential Clustering.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.
Provides functions for the bayesian analysis of extreme
        value models, using MCMC methods.
Functions for setting up and analyzing event history data.
Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages 'edeaR', 'processmapR', etc.
A graphical user interface for open source event detection.
Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.
A user friendly, easy to understand way of doing event
    history regression for marginal estimands of interest,
    including the cumulative incidence and the restricted mean
    survival, using the pseudo observation framework for 
    estimation. For a review of the methodology, see Andersen and
    Pohar Perme (2010) <doi:10.1177/0962280209105020>. The
    interface uses the well known formulation of a generalized
    linear model and allows for features including plotting of 
    residuals, the use of sampling weights, and corrected
    variance estimation.
Functions for analysis of rate changes in sequential events.
Event-driven programming is a programming paradigm where the flow of execution is defined by event.
    In this paradigm an event can be defined as "a change in the state" of an object. This package offers a set
    of functions for creating event-based architectures using three basic functions: events, dispatchers, and
    handlers. The handlers manage the events, the dispatchers are in charge of redirecting the events to each of
    the handlers, finally the events are the objects that carry the information about the change of state.
Stores, manipulates, aggregates and otherwise messes with event
    data from KEDS/TABARI or any other extraction tool with similar output
Implements event extraction and early classification of events in data streams in R. 
    It has the functionality to generate 2-dimensional data streams with events belonging to 
    2 classes. These events can be extracted and features computed. The event features extracted
    from incomplete-events can be classified using a partial-observations-classifier 
    (Kandanaarachchi et al. 2018) <doi:10.13140/RG.2.2.10051.25129>.
A platform for conducting event studies (Fama, Fisher,
             Jensen, Roll (1969) <doi:10.2307/2525569>) and for
             methodological research on event studies. The package
             supports market model, augmented market model, and excess
             returns methods for data modelling along with Wilcox,
             classical t-test, and Bootstrap as inference procedures.
Perform Event Studies from through our <http://EventStudyTools.com> Application Programming Interface, parse the results, visualize it, and / or use the results in further analysis.
Methods for fitting various extreme value distributions with parameters of 
      generalised additive model (GAM) form are provided. For details of distributions 
      see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.
Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package:
        Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) <doi:10.1038/ejhg.2010.47>.
        Strug, L. J., & Hodge, S. E. (2006) <doi:10.1159/000094709>.
        Royall, R. (1997) <ISBN:0-412-04411-0>.
Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach.  The functions in the package also make extensive use of graphical displays for data exploration and model comparison.
Provides tools for integrated sensitivity analysis of evidence factors in observational
	studies. When an observational study allows for multiple independent or nearly
	independent inferences which, if vulnerable, are vulnerable to different biases, we have 
	multiple evidence factors.  This package provides methods that respect type I error rate control. 
	Examples are provided of integrated evidence factors analysis in a longitudinal study with
	continuous outcome and in a case-control study. 
	Karmakar, B., French, B., and Small, D. S. (2019)<DOI:10.1093/biomet/asz003>.
Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. 
  Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.
Contains a collection of examples of evidence factors in observational studies; e.g., Rosenbaum (2017) <doi:10.1214/17-STS621>.  The examples are collected to aid readers of a book in preparation, "Replication and Evidence Factors in Observational Studies".
It allows running 'EViews'(<https://eviews.com>) program from R Markdown. 'EViews' (Econometric Views) is a statistical software for Econometric analysis.  This package serves as an 'EViews' Knit-Engine for 'knitr' package. Write all your 'EViews' commands in R Markdown chunk.
Functions for extreme value theory, which may be 
  divided into the following groups; exploratory data analysis, 
  block maxima, peaks over thresholds (univariate and bivariate), 
  point processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
Provides functions for covariance matrix comparisons, estimation
    of repeatabilities in measurements and matrices, and general evolutionary
    quantitative genetics tools.
A comprehensive set of tools to illustrate the
    core concepts of evolutionary game theory, such as
    evolutionary stability or various evolutionary dynamics, 
    for teaching and academic research.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible 
      package providing optimization driven parameter estimation methods using metaheuristics  and 
      evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization 
      for continuous domains, Tabu Search, Evolutionary Strategies, ...)  which could be more efficient and require, 
      in some cases, fewer model evaluations than alternatives relying on experimental design.  Currently there 
      are built in support for models developed with 'Repast Simphony'  Agent-Based framework (<https://repast.github.io/>)  
      and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP) 
	     and peaks over random threshold (PORT) Hill methodologies. 
             Besides, also computes moment, generalised Hill and mixed moment estimates for EVI. 
             Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.
Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.
Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.
Implements escalation with overdose control (EWOC) trial designs using two drug combinations described by Tighiouart, Li and Rogatko (2017) <doi:10.1002/sim.6961>. It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics.
Function and data sets in the book entitled "R ile Temel Ekonometri", S.Guris, E.C.Akay, B. Guris(2020). The book published in Turkish. It is possible to makes Durbin two stage method for autocorrelation, generalized differencing method for correction autocorrelation, Hausman Test for identification and computes LM, LR and Wald test statistics for redundant variable by using the functions written in this package.
The purpose of Early Warning Systems (EWS) is to detect accurately the occurrence of a crisis, which is represented by a binary variable which takes the value of one when the event occurs, and the value of zero otherwise. EWS are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) <doi:10.1162/rest.90.4.777>. Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) <doi:10.1016/j.ijforecast.2014.03.015>. This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. In addition, this package also provides methods for the analysis of the propagation mechanisms of an exogenous shock, as well as robust confidence intervals for these response functions using a block-bootstrap method as in Lajaunie (2021). This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers.
Performs unconditional exact tests and power calculations for 2x2 contingency tables.  Calculates Barnard's test (1945) <doi:10.1038/156177a0> using the original CSM test (Barnard, 1947 <doi:10.1093/biomet/34.1-2.123>), using Fisher's p-value referred to as Boschloo's test (1970) <doi:10.1111/j.1467-9574.1970.tb00104.x>, or using a Z-statistic (Suissa and Shuster, 1985, <doi:10.2307/2981892>).  Calculations confidence intervals for the difference in proportion.
Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or  exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case (Fay, et al, 2015, <DOI:10.1111/biom.12231>). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, <DOI:10.1002/sim.7579>), an unconditional exact test for the situation where the controls are all expected to fail.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
        difference between two independent or dependent proportions.
Provides a replacement for the 'extract' function from the 'raster' package
    that is suitable for extracting raster values using 'sf' polygons.
Monte Carlo and MCMC goodness of fit tests for log-linear
        models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2020) <arXiv:2008.12682>. Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed.
Computes exact conditional p-values and quantiles using an
 implementation of the Shift-Algorithm by Streitberg & Roehmel.
Contains all data sets for Exam PA: Predictive Analytics at 
    <https://exampa.net/>.
Take the examples written in your documentation of
    functions and use them to create shells (skeletons which must be
    manually completed by the user) of test files to be tested with the
    'testthat' package. Sort of like python 'doctests' for R.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single-choice and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include standalone
	files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOLAT,
	ARSnova, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format
	(NOPS) is provided that can be printed, scanned, and automatically evaluated.
Randomized multiple-select and single-select
  question generation for the 'MyLearn' teaching and learning
  platform. Question templates
  in the form of the R/exams package (see <http://www.r-exams.org/>)
  are transformed into XML format required by 'MyLearn'.
Tools for constructing confidence regions for exceedance regions
    and contour lines.
Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) <arXiv:1803.03356> for more information.
Allows access to data in running instance of Microsoft Excel
    (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
    'xl[a1] = current.graphics()'. Additionally there are function for reading/writing 
    'Excel' files - 'xl.read.file'/'xl.save.file'. They are not fast but able to read/write 
    '*.xlsb'-files and password-protected files. There is an Excel workbook with 
    examples of calling R from Excel in the 'doc' folder. It tries to keep things as
    simple as possible - there are no needs in any additional
    installations besides R, only 'VBA' code in the Excel workbook.
    Microsoft Excel is required for this package.
Implements 'Excel' functions in 'R' for your calculation simplicity.You can use most of the aggregate functions, addressing functions,logical functions and text functions. Helps you a ton in learning how 'R' works as some 'Excel' users might be struggling with the program.
An R interface to 'jExcel' library to create web-based interactive tables and spreadsheets compatible with 'Excel' or any other spreadsheet software.
Removes metadata and summary formatting from Excel reports, so that 
    data from those reports can more easily be loaded into R. Currently works with 
    .xlsx and .xls format files.
This is an R interface to the
    python package 'excerpts' (<https://pypi.python.org/pypi/excerpts>).
Implementation of a function which calculates the empirical excess mass 
	for given \eqn{\lambda} and given maximal number of modes (excessm()). Offering 
	powerful plot features to visualize empirical excess mass (exmplot()). This 
	includes the possibility of drawing several plots (with different maximal 
	number of modes / cut off values) in a single graph.
Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.
Performs frequentist inference for the extremal index of a 
    stationary time series.  Two types of methodology are used.  One type is
    based on a model that relates the distribution of block maxima to the 
    marginal distribution of series and leads to the semiparametric maxima 
    estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and 
    Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>.  Sliding block maxima
    are used to increase precision of estimation. The other type of methodology
    uses a model for the distribution of threshold inter-exceedance times
    (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Two 
    versions of this type of approach are provided, following Suveges (2007) 
    <doi:10.1007/s10687-007-0034-2> and Suveges and Davison (2010)  
    <doi:10.1214/09-AOAS292>.
The goal of this package is to provide an easy to use, fast and
    scalable exhaustive search framework. Exhaustive feature selections 
    typically require a very large number of models to be fitted and evaluated. 
    Execution speed and memory management are crucial factors here. This package 
    provides solutions for both. Execution speed is optimized by using a 
    multi-threaded C++ backend, and memory issues are solved by by only storing 
    the best results during execution and thus keeping memory usage constant.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Reads, writes, and edits EXIF and other file metadata
    using ExifTool <https://exiftool.org/>,
    returning read results as a data frame. ExifTool supports many
    different metadata formats including EXIF, GPS, IPTC, XMP, JFIF,
    GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, as
    well as the maker notes of many digital cameras by Canon, Casio,
    DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf,
    Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson,
    Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung,
    Sanyo, Sigma/Foveon and Sony.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.
Provides a shiny-based front end (the 'ExPanD' app) and
    a set of functions for exploratory data analysis. Run as a web-based 
    app, 'ExPanD' enables users to assess the robustness of empirical evidence 
    without providing them access to the underlying data. You can export a 
    notebook containing the analysis of 'ExPanD' and/or use the functions of the 
    package to support your exploratory data analysis workflow. Refer to the 
    vignettes of the package for more information on how to use 'ExPanD' and/or 
    the functions of this package.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
Expanding Ploidy and Allele Frequency on Nested Subpopulations (expands) characterizes coexisting subpopulations in a single tumor sample using copy number and allele frequencies derived from exome- or whole genome sequencing input data (<http://www.ncbi.nlm.nih.gov/pubmed/24177718>). The model detects coexisting genotypes by leveraging run-specific tradeoffs between depth of coverage and breadth of coverage. This package predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation, tumor purity and phylogeny. The main function runExPANdS() provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of subpopulation predictions increases with the number of mutations provided. It is recommended that at least 200 mutations are used as input to obtain stable results. Updates in version 2.1 include: (i) new parameter ploidy in runExPANdS.R allows specification of non-diploid background ploidies (e.g. for near-triploid cell lines); (ii) parallel computing option is available. Further documentation and FAQ available at <http://dna-discovery.stanford.edu/software/expands>.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test  of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Pacote para análise de delineamentos experimentais (DIC, DBC e DQL), experimentos em esquema fatorial duplo (em DIC e DBC), experimentos em parcelas subdivididas (em DIC e DBC), experimentos em esquema fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em fatorial triplo (em DIC e DBC) e experimentos em esquema fatorial triplo com um tratamento adicional (em DIC e DBC), fazendo analise de variancia e comparacao de multiplas medias (para tratamentos qualitativos), ou ajustando modelos de regressao ate a terceira potencia (para tratamentos quantitativos); analise de residuos (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Distributes samples in batches while making batches homogeneous 
    according to their description. Allows for an arbitrary number of variables, 
    both numeric and categorical. For quality control it provides functions to 
    subset a representative sample.
Provides various statistical methods for
  designing and analyzing randomized experiments. One functionality
  of the package is the implementation of randomized-block and
  matched-pair designs based on possibly multivariate pre-treatment
  covariates. The package also provides the tools to analyze various
  randomized experiments including cluster randomized experiments,
  two-stage randomized experiments, randomized experiments with 
  noncompliance, and randomized experiments with missing data.
Expert opinion (or judgment) is a body of techniques to
  estimate the distribution of a random variable when data is scarce
  or unavailable. Opinions on the quantiles of the distribution are
  sought from experts in the field and aggregated into a final
  estimate. The package supports aggregation by means of the Cooke,
  Mendel-Sheridan and predefined weights models.
Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.
The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.
Generates explanations for classification and regression models and visualizes them.
 Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
 are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
 in the model. The IME method is slower as it samples from all feature subsets.
 For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, 
 and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
 All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided 
 and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Interactive data exploration with one line of code or use an easy to remember set of tidy functions for exploratory data analysis. Introduces three main verbs. explore() to graphically explore a variable or table, describe() to describe a variable or table and report() to create an automated report.
Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.
Computation of the matrix exponential, logarithm, sqrt,
  and related quantities, using traditional and modern methods.
Estimate individual and average joint effects of chemical mixtures, dose-response relationships, and potential interactions in environmental epidemiology.
    The visualization of interactions and the plotting of all the objects. For more information please, check next work: Youssef Oulhote, Marie-Abele Bind, Brent Coull, Chirag, Patel, Philippe Grandjean (2017) <https://www.biorxiv.org/content/early/2017/06/30/147413.article-info>.
A variety of descriptive multivariate analyses with the singular value decomposition,
    such as principal components analysis, correspondence analysis, and multidimensional scaling.
    See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Tools and data to accompany Schlicht, L., Valcu, M., & Kempenaers, B. (2015) <doi:10.1111/1365-2656.12293>. Spatial patterns of extra-pair paternity: beyond paternity gains and losses. Journal of Animal Ecology, 84(2), 518-531.
A set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. It has been written to accompany the forthcoming paper 'Computing expectations and marginal likelihoods for permutations'. Publication details will be updated as soon as they are finalized.
Allows to calculate the probabilities  of occurrences of an event in a 
 great number of repetitions of Bernoulli experiment, through the application of  
 the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. 
 Gives the possibility to show the results graphically and analytically, and to compare
 the results obtained by the application of the above  theorems with those calculated by
 the direct application of the Binomial formula. Is basically useful for educational
 purposes.
Given a continuous-time dynamic network, this package allows one to fit a stochastic blockmodel where nodes belonging to the same group create interactions and non-interactions of similar lengths. This package implements the methodology described by R. Rastelli and M. Fop (2019) <arXiv:1901.09828>.
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Package computes and displays tables with support for 'SPSS'-style 
        labels, multiple and nested banners, weights, multiple-response variables 
        and significance testing. There are facilities for nice output of tables 
        in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods 
        for labelled variables add value labels support to base R functions and to 
        some functions from other packages. Additionally, the package brings 
        popular data transformation functions from 'SPSS' Statistics and 'Excel': 
        'RECODE', 'COUNT', 'COMPUTE', 'DO IF', 'COUNTIF', 'VLOOKUP' and etc. 
        These functions are very useful for data processing in marketing research 
        surveys. Package intended to help people to move data 
        processing from 'Excel' and 'SPSS' to R.
Tests for the composite hypothesis of exponentiality
Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
Estimation for high conditional quantiles based on quantile regression.
The package provides tools for botanists, plant taxonomists,
    curators of plant genebanks and perhaps other biological collections.
A consistent, unified and extensible
    framework for estimation of parameters for probability distributions, including 
    parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.
If 'dplyr' is a grammar for data manipulation, 'extdplyr' is like
    a short paragraph written in 'dplyr'. 'extdplyr' extends 'dplyr' and
    'tidyr' verbs to some common "routines" that manipulate data sets. It uses
    the same interface and preserves all the features from 'dplyr', has good 
    performance, and supports various data sources.
Creates family objects identical to stats
    family but for new links.
If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.
This is a package containing the function extfunnel()
        which produces a funnel plot including additional augmentations
        such as statistical significance contours and heterogeneity
        contours.
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.
For multiple full/partial ranking lists, R package 'ExtMallows' can (1) detect whether the input ranking lists are over-correlated, and (2) use the Mallows model or extended Mallows model to integrate the ranking lists, and (3) use hierarchical extended Mallows model for rank integration if there are groups of over-correlated ranking lists.
This package tests for differences in minor allele
        frequency between groups and is based on an extra-binomial
        variation model for pooled sequencing data.
By using a multispectral image and ESRI shapefile (Point/ Line/ Polygon), a data table will be generated for classification, regression or other processing. The data table will be contained by band wise raster values and shapefile ids (User Defined).
Density, distribution function, quantile function
    and random generation for a number of univariate
    and multivariate distributions. This package implements the
    following distributions: Bernoulli, beta-binomial, beta-negative
    binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
    bivariate normal, bivariate Poisson, categorical, Dirichlet,
    Dirichlet-multinomial, discrete gamma, discrete Laplace,
    discrete normal, discrete uniform, discrete Weibull, Frechet,
    gamma-Poisson, generalized extreme value, Gompertz,
    generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t,
    Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy,
    Laplace, location-scale t, logarithmic, Lomax, multivariate
    hypergeometric, multinomial, negative hypergeometric, 
    non-standard beta, normal mixture, Poisson mixture, Pareto,
    power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam,
    slash, triangular, truncated binomial, truncated normal,
    truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
    zero-inflated negative binomial, zero-inflated Poisson.
Tools to using fonts other than the standard PostScript fonts.
    This package makes it easy to use system TrueType fonts and with PDF or
    PostScript output files, and with bitmap output files in Windows. extrafont
    can also be used with fonts packaged specifically to be used with, such as
    the fontcm package, which has Computer Modern PostScript fonts with math
    symbols. See https://github.com/wch/extrafont for instructions and
    examples.
Package for holding the database for the extrafont package
Speed up common tasks, particularly logical or
  relational comparisons and routine follow up tasks such as finding the
  indices and subsetting. Inspired by mathematics, where something like:
  3 < x < 6 is a standard, elegant and clear way to assert that
  x is both greater than 3 and less than 6
  (see for example <https://en.wikipedia.org/wiki/Relational_operator>),
  a chaining operator is implemented. The chaining operator, %c%,
  allows multiple relational operations to be used in quotes on the right
  hand side for the same object, on the left hand side.
  The %e% operator allows something like set-builder notation 
  (see for example <https://en.wikipedia.org/wiki/Set-builder_notation>) 
  to be used on the right hand side.
  All operators have built in prefixes defined for all, subset, and which
  to reduce the amount of code needed for common tasks, such as return those
  values that are true.
Functions to 'numericise' 'R' objects (coerce to numeric objects)
    and summarise 'MCMC' (Monte Carlo Markov Chain) samples
    as well as 'R' translations of 'BUGS' (Bayesian Using Gibbs Sampling)
    and 'JAGS' (Just Another Gibbs Sampler) functions.
Finding life outside the planet Earth several is the ultimate goal of an astrobiologist. Using known astronomical measurements and assumptions the probability of extraterrestrial life existence could be estimated. Equations such as the Drake equation (1961) as stated in the paper of Molina (2019) <arXiv:1912.01783>, Seager (2013) <https://www.space.com/22648-drake-equation-alien-life-seager.html> and Foucher et al, (2017) <doi:10.3390/life7040040> are included in the 'extraterrestrial' package.
Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method.
Provides summary statistics of local geospatial features within a given geographic area. 
	It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). 
	The geospatial features can be of any type of geospatial data, including point, polygon or line data.
Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) <arXiv:1910.13848>. These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures.
A set of procedures for modelling parametrically and non-parametrically the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. Adapts the methodologies derived in Beranger et al. (2019) <arxiv:1904.08251>, Beranger et al. (2017) <doi:10.1111/sjos.12240>, Beranger and Padoan (2015) <arxiv:1508.05561>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Marcon et al. (2016) <doi:10.1214/16-EJS1162>. It also refers to the works of Bortot (2010) <https://pdfs.semanticscholar.org/b0dc/1cb608d35bf515c76e39aacc14b4de82e281.pdf>, Padoan (2011) <doi:10.1016/j.jmva.2011.01.014>, Cooley et al. (2010) <doi:10.1016/j.jmva.2010.04.007>, Husler and Reiss (1989) <doi:10.1016/0167-7152(89)90106-5>, Engelke et al. (2015) <doi:10.1111/rssb.12074>, Coles and Tawn (1991) <doi:10.1111/j.2517-6161.1991.tb01830.x>, Nikoloulopoulos et al. (2011) <doi:10.1007/s10687-008-0072-4>, Opitz (2013) <doi:10.1016/j.jmva.2013.08.008>, Tawn (1990) <doi:10.2307/2336802>, Azzalini (1985) <https://www.jstor.org/stable/pdf/4615982.pdf>, Azzalini and Capitanio (2014) <doi:10.1017/CBO9781139248891>, Azzalini (2003) <doi:10.1111/1467-9469.00322>, Azzalini and Capitanio (1999) <doi:10.1111/1467-9868.00194>, Azzalini and Dalla Valle (1996) <doi:10.1093/biomet/83.4.715>, Einmahl et al. (2013) <doi:10.1007/s10687-012-0156-z>, Naveau et al (2009) <doi:10.1093/biomet/asp001> and Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Extreme value theory, nonparametric kernel estimation, tail
    conditional probabilities, extreme conditional quantile, adaptive estimation,
    quantile regression, survival probabilities.
An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.
A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations.  The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2020) <arxiv:2004.04078>, Padoan and Stupfler (2020) <arxiv:2007.08944>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>.
General functions for performing extreme value analysis.  In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes.  Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping.  Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included.  For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
Code to fit, plot and compare several (extreme value)
    distribution functions. Can also compute (truncated) distribution quantile estimates and
    draw a plot with return periods on a linear scale.
Detect outliers in one-dimensional data.
Conducts inference in statistical models for extreme values (de Carvalho et al (2012), <doi:10.1080/03610926.2012.709905>; de Carvalho and Davison (2014), <10.1080/01621459.2013.872651>; Einmahl et al (2016), <doi:10.1111/rssb.12099>).
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
It implements the subjectively censored Weibull MLE and censored Weibull mixture methods for the lower quantile estimation. Quantile estimates from these two methods are robust to model misspecification in the lower tail. It also includes functions to evaluation the standard error of the resulting quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for the Type-I or Type-II right censored data.
Testing for and dating periods of explosive
    dynamics (exuberance) in time series using the univariate and panel
    recursive unit root tests proposed by Phillips et al. (2015)
    <doi:10.1111/iere.12132> and Pavlidis et al. (2016)
    <doi:10.1007/s11146-015-9531-2>.  The recursive least-squares
    algorithm utilizes the matrix inversion lemma to avoid matrix
    inversion which results in significant speed improvements. Simulation
    of a variety of periodically-collapsing bubble processes.
There is no ophthalmic researcher who has not had headaches from 
    the handling of visual acuity entries. Different notations, untidy entries. 
    This shall now be a matter of the past. Eye makes it as easy as pie to work
    with VA data - easy cleaning, easy conversion between 
    Snellen, logMAR, ETDRS letters, and qualitative visual acuity 
    shall never pester you again. The eye 
    package automates the pesky task to count number of patients and eyes, 
    and can help to clean data with easy re-coding for right and left eyes. 
    It also contains functions to help reshaping eye side specific variables 
    between wide and long format. Visual acuity conversion is based on 
    Schulze-Bonsel et al. (2006) <doi:10.1167/iovs.05-0981>, 
    Gregori et al. (2010) <doi:10.1097/iae.0b013e3181d87e04>, 
    Beck et al. (2003) <doi:10.1016/s0002-9394(02)01825-1> and 
    Bach (2007) <http:michaelbach.de/sci/acuity.html>.
Open source data allows for reproducible research and helps advance
    our knowledge. The purpose of this package is to collate open source 
    ophthalmic data sets curated for direct use. This is real life data of 
    people with intravitreal injections with anti-vascular endothelial growth
    factor (anti-VEGF), due to age-related macular degeneration or diabetic 
    macular edema. Associated publications of the data sets: 
    Fu et al. (2020) <doi:10.1001/jamaophthalmol.2020.5044>, 
    Moraes et al (2020) <doi:10.1016/j.ophtha.2020.09.025>,
    Fasler et al. (2019) <doi:10.1136/bmjopen-2018-027441>, 
    Arpa et al. (2020) <doi:10.1136/bjophthalmol-2020-317161>,
    Kern et al. 2020, <doi:10.1038/s41433-020-1048-0>.
Imports plain-text ASC data files from EyeLink eye trackers 
    into (relatively) tidy data frames for analysis and visualization.
Functions to prepare and analyse eye tracking data of reading
  exercises. The functions allow some basic data preparations and code fixations 
  as first and second pass. First passes can be further devided into forward and 
  reading. The package further allows for aggregating fixation times per AOI or 
  per AOI and per type of pass (first forward, first rereading, second). These 
  methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> 
  and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is 
  also possible to convert between metric length and visual degrees.
Misc function for working with eyetracking data
A set of functions for organising and analysing datasets from
    experiments run using 'Eyelink' eye-trackers. Organising functions help
    to clean and prepare eye-tracking datasets for analysis, and mark up
    key events such as display changes and responses made by participants.
    Analysing functions help to create means for a wide range of standard
    measures (such as 'mean fixation durations'), which can then be fed into
    the appropriate statistical analyses and graphing packages as necessary.
Facilitates easy analysis of factorial experiments, including
    purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss
    designs, and mixed within-and-between-Ss designs. The functions in this package
    aim to provide simple, intuitive and consistent specification of data analysis
    and visualization. Visualization functions also include design visualization for
    pre-analysis data auditing, and correlation matrix visualization. Finally, this
    package includes functions for non-parametric analysis, including permutation
    tests and bootstrap resampling. The bootstrap function obtains predictions
    either by cell means or by more advanced/powerful mixed effects models, yielding
    predictions and confidence intervals that may be easily visualized at any level
    of the experiment's design.
A tool to operate a batch of univariate or multivariate 
    Cox models and return tidy result.
Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.
Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.
Enables users to create visualizations using functions 
    based on the data analysis task rather than on plotting mechanics. It hides
    the details of the individual 'ggplot2' function calls and 
    allows the user to focus on the end goal. Useful for quick preliminary explorations. 
    Provides functions for common exploration patterns. Some of the ideas in this
    package are motivated by Fox (2015, ISBN:1938377052).
This package implements a simplified version of least
        squares, and logistic regression for efficiently selecting the
        significant non-additive interactions between two variables.
An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.
Serves as a platform for published fluorometric enzyme assay
 protocols. 'ezmmek' calibrates, calculates, and plots enzyme activities as they
 relate to the transformation of synthetic substrates. At present, 'ezmmek'
 implements two common protocols found in the literature, and is modular to accommodate
 additional protocols. Here, these protocols are referred to as the In-Sample
 Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>).
 protocols. By containing multiple protocols, 'ezmmek' aims to stimulate
 discussion about how to best optimize fluorometric enzyme assays. A standardized
 approach would make studies more comparable and reproducible.
Choosing any rectangular data file using interactive GUI dialog box, and seamlessly manipulating tidy data between an 'Excel' window and R session.
Wrapper for the 'ggplot2' package that creates a variety of common
  charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce
  typing.
Runs a Shiny App in the local machine for basic
    statistical and graphical analyses. The point-and-click interface 
    of Shiny App enables obtaining the same analysis outputs (e.g., plots and
    tables) more quickly, as compared with typing the required code in R,
    especially for users without much experience or expertise with coding.
    Examples of possible analyses include tabulating descriptive
    statistics for a variable, creating histograms by experimental groups,
    and creating a scatter plot and calculating the correlation between
    two variables.
ezsim provides a handy way to run simulation and examine its result
Contains two functions that are intended to make
    tuning supervised learning methods easy. The eztune function uses a
    genetic algorithm or Hooke-Jeeves optimizer to find the best 
    set of tuning parameters. The user can choose the optimizer, the 
    learning method, and if optimization will be based on accuracy 
    obtained through validation error, cross validation, or resubstitution. 
    The function eztune.cv will compute a cross validated error rate. The purpose 
    of eztune_cv is to provide a cross validated accuracy or MSE when 
    resubstitution or validation data are used for optimization because 
    error measures from both approaches can be misleading.
Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <DOI:10.1093/biomet/asy009> and  
    'Exact adaptive confidence intervals for linear regression coefficients'
    by Hoff and Yu <DOI:10.1214/18-EJS1517>.
Frequentist assisted by Bayes (FAB) p-values and confidence 
 interval construction. See 
 Hoff (2019) <arXiv:1907.12589> 
 "Smaller p-values via indirect information",
 Hoff and Yu (2019) <doi:10.1214/18-EJS1517> 
 "Exact adaptive confidence intervals for linear regression coefficients", and
 Yu and Hoff (2018) <doi:10.1093/biomet/asy009> 
 "Adaptive multigroup confidence intervals with constant coverage".
Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points.  It also requires minimal assumptions. The  main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021).
Provides a collection of commonly used univariate and multivariate
    time series forecasting models including automatically selected exponential 
    smoothing (ETS) and autoregressive integrated moving average (ARIMA) models.
    These models work within the 'fable' framework provided by the 'fabletools'
    package, which provides the tools to evaluate, visualise, and combine models 
    in a workflow consistent with the tidyverse.
Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.
Provides tools, helpers and data structures for
    developing models and time series functions for 'fable' and extension
    packages. These tools support a consistent and tidy interface for time
    series modelling and analysis.
Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <https://www.jstor.org/stable/25703571>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).
Helps you imagine your data before you collect it. Hierarchical data structures
   and correlated data can be easily simulated, either from random number generators or
   by resampling from existing data sources. This package is faster with 'data.table' and
   'mvnfast' installed.
Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. 
    The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes.
    The package relies on the 'fabricjs' 'JavaScript' library. See <http://fabricjs.com/>.
We implement the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing <doi: 10.1007/s11222-017-9744-8>.
Provides an interface to the 'Kairos' Face Recognition API <https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.
A tool for simulating a variety of spatial individual-based models of
    plant-plant interactions. User-created models can include any number of species, each of
    which can be structured in any number of life-stages, where each life-stage has specific
    death, growth and reproduction rates, as well as specific interaction radius, dispersal
    radius, and interaction effects over each other species/life-stage. Life stages were modeled
    so as to be a stochastic, individual-based version of differential Matrix Population
    Models (Caswell 2001, ISBN:0-87893-096-5). Interactions can be positive
    (facilitation) or negative (competition) and can affect death rates, growth rates or
    reproduction rates. Interactions from multiple numbers are additive, so as to best
    approximate classic population dynamics models such as the logistic model and
    Lotka-Volterra model (Britton 2004, ISBN:9781852335366). All models work in continuous time, implemented as an optimized version
    of the Gillespie algorithm (Gillespie 1976 <doi:10.1016/0021-9991(76)90041-3>) for independent exponential times, and continuous space.
This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.
The package estimates Factor Mixture Analysis via the EM
        algorithm
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.
Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. 
  First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. See, for example: 
      Lebart, L. and Piron, M. and Morineau, A.  (2006).
      Statistique Exploratoire Multidimensionnelle, Dunod, Paris.
  In order to permit to have different weights of the elements to be clustered, the function 'kmeansW', programmed in C++, is included. It is a modification of 'kmeans'.   
  Some graphical functions include the option: 'gg=FALSE'.  When 'gg=TRUE', they  use the 'ggplot2' and 'ggrepel' packages to avoid  the super-position of the labels.
Provides some easy-to-use functions to extract and visualize the
    output of multivariate data analyses, including 'PCA' (Principal Component
    Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence
    Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple
    Factor Analysis) functions from different R packages. It contains also functions
    for simplifying some clustering analysis steps and provides 'ggplot2' - based
    elegant data visualization.
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2020) <arXiv:2004.05105>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.
Tests for Kaiser-Meyer-Olkin (KMO) and
    communalities in a dataset. It provides a final sample by removing
    variables in a iterable manner while keeping account of the variables
    that were removed in each step. It follows the best practices and assumptions according to
    Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).
Estimation, model selection and goodness-of-fit of factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2019) <arXiv:1907.07395>.
Identifies change-points in the common and the idiosyncratic components via factor modelling.
Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2019+), and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.
Used for the design and analysis of a 2x2 factorial trial for
    a time-to-event endpoint.  It performs power calculations and significance
    testing as well as providing estimates of the relevant hazard ratios and the 
    corresponding 95% confidence intervals.  Important reference papers include
    Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609>
    Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507>
    Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020)
    <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.
The Merging Path Plot is a methodology for adaptive fusing of k-groups 
    with likelihood-based model selection. This package contains tools for 
    exploration and visualization of k-group dissimilarities. 
    Comparison of k-groups is one of the most important issues
    in exploratory analyses and it has zillions of applications. 
    The traditional approach is to use pairwise post hoc tests
    in order to verify which groups differ significantly. However, 
    this approach fails with a large number of groups in both interpretation 
    and visualization layer.
    The Merging Path Plot solves this problem by using an easy-to-understand 
    description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic (Sitko, Biecek 2017) <arXiv:1709.04412>.
    'factorMerger' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
    Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
Methods to calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models.
Package to fit Bayesian quantile regression models that
        assume a factor structure for at least part of the design
        matrix.
It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' (<http://www.ipez.es/ModestR>) with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Function factories are functions that make functions. They can be 
    confusing to construct. Straightforward techniques can produce functions 
    that are fragile or hard to understand. While more robust techniques exist 
    to construct function factories, those techniques can be confusing. This 
    package is designed to make it easier to construct function factories.
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
Allow clients to fetch 'analytics' through API for Portfolio 
    'Analytics'('PA'), Style Performance Risk('SPAR') and 'Vault' products of 
    'FactSet'. Visit 
    <https://github.com/factset/analyticsapi-engines-r-sdk/tree/master/Engines>
    for more information on the usage of package. Visit 
    <https://developer.factset.com/> for more information on products.
Generates 'RProtobuf' classes for 'FactSet' 'STACH' tabular 
    format which represents complex multi-dimensional array of data. These 
    classes help in the 'serialization' and 'deserialization' of 'STACH' 
    formatted data. See 'GitHub' repository documentation for more 
    information.
Per the Factual.com website, "Factual is a platform where
        anyone can share and mash open, living data on any subject."
        The data is in the form of tables and is accessible via REST
        API.  The factualR package is a thin wrapper around the
        Factual.com API, to make it even easier for people working with
        R to explore Factual.com data sets.
Compute maximum likelihood estimators of parameters in a Gaussian factor model using
  the the matrix-free methodology described in Dai et al. (2020) <doi:10.1080/10618600.2019.1704296>.
  In contrast to the factanal() function from 'stats' package, fad() can handle high-dimensional datasets where
  number of variables exceed the sample size and is also substantially faster than the EM algorithms.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a  
   factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
    (see Friedman et al. (2010)), sparse linear discriminant analysis (see
    Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
    analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Probability distributions that are sometimes useful in hydrology.
Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.
More comfortable interface to work with R data or source files
    in a key-value fashion.
An implementation of the fair data adaptation with quantile preservation described in
    Plecko & Meinshausen (2019) <arXiv:1911.06685>. 
    The adaptation procedure uses the specified causal graph to pre-process the 
    given training and testing data in such a way to remove the bias caused by 
    the protected attribute. The procedure uses tree ensembles for quantile 
    regression.
Fair machine learning regression models which take sensitive attributes into account in
  model estimation. Currently implementing Komiyama et al. (2018) 
  <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>.
Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented.
Offers calculation, visualization and comparison of algorithmic fairness metrics. Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) <doi:10.1007/s10618-010-0190-x>, Chouldechova (2017) <doi:10.1089/big.2016.0047>, Feldman et al. (2015) <doi:10.1145/2783258.2783311> , Friedler et al. (2018) <doi:10.1145/3287560.3287589> and Zafar et al. (2017) <doi:10.1145/3038912.3052660>. The package also offers convenient visualizations to help understand fairness metrics.
Allows user to obtain subsets of columns of data or vectors within a list.  These subsets will match the original data in terms of average and variation, but have a consistent length of data per column.  It is intended for use on automated data generation which may not always output the same N per replicate or sample.
It is used for systematic analysis of decisions based on attributes and its levels.
Use R as a minimal build system. This might come in
    handy if you are developing R packages and can not use a proper build
    system. Stay away if you can (use a proper build system).
Generates fake data from a dataset of different variable types.
    The package contains the functions simulate_dataset and simulate_dataset_ts 
    to simulate time-independent and time-dependent data. It randomly samples 
    character and factor variables from contingency tables and  numeric and 
    ordered factors from a multivariate normal distribution. It currently supports the 
    simulation of stationary and zero-inflated count time series.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data.  Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.  The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.
Functionality provided for conditional simulation, likelihoods and plotting of pedigrees, mostly as a wrapper for 'paramlink'. Users typically start by exporting from the Windows version of 'Familias'.
Simulates age-at-onset traits associated with a segregating major gene in family data 
     obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment 
     correction is utilized to estimate age-dependent penetrance functions either parametrically from 
     the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm 
     can infer missing genotypes and carrier probabilities estimated from family's genotype and
     phenotype information or from a fitted model. Plot functions include pedigrees of simulated 
     families and predicted penetrance curves based on specified parameter values.
An interface to the core Familias functions (www.familias.name), 
 which are programmed in C++.
Fits penalized linear and logistic regression models with pairwise interaction terms.
Grows families of features by selecting features that maximize a weighted score calculated from empirical feature scores and graphical knowledge. The final weighted score for a feature is determined by summing a feature's family-weighted scores across all families in which the feature appears.
Estimate parameters of univariate probability distributions 
  with maximum likelihood and Bayesian methods.
Given a set of parameters describing model dynamics and a corresponding cost function,
    FAMoS performs a dynamic forward-backward model selection on a specified selection
    criterion. It also applies a non-local swap search method. Works on any cost function. 
    For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.
FamSKAT-RC is a family-based association kernel test for both 
    rare and common variants. This test is general and several special cases 
    are known as other methods: famSKAT, which only focuses on rare variants 
    in family-based data, SKAT, which focuses on rare variants in 
    population-based data (unrelated individuals), and SKAT-RC, which focuses 
    on both rare and common variants in population-based data. When one 
    applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes 
    famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the 
    kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one 
    applies famSKAT-RC and set the kinship matrix (fullkins) to the identity 
    matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also 
    include a small sample synthetic pedigree to demonstrate the method with.  
    For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used.  This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
A collection of R functions to perform nonparametric 
    analysis of covariance for regression curves or surfaces. 
    Testing the equality or parallelism of nonparametric curves 
    or surfaces is equivalent to analysis of variance (ANOVA) or 
    analysis of covariance (ANCOVA) for one-sample functional data. 
    Three different testing methods are available in the package, 
    including one based on L-2 distance, one based on an ANOVA 
    statistic, and one based on variance estimators.
Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the 'RStudio IDE' with 'RStudio' package 'manipulate' loaded. 'RStudio' is freely available (<https://rstudio.com/>), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only. For further information on the method see Fruth, J., Roustant, O., Kuhnt, S. (2014) <doi:10.1016/j.jspi.2013.11.007>.
Visualise sequential distributions using a range of plotting
    styles. Sequential distribution data can be input as either simulations or
    values corresponding to percentiles over time. Plots are added to
    existing graphic devices using the fan function. Users can choose from four
    different styles, including fan chart type plots, where a set of coloured
    polygon, with shadings corresponding to the percentile values are layered
    to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.
Counterparts to R string manipulation functions that account for
   the effects of ANSI text formatting control sequences.
Download Data from the FAOSTAT Database of the Food and Agricultural Organization (FAO) of the United Nations.
    A list of functions to download statistics from FAOSTAT (database of the FAO <http://www.fao.org/faostat/en/#data>) 
    and WDI (database of the World Bank <https://data.worldbank.org/>), and to perform some harmonization operations.
Tools for detecting and summarize influential cases that
    can affect exploratory and confirmatory factor analysis models as well as
    structural equation models more generally (Chalmers, 2015, <doi:10.1177/0146621615597894>; 
    Flora, D. B., LaBrish, C. & Chalmers, R. P., 2012, <doi:10.3389/fpsyg.2012.00055>).
Create Frequently Asked Questions page for Shiny application.
Modelizations and previsions functions for
   Functional AutoRegressive processes using
   nonparametric methods: functional kernel,
   estimation of the covariance operator in
   a subspace, ...
Books are "Practical Regression and ANOVA in R" on CRAN, "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248.
Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.
Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See <http://weka.wikispaces.com/ARFF> for further info on 'ARFF' and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Note that the 'OpenML' package is currently only available from 'Github' at: <https://github.com/openml/openml-r>.
Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.
Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>.
             Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. 
             Extensions to two-sample simultaneous mean comparison problems are also included. 
             As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.
Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, 
    select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. 
    When the model is executed a set of results are displayed which include efficiency scores, peers' determination, scale efficiencies' evaluation 
    and slacks' calculation. Fore more information about the theoretical background of the package, 
    please refer to Bogetoft & Otto (2011) <doi:10.1007/978-1-4419-7961-2>.
The encoding of colour can be handled in many different ways, using
    different colour spaces. As different colour spaces have different uses,
    efficient conversion between these representations are important. The 
    'farver' package provides a set of functions that gives access to very fast
    colour space conversion and comparisons implemented in C++, and offers 
    speed improvements over the 'convertColor' function in the 'grDevices' 
    package.
It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.
Provides functions for pricing
	and valuating Asian Options together with tools for analyzing
    and modeling Exponential Brownian Motion (EBM).
This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.
Provides a  collection of functions 
  to manage, to investigate and to analyze data sets of financial 
  assets from different points of view.
The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various 
    functions to tidy and screen daily stream discharge data; calculate and visualize various summary statistics
    and metrics; and compute annual trending (using 'zyp' package methods <https://CRAN.R-project.org/package=zyp>)
    and volume frequency analyses (using methods similar to HEC-SSP (2019) 
    <https://www.hec.usace.army.mil/software/hec-ssp/>). It features useful function arguments for filtering of and
    handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey
    of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).
A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in
    Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> 
    and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.
Implements Adaboost based on C++ backend code.
             This is blazingly fast and especially useful for large, in memory data sets. 
             The package uses decision trees as weak classifiers. Once the classifiers
             have been trained, they can be used to predict new data. 
             Currently, we support only binary classification tasks.
             The package implements the Adaboost.M1 algorithm and the real
             Adaboost(SAMME.R) algorithm.
The 'fastai' <https://docs.fast.ai/index.html> library 
             simplifies training fast and accurate neural networks 
             using modern best practices. It is based on research 
             in to deep learning best practices undertaken 
             at 'fast.ai', including 'out of the box' support
             for vision, text, tabular, audio, time series, and 
             collaborative filtering models.
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Provides a method of recovering the precision matrix efficiently 
        and solving for the dantzig selector by applying the parametric 
        simplex method.  The computation is based on a linear optimization
        solver. It also contains a generic LP solver and a parameterized LP 
        solver using parametric simplex method.
This is a two-in-one package which provides interfaces to
        both R and 'Python'. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the 'SciPy' package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the 'Python' files, see the file INSTALL in the source distribution.
        Based on the present package, Christoph Dalitz also wrote a pure 'C++'
        interface to 'fastcluster':
        <http://informatik.hsnr.de/~dalitz/data/hclust>.
A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net.
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) DOI: <doi:10.4310/SII.2013.v6.n2.a1>.
For ordinal rating data, consider the accelerated Expectation-Maximization algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis' identity for the observed information matrix.  Best-subset variable selection for CUB regression models is then implemented on such basis. The methods here implemented are illustrated and discussed in the preprint available from Researchgate by Simone R. (2020) <https://tinyurl.com/vvk563e>.
Provides an R interface to Bob Jenkin's streaming, 
 non-cryptographic 'SpookyHash' hash algorithm for use in digest-based 
 comparisons of R objects. 'fastdigest' plugs directly into R's internal 
 serialization machinery, allowing digests of all R objects the serialize() 
 function supports, including reference-style objects via custom hooks. Speed is
 high and scales linearly by object size; memory usage is constant and 
 negligible.
Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
If there is a need to recognise edges on a raster image or a bitmap or any kind of a matrix, one can find packages
    that does only 90 degrees vectorization. Typically the nature of artefact images is linear and can be vectorized in much more
    efficient way than draw a series of 90 degrees lines. The fasteraster package does recognition of lines using only one pass.
    It also allows to calculate mass and the mass centers for the recognized zones or polygons.
Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)(<doi:10.1214/009053604000000067 >) and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library.
Provides a drop-in replacement for rasterize() from the 'raster'
   package that takes 'sf'-type objects, and is much faster. There is support
   for the main options provided by the rasterize() function, including
   setting the field used and background value, and options for 
   aggregating multi-layer rasters. Uses the scan line algorithm attributed to
   Wylie et al. (1967) <doi:10.1145/1465611.1465619>.
Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise.  See the reference: Mengyang Gu and Yanxun Xu (2017) <arXiv:1711.11501>.
Fast, numerically-stable Gauss-Hermite quadrature rules and
    utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A.
    (1994) <doi:10.2307/2337136> for a reference on these methods.
Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares 
    implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard
    against convergence issues.
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
The FastHCS algorithm of Schmitt and Vakili (2015) for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
It implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
Implementation of FastICA algorithm to perform Independent
        Component Analysis (ICA) and Projection Pursuit.
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.
R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 <doi:10.1007/978-3-030-32047-8_16>.
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.
Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data
    and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two 
    datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, 
    tools for preparing, adjusting, and summarizing data merges are included. The package implements methods 
    described in Enamorado, Fifield, and Imai (2019) ''Using a Probabilistic Model to Assist Merging of 
    Large-scale Administrative Records'', American Political Science Review and is available 
    at <http://imai.fas.harvard.edu/research/linkage.html>.
Calculates marginal effects based on logistic model objects such as 'glm' or 'speedglm' at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.
A very fast Log-Rank-Test implementation that is several orders of magnitude faster than the implementation in the 'survival' package.
  Log-Rank-Tests can be computed individually or concurrently using threading.
Estimate spike times from calcium imaging data using an L0 penalty.
Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.
Fast implementation of data structures, including a key-value
    store, stack, and queue. Environments are commonly used as key-value stores
    in R, but every time a new key is used, it is added to R's global symbol
    table, causing a small amount of memory leakage. This can be problematic in
    cases where many different keys are used. Fastmap avoids this memory leak
    issue by implementing the map using data structures in C++.
Fast Marching Method (FMM) first developed by Sethian (1996) <http://www.pnas.org/content/93/4/1591.short>, and further extended by including a second-order approximation, the first-arrival rule, additive weights, and non-homogeneous domains following Silva and Steele (2012) <doi:10.1142/S0219525911003293> and Silva and Steele (2014) <doi:10.1016/j.jas.2014.04.021>.
Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.
Small set of functions to fast computation of some matrices and operations
  useful in statistics.
A mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138> to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) <doi:10.1093/biomet/asz024>); furthermore, we extend it to tackle datasets in metric spaces.
This is an extremely fast implementation of a Naive Bayes classifier. This 
    package is currently the only package that supports a Bernoulli distribution, a Multinomial 
    distribution, and a Gaussian distribution, making it suitable for both binary features, 
    frequency counts, and numerical features. Another feature is the support of a mix of 
    different event models. Only numerical variables are allowed, however, categorical variables 
    can be transformed into dummies and used with the Bernoulli distribution. 
    The implementation is largely based on the paper 
    "A comparison of event models for Naive Bayes anti-spam e-mail filtering" 
    written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be 
    submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
We present an implementation of the algorithms required to simulate
  large-scale social networks and retrieve their most relevant metrics. Details 
  can be found in the accompanying scientific paper on the Journal 
  of Statistical Software, <doi:10.18637/jss.v096.i07>.
The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.
Finds the critical sample size ("critical point of stability") for a 
    correlation to stabilize in Schoenbrodt and Perugini's definition of 
    sequential stability (see <doi:10.1016/j.jrp.2013.05.009>).
Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.
'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.
Data sets and utilities to accompany the second edition of
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2017), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet() function.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Samples generalized random product graph, a
    generalization of a broad class of network models. Given matrices X,
    S, and Y with with non-negative entries, samples a matrix with
    expectation X S Y^T and independent Poisson or Bernoulli entries. The
    algorithm first samples the number of edges and then puts them down
    one-by-one.  As a result it is O(m) where m is the number of edges, a
    dramatic improvement over element-wise algorithms that which require
    O(n^2) operations to sample a random graph, where n is the number of
    nodes.
A data package that hosts all models for the
    'nflfastR' package.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.
Computes fast (relative to other implementations) approximate 
    Shapley values for any supervised learning model. Shapley values help to 
    explain the predictions from any black box model using ideas from game 
    theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> 
    for details.
Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.
When we do statistic work, we need to see the structure of the data.
    list.str() function will help you see the structure of the data quickly. 
    list.plot() function can help you check every variable in your dataframe.
    table_one() function will make it easy to make a baseline table including 
    difference tests. uv_linear(), uv_logit(), uv_cox(), uv_logrank() will give
    you a hand to do univariable regression analysis, while mv_linear(), 
    mv_logit() and mv_cox() will carry out multivariable regression analysis.
An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	An example how to use 'fastTextR' can be found in the 'README' file.
Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
             defined by some design matrix.
Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Kiener distributions K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.
Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.
Provides a replacement for dplyr::na_if().  Allows
    you to specify multiple values to be replaced with NA using a single
    function.
HTTP error helpers. Methods included for general purpose HTTP 
    error handling, as well as individual methods for every HTTP status
    code, both via status code numbers as well as their descriptive names.
    Supports ability to adjust behavior to stop, message or warning.
    Includes ability to use custom whisker template to have any configuration
    of status code, short description, and verbose message. Currently 
    supports integration with 'crul', 'curl', and 'httr'.
Finds the URL to the 'favicon' for a website. This is useful if you
  want to display the 'favicon' in an HTML document or web application,
  especially if the website is behind a firewall.
A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos
             (http://pages.bloomsbury.com/favouritenumber).
Provides functions and datasets from the book "Forest Analytics with R".
A toolkit for Flux Balance Analysis and related
    metabolic modeling techniques. Functions are provided for: parsing
    models in tabular format, converting parsed metabolic models to input
    formats for common linear programming solvers, and
    evaluating and applying gene-protein-reaction mappings. In addition, there
    are wrappers to parse a model, select a solver, find the metabolic fluxes,
    and return the results applied to the original model. Compared to other
    packages in this field, this package puts a much heavier focus on
    providing reusable components that can be used in the design of new
    implementation of new techniques, in particular those that involve large
    parameter sweeps. For a background on the theory, see What is Flux Balance 
    Analysis <doi:10.1038/nbt.1614>.
Provides a collection of functions to 
    explore and to investigate basic properties of financial returns 
    and related quantities.
    The covered fields include techniques of explorative data analysis
    and the investigation of distributional properties, including
    parameter estimation and hypothesis testing. Even more there are
    several utility functions for data handling and management.
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
Normalizes the data from a file containing the raw values
        of the SNP probes of microarrray data by using the FISH probes
        and their corresponding CNs.
It implements the Nelson-Siegel and the Nelson-Siegel-Svensson
	term structures.
Wrapper functions around the Facebook Marketing 'API' to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.
This package uses time dependent Poisson regression and a record of goals scored in matches to rank teams via estimated attack and defense strengths.  The statistical model is based on Dixon and Coles (1997) Modeling Association Football Scores and Inefficiencies in the Football Betting Market, Applied Statistics, Volume 46, Issue 2, 265-280.  The package has a some webscrapers to assist in the development and updating of a match database.  If the match database contains unconnected clusters (i.e. sets of teams that have only played each other and not played teams from other sets), each cluster is ranked separately relative to the median team strength in the cluster.  The package contains functions for predicting and simulating tournaments and leagues from estimated models. The package allows fitting via the glm(), speedglm(), and glmnet() functions.  The latter allows fast and efficient fitting of very large numbers of teams.  The fitting algorithm will analyze the match data and determine which teams form a cluster (a set of teams where there is a path of matches connecting every team) and fit each cluster separately.
Implements a very fast C++ algorithm to quickly bootstrap receiver
    operating characteristics (ROC) curves and derived performance metrics,
    including the area under the curve (AUC) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of one second.
Provides access to a range of functions for computing and visualising the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available. For details on the computation and theory of the FBST see <arXiv:2005.13181>.
Provides a streamlined, standard evaluation-based approach to multivariate function composition. Allows for chaining commands via a forward-pipe operator, %>%.
Provides tools to perform fuzzy formal concept
    analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23>
    and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>.  It
    provides functions to load and save a formal context, extract its
    concept lattice and implications.  In addition, one can use the
    implications to compute semantic closures of fuzzy sets and, thus,
    build recommendation systems.
Efficient procedures for community detection in network studies, especially for sparse networks with not very obvious community structure. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Collection of pricing by duplication methods for popular structured products ("Zertifikate").
Fatigue Crack Growth in Reliability estimates the distribution
             of material lifetime due to mechanical fatigue efforts. The FCGR
             package provides simultaneous crack growth curves fitting to 
             different specimens in materials under mechanical stress efforts. 
             Linear mixed-effects models (LME) with smoothing B-Splines 
             and the linearized Paris-Erdogan law are applied. Once defined
             the fail for a determined crack length, the distribution function
             of failure times to fatigue is obtained. The density function is
             estimated by applying nonparametric binned kernel density estimate 
             (bkde) and the kernel estimator of the distribution function (kde). 
             The results of Pinheiro and Bates method based on nonlinear 
             mixed-effects regression (nlme) can be also retrieved. The package 
             contains the crack.growth, PLOT.cg, IB.F, and Alea.A (database) functions.
Extract full length circular RNA sequences and classify circular RNA
             using the output of circular RNA prediction tools, reference genome and the annotation file corresponding to the reference genome.
             This package uses the output of circular RNA prediction tools such as 'CIRI', 'CIRCExplorer' and the output of other state-of-the-art circular RNA prediction tools.
             Details about the circular RNA prediction procedure can be found in
             'Yuan Gao, Jinfeng Wang and Fangqing Zhao' (2015) <doi:10.1186/s13059-014-0571-3>
             and 'Zhang XO, Wang HB, Zhang Y, Lu X, Chen LL and Yang L' (2014) <doi:10.1016/j.cell.2014.09.001>.
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
Provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the 'fcm' package returns a data frame of the concepts' values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.
Provides several functions to create and manipulate fuzzy
    cognitive maps. It is based on 'FCMapper' for Excel, distributed at <http://
    www.fcmappers.net/joomla/>, developed by Michael Bachhofer and Martin Wildenberg.
    Maps are inputted as adjacency matrices. Attributes of the maps and the
    equilibrium values of the concepts (including with user-defined constrained
    values) can be calculated. The maps can be graphed with a function that calls
    'igraph'. Multiple maps with shared concepts can be aggregated.
Provides a  collection of functions to 
	manage, to investigate and to analyze bivariate financial returns by  
	Copulae. Included are the families of Archemedean, Elliptical, 
	Extreme Value, and Empirical Copulae.
Over fifty conventional clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.
Dynamic prediction in functional concurrent regression with an application to child growth. Extends the pffr() function from the 'refund' package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured.  Leroux et al. (2017), Statistics in Medicine, <doi:10.1002/sim.7582>.
A fold change rank based method is presented to search for genes with changing
             expression and to detect recurrent chromosomal copy number aberrations. This 
             method may be useful for high-throughput biological data (micro-array, sequencing, ...).
             Probabilities are associated with genes or probes in the data set and there is no
             problem of multiple tests when using this method. For array-based comparative genomic
             hybridization data, segmentation results are obtained by merging the significant
             probes detected.
This is a package for fluorescence fluctuation spectroscopy data analysis methods such as spFCS, FCCS, scanning-FCS, pCF, N&B and pCOMB, among others.
    In addition, several data detrending tools are provided. For an extensive user's guide for the use of FCSlib, please navigate to (<https://github.com/FCSlib/FCSlib/tree/master/Documentation>).
    Sample data can be found at (<https://github.com/FCSlib/FCSlib/tree/master/Sample%20Data>).
    The original paper where this package is presented can be found at (<doi:10.1093/bioinformatics/btaa876>).
Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages.
    A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$.
    First- and second-order derivatives are also included. These are the mathematically correct ones, no approximations applied.
    As of version 1.0, this package includes B-splines, Fourier bases and polynomials.
Automatically suggests a correction when a typo occurs.
FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.
These functions were developed to support functional data
 analysis as described in Ramsay, J. O. and Silverman, B. W.
 (2005) Functional Data Analysis. New York: Springer and in 
 Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). 
 Functional Data Analysis with R and Matlab (Springer). 
 The package includes data sets and script files working many examples 
 including all but one of the 76 figures in this latter book.  Matlab versions 
 are available by ftp from 
 <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
Quantify the serial correlation across lags of a given functional 
    time series using the autocorrelation function and a partial autocorrelation
    function for functional time series proposed in 
    Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>.
    The autocorrelation functions are based on the L2 norm of the lagged covariance 
    operators of the series. Functions are available for estimating the 
    distribution of the autocorrelation functions under the assumption 
    of strong functional white noise.
An implementation of the methodology described in
    Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional
    data analysis of samples of density functions.  Densities are first
    transformed to their corresponding log quantile densities, followed by
    ordinary Functional Principal Components Analysis (FPCA).  Transformation
    modes of variation yield improved interpretation of the variability in the
    data as compared to FPCA on the densities themselves.  The standard
    fraction of variance explained (FVE) criterion commonly used for functional
    data is adapted to the transformation setting, also allowing for an
    alternative quantification of variability for density data through the
    Wasserstein metric of optimal transport.
It performs simultaneously clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.
Likelihood based analysis of 1-dimension functional data
        in a mixed-effects model framework. Matrix computation are
        approximated by semi-explicit operator equivalents with linear
        computational complexity. Markussen (2013) <doi:10.3150/11-BEJ389>.
Performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) <doi:10.1007/s11749-010-0185-3>, Gorecki and Smaga (2015) <doi:10.1007/s00180-015-0555-0>, Gorecki and Smaga (2017) <doi:10.1080/02664763.2016.1247791>, Zhang et al. (2018) <doi:10.1016/j.csda.2018.05.004>).
A collection of functions for outlier detection in functional data analysis. 
  Methods implemented include directional outlyingness by 
  Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>,
  MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>,
  total variation depth and modified shape similarity index by 
  Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by
  Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection
  tools and depths for functional data like functional boxplot, (modified) band depth etc.,
  are also available.
A versatile package that provides implementation of various
    methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this
    package is Functional Principal Component Analysis (FPCA), a key technique for
    functional data analysis, for sparsely or densely sampled random trajectories
    and time courses, via the Principal Analysis by Conditional Estimation
    (PACE) algorithm. This core algorithm yields covariance and mean functions,
    eigenfunctions and principal component (scores), for both functional data and
    derivatives, for both dense (functional) and sparse (longitudinal) sampling designs.
    For sparse designs, it provides fitted continuous trajectories with confidence bands,
    even for subjects with very few longitudinal observations. PACE is a viable and
    flexible alternative to random effects modeling of longitudinal data. There is also a
    Matlab version (PACE) that contains some methods not available on fdapace and vice
    versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry).
    References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>;
    Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L.M., Ramsay, J.O., Ramsay, T.O. (2013), Spatial spline regression models for an overview.
Integrated Depths for Partially Observed Functional Data (PoFD). Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2020), "Integrated Depth for Partially Observed Functional Data".
Performs alignment, PCA, and modeling of multidimensional and
            unidimensional functions using the square-root velocity framework
            (Srivastava et al., 2011 <arXiv:1103.3817> and
            Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework
            allows for elastic analysis of functional data through phase and
            amplitude separation.
Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
Regression models for functional data, i.e., scalar-on-function,
    function-on-scalar and function-on-function regression models, are fitted
    by a component-wise gradient boosting algorithm. 
	For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.
Provides a variety of tools for the analysis of covariance operators including k-sample tests for equality and classification and clustering methods found in the works of Cabassi et al (2017) <doi:10.1214/17-EJS1347>, Kashlak et al (2017) <arXiv:1604.06310>, Pigoli et al (2014) <doi:10.1093/biomet/asu008>, and Panaretos et al (2010) <doi:10.1198/jasa.2010.tm09239>.
Provides the probability density function (PDF) of the diffusion decision
  model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>)
  with across-trial variability in the drift rate.
  Because the PDF of the DDM contains an infinite sum, it needs to be
  approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009,
  <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014,
  <doi:10.1016/j.jmp.2014.05.002>) plus new approximations. All approximations
  are implemented purely in 'C++' providing faster speed than existing packages.
Provides functionality to generate a frequency 
    distribution table from a set of observations and plot the frequency
    distribution using a Pareto chart.
Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".
Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.
Forest data quality is a package containing nine methods of analysis for forest databases,
    from databases containing inventory data and growth models, the focus of the analyzes is related to the
    quality of the data present in the database with a focus on consistency , punctuality and completeness of data.
FDR functions for permutation-based estimators, including pi0 as well as FDR
             confidence intervals. The confidence intervals account for dependencies between
             tests by the incorporation of an overdispersion parameter, which is estimated
             from the permuted data.
It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.
The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines. Megan H. Murray and Jeffrey D. Blume (2020) <arXiv:2010.04680>.
Tools for FDR problems, including false discovery rate regression.
    See corresponding paper: "False discovery rate regression: application to
    neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
    C. Kelly, Matthew A. Smith, Robert E. Kass.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.
Estimate step functions via multiscale inference with controlled false discovery rate (FDR). For details see H. Li, A. Munk and H. Sieling (2016) <doi:10.1214/16-EJS1131>.
Estimates both tail area-based false 
   discovery rates (Fdr) as well as local false discovery rates (fdr) for a 
   variety of null models (p-values, z-scores, correlation coefficients,
   t-scores).  The proportion of null values and the parameters of the null 
   distribution are adaptively estimated from the data.  In addition, the package 
   contains functions for non-parametric density estimation (Grenander estimator), 
   for monotone regression (isotonic regression and antitonic regression with weights),
   for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), 
   for the half-normal and correlation distributions, and for computing
   empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.
Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2019) <arXiv:1912.04607v1>. The main algorithms of the paper are available as continuous, discrete and weighted versions.
For each feature, a score is computed that can be useful
        for feature selection. Several random subsets are sampled from
        the input data and for each random subset, various linear
        models are fitted using lars method. A score is assigned to
        each feature based on the tendency of LASSO in including that
        feature in the models.Finally, the average score and the models
        are returned as the output. The features with relatively low
        scores are recommended to be ignored because they can lead to
        overfitting of the model to the training data. Moreover, for
        each random subset, the best set of features in terms of global
        error is returned. They are useful for applying Bolasso, the
        alternative feature selection method that recommends the
        intersection of features subsets.
Comprises a pipeline for predicting microRNA/mRNA interactions, as detailed in Williams, Calinescu, Mohorianu (2020) <doi:10.1101/2020.12.23.424130>. Its input consists of [a] a messenger RNA (mRNA) dataset (either in fasta format, focused on 3' UTRs or in gtf format; for the latter, the sequences of the 3’ UTRs are generated using the genomic coordinates), [b] a microRNA dataset (in fasta format, retrieved from miRBase,  <http://www.mirbase.org/>) and [c] an interaction dataset (in csv format, from miRTarBase <http://mirtarbase.cuhk.edu.cn/php/index.php>). To characterise and predict microRNA/mRNA interactions, we use [a] statistical analyses based on Chi-squared and Fisher exact tests and [b] Machine Learning classifiers (decision trees, random forests and support vector machines). To enhance the accuracy of the classifiers we also employ feature selection approaches used in on conjunction with the classifiers. The feature selection approaches include a voting scheme for decision trees, a measure based on Gini index for random forests, forward feature selection and Genetic Algorithms on SVMs. The pipeline also includes a novel approach based on embryonic Genetic Algorithms which combines and optimises the forward feature selection and Genetic Algorithms. All analyses, including the classification and feature selection, are applicable on the microRNA seed features (default), on the full microRNA features and/or flanking features on the mRNA. The sets of features can be combined.
Provides a collection of features, decomposition methods, 
    statistical summaries and graphics functions for the analysing tidy time
    series data. The package name 'feasts' is an acronym comprising of its key
    features: Feature Extraction And Statistics for Time Series.
Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.
Local inferential feature significance for multivariate kernel density estimation.
Uses three different correlation coefficients to
    calculate measurement-level adequate correlations in a feature matrix: 
    Pearson product-moment correlation coefficient,
    Intraclass correlation and Cramer's V.
Finds modelling features through a detailed analysis of model residuals using 'rpart' classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model prediction differs from the actual target variable. S. Chatterjee, A. S. Hadi (2006) <doi:10.1002/0470055464>.
Feature flags allow developers to turn features of their
    software on and off in form of configuration. This package provides
    functions for creating feature flags in code. It exposes an interface
    for defining own feature flags which are enabled based on custom criteria.
Feature hashing, also called as the hashing trick, is a method to transform 
  features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. 
  Without looking up the indices in an associative array, 
  it applies a hash function to the features and uses their hash values as indices directly.
  The method of feature hashing in this package was proposed in Weinberger et al. (2009) <arXiv:0902.2206>. 
  The hashing algorithm is the murmurhash3 from the 'digest' package. 
  Please see the README in <https://github.com/wush978/FeatureHashing> for more information.
Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values.
Discretely-sampled function is first smoothed.  Features
        of the smoothed function are then extracted.  Some of the key
        features include mean value, first and second derivatives,
        critical points (i.e. local maxima and minima), curvature of
        cunction at critical points, wiggliness of the function, noise
        in data, and outliers in data.
A 'reticulate'-based interface to the 'Python' module 'Featuretools'.
  The package grants functionality to interact with 'Pythons' 'Featuretools' module, which allows 
  for automated feature engineering on any data frame. Valid features and new data sets can, after
  feature synthesis, easily be extracted.
A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.
Easily analyze relational data from the United States 2016 federal 
    election cycle as reported by the Federal Election Commission.
    This package contains data about candidates, committees, and a
    variety of different financial expenditures. Data is from <https://www.fec.gov/data/browse-data/?tab=bulk-data>.
Functions and example datasets for Fechnerian scaling of discrete
  object sets.  User can compute Fechnerian distances among objects representing
  subjective dissimilarities, and other related information.  See
  package?fechner for an overview.
Functions to automate downloading geospatial data available from
    several federated data sources (mainly sources maintained by the US Federal
    government). Currently, the package enables extraction from seven datasets:
    The National Elevation Dataset digital elevation models (1 and 1/3 arc-second;
    USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic
    (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is
    led by the Natural Resources Conservation Service (NRCS) under the USDA; the
    Global Historical Climatology Network (GHCN), coordinated by National Climatic
    Data Center at NOAA; the Daymet gridded estimates of daily weather parameters 
    for North America, version 3, available from the Oak Ridge National Laboratory's
    Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; 
    and the National Land Cover Database (NLCD).
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
The Code of Federal Regulations (CFR) annual edition is the codification 
    of the general and permanent rules published in the Federal Register by the departments
    and agencies of the Federal Government of the United States of America. Simply, the 
    'fedregs' package facilitates word processing and sentiment analysis of the CFR using tidy 
    principles. Note: According to the Code of Federal Regulations XML Rendition User Guide Document: 
    "In general, there are no restrictions on re-use of information in Code of Federal Regulations
    material because U.S. Government works are not subject to copyright. OFR and GPO do not
    restrict downstream uses of Code of Federal Regulations data, except that independent providers
    should be aware that only the OFR and GPO are entitled to represent that they are the providers
    of the official versions of the Code of Federal Regulations and related Federal Register
    publications."
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Provides the function feis() to estimate fixed effects individual 
    slope (FEIS) models. The FEIS model constitutes a more general version of 
    the often-used fixed effects (FE) panel model, as implemented in the 
    package 'plm' by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. 
    In FEIS models, data are not only person demeaned like in conventional 
    FE models, but detrended by the predicted individual slope of each 
    person or group. Estimation is performed by applying least squares lm() 
    to the transformed data. For more details on FEIS models see Bruederl and 
    Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; 
    Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; 
	Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>;
    Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE 
    and random effects estimators against heterogeneous slopes, the package 
    also provides the functions feistest() for an artificial regression test 
    and bsfeistest() for a bootstrapped version of the Hausman test.
A small package for determining if n-dimensional ellipses overlap.
Provides pseudo-postfix operators and more to enhance displaying documents.
    The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects.
    The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo).
Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported.
A structured profile likelihood algorithm for the logistic fixed effects model and an approximate expectation maximization (EM) algorithm for the logistic mixed effects model. Based on He, K., Kalbfleisch, J.D., Li, Y. and Li, Y. (2013) <doi:10.1007/s10985-013-9264-6>.
R implementations of standard financial engineering codes;
  vanilla option pricing models such as Black-Scholes, Bachelier, CEV, and
  SABR.
Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>.
    Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.
Data from various catalogs of astrophysical gamma-ray sources
    detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071,
    2009 June 1), on board the Fermi gamma-ray satellite. More information on
    Fermi and its data products is available from the Fermi Science Support Center
    (http://fermi.gsfc.nasa.gov/ssc/).
Quantify variability (such as confidence interval) of fertilizer response curves and optimum fertilizer rates using bootstrapping residuals with several popular non-linear and linear models.
Original idea was presented in the reference paper. Varghese et al. (2020, 74(1):35-42) "Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery". Marine fisheries governance and management practices are very essential to ensure the sustainability of the marine resources. A widely accepted resource management strategy towards this is to derive sustainable fish harvest levels based on the status of marine fish stock. Various fish stock assessment models that describe the biomass dynamics using time series data on fish catch and fishing effort are generally used for this purpose. In the scenario of complex multi-species marine fishery in which different species are caught by a number of fishing gears and each gear harvests a number of species make it difficult to obtain the fishing effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum of efforts expended by different fishing gears. This necessitates standardisation of fishing effort in unit base.
Provides a collection of functions to evaluate
	barrier options, Asian options, binary options, currency 
    translated options, lookback options, multiple asset options
	and multiple exercise options.
Provides pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables).
Provides functions for analysing
  and modelling extreme events in financial time Series. The
  topics include: (i) data pre-processing, (ii) explorative 
  data analysis, (iii) peak over threshold modelling, (iv) block
  maxima modelling, (v) estimation of VaR and CVaR, and (vi) the
  computation of the extreme index.
Extends the out of memory vectors of 'ff' with
    statistical functions and other utilities to ease their usage.
Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997, <doi:10.1016/s0167-5877(97)00081-0>). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002, <doi:10.1016/S0167-5877(01)00245-8>). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.
Downloads all the datasets (you can exclude the daily ones or specify a list of those you are targeting specifically) from Kenneth French's Website at <https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>, process them and convert them to list of 'xts' (time series).
Force field simulation of interaction of set of points.
        Very useful for placing text labels on graphs, such as
        scatterplots.
Format the raw data from the ESPN fantasy football
    API <https://fantasy.espn.com/apis/v3/games/ffl/> into tidy tables.
    Scrape data on public leagues, rosters, athletes, and matches.
General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.
A collection of functions that allows users to retrieve 
  metadata for the Fragile Families challenge via a Web API 
  (<http://api.metadata.fragilefamilies.princeton.edu>).  
  Users can select and search metadata for relevant variables by 
  filtering on different attribute names.
Helps access various Fantasy Football APIs by handling authentication and
    rate-limiting, forming appropriate calls, and returning tidy
    dataframes which can be easily connected to other data sources.
An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in C++ and uses Rcpp. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic CUSUM and EWMA methods, are included.
Provides a simple and efficient wrapper around the fastest
        Fourier transform in the west (FFTW) library <http://www.fftw.org/>.
Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
Bi-variate data fitting is done by two stochastic
        components: the marginal distributions and the dependency
        structure. The dependency structure is modeled through a
        copula. An algorithm was implemented considering seven families
        of copulas (Generalized Archimedean Copulas), the best fitting
        can be obtained looking all copula's options (totally positive
        of order 2 and stochastically increasing models).
This is a package for implementation of Flury-Gautschi
        algorithms.
Provides a collection of functions to 
  analyze and model heteroskedastic behavior in financial time
  series models.
Read and Parse for Fundamental Geo-Spatial Data (FGD) which downloads XML file 
    from providing site (<https://fgd.gsi.go.jp/download/menu.php>). The JPGIS format file 
    provided by FGD so that it can be handled as an R spatial object such as 'sf' and 
    'raster', 'terra' or 'stars'.
    Supports the FGD version 4.1, and accepts fundamental items and digital elevation models.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to install and load the entire package-collection with a
    single R command, and provides convenient ways to find relevant
    documentation. Most commonly, you should not worry about the
    individual packages that make up the package-collection as you can
    access all features via this package. To learn more about ForestGEO
    visit <http://www.forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to implement analyses of plot species distributions,
    topography, demography, and biomass. It also includes a torus
    translation test to determine habitat associations of tree species as
    described by Zuleta et al. (2018) <doi:10.1007/s11104-018-3878-0>. To
    learn more about ForestGEO visit <https://forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to plot ForestGEO data. To learn more about ForestGEO visit
    <https://forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to easily import, filter, and modify ForestGEO data. To
    learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.
Access small example datasets from Luquillo, a
    ForestGEO site in Puerto Rico
    (<https://forestgeo.si.edu/sites/north-america/luquillo>).
Estimates a functional graphical model and a partially separable Karhunen-Loève decomposition for a multivariate Gaussian process. See Zapata J., Oh S. and Petersen A. (2019) <arXiv:1910.03134>.
Growth models and forest production require existing data
    manipulation and the creation of new data, structured from basic forest
    inventory data. The purpose of this package is provide functions to support
    these activities.
Goodness-of-fit test with multiplier or parametric
        bootstrap.
A permutation technique to explore and control for spatial autocorrelation. This package contains low level functions for performing permutations and calculating statistics as well as higher level functions. Higher level functions are an easy to use function for performing spatially restricted permutation tests and summarize and plot results.
Implement algorithms for feature grouping and selection over an undirected graph, solves problems like graph fused lasso, graph OSCAR and so on.
Rapidly create a GUI interface for a function you created by automatically creating widgets for arguments of the function. Automatically parses help routines for context-sensitive help to these arguments. The interface essentially a wrapper to some Tcl/Tk routines to both simplify and facilitate GUI creation. More advanced Tcl/Tk routines/GUI objects can be incorporated into the interface for greater customization for the more experienced.
Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.
Provides structural data for Norway. Datasets relating to maps, population in municipalities, vaccination coverage for childhood vaccines, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2019.
Useful tools for conveniently downloading FHIR resources in xml format 
    and converting them to R data frames. The package uses FHIR-search to download bundles 
    from a FHIR server, provides functions to save and read xml-files containing such bundles 
    and allows flattening the bundles to data.frames using XPath expressions.
Fitting (hierarchical) hidden Markov models 
    to daily share prices provided by <https://finance.yahoo.com/>.
    See <https://github.com/loelschlaeger/fHMM#readme> for documentation and examples.
Functions to compare two or more survival curves with:
             a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
             b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
        Common volumetric equations (Smalian, Newton and Huber) as well
        stacking factor and form
Routines for estimating tree fiber (tracheid) length distributions in the standing tree
           based on increment core samples. Two types of data can be used with the package, increment 
           core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani 
          Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell
          lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells)
           without being able to identify which cells are cut or if they are fines or fibres. The
          microscopy measured data consist of the observed lengths of the uncut fibres in the increment
         core. A censored version of a mixture of the fine and fiber length distributions is proposed to
         fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the
        assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers
        (fines) that at least partially appear in the increment core, being the generalized gamma and 
        the log normal densities.
Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes.  Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models.  The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>. The package is described in Miettinen, Nordhausen and Taskinen (2018) <doi:10.32614/RJ-2018-046>.
In remote sensing, designing a field campaign to collect ground-truth data can be a challenging task. We need to collect representative samples while accounting for issues such as budget constraints and limited accessibility created by e.g. poor infrastructure. As suggested by Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015>, this demands the establishment of best-practices to collect ground-truth data that avoid the waste of time and funds. 'fieldRS' addresses this issue by helping scientists and practitioners design field campaigns through the identification of priority sampling sites, the extraction of potential sampling plots and the conversion of plots into consistent training and validation samples that can be used in e.g. land cover classification.
For curve, surface and function fitting with an emphasis
 on splines, spatial data, geostatistics,  and spatial statistics. The major methods
 include cubic, and thin plate splines, Kriging, and compactly supported
 covariance functions for large data sets. The splines and Kriging methods are
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance function parameters by cross
 validation and also by restricted maximum likelihood. For Kriging
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple format can be used for
 spatial prediction. There are also many useful functions for plotting
 and working with spatial data as images. This package also contains
 an implementation of sparse matrix methods for large spatial data
 sets and currently requires the sparse matrix (spam) package. Use
 help(fields) to get started and for an overview.  The fields source
 code is deliberately commented and provides useful explanations of
 numerical details as a companion to the manual pages. The commented
 source code can be viewed by expanding the source code version
 and looking in the R subdirectory. The reference for fields can be generated
 by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
 of this package was supported in part by the National Science Foundation  Grant
 1417857,  the National Center for Atmospheric Research, and Colorado School of Mines.
 See the Fields URL
 for a vignette on using this package and some background on spatial statistics.
Tools for random fields and bridges simulations.
A very flexible framework for building server side logic in R. The 
    framework is unopinionated when it comes to how HTTP requests and WebSocket
    messages are handled and supports all levels of app complexity; from serving
    static content to full-blown dynamic web-apps. Fiery does not hold your hand
    as much as e.g. the shiny package does, but instead sets you free to create
    your web app the way you want.
Implements a simple key-value style database where character string keys
  are associated with data values that are stored on the disk. A simple interface is provided for inserting,
  retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be
  treated much like environments and lists are already used in R. These utilities are provided to encourage
  interactive and exploratory analysis on large datasets. Three different file formats for representing the
  database are currently available and new formats can easily be incorporated by third parties for use in the
  'filehash' framework.
Simple key-value database using SQLite as the backend
Place an exclusive or shared lock on a file. It uses 'LockFile'
    on Windows and 'fcntl' locks on Unix-like systems.
Interface for working with large matrices stored in files,
        not in computer memory. Supports multiple non-character
        data types (double, integer, logical and raw) of
        various sizes (e.g. 8 and 4 byte real values).
        Access to parts of the matrix is done by indexing, 
        exactly as with usual R matrices.
        Supports very large matrices.
        Tested on multi-terabyte matrices.
        Allows for more than 2^32 rows or columns.
        Allows for quick addition of extra columns to a filematrix.
        Cross-platform as the package has R code only.
Create descriptive file names with ease. New file names are
    automatically (but optionally) time stamped and placed in date stamped
    directories. Streamline your analysis pipeline with input and output file
    names that have informative tags and proper file extensions.
Functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for
  viewing the details (size, time created, time modified, etc.) of a folder's top level contents.
This started out as a package for file and string
    manipulation.  Since then, the 'fs' and 'strex' packages emerged,
    offering functionality previously given by this package (but it's done
    better in these new ones).  Those packages have hence almost pushed
    'filesstrings' into extinction.  However, it still has a small number
    of unique, handy file manipulation functions which can be seen in the
    vignette.  One example is a function to remove spaces from all file
    names in a directory.
A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) <doi:10.1007/BF01441146>. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.
Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.
Edit vectors to fill missing values, based on the vector itself.
Provides a collection of utility functions 
    to download and manage data sets from the Internet or from other 
    sources.
Generate regression results tables and plots in final 
    format for publication. Explore models and export directly to PDF 
    and 'Word' using 'RMarkdown'.
Functions for financial analysis and financial modeling, 
             including batch graphs generation, beta calculation, 
             descriptive statistics, annuity calculation, bond pricing 
             and financial data download.
Infrastructure for defining meta-data and
    relationships for financial instruments.
Contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam, and some topics in the 'Models for Financial Economics' exam.
This package accomplishes two tasks: a) it classifies
        implicit trading activity from quotes in OTC markets using the
        algorithm of Lee and Ready (1991); b) based on information for
        trade initiation, the package computes the probability of
        informed trading of Easley and O'Hara (1987).
A programmatic interface to the 'Finnish Biodiversity Information
    Facility' ('FinBIF') API (<https://api.laji.fi>). 'FinBIF' aggregates
    Finnish biodiversity data from multiple sources in a single open access
    portal for researchers, citizen scientists, industry and government.
    'FinBIF' allows users of biodiversity information to find, access, combine
    and visualise data on Finnish plants, animals and microorganisms. The
    'finbif' package makes the publicly available data in 'FinBIF' easily
    accessible to programmers. Biodiversity information is available on taxonomy
    and taxon occurrence. Occurrence data can be filtered by taxon, time,
    location and other variables. The data accessed are conveniently
    preformatted for subsequent analyses.
Package for time value of money calculation, time series analysis and computational finance.
Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple
    and Archives. Functionality includes reading and parsing all the
    files in a Darwin Core Archive, including the datasets and metadata;
    read and parse simple Darwin Core files; and validation of Darwin
    Core Archives.
Estimation and regularization for covariance matrix of asset
    returns. For covariance matrix estimation, three major types of factor
    models are included: macroeconomic factor model, fundamental factor model and
    statistical factor model. For covariance matrix regularization, four regularized
    estimators are included: banding, tapering, hard-thresholding and soft-
    thresholding. The tuning parameters of these regularized estimators are selected
    via cross-validation.
Find all root(s) of the equation,including complex
        roots;Find root(s) of the equation by dichotomy.Besides,in
        dichotomy, more than one interval can be given at a time.
Creates a HTML widget which displays the results of searching for a pattern in files in a given folder. The results can be viewed in the 'RStudio' viewer pane, included in a 'R Markdown' document or in a 'Shiny' app.
The heterogeneous treatment effect estimation procedure 
        proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.  
        The proposed method is applicable, for
        example, when selecting a small number of most (or least)
        efficacious treatments from a large number of alternative
        treatments as well as when identifying subsets of the
        population who benefit (or are harmed by) a treatment of
        interest. The method adapts the Support Vector Machine
        classifier by placing separate LASSO constraints over the
        pre-treatment parameters and causal heterogeneity parameters of
        interest. This allows for the qualitative distinction between
        causal and other parameters, thereby making the variable
        selection suitable for the exploration of causal heterogeneity. 	
        The package also contains a class of functions, CausalANOVA, 
        which estimates the average marginal interaction effects (AMIEs)
        by a regularized ANOVA as proposed by Egami and Imai (2019)<DOI:10.1080/01621459.2018.1476246>. 
        It contains a variety of regularization techniques to facilitate 
	analysis of large factorial experiments.
Package designed to find an acceptable python binary.
Scans all directories and subdirectories of a path for code snippets, R scripts,
    R Markdown, PDF or text files containing a specific pattern.  Files found can be copied to a new folder.
A tool to explore wide data sets, by detecting, ranking
    and plotting groups of statistically dependent columns.
Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.
Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.
The ability to tune models is important. 'finetune' enhances the
 'tune' package by providing more  specialized methods for finding reasonable
 values of model tuning parameters.  Two racing methods described by Kuhn (2014)
 <arXiv:1405.6974> are included. An  iterative search method using generalized
 simulated annealing (Bohachevsky, Johnson and Stein, 1986)
 <doi:10.1080/00401706.1986.10488128> is also included.
Functions to manipulate binary fingerprints
 of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
 which is internally represented a vector of integers, such
 that each element represents the position in the fingerprint that is set to 1.
 The bitwise logical functions in R are overridden so that they can be used directly
 with 'fingerprint' objects. A number of distance metrics are also
 available (many contributed by Michael Fadock). Fingerprints 
 can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
 can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
 handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.
Quantifies the provenance of the sediments in a catchment or study area. Based on a comprehensive characterization of the sediment sources and the end sediment mixtures a mixing model algorithm is applied to the sediment mixtures in order to estimate the relative contribution of each potential source. The package includes several statistical methods such as Kruskal-Wallis test, discriminant function analysis ('DFA'), principal component plot ('PCA') to select the optimal subset of tracer properties. The variability within each sediment source is also considered to estimate the statistical distribution of the sources contribution.
In the Cramér–Lundberg risk process perturbed by a Wiener
    process, this packages provides approximations to the probability of
    ruin within a finite time horizon.  Currently, there are three methods
    implemented: The first one uses saddlepoint approximation (two
    variants are provided), the second one uses importance sampling and
    the third one is based on the simulation of a dual process.  This last
    method is not very accurate and only given here for completeness.
The purpose of this package is to tests whether a given 
             moment of the distribution of a given sample is finite or not. For 
             heavy-tailed distributions with tail exponent b, only moments of 
             order smaller than b are finite. Tail exponent and heavy-
             tailedness are notoriously difficult to ascertain. But the 
             finiteness of moments (including fractional moments) can be tested 
             directly. This package does that following the test suggested by 
             Trapani (2016) <doi:10.1016/j.jeconom.2015.08.006>.
Download and display company financial data from the U.S. Securities
    and Exchange Commission's EDGAR database. It contains a suite of functions with
    web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR 
    in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
    for more information.
R companion to Tsay (2005)
 Analysis of Financial Time Series, second edition (Wiley).
 Includes data sets, functions and script files
 required to work some of the examples.  Version 0.3-x
 includes R objects for all data files used in the text
 and script files to recreate most of the analyses in
 chapters 1-3 and 9 plus parts of chapters 4 and 11.
The Brazilian vehicle purchase pricing table is provided by 
  the Institute of Economic Research Foundation (Fipe) and used in purchase 
  negotiations according to region, vehicle’s conservation, color, accessories 
  or any other factor that might influence the demand and supply for a specific 
  vehicle. For more on the data themselves and web access, please see 
  <https://www.fipe.org.br/en-us/home/>.
Computes implicitly induced quantities from prior/hyperparameter    
    specifications of three Mixtures of Finite Mixtures models: Dirichlet 
    Process Mixtures (DPMs; Escobar and West (1995) 
    <doi:10.1080/01621459.1995.10476550>), Static Mixtures of Finite Mixtures 
    (Static MFMs; Miller and Harrison (2018) 
    <doi:10.1080/01621459.2016.1255636>), and Dynamic Mixtures of Finite 
    Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020)
    <arXiv:2005.09918>). For methodological details, please refer to 
    Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020)
    <arXiv:2012.12337>) as well as the package vignette.
The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets.
    The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.
Authenticate users in 'Shiny' applications using 'Google Firebase' 
    with any of the many methods provided; email and password, email link, or
    using a third-party provider such as 'Github', 'Twitter', or 'Google'.
Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <DOI:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <DOI:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.
Proposes non-parametric estimates of the Fisher information measure and the 
              Shannon entropy power. The state-of-the-art studies related to the Fisher-Shannon 
              measures, with new analytical formulas for positive unimodal skewed distributions 
              are presented in Guignard et al. <arXiv:1912.02452>. A 'python' version of this 
              work is available on 'github' and 'PyPi' ('FiShPy').
Provides raw and curated data on the codes,
    classification and conservation status of freshwater fishes in British
    Columbia. Marine fishes will be added in a future release.
A collection of four datasets 
    based around the population dynamics of migratory fish. Datasets 
    contain both basic size information on a per fish basis, as well as
    otolith data that contains a per day record of fish growth history.
    All data in this package was collected by the author, from 
    2015-2016, in the Wellington region of New Zealand.
The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) <doi:10.1007/s11222-011-9249-9>,
        is an efficient method for the clustering of high-dimensional data. FisherEM models and 
        clusters the data in a discriminative and low-dimensional latent subspace. It also provides
        a low-dimensional representation of the clustered data. A sparse version of Fisher-EM
        algorithm is also provided.
fisheyeR provides tools for creating Interactive Data
        Visualizations by implementing ideas from Furnas, Munzner,
        Costa and Venturini.
Model fluxes of carbon, nitrogen, and phosphorus with the use of a coupled 
    bioenergetics and stoichiometric model that incorporates flexible elemental limitation. 
    Additional functions to help the user to find parameters are included. Finally, functions to 
    extract and visualize results are available as well. For an introduction, see vignette. 
    For more information on the theoretical background of this model, 
    see Schiettekatte et al. (2020) <doi:10.1111/1365-2435.13618>.
FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.
Dataset of 302 measurements of 11 fish species to accompany the
    manuscript "Length-weight relationships of six freshwater fish species
    from lake Kirkkojarvi, Finland".
Functions for applying a wide range of fisheries stock assessment methods.
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs.
Functions to predict fish movement parameters plotting leptokurtic fish dispersal kernels (see Radinger and Wolter, 2014: Patterns and predictors of fish dispersal in rivers. Fish and Fisheries. 15:456-473.)
Calculates metabolic rate of fish and other aquatic organisms measured using
             an intermittent-flow respirometry approach. The tool is used to run a set of graphical
             QC tests of raw respirometry data, correct it for background respiration and chamber
             effect, filter and extract target values of absolute and mass-specific metabolic rate.
             Experimental design should include background respiration tests and measuring of one
             or more metabolic rate traits. The package allows a user to import raw respirometry
             data obtained from 'AquaResp' (free software), 'AutoResp' ('LoligoSystems'), 'OxyView'
             ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and 'Q-box Aqua' ('QubitSystems').
An interface to the Fish Tree of Life API to download taxonomies,
    phylogenies, fossil calibrations, and diversification rate information for
    ray-finned fishes.
Implementation of color palettes based on fish species.
Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. 
  This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software 'FisPro' <https://www.fispro.org>. 
  'FisPro' allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system.
Provides functionality for constructing
    statistical models of transcriptomic dynamics in field conditions.
    It further offers the function to predict expression of a gene given 
    the attributes of samples and meteorological data. Nagano, A. J., Sato, 
    Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and 
    Izawa, T. (2012). <doi:10.1016/j.cell.2012.10.048>. Iwayama, K., Aisaka, Y., 
    Kutsuna, N., and Nagano, A. J. (2017). <doi:10.1093/bioinformatics/btx049>.
The fit.models function and its associated methods (coefficients, print,
  summary, plot, etc.) were originally provided in the robust package to compare robustly
  and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002)
  'Robust Library User's Guide' <http://robust.r-forge.r-project.org/Robust.pdf>). The aim
  of the fit.models package is to separate this fitted model object comparison functionality
  from the robust package and to extend it to support fitting methods (e.g., classical,
  robust, Bayesian, regularized, etc.) more generally.
Comprehensive model building function for identification,
        estimation and diagnostic checking for AR and subset AR models.
        Two types of subset AR models are supported.  One family of
        subset AR models, denoted by ARp, is formed by taking subet of
        the original AR coefficients and in the other, denoted by ARz,
        subsets of the partial autocorrelations are used. The main
        advantage of the ARz model is its applicability to very large
        order models.
Implements fast maximum likelihood algorithm for fitting ARMA time series. Uses S3 methods print, summary, fitted, residuals. Fast exact Gaussian ARMA simulation.
Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official
    API, but instead uses the API that the web dashboard uses to generate the graphs
    displayed on the dashboard after login at <http://www.fitbit.com>.
Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an  arbitrary angle and the fit will still succeed. Helper functions are  provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by  least squares" <http://people.cas.uab.edu/~mosya/cl/>; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI,  Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma  (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302.
A pure R package for decoding activity files written in the FIT ("Flexible and Interoperable Data Transfer") format. A format that is fast becoming the standard for recording running and cycling data. Details of the FIT protocol can be found at <https://www.thisisant.com/resources/fit>.
Extends the fitdistr() function (of the MASS package) with several functions 
  to help the fit of a parametric distribution to non-censored or censored data. 
  Censored data may contain left censored, right censored and interval censored values, 
  with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), 
  the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit 
  estimation (MGE) and maximum spacing estimation (MSE) methods (available only for 
  non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. 
  Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction 
  to parametric estimation.
Fits Density Ratio Classes to elicited
        probability-quantile points or intervals.
Robust estimation methods for the mean vector and covariance matrix 
    from data (possibly containing NAs) under multivariate heavy-tailed 
    distributions such as angular Gaussian (via Tyler's method), Cauchy, 
    and Student's t. 
    Additionally, a factor model structure can be specified for the covariance 
    matrix.
    The package is based on the papers: Sun, Babu, and Palomar (2014),
    Sun, Babu, and Palomar (2015), Liu and Rubin (1995), and 
    Zhou, Liu, Kumar, and Palomar (2019).
Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>), also Journal article to use the package(<doi:10.21105/joss.01505>).
Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.
Genotyping assays for bi-allelic markers (e.g. SNPs) produce
	signal intensities for the two alleles. 'fitPoly' assigns genotypes 
	(allele dosages) to a collection of polyploid samples based on these
	signal intensities. 'fitPoly' replaces the older package 'fitTetra' that was
	limited (a.o.) to only tetraploid populations whereas 'fitPoly' accepts any
	ploidy level. Reference: Voorrips RE, Gort G, Vosman B (2011)
	<doi:10.1186/1471-2105-12-172>.
Utilities to read and write files in the FITS (Flexible
  Image Transport System) format, a standard format in astronomy (see
  e.g. <https://en.wikipedia.org/wiki/FITS> for more information).
  Present low-level routines allow: reading, parsing, and modifying
  FITS headers; reading FITS images (multi-dimensional arrays);
  reading FITS binary and ASCII tables; and writing FITS images
  (multi-dimensional arrays).  Higher-level functions allow: reading
  files composed of one or more headers and a single (perhaps
  multidimensional) image or single table; reading tables into
  data frames; generating vectors for image array axes; scaling and
  writing images as 16-bit integers.  Known incompletenesses are
  reading random group extensions, as well as
  bit, complex, and array descriptor data types in binary tables.
Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a 'shiny' app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.
Package fitTetra contains three functions that can be used
        to assign genotypes to a collection of tetraploid samples based
        on bialleleic marker assays. Functions fitTetra (to fit several
        models for one marker from the data and select the best
        fitting) or saveMarkerModels (calls fitTetra for multiple
        markers and saves the results to files) will probably be the
        most convenient to use. Function CodomMarker offers more
        control and fits one specified model for a given marker.
Extends the fitdist() (from 'fitdistrplus') adding the Anderson-Darling ad.test() (from 'ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from 'stats' package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from 'mclust').
Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.
An easy package for scraping and processing Australia Rules Football (AFL)
    data. 'fitzRoy' provides a range of functions for accessing publicly available data 
    from 'AFL Tables' <https://afltables.com/afl/afl_index.html>, 'Footy Wire' <https://www.footywire.com> and
    'The Squiggle' <https://squiggle.com.au>. Further functions allow for easy processing, 
    cleaning and transformation of this data into formats that can be used for analysis.
Datasets and code published by the data journalism website 
    'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>. 
    Note that while we received guidance from editors at 'FiveThirtyEight', this 
    package is not officially published by 'FiveThirtyEight'.
For functions that take and return vectors (or scalars), this package provides 8 algorithms for finding fixed point vectors (vectors for which the inputs and outputs to the function are the same vector). These algorithms include Anderson (1965) acceleration <doi:10.1145/321296.321305>, epsilon extrapolation methods (Wynn 1962 <doi:10.2307/2004051>) and minimal polynomial methods (Cabay and Jackson 1976 <doi:10.1137/0713060>).
Distribution functions and test for over-representation of short
    distances in the Liland distribution. Simulation functions are included for
    comparison.
An R client for the "fixer.io" currency conversion and exchange 
  rate API. The API requires registration and some features are only available 
  on paid accounts. The full API documentation is available at 
  <https://fixer.io/documentation>.
Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial.
    The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://wwwen.uni.lu/content/download/110162/1299525/file/2018_13>.
    Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
Several generalized / directional Fixed Sequence Multiple Testing
    Procedures (FSMTPs) are developed for testing a sequence of pre-ordered
    hypotheses while controlling the FWER, FDR and Directional Error (mdFWER).
    All three FWER controlling generalized FSMTPs are designed under arbitrary
    dependence, which allow any number of acceptances. Two FDR controlling
    generalized FSMTPs are respectively designed under arbitrary dependence and
    independence, which allow more but a given number of acceptances. Two mdFWER
    controlling directional FSMTPs are respectively designed under arbitrary
    dependence and independence, which can also make directional decisions based
    on the signs of the test statistics. The main functions for each proposed
    generalized / directional FSMTPs are designed to calculate adjusted p-values
    and critical values, respectively. For users' convenience, the functions also
    provide the output option for printing decision rules.
An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. 
    It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start 
    and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives 
    interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm
    themselves.
This is a fast and flexible implementation of the Kalman
        filter, which can deal with NAs. It is entirely written in C
        and relies fully on linear algebra subroutines contained in
        BLAS and LAPACK. Due to the speed of the filter, the fitting of
        high-dimensional linear state space models to large datasets
        becomes possible. This package also contains a plot function
        for the visualization of the state vector and graphical
        diagnostics of the residuals.
Fast and flexible Kalman filtering implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter.
Implements the method of Hofmeyr, D.P. (2019) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations.
  Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods
  for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected
  covariates.
Tools and features for "Exploratory Landscape Analysis (ELA)" of
	single-objective continuous optimization problems.
    Those features are able to quantify rather complex properties, such as the
    global structure, separability, etc., of the optimization problems.
Three methods are implemented in R to facilitate the aggregations of flags in official statistics.  
            From the underlying flags the highest in the hierarchy, the most frequent, or with the highest total weight
            is propagated to the flag(s) for EU or other aggregates. Below there are some reference documents for the topic: 
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_v2_1.docx>,
            <https://sdmx.org/wp-content/uploads/CL_CONF_STATUS_1_2_2018.docx>,
            <http://ec.europa.eu/eurostat/data/database/information>,
            <http://www.oecd.org/sdd/33869551.pdf>,
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.pdf>.
Facilitates easier formatting and highlighting of R
    source code in a R Markdown-based presentation. The main goal of the
    package is to allow users to preserve their code creation process
    within code chunks, then to specify formatting details for the source
    code, such as highlighting of particular syntactical elements.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.
Efficient implementations of the algorithms in the 
    Almost-Matching-Exactly framework for interpretable matching in causal
    inference. These algorithms match units via a learned, weighted Hamming
    distance that determines which covariates are more important to match on.
    For more information and examples, see the Almost-Matching-Exactly website.
Provides a variety of original and flexible user-friendly 
    statistical latent variable models for the simultaneous clustering and 
    segmentation of heterogeneous functional data (i.e time series, or more 
    generally longitudinal data, fitted by unsupervised algorithms, including 
    EM algorithms. Functional Latent Data Models for Clustering heterogeneous 
    curves ('FLaMingos') are originally introduced and written in 'Matlab' by
    Faicel Chamroukhi 
    <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. 
    The references are mainly the following ones.
    Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>.
    Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>.
    Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). <doi:10.1109/IJCNN.2011.6033590>.
    Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>.
    Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>.
    Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>.
    Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>.
    Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.
Tools for fluctuations analysis of mutant cells counts.
Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output. For more information, please refer to <https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.
Variable selection algorithm for functional linear regression with scalar response variable and mixed scalar/functional predictors.
Create a flip over style Flash Card with desired data frame for Shiny application.
Fast implementation of hierarchical clustering
Shed light on black box machine learning models by
    the help of model performance, variable importance, global surrogate
    models, ICE profiles, partial dependence (Friedman J. H. (2001)
    <doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W.
    (2016) <arXiv:1612.08468>), further effects plots, scatter plots,
    interaction strength, and variable contribution breakdown (approximate
    SHAP) for single observations (Gosiewska and Biecek (2019)
    <arxiv:1903.11420>). All tools are implemented to work with case
    weights and allow for stratified analysis. Furthermore, multiple
    flashlights can be combined and analyzed together.
Contingency Tables are a pain to work with when you want to run regressions.
  This package takes them, flattens them into a long data frame, so you can more easily analyse them!
  As well, you can calculate other related statistics. All of this is done so in a 'tidy' manner,
  so it should tie in nicely with 'tidyverse' series of packages.
Transformations that allow obtaining a flat table from 
    reports in text or Excel format that contain data in the form of 
    pivot tables. They can be defined for a single report and applied 
    to a set of reports.
On import, the XML information is converted to a dataframe that reflects the hierarchical XML structure. Intuitive functions allow to navigate within this transparent XML data structure (without any knowledge of 'XPath'). 'flatXML' also provides tools to extract data from the XML into a flat dataframe that can be used to perform statistical operations. It also supports converting dataframes to XML.
The main function kcca implements a general framework for
  k-centroids cluster analysis supporting arbitrary distance measures
  and centroid computation. Further cluster methods include hard
  competitive learning, neural gas, and QT clustering. There are
  numerous visualization methods for cluster results (neighborhood
  graphs, convex cluster hulls, barcharts of centroids, ...), and
  bootstrap methods for the analysis of cluster stability.
Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. 
            Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.
Format for converting an R Markdown document to a grid oriented
  dashboard. The dashboard flexibly adapts the size of it's components to the
  containing web page.
Provides tools to work with the Flexible Dirichlet
    distribution. The main features are an E-M algorithm for computing the maximum
    likelihood estimate of the parameter vector and a function based on conditional
    bootstrap to estimate its asymptotic variance-covariance matrix. It contains
    also functions to plot graphs, to generate random observations and to handle
    compositional data.
Standard generalized additive models assume a response function, 
    which induces an assumption on the shape of the distribution of the 
    response. However, miss-specifying the response function results in biased 
    estimates. Therefore in Spiegel et al. (2017) 
    <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function
    jointly with the covariate effects. This package provides the underlying 
    functions to estimate these generalized additive models with flexible 
    response functions. The estimation is based on an iterative algorithm. In 
    the outer loop the response function is estimated, while in the inner loop 
    the covariate effects are determined. For the response function a strictly
    monotone P-spline is used while the covariate effects are estimated based on
    a modified Fisher-Scoring algorithm. Overall the estimation relies on the 
    'mgcv'-package.
Provides functions to switch the 'BLAS'/'LAPACK' optimized backend
    and change the number of threads without leaving the R session, which needs
    to be linked against the 'FlexiBLAS' wrapper library
    <https://www.mpi-magdeburg.mpg.de/projects/flexiblas>.
Application of the filtered monotonic polynomial (FMP) item response
    model to flexibly fit item response models. The package includes tools that
    allow the item response model to be build on any monotonic transformation of
    the latent trait metric, as described by Feuerstahler (2016) <http://hdl.handle.net/11299/182267>.
A general framework for finite mixtures of regression
  models using the EM algorithm is implemented. The E-step and all
  data handling are provided, while the M-step can be supplied by the
  user to easily define new models. Existing drivers implement
  mixtures of standard linear models, generalized linear models and
  model-based clustering.
The fitting of mixtures of generalized nonlinear models is implemented as an extension of the existing package 'flexmix'.
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.
Binding to the C++ implementation of the flexible polyline
    encoding by HERE <https://github.com/heremaps/flexible-polyline>. The
    flexible polyline encoding is a lossy compressed representation of a list of
    coordinate pairs or coordinate triples. The encoding is achieved by:
    (1) Reducing the decimal digits of each value;
    (2) encoding only the offset from the previous point;
    (3) using variable length for each coordinate delta; and
    (4) using 64 URL-safe characters to display the result.
Functions to fit regression models for bounded responses, such as rates and proportions. Available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), and the beta one (Ferrari, S.L.P., and Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>). Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A.; Carlin, J. B.; Stern, H. S. and Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness-of-fit measures, convergence diagnostics, and graphical representations are provided.
Package for parametric relative survival analyses. It allows to model non-linear and 
        non-proportional effects using splines (B-spline and truncated power basis). It also includes 
        both non proportional and non linear effects of 
			Remontet, L. et al. (2007) <DOI:10.1002/sim.2656> and 
			Mahboubi, A. et al. (2011) <DOI:10.1002/sim.4208>.
An easy way to conduct flexible scan.
    Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile.
    A table with formal style and a map with clusters are included in the result report.
    The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
Flexible parametric models for time-to-event data,
    including the Royston-Parmar spline model, generalized gamma and
    generalized F distributions.  Any user-defined parametric
    distribution can be fitted, given at least an R function defining
    the probability density or hazard. There are also tools for
    fitting and predicting from fully parametric multi-state models,
    based on either cause-specific hazards or mixture models.
Flexible parametric mixture and non-mixture cure models for time-to-event data.
Create pretty tables for 'HTML', 'PDF', 'Microsoft Word' and 'Microsoft PowerPoint' 
  documents from 'R Markdown'. Functions are provided to let users create tables, modify and format 
  their content. It also extends package 'officer' that does not contain any feature for customized 
  tabular reporting.
Provides an interface to the Flickr API <https://www.flickr.com/services/api/>
      and allows R users to download data on Flickr.
Functions to create and manipulate 
    FIFO (First In First Out), LIFO (Last In First Out), and NINO (Not In or Never Out) 
    stacks in R.
Utility functions for creating flight plans for unmanned aerial vehicles (UAV), specially for the Litchi Hub platform. It calculates the flight and camera settings based on the camera specifications, exporting the flight plan CSV format ready to import into Litchi Hub.
Provides functionality to plot airplane flight paths on maps. The plotted flight paths follow the great circle of the Earth.
Spatio-temporal locations of an animal are computed 
    from annotated data with a hidden Markov  model via particle
    filter algorithm. The package is relatively robust to varying
    degrees of shading.
    The hidden Markov model is described in Movement Ecology (Rakhimberdiev et al., 2015) <doi:10.1186/s40462-015-0062-5>,
    general package description is in the Methods in Ecology and Evolution (Rakhimberdiev et al., 2017) <doi:10.1111/2041-210X.12765>
    and package accuracy assessed in the Journal of Avian Biology (Rakhimberdiev et al. 2016) <doi:10.1111/jav.00891>.
It implements many univariate and multivariate permutation (and
    rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear
    models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney,
    Kruskal-Wallis), Sign test and Mc Nemar. Test on Linear Models are
    performed also in presence of covariates (i.e. nuisance parameters).
    The permutation and the rotation methods to get the null distribution of
    the test statistics are available. It also implements methods for
    multiplicity control such as Westfall & Young minP procedure and Closed
    Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed
    effects in mixed effects models.
Allows the user to create a countdown in 'RMarkdown' documents and 'shiny' applications. 
    The package is a wrapper of the 'JavaScript' library 'flipdown.js'. See <https://pbutcher.uk/flipdown/> for more info.
The lipid scrambling activity of protein extracts and purified
    scramblases is often determined using a fluorescence-based assay involving
    many manual steps. flippant offers an integrated solution for the analysis
    and publication-grade graphical presentation of dithionite scramblase
    assays, as well as a platform for review, dissemination and extension of the
    strategies it employs. The package's name derives from a play on the fact
    that lipid scrambling is also sometimes referred to as 'flipping'.
    The package is originally published as Cotton, R.J., Ploier, B., Goren,
    M.A., Menon, A.K., and Graumann, J. (2017). flippant–An R package for the
    automated analysis of fluorescence-based scramblase assays. BMC
    Bioinformatics 18, 146. <DOI: 10.1186/s12859-017-1542-y>.
A flexible permutation framework for hypothesis 
  testing, ANOVA and regression analysis of complex data.
Provides robust tests for testing in GLMs, by sign-flipping score contributions. The tests are robust against overdispersion, heteroscedasticity and, in some cases, ignored nuisance variables. See Hemerik, Goeman and Finos (2020) <doi:10.1111/rssb.12369>.
Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV).  Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample.  Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
R comes with a suite of utilities for linear algebra with "numeric"
    (double precision) vectors/matrices. However, sometimes single precision (or
    less!) is more than enough for a particular task.  This package extends R's
    linear algebra facilities to include 32-bit float (single precision) data.
    Float vectors/matrices have half the precision of their "numeric"-type
    counterparts but are generally faster to numerically operate on, for a
    performance vs accuracy trade-off.  The internal representation is an S4
    class, which allows us to keep the syntax identical to that of base R's.
    Interaction between floats and base types for binary operators is generally
    possible; in these cases, type promotion always defaults to the higher
    precision.  The package ships with copies of the single precision 'BLAS' and
    'LAPACK', which are automatically built in the event they are not available
    on the system.
Converts files to and from flobs. 
    A flob is a file that was 
    read into binary in integer-mode as little endian, 
    saved as the single element of a named list (where the name is the name 
    of the original file) and then serialized before being coerced into a blob.
    Flobs are useful for writing and reading files to and from databases.
Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.
Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations).
	For details see Kinsvater et al. (2017) <arXiv:1701.06455>.
Tools to quickly compile taxonomic and distribution data from
    the Brazilian Flora 2020.
The functions return sampling parameters for forest inventories with tables and graphics. Methods used in the 
  package refers to Pellico e Brena (1997) <https://bit.ly/2BDbHJI>.
Flowering is an important life history trait of flowering plants. It has been mainly analyzed with respect to flowering onset and duration of flowering. This tools provide some functions to compute the temporal distribution of an flowering individual related to other population members. fCV() measures the temporal variation in flowering. RIind() measures the rank order of flowering for individual plants within a population. SI(), SI2(), SI3(), and SI4() calculate flowering synchrony with different methods.
Flow field forecasting draws information from an interpolated flow field of the observed time series to incrementally build a forecast.
This framework allows you to design and implement complex
    pipelines, and deploy them on your institution's computing cluster. This has
    been built keeping in mind the needs of bioinformatics workflows. However, it is
    easily extendable to any field where a series of steps (shell commands) are to
    be executed in a (work)flow.
An application to calculate the daily environmental costs of river flow regulation by dams based on García de Jalon et al. 2017 <doi:10.1007/s11269-017-1663-0>.
Screens daily streamflow time series for temporal trends and
    change-points. This package has been primarily developed for assessing the
    quality of daily streamflow time series. It also contains tools for plotting
    and calculating many different streamflow metrics. The package can be used to
    produce summary screening plots showing change-points and significant temporal
    trends for high flow, low flow, and/or baseflow statistics, or it can be used
    to perform more detailed hydrological time series analyses. The package was
    designed for screening daily streamflow time series from Water Survey Canada
    and the United States Geological Survey but will also work with streamflow time
    series from many other agencies.
Implements a path algorithm for the Fused Lasso Signal Approximator. 
    For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>.
Specialized solvers for combinatorial optimization problems in the Subset Sum family. These solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value sets with predefined subset sum errors, and (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The multi-threaded framework for the latter offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. Historical updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) an option of mapping real-domain problems to the integer domain with user-controlled precision loss, and those integers are further zipped non-uniformly in 64-bit buffers. Arithmetic on compressed integers is done by bit-manipulation and the design has virtually zero speed lag relative to normal integer arithmetic. Reduction in dimensionality from the compression may yield substantial acceleration; (e) distributed computing infrastructure for multidimensional subset sum. Compilation with g++ '-Ofast' is recommended. See package vignette (<arXiv:1612.04484v3>) for details. Functions prefixed with 'aux' (auxiliary) are or will be implementations of existing foundational or cutting-edge algorithms for solving optimization problems of interest.
Functions to estimate the mortality attributable to 
    influenza and temperature, using distributed-lag nonlinear models
    (DLNMs), as first implemented in 
    Lytras et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.14.1800118>.
    Full descriptions of underlying DLNM methodology in Gasparrini et al. 
    <doi:10.1002/sim.3940> (DLNMs), 
    <doi:10.1186/1471-2288-14-55> (attributable risk from DLNMs) and 
    <doi:10.1002/sim.5471> (multivariate meta-analysis).
Use spectrophotometry measurements performed on insects as a way to infer pathogens 
      virulence. Insect movements cause fluctuations in fluorescence signal, and functions are 
      provided to estimate when the insect has died as the moment when variance in autofluorescence 
      signal drops to zero. The package provides functions to obtain this estimate together with 
      functions to import spectrophotometry data from a Biotek microplate reader. Details of the method 
      are given in Parthuisot et al. (2018) <doi:10.1101/297929>.
A model for leaf fluorescence, reflectance and transmittance spectra. It implements
    the model introduced by Vilfan et al. (2016) <DOI:10.1016/j.rse.2016.09.017>. Fluspect-B
    calculates the emission of ChlF on both the illuminated and shaded side of the leaf.
    Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter 
    and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF 
    quantum efficiency for the two photosystems, PS-I and PS-II.
Functions for the calculation of greenhouse gas flux rates 
	from closed chamber concentration measurements. The package follows 
	a modular concept: Fluxes can be calculated in just two simple steps 
	or in several steps if more control in details is wanted. Additionally 
	plot and preparation functions as well as functions for modelling
	gpp and reco are provided.
Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.
Functions for range estimation in birds based on Pennycuick (2008)
    and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008)
    requires manual entry of birds which can be tedious when there are thousands
    of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975)
    and time-marching computation method "constant muscle mass" as in Pennycuick (1998).
    See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>,
    and Pennycuick (2008, ISBN:9780080557816).
Functions for range estimation in birds based on Pennycuick (2008)
    and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008)
    requires manual entry of birds which can be tedious when there are hundreds
    of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975)
    and time-marching computation methods as in Pennycuick (1998) and Pennycuick (2008).
    See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>,
    and Pennycuick (2008, ISBN:9780080557816).
All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.
Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.
Implements basic financial market objects like currencies, currency
  pairs, interest rates and interest rate indices. You will be able to use
  Benchmark instances of these objects which have been defined using their most
  common conventions or those defined by International Swap Dealer Association
  (ISDA, <https://www.isda.org>) legal documentation.
Generate cost effective minimally changed run sequences 
              for symmetrical as well as asymmetrical factorial 
              designs.
Current status data abounds
            in the field of epidemiology and public health, where the only observable data for a subject is
            the random inspection time and the event status at inspection. Motivated by such a current status
            data from a periodontal study where data are inherently clustered,
            we propose a unified methodology to analyze such complex data.
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC)
         framework for implementing Metropolis-Hastings algorithm in a modular way
         allowing users to specify automatic convergence checker, personalized
         transition kernels, and out-of-the-box multiple MCMC chains using
         parallel computing. Most of the methods implemented in this package can
         be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods
         included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5>
         Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5>
         Robust Adaptive Metropolis, and Thawornwattana et
         al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.
Implements common date calculations relevant for specifying
  the economic nature of financial market contracts that are typically defined
  by International Swap Dealer Association (ISDA, <http://www2.isda.org>) legal
  documentation. This includes methods to check whether dates are business
  days in certain locales, functions to adjust and shift dates and time length
  (or day counter) calculations.
Provides functions to help in fitting models to data, to
  perform Monte Carlo, sensitivity and identifiability analysis. It is
  intended to work with models be written as a set of differential
  equations that are solved either by an integration routine from
  package 'deSolve', or a steady-state solver from package
  'rootSolve'. However, the methods can also be used with other types of
  functions.
Heterogeneity pursuit methodologies for regularized finite mixture regression by effects-model formulation proposed by Li et al. (2021) <arXiv:2003.04787>.
A fast class noise detector which provides noise score for each observations. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
Provides a collection of functions to fit and explore single, multi-component and restricted Frequency Modulated Moebius (FMM) models. 'FMM' is a nonlinear parametric regression model capable of fitting non-sinusoidal shapes in rhythmic patterns. Details about the mathematical formulation of 'FMM' models can be found in Rueda et al. (2019) <doi:10.1038/s41598-019-54569-1>.
Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.
Use R to access to the 'FMP Cloud' API <https://fmpcloud.io/> and 
    'Financial Modeling Prep' API <https://financialmodelingprep.com/developer/docs/>.
    Data available includes stock prices, market indexes, company fundamentals,
    13F holdings data, and much more. A valid API token must be set to enable
    functions.
Functions that support stable prediction and classification with radiomics data through factor-analytic modeling. For details, see Peeters et al. (2019) <arXiv:1903.11696>.
Contains R-functions to perform an fMRI analysis as described in
             Polzehl and Tabelow (2019) <DOI:10.1007/978-3-030-29184-6>,
             Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>,
             Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>,
             Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Methods for performing fMRI quality assurance (QA) measurements of
  test objects. Heavily  based on the fBIRN procedures detailed by Friedman and 
  Glover (2006) <doi:10.1002/jmri.20583>.
Several utility functions for the book entitled 
	"Practices of Medical and Health Data Analysis using R"
	(Pearson Education Japan, 2007) with Japanese demographic
	data and some demographic analysis related functions.
Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.
This package implements some basic procedures for dealing
        with log maximally skew stable distributions, which are also
        called finite moment log stable distributions.
Contains a set of functions that can be used to apply
  formats to data frames or vectors.  The package aims to provide to 
  functionality similar to that of SAS® formats. Formats are assigned to
  the format attribute on data frame columns.  Then when the fdata() 
  function is called, a new data frame is created with the column data
  formatted as specified.  The package also contains a value() function
  to create a user-defined format, similar to a SAS® user-defined format.
Provides a  collection of functions 
	to manage, to investigate and to analyze bivariate and multivariate 
	data sets of financial returns.
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
        including KNN classification, regression and information measures are implemented.
Provides a collection of functions for testing various aspects of
	univariate time series including independence and neglected
	nonlinearities. Further provides functions to investigate the chaotic
	behavior of time series processes and to simulate different types of chaotic
	time series maps.
Feature Ordering by Conditional Independence (FOCI) is a variable selection algorithm based on the measure of conditional dependence.
    For more information, see the paper: Azadkia and Chatterjee (2019),"A simple measure of conditional dependence" <arXiv:1910.12327>.
Takes a distance matrix and plots it as an 
    interactive graph. One point is focused at the center of the graph,
    around which all other points are plotted in their exact distances as
    given in the distance matrix. All other non-focus points are plotted 
    as best as possible in relation to one another. Double click on any 
    point to choose a new focus point, and hover over points to see their
    ID labels. If color label categories are given, hover over colors in 
    the legend to highlight only those points and click on colors to 
    highlight multiple groups. For more information on the rationale 
    and mathematical background, as well as an interactive introduction,
    see <https://lea-urpa.github.io/focusedMDS.html>.
The CRAN check results and where your package stands in the
    CRAN submission queue in your R terminal.
Fits continuous-time random walk and correlated random walk state-space models for quality control animal tracking data ('Argos', processed light-level 'geolocation', 'GPS'). Template Model Builder ('TMB') is used for fast estimation. The 'Argos' data can be: (older) least squares-based locations; (newer) Kalman filter-based locations with error ellipse information; or a mixture of both. The models estimate two sets of location states corresponding to: 1) each observation, which are (usually) irregularly timed; and 2) user-specified time intervals (regular or irregular). Latent variable models are provided to estimate move persistence along tracks as an index of behaviour. 'Jonsen I', 'McMahon CR', 'Patterson TA', 'Auger-Méthé M', 'Harcourt R', 'Hindell MA', 'Bestley S' (2019) Movement responses to environment: fast inference of variation among southern elephant seals with a mixed effects model. Ecology 100:e02566 <doi:10.1002/ecy.2566>.
If you find yourself working on multiple different projects in R, you'll want a 
	series of folders pointing to raw data, processed data, plot results, intermediate table 
	outputs, etc. This package makes it easier to do that by providing a quick and easy way 
	to create and use functions for project-level directories.
Datasets for teaching quantitative approaches and
    modeling in archaeology and paleontology. This package provides
    several types of data related to broad topics (cultural evolution,
    radiocarbon dating, paleoenvironments, etc.), which can be used to
    illustrate statistical methods in the classroom (multivariate data
    analysis, compositional data analysis, diversity measurement, etc.).
Easily and flexibly insert 'Font Awesome' icons into 'R Markdown'
    documents and 'Shiny' apps. These icons can be inserted into HTML content
    through inline 'SVG' tags or 'i' tags. There is also a utility function for
    exporting 'Font Awesome' icons as 'PNG' images for those situations where
    raster graphics are needed.
Provides fonts licensed under the 'Bitstream Vera Fonts'
    license for the 'fontquiver' package.
Computer Modern font for use with extrafont package
Provides 'ggplot2' themes based on the 'Hind' fonts.
  'Hind' is an open source 'typeface' supporting the 'Devanagari' and Latin scripts.
  Developed explicitly for use in User Interface design, the 'Hind' font family includes five styles.
  More information about the font can be found at <https://fonts.google.com/specimen/Hind>.
A placeholder for the Liberation fontset intended for the
    `fontquiver` package. This fontset covers the 12 combinations of
    families (sans, serif, mono) and faces (plain, bold, italic, bold
    italic) supported in R graphics devices.
Provides 'ggplot2' themes based on the 'M+' fonts.
  The 'M+' fonts are a font family under a free license. The font family provides
  multilingual glyphs. The fonts provide 'Kana', over 5,000 'Kanji', Basic Latin,
  Latin-1 Supplement, Latin Extended-A, and 'IPA' Extensions glyphs. Most of the Greek,
  Cyrillic, Vietnamese, and extended glyphs and symbols are included too.
  So the fonts are in conformity with ISO-8859-1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 16,
  Windows-1252, T1, and VISCII encoding.
  More information about the fonts can be found at <http://mplus-fonts.osdn.jp/about-en.html>.
Provides a set of fonts with permissive licences. This is
    useful when you want to avoid system fonts to make sure your
    outputs are reproducible.
Displays a weighted undirected food graph from an adjacency matrix.
    Can perform confidence-interval bootstrap inference with mutual information
    or maximal information coefficient.
    Based on my Master 1 internship at the Bordeaux Population Health center.
    References : Reshef et al. (2011) <doi:10.1126/science.1205438>,
    Meyer et al. (2008) <doi:10.1186/1471-2105-9-461>,
    Liu et al. (2016) <doi:10.1371/journal.pone.0158247>.
Calculates twelve commonly-used, basic measures of food
        web network structure from binary, predator-prey matrices:
        species richness, connectance, total number of links, link
        density, number of trophic positions, predator:prey ratio, and
        fraction of carnivores, herbivores, top species and
        intermediate species.  Employs food web language in the code
        and output, translates between a couple of common food web
        formats, can handle food webs consisting of multiple levels,
        and can automate the analysis for a large number of webs. The
        program produces 3-dimensional graphs of high quality that can
        be customized by the user.
Provides functionality for manipulating functions and translating them
    in metaprogramming.
A handy tool to calculate carbon footprints from
    air travel based on three-letter International Air Transport Association (IATA) airport codes or latitude and longitude.
    footprint first calculates the great-circle distance between departure and arrival 
    destinations. It then uses the Department of Environment, Food & Rural Affairs (DEFRA) 
    greenhouse gas conversion factors for business air travel to estimate the carbon footprint.
    These conversion factors consider trip length, flight class (e.g. economy, business), and emissions 
    metric (e.g. carbon dioxide equivalent, methane).
Provides a collection of functions to 
    valuate basic options. This includes the generalized
	Black-Scholes option, options on futures and options on
	commodity futures.
SHE, FORAM Index and ABC Method analyses and custom plot
        functions for community data.
Helpers for reordering factor levels (including
    moving specified levels to front, ordering by first appearance,
    reversing, and randomly shuffling), and tools for modifying factor
    levels (including collapsing rare levels into other, 'anonymising',
    and manually 'recoding').
Support for the foreach looping construct.  Foreach is an
        idiom that allows for iterating over elements in a collection,
        without the use of an explicit loop counter.  This package in
        particular is intended to be used for its return value, rather
        than for its side effects.  In that sense, it is similar to the
        standard lapply function, but doesn't require the evaluation
        of a function.  Using foreach without side effects also
        facilitates executing the loop in parallel.
Implementation of Forecastable Component Analysis ('ForeCA'),
    including main algorithms and auxiliary function (summary, plotting, etc.) to
    apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension
    reduction (DR) technique for temporally dependent signals. Contrary to other
    popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency
    explicitly into account and searches for the most ''forecastable'' signal.
    The measure of forecastability is based on the Shannon entropy of the spectral
    density of the transformed signal.
Methods and tools for displaying and analysing
             univariate time series forecasts including exponential smoothing
             via state space models and automatic ARIMA modelling.
Provides geometric- and regression-based forecast
    combination methods under a unified user interface for the packages 'ForecastCombinations'
    and 'GeomComb'. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity). For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>,
    Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>, 
    and Clemen RT (1989). <doi:10.1016/0169-2070(89)90012-5>.
Aim: Supports the most frequently used methods to combine forecasts. Among others: Simple average, Ordinary Least Squares, Least Absolute Deviation, Constrained Least Squares, Variance-based, Best Individual model, Complete subset regressions and Information-theoretic (information criteria based).
Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights
    based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>),
    or cross-validated weights. Cross validation for time series data with user-supplied models
    and forecasting functions is also supported to evaluate model accuracy.
The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
Methods to compute linear h-step ahead prediction coefficients based
    on localised and iterated Yule-Walker estimates and empirical mean squared
    and absolute prediction errors for the resulting predictors. Also, functions
    to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time
    series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics,
    forthcoming. Preprint <arXiv:1611.04460>.
Provides a test bench for the comparison of forecasting
    methods in uni-variate time series. Forecasting methods are compared using 
    different error metrics. Proposed forecasting methods and alternative error 
    metrics can be used. Detailed discussion is provided in the vignette.
Provides classical (bottom-up), optimal and heuristic combination 
    forecast reconciliation procedures for cross-sectional, temporal, and 
    cross-temporal linearly constrained time series.
Routines for forecasting univariate time series using Theta Models. Contains several cross-validation routines.
Reading and writing data stored by some versions of
	'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
	and for reading and writing some 'dBase' files.
The statistical evaluation of DNA mixtures, DNA profile
        match probability
Statistical methods and simulation tools for the
        interpretation of forensic DNA mixtures
A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) <doi:10.1029/2012EO410001> and Prudhomme et al. (2010) <doi:10.1016/j.jhydrol.2010.06.043>). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) <doi:10.1002/2015WR018253>) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) <doi:10.1016/j.jhydrol.2016.03.025>). It incorporates 'Richardson-type' weather generator model configurations documented in Richardson (1981) <doi:10.1029/WR017i001p00182>, Richardson and Wright (1984), as well as latent variable type model configurations documented in Bennett et al. (2018) <doi:10.1016/j.jhydrol.2016.12.043>, Rasmussen (2013) <doi:10.1002/wrcr.20164>, Bennett et al. (2019) <doi:10.5194/hess-23-4783-2019> to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances.
Approximate false positive rate control in selection frequency for
    random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>.
    Methods for calculating the selection frequency threshold at false positive rates
    and selection frequency false positive rate feature selection.
Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.
Estimates the conditional error distributions of random forest
    predictions and common parameters of those distributions, including
    conditional mean squared prediction errors, conditional biases, and 
    conditional quantiles, by out-of-bag weighting of out-of-bag prediction
    errors as proposed by Lu and Hardin (2021). This package is compatible
    with several existing packages that implement random forests in R.
Developed for the following tasks. 1 ) Computing the probability density function,
             cumulative distribution function, random generation, and estimating the parameters
 			 of the eleven mixture models. 2 ) Point estimation of the parameters of two - 
			 parameter Weibull distribution using twelve methods and three - parameter Weibull 
			 distribution using nine methods. 3 ) The Bayesian inference for the three - 
			 parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter
			 Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to
			 grouped data using three methods including approximated maximum likelihood, 
			 expectation maximization, and maximum likelihood. 5 ) Estimating the parameters
			 of the gamma, log-normal, and Weibull mixture models fitted to the grouped data
			 through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve
			 fitted to the height - diameter observation, 7 ) Estimating parameters, computing
			 probability density function, cumulative distribution function, and generating
			 realizations from gamma shape mixture model introduced by Venturini et al. (2008)
			 <doi:10.1214/07-AOAS156> , 8 ) The Bayesian inference, computing probability
			 density function, cumulative distribution function, and generating realizations
			 from four-parameter Johnson SB distribution, 9 ) Robust multiple linear regression
			 analysis when error term follows skewed t distribution, and 10 ) Estimating 
			 parameters of a given distribution fitted to grouped data using method of maximum
			 likelihood.
Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps.
Assessing forest ecosystem health is an effective way for forest
    resource management.The national forest health evaluation system at the forest 
    stand level using analytic hierarchy process, has a high application value 
    and practical significance. The package can effectively and easily realize the 
    total assessment process, and help foresters to further assess and management 
    forest resources.
Extensive global and small-area estimation procedures for multiphase
    forest inventories under the design-based Monte-Carlo approach are provided.
    The implementation has been published in the Journal of Statistical Software (<doi:10.18637/jss.v097.i04>) 
    and includes estimators for simple and cluster sampling
    published by Daniel Mandallaz in 2007 (<doi:10.1201/9781584889779>),
    2013 (<doi:10.1139/cjfr-2012-0381>, <doi:10.1139/cjfr-2013-0181>,
    <doi:10.1139/cjfr-2013-0449>, <doi:10.3929/ethz-a-009990020>)
    and 2016 (<doi:10.3929/ethz-a-010579388>). It provides point estimates,
    their external- and design-based variances and confidence intervals, as well as a
    set of functions to analyze and visualize the produced estimates.
    The procedures have also been optimized for the use of remote sensing data
    as auxiliary information, as demonstrated in 2018 by Hill et al. (<doi:10.3390/rs10071052>).
Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>.
Produces forest plots using 'ggplot2' from models produced by functions
    such as stats::lm(), stats::glm() and survival::coxph().
A forest plot that allows for
    multiple confidence intervals per row,
    custom fonts for each text element,
    custom confidence intervals,
    text mixed with expressions, and more.
    The aim is to extend the use of forest plots beyond meta-analyses.
    This is a more general version of the original 'rmeta' package's forestplot()
    function and relies heavily on the 'grid' package.
Provides a toolkit for calculating forest and canopy structural complexity metrics from
    terrestrial LiDAR (light detection and ranging). References:  Atkins et al. 2018 <doi:10.1111/2041-210X.13061>; Hardiman et al. 2013 <doi:10.3390/f4030537>;
    Parker et al. 2004 <doi:10.1111/j.0021-8901.2004.00925.x>.
Provides functions that calculates common types of splitting
    criteria used in random forests for classification problems, as well as 
    functions that make predictions based on a single tree or a Forest-R.K. model; 
    the package also provides functions to generate importance plot for a 
    Forest-R.K. model, as well as the 2D multidimensional-scaling plot of 
    data points that are colour coded by their predicted class types by the 
    Forest-R.K. model. This package is based on: 
    Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) 
    "Forest-R.K.: A New Random Forest Induction Method", 
    Fourth International Conference on Intelligent Computing,
    September 2008, Shanghai, China, pp.430-437.
A series of utility functions to help with 
    reshaping hierarchy of data tree, and reform the structure of data tree.
Provides tools for analyzing remotely sensed forest data, including functions for detecting treetops from canopy models (Popescu & Wynne, 2004), outlining tree crowns (Meyer & Beucher, 1990) and generating spatial statistics.
Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class 
    Discrete Weibull Models.This package is based on Fader and Hardie (2007) 
    <doi:10.1002/dir.20074> and Fader and Hardie et al. (2018) <doi:10.1016/j.intmar.2018.01.002>.
Helper functions with a consistent interface to coerce and verify
    the types and shapes of values for input checking.
The goal of this collection of functions is to provide an easy to use tool for the measurement of foraminifera and other unicellulars organisms size. With functions developed to guide foraminiferal test biovolume calculations and cell biomass estimations. The volume function includes several microalgae models geometric adaptations based on Hillebrand et al. (1999) <doi:10.1046/j.1529-8817.1999.3520403.x>, Sun and Liu (2003) <doi:10.1093/plankt/fbg096> and Vadrucci, Cabrini and Basset (2007) <doi:10.1285/i1825229Xv1n2p83>.
Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm
This package provides estimates of tree volume and biomass from
    Italian NFI models
Provides a function tidy_source() to format R source code. Spaces
    and indent will be added to the code automatically, and comments will be
    preserved under certain conditions, so that R code will be more
    human-readable and tidy. There is also a Shiny app as a user interface in
    this package (see tidy_app()).
Provides functions to create formattable vectors and data frames.
    'Formattable' vectors are printed with text formatting, and formattable
    data frames are printed with multiple types of formatting in HTML
    to improve the readability of data presented in tabular form rendered in
    web pages.
Infrastructure for extended formulas with multiple parts on the
             right-hand side and/or multiple responses on the left-hand side
	     (see <doi:10.18637/jss.v034.i01>).
These utilities facilitate the programmatic manipulations of
    formulas, expressions, calls, assignments and other R language objects. 
    These objects all share the same structure: a left-hand side, operator and 
    right-hand side. This packages provides methods for accessing and 
    modifying this structures as well as extracting and replacing names and 
    symbols from these objects.
Many statistical models and analyses in R are implemented through formula objects. The formulaic package creates a unified approach for programmatically and dynamically generating formula objects. Users may specify the outcome and inputs of a model directly, search for variables to include based upon naming patterns, incorporate interactions, and identify variables to exclude. A wide range of quality checks are implemented to identify issues such as misspecified variables, duplication, a lack of contrast in the inputs, and a large number of levels in categorical data.  Variables that do not meet these quality checks can be automatically excluded from the model.  These issues are documented and reported in a manner that provides greater accountability and useful information to guide an investigation of the data.
Perform mathematical operations on R formula (add, subtract, multiply, etc.) and substitute parts of formula.
Forensic applications of pedigree analysis, including likelihood ratios 
    for relationship testing, general relatedness inference, marker simulation, and 
    power analysis. General computation of exclusion powers is based on Egeland et 
    al. (2014) <doi:10.1016/j.fsigen.2013.05.001>. Several functions deal 
    specifically with family reunion cases, implementing and developing ideas from 
    Kling et al. (2017) <doi:10.1016/j.fsigen.2017.08.006>. A novelty of 'forrel' 
    is the ability to model background inbreeding in forensic pedigree computations.
    This can have significant impact in applications, as exemplified in Vigeland 
    and Egeland (2019) <doi:10.1016/j.fsigss.2019.10.175>. 'forrel' is part of the
    ped suite, a collection of packages for pedigree analysis. In particular, 
    'forrel' imports 'pedtools' for creating and manipulating pedigrees and markers,
    'pedprobr' for likelihood computations, and 'pedmut' for mutation modelling. 
    Pedigree data may be created from scratch, or loaded from text files. Data 
    import from the 'Familias' software (Egeland et al. (2000) 
    <doi:10.1016/S0379-0738(00)00147-X>) is supported.
Process automation of Terrestrial Laser Scanner (TLS) point cloud data derived from single scans. 'FORTLS' enables (i) detection of trees and estimation of diameter at breast height (dbh), (ii) estimation of some stand variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories (FIs) at stand level and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2020, <doi:10.3390/IECF2020-08066>).
A collection of fortunes from the R community.
Robust analysis using forward search in linear and
        generalized linear regression models, as described in Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer.
Anonymized data from surveys conducted by Forwards <https://forwards.github.io/>, the R Foundation task force on women and other under-represented groups. Currently, a single data set of responses to a survey of attendees at useR! 2016 <https://www.r-project.org/useR-2016/>, the R user conference held at Stanford University, Stanford, California, USA, June 27 - June 30 2016.
Forward Search analysis of time series regressions. Implements the asymptotic theory developed in Johansen and Nielsen (2013, 2014).
A set of analytical tools useful in analysing ecological and geographical data sets, both ancient and modern. The package includes functions for estimating species richness (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta diversity, species area curves and geographic distances and areas.
Simulating taxonomy and fossil data on phylogenetic trees under mechanistic
    models of speciation, preservation and sampling.
Set of tools to streamline the modeling of the relationship between 
    satellite imagery time series or any other environmental information, 
    such as terrain elevation, with forest structural attributes derived from 
    3D point cloud data and their subsequent imputation over the broader 
    landscape.
The Fourier Transform Textural Ordination method 
  uses a principal component analysis on radially averaged
  two dimensional Fourier spectra to characterize image texture.
The four-gamete test is based on the infinite-sites model which assumes that the probability of the same mutation occurring twice (recurrent or parallel mutations) and the probability of a mutation back to the original state (reverse mutations) are close to zero. Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility.
Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation.
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> 
    four parameter IRT model with lower and upper asymptotes using Bayesian
    formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.
Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.
Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Standardisation of cluster validation statistics by random clusterings and 
  comparison between many clustering methods and numbers of clusters based on
  this.  
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.
A geometric approach to MLE for functional principal
        components
Compute the two dimension functional principal component scores for a series of two dimension images.
Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.
Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    'The R Inferno' <http://www.burns-stat.com/pages/Tutor/R_inferno.pdf>).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. GPDC and TPDC uses a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets.
Tools to help text files importation. It can return 
 the number of lines; print the first and last lines; convert 
 encoding. Operations are made without reading the entire file 
 before starting, resulting in good performances with large files. 
 This package provides an alternative to a simple use of the 
 'head', 'tail', 'wc' and 'iconv' programs that are not always 
 available on machine where R is installed.
Given the values of sampled units and selection probabilities 
             the desraj function  in the package computes the estimated value of the total
             as well as estimated variance.
Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and boxplots.
Read and process a large delimited file block by
    block. A block consists of all the contiguous rows that have the same value
    in the first field. The result can be returned as a list or a data.table,
    or even directly printed to an output file.
Algorithms for detection of outliers based on frequent pattern
    mining. Such algorithms follow the paradigm: if an instance contains more frequent patterns, 
    it means that this data instance is unlikely to be an anomaly (He Zengyou, Xu Xiaofei, Huang 
    Zhexue Joshua, Deng Shengchun (2005) <doi:10.2298/CSIS0501103H>).
    The package implements a list of existing state of the art algorithms as well as other published 
    approaches: FPI, WFPI, FPOF, FPCOF, LFPOF, MFPOF, WCFPOF and WFPOF.
A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles.
  The 'fpop'  package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017
  <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated 
  in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error 
  for a penalty linear in the number of changepoints.
Provides a collection
	of functions to optimize portfolios and to analyze them from
    different points of view.
Returns the noncentrality parameter of the noncentral F
        distribution if probability of type I and type II error,
        degrees of freedom of the numerator and the denominator are
        given.  It may be useful for computing minimal detectable
        differences for general ANOVA models.  This program is
        documented in the paper of A. Baharev, S. Kemeny, On the
        computation of the noncentral F and noncentral beta
        distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
        the book "Forecasting: principles and practice" by Rob J
        Hyndman and George Athanasopoulos. All packages required to run
        the examples are also loaded.
All data sets required for the examples and exercises
  in the book "Forecasting: principles and practice" (2nd ed, 2018)
  by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>.
  All packages required to run the examples are also loaded.
All data sets required for the examples and exercises in the book
    "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos
    <https://OTexts.com/fpp3/>.  All packages required to run the examples are also
    loaded.
Efficient approximation of first-passage-time densities for diffusion processes based on the First-Passage-Time Location (FPTL) function.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, see Filzmoser and Viertl (2004) <doi:10.1007/s001840300269>, (2) testing fuzzy hypotheses based on crisp data, see Parchami et al. (2010) <doi:10.1007/s00362-008-0133-4>, and (3) testing fuzzy hypotheses based on fuzzy data, see Parchami et al. (2012) <doi:10.1007/s00362-010-0353-2>. In all cases, the fuzziness of data or / and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Maximum likelihood estimation of the parameters of a fractionally
   differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989);
   including inference and basic methods.  Some alternative algorithms to estimate "H".
Functions for fitting data to a quiescent growth model,
        i.e. a growth process that involves members of the population
        who stop dividing or propagating.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data.
The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) <doi:10.1002/sim.6497>.
Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) <doi:10.1007/BF02065874>.
This is the package which can help you turn
        numeric,dataframe,matrix into fraction form.
The main function of this package allows numerical vector objects to
  be displayed with their values in vulgar fractional form.  This is convenient if
  patterns can then be more easily detected.  In some cases replacing the components
  of a numeric vector by a rational approximation can also be expected to remove
  some component of round-off error.  The main functions form a re-implementation
  of the functions 'fractions' and 'rational' of the MASS package, but using a
  radically improved programming strategy.
Provides functions for converting decimals to a
    matrix of numerators and denominators or a character vector of
    fractions.  Supports mixed or improper fractions, finding common
    denominators for vectors of fractions, limiting denominators to powers
    of ten, and limiting denominators to a maximum value.  Also includes
    helper functions for finding the least common multiple and greatest
    common divisor for a vector of integers.  Implemented using C++ for
    maximum speed.
A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 <doi:10.1016/j.jclinepi.2013.10.019>; Lin, 2020 <doi:10.1111/jep.13428>), conventional pairwise meta-analyses (Atal et al., 2019 <doi:10.1016/j.jclinepi.2019.03.012>), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 <doi:10.1016/j.jclinepi.2020.07.003>). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures.
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).
Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.
Implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.
Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.
Tools to support sensible statistics for functional response analysis.
Generation of theoretical size distributions of framboidal or sunflower pyrite. 
        The growth mechanisms used are surface and supply controlled and dependent
        or independent of size. The algorithms are fully described in the published work 
        in Mineralogy and Petrology journal:
        "Theoretical growth of framboidal and sunflower pyrite using the R-package frambgrowth"
        The authors Merinero, R., and Cardenes, V. (2018).
        <DOI:10.1007/s00710-017-0535-x>.
Point and interval estimation in dual frame surveys. In contrast
    to classic sampling theory, where only one sampling frame is considered,
    dual frame methodology assumes that there are two frames available for
    sampling and that, overall, they cover the entire target population. Then,
    two probability samples (one from each frame) are drawn and information
    collected is suitably combined to get estimators of the parameter of
    interest.
With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.
Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.
Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.
An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. 
    FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
    representing the reasoning of human experts in a set of IF-THEN rules, to
    handle real-life problems in, e.g., control, prediction and inference, data
    mining, bioinformatics data processing, and robotics. FRBSs are also known
    as fuzzy inference systems and fuzzy models. During the modeling of an
    FRBS, there are two important steps that need to be conducted: structure
    identification and parameter estimation. Nowadays, there exists a wide
    variety of algorithms to generate fuzzy IF-THEN rules automatically from
    numerical data, covering both steps. Approaches that have been used in the
    past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
    methods, genetic algorithms, squares methods, etc. Furthermore, in this
    version we provide a universal framework named 'frbsPMML', which is adopted
    from the Predictive Model Markup Language (PMML), for representing FRBS
    models. PMML is an XML-based language to provide a standard for describing
    models produced by data mining and machine learning algorithms. Therefore,
    we are allowed to export and import an FRBS model to/from 'frbsPMML'.
    Finally, this package aims to implement the most widely used standard
    procedures, thus offering a standard package for FRBS modeling to the R
    community.
This package implements the functions associated with Fast
        Regularized Canonical Correlation Analysis.
Provides implementation of statistical methods for random objects 
    lying in various metric spaces, which are not necessarily linear spaces. 
    The core of this package is Fréchet regression for random objects with 
    Euclidean predictors, which allows one to perform regression analysis 
    for non-Euclidean responses under some mild conditions. 
    Examples include distributions in L^2-Wasserstein space, 
    covariance matrices endowed with power metric (with Frobenius metric 
    as a special case), Cholesky and log-Cholesky metrics.  
    References: Petersen, A., & Müller, H.-G. (2019) <doi:10.1214/17-AOS1624>.
An R client for the 'Federal Reserve Economic Data'
    ('FRED') API <https://research.stlouisfed.org/docs/api/>.  Functions
    to retrieve economic time series and other data from 'FRED'.
The free algebra in R; multivariate polynomials with non-commuting indeterminates.
Implements the formulae required to calculate freedom
    from disease according to Cameron and Baldock (1998)
    <doi:10.1016/S0167-5877(97)00081-0>. These are the
    methods used at the Swedish national veterinary institute (SVA) to
    evaluate the performance of our nation animal disease
    surveillance programmes.
Provides functionality for manipulating elements of the free group (juxtaposition is represented by a plus) including inversion, multiplication by a scalar, group-theoretic power operation, and Tietze forms.  The package is fully vectorized.
Provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
Wrapper functions that interface with 'Freesurfer' 
    <https://surfer.nmr.mgh.harvard.edu/>, a powerful and 
    commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with
    'Freesurfer' completely in R, where you pass R objects of class 'nifti',
    implemented by package 'oro.nifti', and the function executes an 'Freesurfer'
    command and returns an R object of class 'nifti' or necessary output.
Provides functions to read and write neuroimaging data in various file formats, with a focus on 'FreeSurfer' <http://freesurfer.net/> formats. This includes, but is not limited to, the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.
This tree-based method deals with high dimensional longitudinal 
	data with correlated features through the use of a piecewise random effect 
	model. FREE tree also exploits the network structure of the features, by 
	first clustering them using Weighted Gene Co-expression Network Analysis 
	('WGCNA'). It then conducts a screening step within each cluster of features 
	and a selecting step among the surviving features, which provides a relatively
	unbiased way to do feature selection. By using dominant principle components 
	as regression variables at each leaf and the original features as splitting 
	variables at splitting nodes, FREE tree delivers easily interpretable results
	while improving computational efficiency.
Unlike other tools that dynamically link to the 'Cairo'
    stack, 'freetypeharfbuzz' is statically linked to specific
    versions of the 'FreeType' and 'harfbuzz' libraries (2.9 and 1.7.6
    respectively). This ensures deterministic computation of text box
    extents for situations where reproducible results are crucial (for
    instance unit tests of graphics).
A collection of functions for linear and non-linear regression modelling. 
	It implements a wrapper for several regression models available in the base and
	contributed packages of R.
Functions for finding smooth interpolating curves connecting
    a series of points in the plane.  Curves may be open or closed,
    that is, with the first and last point of the curve at the initial point.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
Generates a frequency distribution. The frequency
    distribution includes raw frequencies, percentages in each category, and
    cumulative frequencies. The frequency distribution can be stored as a data
    frame.
Implementation of dynamic principal component
    analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. 
    These frequency domain methods for dimensionality reduction of multivariate time series
    were introduced by David Brillinger in his book Time Series (1974). We follow implementation
    guidelines as described in Hormann, Kidzinski and Hallin (2016),
    Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
Implementations of functional dynamic principle components analysis. Related graphic tools and frequency domain methods.
  These methods directly use multivariate dynamic principal components implementation,
  following the guidelines from Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
New approaches to parallel coordinates plots for
   multivariate data visualization, including applications to clustering,
   outlier hunting and regression diagnostics.  Includes general functions
   for multivariate nonparametric density and regression estimation, 
   using parallel computation.
Tools for generating an informative type of line graph, the frequency profile, 
    which allows single behaviors, multiple behaviors, or the specific behavioral patterns 
    of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
Quickly make tables of descriptive statistics (i.e., counts, 
    percentages, confidence intervals) for categorical variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to Microsoft Word ® with minimal pain.
Generate 'SPSS'/'SAS' styled frequency tables. Frequency tables are 
    generated with variable and value label attributes where applicable with optional
    html output to quickly examine datasets.
Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
Implement frequent-directions algorithm for efficient matrix sketching. 
  (Edo Liberty (2013) <doi:10.1145/2487575.2487623>).
Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
Customize 'Bootstrap' and 'Bootswatch' themes, like colors, fonts, grid layout, 
  to use in 'Shiny' applications, 'rmarkdown' documents and 'flexdashboard'.
Regular and non-regular Fractional Factorial 2-level designs 
        can be created. Furthermore, analysis tools for Fractional
        Factorial designs with 2-level factors are offered (main
        effects and interaction plots for all factors simultaneously,
        cube plot for looking at the simultaneous effects of three
        factors, full or half normal plot, alias structure in a more
        readable format than with the built-in function alias).
This package provides catalogues of resolution IV regular
        fractional factorial designs in 128 runs for up to 33 2-level
        factors. The catalogues are complete, excluding resolution IV
        designs without 5-letter words, because these do not add value
        for a search for clear designs. The previous package version
        1.0 with complete catalogues up to 24 runs (24 runs and a
        namespace added later) can be downloaded from the authors
        website.
The complete scripts from the American sitcom Friends in tibble 
    format. Use this package to practice data wrangling, text analysis and 
    network analysis.
Miscellaneous utilities, tools and helper
    functions for finding and searching files on disk, searching for and
    removing R objects from the workspace.
    These are utilities for packages
    <https://CRAN.R-project.org/package=cleanr>,
    <https://CRAN.R-project.org/package=document>,
    <https://CRAN.R-project.org/package=fakemake>,
    <https://CRAN.R-project.org/package=packager> and
    <https://CRAN.R-project.org/package=rasciidoc>.
    Does not import or depend on any third party party package, but on core R 
    only (i.e it may depend on packages with priority 'base').
Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling
    and prediction with large datasets. The approach, discussed in Cressie and
    Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, 
    and hence the covariance function, using a fixed set of n basis functions, 
    where n is typically much smaller than the number of data points (or polygons) m. 
    The method naturally allows for non-stationary, anisotropic covariance functions 
    and the use of observations with varying support (with known error variance). The 
    projected field is a key building block of the Spatial Random Effects (SRE) model, 
    on which this package is based. The package FRK provides helper functions to model, 
    fit, and predict using an SRE with relative ease.
When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.
Estimation and specification analysis of one- and two-part fractional regression models and calculation of partial effects.
Estimation and specification analysis of fractional regression models with neglected heterogeneity and/or endogenous covariates.
Estimation of panel data regression models for fractional responses.
A collection of R and C++ functions to work with the
        generalized hyperbolic distribution, related distributions and
        their applications in financial risk management and
        quantitative analysis.
Fast, numerically robust computation of weighted moments via 'Rcpp'. 
   Supports computation on vectors and matrices, and Monoidal append of moments. 
   Moments and cumulants over running fixed length windows can be computed, 
   as well as over time-based windows.
   Moment computations are via a generalization of Welford's method, as described
   by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.
Maximum Likelihood Estimation of
   Stochastic Frontier Production and Cost Functions.
   Two specifications are available:
   the error components specification with time-varying efficiencies
   (Battese and Coelli, 1992, <doi:10.1007/BF00158774>)
   and a model specification in which the firm effects are directly 
   influenced by a number of variables (Battese and Coelli, 1995,
   <doi:10.1007/BF01205442>).
It calculates the alpha-quantile and order-m efficiency score in multi-dimension and computes several summaries and representation of the associated frontiers in 2d and 3d.
A compilation of empirical methods used by farmers and agronomic engineers to predict the minimum temperature to detect a frost night. These functions use variables such as environmental temperature, relative humidity, and dew point. See <http://sedici.unlp.edu.ar/handle/10915/72102> <http://www.fao.org/docrep/008/y7223e/y7223e0b.htm#bm11.8> for details.
An R API to MET Norway's 'Frost' API <https://frost.met.no/index.html> 
    to retrieve data as data frames. The 'Frost' API, and the underlying data, is 
    made available by the Norwegian Meteorological Institute (MET Norway). The data
    and products are distributed under the 
    Norwegian License for Open Data 2.0 (NLOD) <https://data.norge.no/nlod/en/2.0>
    and Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.
Climate is a critical component limiting growing range of plant species, which
    also determines cultivar adaptation to a region. The evaluation of climate influence on
    fruit production is critical for decision-making in the design stage of orchards and 
    vineyards and in the evaluation of the potential consequences of future climate. Bio-
    climatic indices and plant phenology are commonly used to describe the suitability of 
    climate for growing quality fruit and to provide temporal and spatial information about 
    regarding ongoing and future changes. 'fruclimadapt' streamlines the assessment of 
    climate adaptation and the identification of potential risks for grapevines and fruit 
    trees. Procedures in the package allow to i) downscale daily meteorological variables
    to hourly values (Forster et al (2016) <doi:10.5194/gmd-9-2315-2016>),
    ii) estimate chilling and forcing heat accumulation (Miranda et al (2019)
    <https://ec.europa.eu/eip/agriculture/sites/agri-eip/files/fg30_mp5_phenology_critical_temperatures.pdf>),
    iii) estimate plant phenology (Schwartz (2012) <doi:10.1007/978-94-007-6925-0>), iv) 
    calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr 
    et al (2017) <doi:10.3354/cr01532>), v) estimate the incidence of weather-related disorders 
    in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi)
    estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)).
A cross-platform interface to file system operations, built on
  top of the 'libuv' C library.
A variety of simple fish stock assessment methods.
    Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.
The datasets to support the Fish Stock Assessment ('FSA') package.
Provides high-level access to neuroimaging data from standard software packages like 'FreeSurfer' <http://freesurfer.net/> on the level of subjects and groups. Load morphometry data, surfaces and brain parcellations based on atlases. Mask data using labels, load data for specific atlas regions only, and visualize data and statistical results directly in 'R'.
Automated feature selection using variety of models
        provided by 'caret' package.
        This work was funded by Poland-Singapore bilateral cooperation
        project no 2/3/POL-SIN/2012.
FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints.
Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis
    (FSDA)' which is a comprehensive and computationally efficient
    software package for robust statistics in regression, multivariate
    and categorical data analysis. The current R version implements tools
    for regression: (forward search, S- and MM-estimation, least trimmed
    squares (LTS) and least median of squares (LMS)), for multivariate analysis
    (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression.
    The distinctive feature of our package is the possibility of
    monitoring the statistics of interest as function of breakdown point,
    efficiency or subset size, depending on the estimator. This is
    accompanied by a rich set of graphical features, such as dynamic
    brushing, linking, particularly useful for exploratory data analysis.
Functions for selecting attributes from a given
    dataset. Attribute subset selection is the process of identifying and
    removing as much of the irrelevant and redundant information as
    possible.
'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection 
 algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
 In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf>
 with a sparse matrix support.
Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from <http://sourceforge.net/projects/formscanner/>. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects.
Feature subset selection algorithms modularized in search algorithms and measure utilities. Full list and more information available at <https://dicits.ugr.es/software/FSinR/>.
Performs fast detection of interactions in large-scale data using the
    method of random intersection trees introduced in
    Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. 
    The algorithm finds potentially high-order interactions in high-dimensional binary
    two-class classification data, without requiring lower order interactions
    to be informative.  The search is particularly fast when the matrices of
    predictors are sparse.  It can also be used to perform market basket analysis
    when supplied with a single binary data matrix.  Here it will find collections
    of columns which for many rows contain all 1's.
Functions for importing, creating, editing and
    exporting 'FSK' files <https://foodrisklabs.bfr.bund.de/fsk-ml-food-safety-knowledge-markup-language/>
    using the 'R' programming environment. Furthermore, it enables users
    to run simulations contained in the 'FSK' files and visualize the results.
Wrapper functions that interface with 'FSL' 
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with 'FSL'
    completely in R, where you pass R objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' if desired.
Implements feature selection routines for multivariate time series (MTS). 
    The list of implemented algorithms includes: 
    own lags (independent MTS components), 
    distance-based (using external structure, e.g. Pfeifer and Deutsch (1980) <doi:10.2307/1268381>),
    cross-correlation (see  Schelter et al. (2006, ISBN:9783527406234)),
    graphical LASSO (see Haworth and Cheng (2014) <https://www.gla.ac.uk/media/Media_401739_smxx.pdf>),
    random forest (see Pavlyuk (2020) "Random Forest Variable Selection for Sparse Vector Autoregressive Models" in Contributions to Statistics, in production),
    least angle regression (see Gelper and Croux (2008) <https://lirias.kuleuven.be/retrieve/16024>), 
    mutual information (see  Schelter et al. (2006, ISBN:9783527406234), Liu et al. (2016) <doi:10.1109/ChiCC.2016.7554480>),
    and partial spectral coherence (see Davis et al.(2016) <doi:10.1080/10618600.2015.1092978>). 
    In addition, the package implements functions for ensemble feature selection (using feature ranking and majority voting).
    The package is implemented within Dmitry Pavlyuk's research project No. 1.1.1.2/VIAA/1/16/112 "Spatiotemporal urban traffic modelling using big data".
Filling large gaps in low or uncorrelated multivariate time series uses a new fuzzy weighted similarity measure. It contains all required functions to create large missing consecutive values within time series and then fill these gaps, according to the paper Phan et al. (2018), <DOI:10.1155/2018/9095683>. Performance indicators are also provided to compare similarity between two univariate signals (incomplete signal and imputed signal).
Estimation of Rosenthal's fail safe number including confidence intervals. The relevant papers are the following. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2014). "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number". International Scholarly Research Notices, Volume 2014. <doi:10.1155/2014/825383>. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2017). "Exploring the distribution for the estimator of Rosenthal's fail-safe number of unpublished studies in meta-analysis". Communications in Statistics-Theory and Methods, 46(11):5672--5684. <doi:10.1080/03610926.2015.1109664>.
Fuzzy set ordination is a multivariate analysis used in ecology to
    relate the composition of samples to possible explanatory variables.  While
    differing in theory and method, in practice, the use is similar to 'constrained
    ordination.'  The package contains plotting and summary functions as well as
    the analyses.
Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.
Social Relations Analysis with roles ("Family SRM") are computed,
    using a structural equation modeling approach. Groups ranging from three members
    up to an unlimited number of members are supported and the mean structure can
    be computed. Means and variances can be compared between different groups of
    families and between roles.
An optimizer of Fused-Sparse Structural Equation Models, which is 
 the state of the art jointly fused sparse maximum likelihood function 
 for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 
 <doi:10.1101/466623>).
Provides three methods proposed by Shang and Apley (2019) <doi:10.1080/00224065.2019.1705207> to generate fully-sequential space-filling designs inside a unit hypercube. A 'fully-sequential space-filling design' means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design.
Multithreaded serialization of compressed data frames using the
    'fst' format. The 'fst' format allows for random access of stored data and
    compression with the LZ4 and ZSTD compressors created by Yann Collet. The ZSTD
    compression library is owned by Facebook Inc.
Has two functions to help with calculating feature selection stability. 'Lump' is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length.
    'ASM' is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection.
    The calculation for 'asm' uses the Adjusted Stability Measure proposed in: 'Lustgarten', 'Gopalakrishnan', & 'Visweswaran' (2009)<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815476/>.
The 'fstlib' library provides multithreaded serialization of compressed data frames using the
    'fst' format. The 'fst' format allows for random access of stored data and compression with the 'LZ4' and 'ZSTD'
    compressors.
A program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.
Functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.
A wrapper for the API of the Danish Parliament. It makes it 
    possible to get data from the API easily into a data frame. Learn more at 
    <http://www.ft.dk/dokumenter/aabne_data>.
Build display tables easily by extending the functionality of the
    'flextable' package. Features include spanning header, grouping rows,
    parsing markdown and so on.
The package contains R-functions to perform the methods in
        nonparametric regression and density estimation, described in
        Davies, P. L. and Kovac, A. (2001) Local Extremes, Runs,
        Strings and Multiresolution (with discussion) Annals of
        Statistics. 29. p1-65 Davies, P. L. and Kovac, A. (2004)
        Densities, Spectral Densities and Modality Annals of
        Statistics. Annals of Statistics. 32. p1093-1136 Kovac, A.
        (2006) Smooth functions and local extreme values. Computational
        Statistics and Data Analysis (to appear) D\"umbgen, L. and
        Kovac, A. (2006) Extensions of smoothing via taut strings
        Davies, P. L. (1995) Data features. Statistica Neerlandica
        49,185-245.
A collection of functions for trading and rebalancing financial
	instruments. It implements various technical indicators to analyse time series such
	as moving averages or stochastic oscillators.
Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) <DOI:10.1093/bib/bbz041>. 'iFeature' 'Z. Chen et al.' (2018) <DOI:10.1093/bioinformatics/bty140>. <https://CRAN.R-project.org/package=rDNAse>. 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) <DOI:10.1093/bioinformatics/btw564>. 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) <DOI:10.1016/j.ygeno.2018.01.005>. 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) <DOI:10.1073/pnas.92.19.8700>. 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) <DOI:10.1038/srep13859>.
Fast operations for time series objects.
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
Functions for estimating spectral density operator of functional
    time series (FTS) and comparing the spectral density operator of two
    functional time series, in a way that allows detection of differences of
    the spectral density operator in frequencies and along the curve length.
Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package 'fuel' implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.
Fuel economy data from the EPA, 1985-2015,
    conveniently packaged for consumption by R users.
This is an evolutionary algorithm for fuzzy systems, a
        genetic algorithm is used to construct a fuzzy system able to
        fit the given training data.  This fuzzy system can then be
        used as a prediction model, it's composed of fuzzy logic rules
        that provide a good lingustic representation.
As in music, a fugue statistic repeats a theme in small variations.  Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes.  The main functions in the package are sen() and senCI().  For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.
We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016) <doi:10.1002/ece3.1943>.
Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). 
  References: Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". <doi:10.13140/RG.2.2.20415.94885/1>  ,
              Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. <doi:10.1177/1745691620902426>.
Provides a single interface to many sources of full text
    'scholarly' data, including 'Biomed Central', Public Library of
    Science, 'Pubmed Central', 'eLife', 'F1000Research', 'PeerJ',
    'Pensoft', 'Hindawi', 'arXiv' 'preprints', and more. Functionality
    included for searching for articles, downloading full or partial
    text, downloading supplementary materials, converting to various
    data formats.
This is a collection of R games and other funny stuff, such as the
    classic Mine sweeper and sliding puzzles.
Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, 
    protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' 
    from gene set over-expression analysis. Performs clustering of pathway based on similarity 
    of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) 
    when up and down regulated genes are known. The methods are described in a paper currently
    submitted by Orecchioni et al, 2020 in Nanoscale.
The FunCC algorithm allows to apply the FunCC algorithm to simultaneously cluster the rows and the columns of a data matrix whose inputs are functions.
YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. abbr_to_colClass() is a much more concise way of feeding many types to a colClass argument in a data reader. stale_package_check() tries to identify any library() calls to unused packages.
Statistical hypothesis testing methods for
 inferring model-free functional dependency using asymptotic
 chi-squared or exact distributions. Functional test
 statistics are asymmetric and functionally optimal, unique
 from other related statistics. Tests in this package reveal
 evidence for causality based on the
 causality-by-functionality principle. They include
 asymptotic functional chi-squared tests
 (Zhang & Song 2013) <arXiv:1311.2707> and an exact
 functional test (Zhong & Song 2019)
 <doi:10.1109/TCBB.2018.2809743>. The normalized functional
 chi-squared test was used by Best Performer 'NMSUSongLab'
 in HPN-DREAM (DREAM8) Breast Cancer Network Inference
 Challenges (Hill et al 2016) <doi:10.1038/nmeth.3773>. A
 function index (Zhong & Song 2019)
 <doi:10.1186/s12920-019-0565-9> (Kumar et al 2018)
 <doi:10.1109/BIBM.2018.8621502> derived from the
 functional test statistic offers a new effect size measure
 for the strength of functional dependency, a better
 alternative to conditional entropy in many aspects. For
 continuous data, these tests offer an advantage over
 regression analysis when a parametric functional form
 cannot be assumed; for categorical data, they provide a
 novel means to assess directional dependency not possible
 with symmetrical Pearson's chi-squared or Fisher's exact
 tests.
FunCluster performs a functional analysis of microarray
        expression data based on Gene Ontology & KEGG functional
        annotations. From expression data and functional annotations
        FunCluster builds classes of putatively co-regulated biological
        processes through a specially designed clustering procedure.
Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers).  Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank.  FuncMap should be useful for comparing packages at a high level for their overall design.  Plus, it's just plain fun.  The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package.  Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.
A collection of functions which fit functional neural network models. In
            other words, this package will allow users to build deep learning models 
            that have either functional or scalar responses paired with functional and 
            scalar covariates. We implement the theoretical discussion found 
            in Thind, Multani and Cao (2020) <arXiv:2006.09590> through the help of a main fitting and 
            prediction function as well as a number of helper functions to assist with 
            cross-validation, tuning, and the display of estimated functional weights.
Cluster together the components that make up an interactive
 system on the basis of their functional redundancy 
 for one or more collective, systemic performances. 
 Plot the hierarchical tree of component clusters,
 the modelled and predicted performances of component assemblages,
 and other results associated with a functional clustering.
 Test and prioritize the significance of the different components 
 that make up the interactive system, 
 of the different assemblages of components that make up the dataset, 
 and of the different performances observed on the component assemblages.
 The method finds application in ecology, for instance,
 where the system is an ecosystem, the components are organisms or species,
 and the systemic performance is the production of biomass 
 or the respiration of the ecosystem. 
 The method is extensively described in 
 Jaillard B, Deleporte P, Loreau M, Violle C (2018) 
 "A combinatorial analysis using observational data identifies species
 that govern ecosystem functioning" 
 <doi:10.1371/journal.pone.0201135>.
Curry, Compose, and other higher-order functions
Find functions in an unstructured directory and explore their dependencies. 
    Sourcing of R source files is performed without side-effects: from R scripts that have 
    executable code and function definitions only functions are sourced.
Extends functional programming in R by
    providing support to the usual higher order functional
    suspects (Map, Reduce, Filter, etc.).
S4 classes for univariate and multivariate functional data with
    utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description 
    of the package functionalities and its interplay with the MFPCA package for 
    multivariate functional principal component analysis 
    <https://CRAN.R-project.org/package=MFPCA>.
Computes 4 alpha-functional diversity indices: Functional
  Divergence FDiv, Function Evenness Feve, Functional Richness FRic, and Rao's
  entropy Q (reviewed in Villéger et al. 2008 <doi:10.1890/07-1206.1>). Provides
  efficient and modular functions to compute functional diversity indices.
A fast implementation of functional ensemble survival tree is provided to facilitate dynamic prediction with right-censored data. Multiple time-varying covariates can be accommodated via multivariate principal component analysis. These extracted features along with baseline covariates are nested within the ensemble survival tree where dynamic prediction can be done under user-specified sliding windows. Prediction accuracy measures, Area under the receiver operating characteristic (ROC) curve and Brier score, are provided in this package.
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>.
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights.
    Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices:
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <DOI:10.1080/00273171.2016.1178566>.
    Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF)
    covariance matrix of standardized regression coefficients: theoretical extensions
    and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>.
    Waller, N. G.  (2018).  Direct Schmid-Leiman transformations and rank-deficient loadings matrices.  Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.
Construction and smart selection of Gaussian process models
	with emphasis on treatment of functional inputs. This package
	offers: (i) flexible modeling of functional-input regression
	problems through the fairly general Gaussian process model; (ii)
	built-in dimension reduction for functional inputs; (iii)
	heuristic optimization of the structural parameters of the model
	(e.g., active inputs, kernel function, type of distance).
	Metamodeling background is provided in
	Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
	The algorithm for structural parameter optimization is described
	in <https://hal.archives-ouvertes.fr/hal-02532713>.
The funHDDC algorithm  allows to cluster functional univariate (Bouveyron and Jacques, 2011, <doi:10.1007/s11634-011-0095-6>) or multivariate data (Schmutz et al., 2018) by modeling each group within a specific functional subspace.
Similar to base's unique function, only optimized for working with 
    data frames, especially those that contain date-time columns.
Provides four addons for analyzing trends and
    unit roots in financial time series: (i) functions for the density
    and probability of the augmented Dickey-Fuller Test, (ii) functions 
    for the density and probability of MacKinnon's unit root test 
    statistics, (iii) reimplementations for the ADF and MacKinnon
    Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's
    unit root test suite.
The funLBM algorithm allows to simultaneously cluster the rows and the columns of a data matrix where each entry of the matrix is a function or a time series.
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
Time-based joins to analyze sequence of events, 
    both in memory and out of memory. after_join() joins two 
    tables of events, while funnel_start() and funnel_step() 
    join events in the same table. With the type argument, you
    can switch between different funnel types, like first-first
    and last-firstafter.
An implementation of methods presented by Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots for comparing institutional performance, for standardised ratios, ratios of counts and proportions with additive overdispersion adjustment.
A set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.
High-order functions for data manipulation : sort or group data, given one
    or more auxiliary functions. Functions are inspired by other pure
    functional programming languages ('Haskell' mainly). The package also 
    provides built-in function operators for creating compact anonymous
    functions, as well as the possibility to use the 'purrr' package syntax.
A small utility which wraps Rscript and provides access to all R
    functions from the shell.
Computes functional rarity indices as proposed by Violle et al.
    (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed
    using both regional and local information. Functional Rarity combines both
    the functional aspect of rarity as well as the extent aspect of rarity.
    'funrar' is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.
Performs functional regression, and some related
    approaches, for intensive longitudinal data (see the book by Walls & Schafer, 
    2006, Models for Intensive Longitudinal Data, Oxford) when such data is not
    necessarily observed on an equally spaced grid of times.  The
    approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
    Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but
    with some modifications to make it more feasible to use with long rather
    than wide, non-rectangular longitudinal datasets with unequal and
    potentially random measurement times.  It also allows easy plotting of the
    correlation between the smoothed covariate and the outcome as a function of
    time, which can add additional insights on how to interpret a functional
    regression.  Additionally, it also provides several permutation tests for
    the significance of the functional predictor.  The heuristic interpretation
    of ``time'' is used to describe the index of the functional predictor, but
    the same methods can equally be used for another unidimensional continuous
    index, such as space along a north-south axis.  The development of this
    package was part of a research project supported by Award R03 CA171809-01
    from the National Cancer Institute and Award P50 DA010075 from the National
    Institute on Drug Abuse. The content is solely the responsibility of the
    authors and does not necessarily represent the official views of the
    National Institute on Drug Abuse, the National Cancer Institute, or the
    National Institutes of Health.
Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.
Includes nonparametric estimators and tests for time series analysis. The functions are to test for presence of possibly non-monotonic trends and for synchronism of trends in multiple time series, using bootstrap techniques and robust nonparametric difference-based estimators.
A collection of toys to do things like generate Collatz sequences, convert a fraction to "continued fraction" form, calculate a fraction which is a close approximation to some value (e.g., 22/7 or 355/113 or pi), and so on.
Contains four main functions (i.e., four pieces of furniture):
    table1() which produces a well-formatted table of descriptive statistics common as Table 1
    in research articles, tableC() which produces a well-formatted table of correlations,
    tableF() which provides frequency counts, and washer() which 
    is helpful in cleaning up the data. These furniture-themed functions are designed 
    to simplify common tasks in quantitative analysis. Other data summary and cleaning tools 
    are also available.
Implementations of the family of map() functions
    from 'purrr' that can be resolved using any 'future'-supported
    backend, e.g. parallel on the local machine or distributed on a
    compute cluster.
Provides methods fusedest_normal() and fusedest_logit() for carrying out block splitting algorithms for fused penalty estimation. For details, please see Tso-Jung Yen (2019) <doi.10618600.2019.1660178>.
Enables high-dimensional penalized regression across heterogeneous 
    subgroups. Fusion penalties are used to share information about the linear 
    parameters across subgroups. The underlying model is described 
    in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.
FusionCharts provides awesome and minimalist functions to make beautiful interactive charts <https://www.fusioncharts.com/>.
Provides the Big Merge Tracker and COSCI algorithms for convex clustering and 
    feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and 
    T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.
The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.
Provides a simple yet powerful logging utility. Based loosely on
    log4j, futile.logger takes advantage of R idioms to make logging a
    convenient and easy to use replacement for cat and print statements.
A scoped options management framework. Used in other packages.
Provides dispatching implementations suitable for
        functional programming paradigms. The framework provides a
        mechanism for attaching guards to functions similar to Erlang,
        while also providing the safety of assertions reminiscent of
        Eiffel.
Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results.
The purpose of this package is to provide a lightweight and
    unified Future API for sequential and parallel processing of R
    expression via futures.  The simplest way to evaluate an expression
    in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`.
    This package implements sequential, multicore, multisession, and
    cluster futures.  With these, R expressions can be evaluated on the
    local machine, in parallel a set of local machines, or distributed
    on a mix of local and remote machines.
    Extensions to this package implement additional backends for
    processing futures via compute cluster schedulers etc.
    Because of its unified API, there is no need to modify any code in order
    switch from sequential on the local machine to, say, distributed
    processing on a remote compute cluster.
    Another strength of this package is that global variables and functions
    are automatically identified and exported as needed, making it
    straightforward to tweak existing code to make use of futures.
Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.  These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
Implementation of the Future API on top of the 'batchtools' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.
Implementation of the Future API on top of the 'callr' package.  This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine.  Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession'), the 'callr' backend provided here can run more than 125 parallel R processes.
Backends implementing the 'Future' API, as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API.  The tests can be performed easily from within R or from outside of R from the command line making it easy to include them package tests and in Continuous Integration (CI) pipelines.
Inputs a directory of climate projection files and, for each,
    identifies and characterizes heat waves for specified study locations. The
    definition used to identify heat waves can be customized. Heat wave
    characterizations include several metrics of heat wave length, intensity,
    and timing in the year. The heat waves that are identified can be
    explored using a function to apply user-created functions across all
    generated heat wave files.This work was supported in part by grants from
    the National Institute of Environmental Health Sciences (R00ES022631), the
    National Science Foundation (1331399), and the Colorado State University
    Vice President for Research.
Test function arguments with a wide array of inputs, and produce
  reports summarizing messages, warnings, errors, and returned values.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Calculation of AHP (Analytic Hierarchy Process -
    <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>)
    with classic and fuzzy weights based on Saaty's pairwise
    comparison method for determination of weights.
Exact calculation of fuzzy decision rules for multiple
	testing. Choose to control FDR (false discovery rate) using the
	Benjamini and Hochberg method, or FWER (family wise error rate)
	using the Bonferroni method. Kulinsakaya and Lewin (2007).
Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
    See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
    for further details.
Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.
Provides methods to solve Fuzzy Linear Programming Problems with
             fuzzy constraints (following different approaches proposed by 
             Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy
             technological matrix.
Implementation of several MCDM methods for fuzzy data (triangular
    fuzzy numbers) for decision making problems. The methods that are implemented in
    this package are Fuzzy TOPSIS (with two normalization procedures), Fuzzy VIKOR,
    Fuzzy Multi-MOORA and Fuzzy WASPAS. In addition, function MetaRanking() calculates
    a new ranking from the sum of the rankings calculated, as well as an aggregated ranking.
S4 classes and methods
    to deal with fuzzy numbers. They allow for computing any arithmetic
    operations (e.g., by using the Zadeh extension principle),
    performing approximation of arbitrary fuzzy numbers by trapezoidal
    and piecewise linear ones, preparing plots for publications, computing
    possibility and necessity values for comparisons, etc.
One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.
Fuzzy clustering of species in an ecological community as common or
    rare based on their abundance and occupancy. It also includes functions to
    compute confidence intervals of classification metrics and plot results. See
    Balbuena et al. (2020, <doi:10.1101/2020.08.12.247502>).
Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic.
    This toolkit includes with graphical user interface (GUI) and an adaptive neuro-
    fuzzy inference system (ANFIS). This toolkit is a continuation from the previous
    package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA)
    and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham.
Does fuzzy tests and confidence intervals (following Geyer
    and Meeden, Statistical Science, 2005, <doi:10.1214/088342305000000340>)
    for sign test and Wilcoxon signed rank and rank sum tests.
Estimators for fuzzy linear regression. The functions estimate parameters of 
    fuzzy linear regression models with crisp or fuzzy independent variables (triangular 
    fuzzy numbers are supported). Implements multiple methods for parameter estimation and 
    algebraic operations with triangular fuzzy numbers. Includes functions for 
    summarising, printing and plotting the model fit. Calculates predictions from the 
    model and total error of fit. 
    Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>,
	Hung & Yang (2006) <doi:10.1016/j.fss.2006.08.004>,
	Lee & Tanaka (1999) <doi:10.15807/jorsj.42.98>,
	Nasrabadi, Nasrabadi & Nasrabady (2005) <doi:10.1016/j.amc.2004.02.008>,
	Tanaka, Hayashi & Watada (1989) <doi:10.1016/0377-2217(89)90431-1>,
	Zeng, Feng & Li (2017) <doi:10.1016/j.asoc.2016.09.029>.
Functions to calculate fuzzy versions of species' occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis and prevalence-independent favourability GLM), and pair-wise fuzzy similarity (based on fuzzy versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model comparison (overlap and fuzzy similarity, loss or gain), and for data preparation, such as obtaining unique abbreviations of species names, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, assessing the false discovery rate, or analysing and dealing with multicollinearity among variables. Includes also sample datasets for providing practical examples.
An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
        from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley 
        in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities.
The aim of the package is to provide some basic functions
 for doing statistics with trapezoidal fuzzy numbers. In particular,
  the package contains several functions for simulating trapezoidal 
  fuzzy numbers, as well as for calculating some central tendency 
  measures (mean and two types of median), some scale measures 
  (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and 
  one diversity index and one inequality index. Moreover, 
  functions for calculating the 1-norm distance, the mid/spr 
  distance and the (phi,theta)-wabl/ldev/rdev distance between 
  fuzzy numbers are included, and a function to calculate the 
  value phi-wabl given a sample of trapezoidal fuzzy numbers.
The main goal of this package is to present various fuzzy statistical tools. It intends to provide an implementation of the theoretical and empirical approaches presented in the thesis entitled "The signed distance measure in fuzzy statistical analysis. Some theoretical, empirical and programming advances" (Thesis to be published soon. For the theoretical approaches, see Berkachy R. and Donze L. (2019) <doi:10.1007/978-3-030-03368-2_1>. For the empirical approaches, see Berkachy R. and Donze L. (2016) <ISBN: 978-989-758-201-1>). Important (non-exhaustive) implementation highlights of this package are as follows: (1) a numerical procedure to estimate the fuzzy difference and the fuzzy square. (2) two numerical methods of fuzzification. (3) a function performing different possibilities of distances, including the signed distance and the generalized signed distance for instance. (4) numerical estimations of fuzzy statistical measures such as the variance, the moment, etc. (5) two methods of estimation of the bootstrap distribution of the likelihood ratio in the fuzzy context. (6) an estimation of a fuzzy confidence interval by the likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data. (9) a method of estimation of global and individual evaluations of linguistic questionnaires. (10) numerical estimations of multi-ways analysis of variance models in the fuzzy context. The unbalance in the considered designs are also foreseen.
A custom framework for working with Type 1 Fuzzy Logic,
        produced by the University of Nottingham IMA Group.
Fuzzy string matching implementation of the 'fuzzywuzzy' <https://github.com/seatgeek/fuzzywuzzy> 'python' package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.
A simple method
    to select the best model or best subset of variables using
    different types of data (binary, Gaussian or Poisson) and
    applying it in different contexts (parametric or non-parametric).
Provides three functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.
Implementation of the fast algorithm for wild cluster bootstrap 
             inference developed in Roodman et al (2019, STATA Journal) for 
             linear regression models 
             <https://journals.sagepub.com/doi/full/10.1177/1536867X19830877>, 
             which makes it feasible to quickly calculate bootstrap test 
             statistics based on a large number of bootstrap draws even for 
             large samples - as long as the number of bootstrapping clusters 
             is not too large. Multiway clustering, regression weights, 
             bootstrap weights, fixed effects and subcluster bootstrapping
             are supported. Further, both restricted (WCR) and unrestricted
             (WCU) bootstrap are supported. Methods are provided for a variety 
             of fitted models, including 'lm()', 'feols()' 
             (from package 'fixest') and 'felm()' (from package 'lfe').
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
Exchange rate regression and structural change tools
             for estimating, testing, dating, and monitoring
	     (de facto) exchange rate regimes.
The goal of this package is to provide an improved version of 
    WA-PLS (Weighted Averaging Partial Least Squares) by including the 
    tolerances of taxa and the frequency of the sampled climate variable. 
    This package also provides a way of leave-out cross-validation that 
    removes both the test site and sites that are both geographically 
    close and climatically close for each cycle, to avoid the risk of 
    pseudo-replication.
In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June
     of the following calendar year. As such, many databases need to represent and 
     validate financial years efficiently. While the use of integer years with a convention that  
     they represent the year ending is common, it may lead to ambiguity with calendar years.
     On the other hand, string representations may be too inefficient and do not easily admit
     arithmetic operations. This package tries to make validation of financial years quicker while
     retaining clarity.
Create and maintain delayed-data packages (ddp's).  Data stored in
  a ddp are available on demand, but do not take up memory until requested.
  You attach a ddp with g.data.attach(), then read from it and assign to it in
  a manner similar to S-PLUS, except that you must run g.data.save() to
  actually commit to disk.
G1DBN performs DBN inference using 1st order conditional
        dependencies.
For a given metabolic network, this package finds the gaps (metabolites not produced or not consumed in any other reaction), and fills it from the stoichiometric reactions of a reference metabolic reconstruction using a weighting function. Also the option to download all the set of gene-associated stoichiometric reactions for a specific organism from the KEGG database <http://www.genome.jp/kegg/> is available.
Full descriptive statistics, physical description of sediment,
            metric or phi sieves.
R interface for 'g3-lollipop' JavaScript library.
    Visualize genetic mutation data using an interactive lollipop diagram in RStudio or your browser.
Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.
Scale abbreviation using Genetic Algorithms that maximally capture the variance in the original data.
Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.
The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method.
  By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed.
  This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others.
  First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user.
  By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions.
  The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.
This package analyses complex ANOVA models with any
        combination of orthogonal/nested and fixed/random factors, as
        described by Underwood (1997). There are two restrictions: (i)
        data must be balanced; (ii) fixed nested factors are not
        allowed. Homogeneity of variances is checked using Cochran's C
        test and 'a posteriori' comparisons of means are done using
        Student-Newman-Keuls (SNK) procedure.
Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).
A statistical ecosystem modelling package, taking many features of
             the ecosystem into account. Gadget works by running an internal
             model based on many parameters, and then comparing the data from
             the output of this model to real data to get a goodness-of-fit
             likelihood score. These parameters can then be adjusted, and the
             model re-run, until an optimum is found, which corresponds to the
             model with the lowest likelihood score. Gadget allows the user to
             include a number of features into an ecosystem model: One or more
             species, each of which may be split into multiple stocks; multiple
             areas with migration between areas; predation between and within
             species; maturation; reproduction and recruitment; multiple
             commercial and survey fleets taking catches from the populations.
             For more details see <https://hafro.github.io/gadget2/>.
             This is the C++ Gadget2 runtime, making it available for R.
In this package we consider Gaussian Diffusion processes and smooth thresholds. After evaluating the mean of the process to check the subthreshold regimen hypothesis, the FPT density function is reconstructed via the numerical quadrature of the integral equation in (Buonocore 1987); first passage times are also generated by the method in (Buonocore 2014) and results are compared. The timestep of the simulations can iteratively be refined. User should provide the functional form for the drift and the infinitesimal variance in the script 'userfunc.R' and for the threshold in the script 'userthresh.R'. All the parameters required by the implementation are to be set in the script 'userparam.R'. Example scripts for common drifts and thresholds are given.
Manipulate, assemble, export <https://gadm.org/> maps. Create 'choropleth', 'isopleth', dots plot, proportional dots,
   dot-density and more.
A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).
Tools for using genetic markers, stable isotope data, and habitat
    suitability data to calculate posterior probabilities of breeding origin of
    migrating birds.
Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators.
  H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.
Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.  The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11.
GAIPE implements graphical representation of accuracy in
        parameter estimation (AIPE) on RMSEA for sample size planning
        in structural equation modeling. Sample sizes suggested by
        RMSEA with AIPE method and power analysis approach can also be
        obtained separately using the provided functions.
The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.
Includes the ga.lts() function that estimates
        LTS (Least Trimmed Squares) parameters using genetic algorithms
        and C-steps. ga.lts() constructs a genetic algorithm to form a
        basic subset and iterates C-steps as defined in Rousseeuw and
        van-Driessen (2006) to calculate the cost value of the LTS
        criterion. OLS (Ordinary Least Squares) regression is known to
        be sensitive to outliers. A single outlying observation can
        change the values of estimated parameters. LTS is a resistant
        estimator even the number of outliers is up to half of the
        data. This package is for estimating the LTS parameters with
        lower bias and variance in a reasonable time. Version >=1.3 
        includes the function medmad for fast outlier detection in
        linear regression.
An R interface to the Galvanize 'Highbond' API <https://docs-apis.highbond.com>.
Functions for fitting and working with generalized
		additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).
An evolutionary approach to performing hard partitional clustering. The algorithm uses genetic operators guided by information about the quality of individual partitions. The method looks for the best barycenters/centroids configuration (encoded as real-value) to maximize or minimize one of the given clustering validation criteria: Silhouette, Dunn Index, C-Index or Calinski-Harabasz Index. As many other clustering algorithms, 'gama' asks for k: a fixed a priori established number of partitions. If the user does not know the best value for k, the algorithm estimates it by using one of two user-specified options: minimum or broad. The first method uses an approximation of the second derivative of a set of points to automatically detect the maximum curvature (the 'elbow') in the within-cluster sum of squares error (WCSSE) graph. The second method estimates the best k value through majority voting of 24 indices. One of the major advantages of 'gama' is to introduce a bias to detect partitions which attend a particular criterion. References: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04>; CHARRAD, Malika et al. (2014) <doi:10.18637/jss.v061.i06>; Tsagris M, Papadakis M. (2018) <doi:10.7287/peerj.preprints.26605v1>; Kaufman, L., & Rousseeuw, P. (1990, ISBN:0-47 1-73578-7).
Data sets and scripts used in the book 'Generalized Additive 
             Models: An Introduction with R', Wood (2006,2017) CRC.
Fits unimodal and multimodal gambin distributions to species-abundance distributions
    from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. 
    'gambin' is short for 'gamma-binomial'. The main function is fit_abundances(), which estimates 
    the 'alpha' parameter(s) of the gambin distribution using maximum likelihood. Functions are 
    also provided to generate the gambin distribution and for calculating likelihood statistics.
Boosting models for fitting generalized additive models for
  location, shape and scale ('GAMLSS') to potentially high dimensional
  data.
Provides features to use function gamboost() from  package mboost for estimation of multistate models
Functions and data are provided that support a course
        that emphasizes statistical issues of inference and generalizability.  
        The functions are designed to make it straightforward to illustrate
        the use of cross-validation, the training/test approach, simulation, 
        and model-based estimates of accuracy.  Methods considered are
        Generalized Additive Modeling, Linear and Quadratic Discriminant
        Analysis, Tree-based methods, and Random Forests.
Implementation of various inference and simulation tools to
    apply generalized additive models to bivariate dependence structures and
    non-simplified vine copulas.
Implements the GAMbag, GAMrsm and GAMens ensemble
    classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles
    implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601>
    , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.
Implementation of the characteristic palettes from the TV show 'Game of Thrones'.
Provides estimation and analysis functions for
    strategic statistical models.
Finds adaptive strategies for sequential symmetric 
    games using a genetic algorithm. Currently, any symmetric two by two matrix
    is allowed, and strategies can remember the history of an opponent's play
    from the previous three rounds of moves in iterated interactions between
    players. The genetic algorithm returns a list of adaptive strategies given
    payoffs, and the mean fitness of strategies in each generation.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', <arXiv:1308.5623>.
Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Interface for extra smooth functions including tensor products, neural networks and decision trees.
This is an add-on package to GAMLSS. The purpose of this
        package is to allow users to fit interval response variables in
        GAMLSS models. The main function gen.cens() generates a
        censored version of an existing GAMLSS family distribution.
This is an add on package to 'GAMLSS'. The main purpose of this package is generating and fitting inflated distributions at any desired point (0, 1, 2, ...). The function gen.Kinf() generates K-inflated version of an existing discrete 'GAMLSS' family distribution.
Data used as examples to demonstrate GAMLSS models.
Demos for smoothing and gamlss.family distributions.
A set of distributions  which can be used  for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed  distributions.  Extra distributions can be created, by transforming, any continuous distribution defined on the real line,  to  a distribution defined on ranges 0 to infinity  or  0 to 1,  by using a ''log'' or a ''logit' transformation respectively.
This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.
The main purpose of this package is to allow fitting of
        mixture distributions with generalised additive models for location 
        scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.
This is an add on package to GAMLSS. It allows one extra
        method for fitting GAMLSS models. The main function nlgamlss()
        can fit any parametric (up to four parameter) GAMLSS
        distribution.
It allows us to fit Gaussian Markov Random Field within the
    Generalized Additive Models for Location Scale and Shape algorithms.
This is an add on package to GAMLSS. The purpose of this
        package is to allow users to defined truncated distributions in
        GAMLSS models. The main function gen.trun() generates truncated
        version of an existing GAMLSS family distribution.
Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.
Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', using 'lme4' for estimation.
To compare nonlinear curves and surface estimations between groups using semiparametric methods for cross-sectional and longitudinal dataset.
Process in-situ Gamma-Ray Spectrometry for
    Luminescence Dating. This package allows to import, inspect and
    correct the energy shifts of Gamma-ray spectra. It provides methods
    for estimating the gamma dose rate by the use of a calibration curve.
    The package only supports Canberra CNF and TKA files.
Performs Gamma regression, where both mean and shape parameters follows lineal regression structures.
Uses a slice sampling-based Markov chain Monte Carlo to
        conduct Bayesian fitting and inference for generalized additive
        mixed models.  Generalized linear mixed models and generalized 
        additive models are also handled as special cases of generalized
        additive mixed models. The methodology and software is described 
        in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand
        Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.
Robust regression via gamma-divergence with L1, elastic net and ridge.
To calculate the relative risk (RR) for the generalized additive model.
Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.
Functions for querying the 'Google Analytics' core reporting,
    real-time, multi-channel funnel and management APIs, as well as the 'Google
    Tag Manager' (GTM) API. Write methods are also provided for the management
    and GTM APIs so that you can change tag, property or view settings, for
    example. Define reporting queries using natural R expressions instead of
    being concerned as much about API technical intricacies like query syntax,
    character code escaping, and API limitations.
A network-based gene weighting algorithm for pathway enrichment analysis, using either RNA-seq or microarray data. Zhaoyuan Fang, Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.
This is a dataset package for GANPA, which implements a
        network-based gene weighting approach to pathway analysis. This
        package includes data useful for GANPA, such as a functional
        association network, pathways, an expression dataset and
        multi-subunit proteins.
It is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it
        contains functions for sample size calculations of both
        population-based and family-based designs, probability of
        familial disease aggregation, kinship calculation, statistics
        in linkage analysis, and association analysis involving genetic
        markers including haplotype analysis with or without environmental
        covariates.
Datasets associated with the 'gap' package. Currently,
             it includes an example data for regional association
             plot (CDKN), an example data for a genomewide association
             meta-analysis (OPG), Manhattan plots with (hr1420, mhtdata)
             and without (w4) gene annotations.
Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.
Tools to fill missing values in satellite data and to develop new
    gap-fill algorithms. The methods are tailored to data (images) observed
    at equally-spaced points in time. The package is illustrated with MODIS
    NDVI data.
An excerpt of the data available at Gapminder.org. For each of 142
    countries, the package provides values for life expectancy, GDP per capita,
    and population, every five years, from 1952 to 2007.
Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.
Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time.
Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.
Provides utilities for working with Google APIs
    <https://developers.google.com/apis-explorer>.  This includes
    functions and classes for handling common credential types and for
    preparing, executing, and processing HTTP requests.
An event-Based framework for building 'Shiny' apps.
    Instead of relying on standard 'Shiny' reactive objects, this 
    package allow to relying on a lighter set of triggers, so that 
    reactive contexts can be invalidated with more control.
Methods for estimating univariate long memory-seasonal/cyclical
             Gegenbauer time series processes. See for example (2018) <doi:10.1214/18-STS649>.
             Refer to the vignette for details of fitting these processes.
Organize a so-called ragged array as generalized arrays, which
	is simply an array with sub-dimensions denoting the subdivision of
	dimensions (grouping of members within dimensions).
	By the margins (names of dimensions and sub-dimensions) in generalized
	arrays, operators and utility functions provided in this package
	automatically match the margins,
	doing map-reduce style parallel computation
	along margins.  Generalized arrays are also cooperative to R's native
	functions that work on simple arrays.
Simulate, estimate and forecast using univariate and multivariate GAS models 
  as described in Ardia et al. (2019) <doi:10.18637/jss.v088.i06>.
Provides a genetic algorithm for finding variable
    subsets in high dimensional data with high prediction performance. The
    genetic algorithm can use ordinary least squares (OLS) regression models or
    partial least squares (PLS) regression models to evaluate the prediction
    power of variable subsets. By supporting different cross-validation
    schemes, the user can fine-tune the tradeoff between speed and quality of
    the solution.
Functions for greenhouse gas flux calculation from chamber
    measurements.
Provides the standard operations for signal processing on graphs: 
    graph Fourier transform, spectral graph wavelet transform, 
    visualization tools. It also implements a data driven method
    for graph signal denoising/regression, for details see 
    De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. 
    The package also provides an interface to the SuiteSparse Matrix Collection, 
    <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix 
    benchmarks collected from a wide range of applications.
Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al <doi:10.1159/000488519>.
Allows user to choose/gate a region on the plot and returns points
    within it.
Estimates statistically significant marker combination values within
        which one immunologically distinctive group (i.e., disease case) is more associated than
        another group (i.e., healthy control), successively, using various combinations (i.e.,
        "gates") of markers to examine features of cells that may be different between
        groups. For a two-group comparison, the 'gateR' package uses the spatial relative risk
        function that is estimated using the 'sparr' package. Details about the 'sparr' package
        methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details
        about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.
        More information about relative risk functions using kernel density estimation can be
        found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel (not for Windows), and 'Rcpp'
 has been used to speed up calculations. Other R packages that perform
 similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.
A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. 'gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence' in the journal Ecology and Evolution.
Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is  zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets.  Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", 2020, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633>.
A collection difference measures for multivariate Gaussian
        probability density functions, such as the Euclidea mean, the
        Mahalanobis distance, the Kullback-Leibler divergence, the
        J-Coefficient, the Minkowski L2-distance, the Chi-square
        divergence and the Hellinger Coefficient.
Display a random fact about Carl Friedrich Gauss
 based the on collection curated by Mike Cavers via the
 <http://gaussfacts.com> site.
Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.
Functions to fit two-dimensional Gaussian functions, predict values from
    fits, and produce plots of predicted data via either 'ggplot2' or base R 
    plotting.
A collection of functions to perform Gaussian quadrature
        with different weight functions corresponding to the orthogonal
        polynomials in package orthopolynom.  Examples verify the
        orthogonality and inner products of the polynomials.
R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.
Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.
A collection of algorithms and functions 
  for fitting data to a generalized lambda distribution 
  via moment matching methods, and generalized 
  bootstrapping.
Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.
Estimation of the generalized beta distribution of the second 
    kind (GB2) and related models using grouped data in form of income shares. 
    The GB2 family is a general class of distributions that provides an accurate 
    fit to income data. 'GB2group' includes functions to estimate the GB2, the 
    Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 
    'GB2group' deploys two different econometric strategies to estimate these 
    parametric distributions, the equally weighted minimum distance (EWMD) estimator and the  
    optimally weighted minimum distance (OMD) estimator. Asymptotic standard errors are reported for the 
    OMD estimates. Standard errors of the EWMD estimates are obtained by Monte 
    Carlo simulation. See Jorda et al. (2018) <arXiv:1808.09831> for a detailed 
    description of the estimation procedure.
Downloads and interpolates the Brazilian, Chinese, and Russian yield curves directly from <http://www.b3.com.br/>, <http://yield.chinabond.com.cn>, and <https://www.cbr.ru>, respectively.
Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) <doi:10.1214/11-BJPS139>, Hamza & Vallois (2016) <doi:10.1016/j.spl.2016.03.014>, Chen & Novick (1984) <doi:10.3102/10769986009002163>.
Supplies a set of functions to interface with bikeshare data
    following the General Bikeshare Feed Specification, allowing users to query
    and accumulate tidy datasets for specified cities/bikeshare programs.
Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
	association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ),
    Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence
    Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented 
    in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests.
    The GBJ has been shown to outperform other tests in genetic association studies when signals
    are correlated and moderately sparse. Please see the vignette for a quickstart guide or the paper at
    <doi:10.1080/01621459.2019.1660170> for full details.
An implementation of extensions to Freund and Schapire's AdaBoost 
  algorithm and Friedman's gradient boosting machine. Includes regression 
  methods for least squares, absolute loss, t-distribution loss, quantile 
  regression, logistic, multinomial logistic, Poisson, Cox proportional hazards 
  partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and 
  Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.
Automates delta log-normal boosted regression tree abundance
    prediction. Loops through parameters provided (LR (learning rate), TC (tree 
    complexity), BF (bag fraction)), chooses best, simplifies, & generates line, 
    dot & bar plots, & outputs these & predictions & a report, makes predicted 
    abundance maps, and Unrepresentativeness surfaces.
    Package core built around 'gbm' (gradient boosting machine) functions in 
    'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself 
    built around 'gbm' (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & 
    ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 
    'Working Guide' <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows 
    Appendix S3. See <http://www.simondedman.com/> for published guides and 
    papers using this package.
Writes SAS code to get predicted values from every tree of a gbm.object.
Basic infrastructure and several algorithms for 1d-4d bin packing
    problem. This package provides a set of c-level classes and solvers for
    1d-4d bin packing problem, and an r-level solver for 4d bin packing problem,
    which is a wrapper over the c-level 4d bin packing problem solver.
    The 4d bin packing problem solver aims to solve bin packing problem, a.k.a
    container loading problem, with an additional constraint on weight.
    Given a set of rectangular-shaped items, and a set of rectangular-shaped bins
    with weight limit, the solver looks for an orthogonal packing solution
    such that minimizes the number of bins and maximize volume utilization.
    Each rectangular-shaped item i = 1, .. , n is characterized by length l_i,
    depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin
    j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j,
    and weight limit w_j.
    The item can be rotated into any orthogonal direction, and no further
    restrictions implied.
Provides utilities for processing Rd objects and files.
        Extract argument descriptions and other parts of the help pages
        of functions.
Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
An implementation of hyperparameter optimization for Gradient
    Boosted Trees on binary classification and regression problems. The current
    version provides two optimization methods: Bayesian optimization and random
    search. Instead of giving the single best model, the final output is an 
    ensemble of Gradient Boosted Trees constructed via the method of ensemble 
    selection.
Simulate real and complex numbers from distributions of their
       magnitude and arguments. Optionally, the magnitudes and/or arguments may
       be fixed in almost arbitrary ways. Plot density and distribution
       functions with automatic selection of suitable regions.  Small programming
       utilities: check if an object is identical to NA, count positional
       arguments in a call, set intersection of more than two sets, check if an
       argument is unnamed, compute the graph of S4 classes in packages.
Many inherited biases and effects exists in RNA-seq due to both biological and technical effects. We observed the biological variance of testing target transcripts can influence the yield of sequencing reads, which might indicate a resource competition existing in RNA-seq. We developed this package to capture the bias depending on local sequence and perform the correction of this type of bias by borrowing information from spike-in measurement.
Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.
Aligns peak based on peak retention times and matches homologous peaks
    across samples. The underlying alignment procedure comprises three sequential steps.
    (1) Full alignment of samples by linear transformation of retention times to 
    maximise similarity among homologous peaks (2) Partial alignment of peaks within 
    a user-defined retention time window to cluster homologous peaks (3) Merging rows
    that are likely representing homologous substances (i.e. no sample shows peaks in 
    both rows and the rows have similar retention time means). The algorithm is described in detail
    in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
'GPU'/CPU Benchmarking on Debian-package based systems
 This package benchmarks performance of a few standard linear algebra
 operations (such as a matrix product and QR, SVD and LU decompositions)
 across a number of different 'BLAS' libraries as well as a 'GPU' implementation.
 To do so, it takes advantage of the ability to 'plug and play' different
 'BLAS' implementations easily on a Debian and/or Ubuntu system.  The current
 version supports
  - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline
  - Atlas which are tuned but typically configure single-threaded
  - Atlas39 which are tuned and configured for multi-threaded mode
  - 'Goto Blas' which are accelerated and multi-threaded
  - 'Intel MKL' which is a commercial accelerated and multithreaded version.
 As for 'GPU' computing, we use the CRAN package
  - 'gputools'
 For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be
 used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10.
Contains the Global Charcoal database data. Data include charcoal
    series (age, depth, charcoal quantity, associated units and methods) and
    information on sedimentary sites (localisation, depositional environment, biome,
    etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.
A generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the LASSO and elastic net (adaptive) penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.
Generalized competing event model based on Cox PH model and Fine-Gray model.
    This function is designed to develop optimized risk-stratification methods for competing
    risks data, such as described in:
    1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F,
    and Mell LK (2014) <DOI:10.1016/j.ijrobp.2014.03.047>.
    2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose
    BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>.
    3. Lunn, Mary, and Don McNeil (1995) <DOI:10.2307/2532940>.
Scrapes Google Citation pages and creates data frames of 
    citations over time.
Provides a variety of functions to analyze and model
    geostatistical count data with Gaussian copulas, including
 1) data simulation and visualization; 
 2) correlation structure assessment (here also known as the Normal To Anything); 
 3) calculate multivariate normal rectangle probabilities; 
 4) likelihood inference and parallel prediction at predictive locations.
Estimation of covariance matrices as solutions of 
             continuous time Lyapunov equations. 
             Sparse coefficient matrix and diagonal noise are estimated 
             with a proximal gradient 
             method for an l1-penalized loss minimization problem.
             Varando G, Hansen NR (2020) <arXiv:2005.10483>.
Orders panels in scatterplot matrices and parallel coordinate
 displays by some merit index. Package contains various indices of merit,
 ordering functions, and enhanced versions of pairs and parcoord which
 color panels according to their merit level.
Likelihood inference in Gaussian copula marginal
        regression models.
Estimates a sparse inverse covariance matrix from a convex
    pseudo-likelihood function with L1 penalty
An implementation of the International Bureau of Weights and Measures (BIPM) generalized consensus estimators used to assign the reference value in a key comparison exercise. This can also be applied to any interlaboratory study. Given a set of different sources, primary laboratories or measurement methods this package provides an evaluation of the variance components according to the selected statistical method for consensus building. It also implements the comparison among different consensus builders and evaluates the participating method or sources against the consensus reference value. Based on a diverse set of references, DerSimonian-Laird (1986) <doi:10.1016/0197-2456(86)90046-2>, Vangel-Ruhkin (1999) <doi:10.1111/j.0006-341X.1999.00129.x>, for a complete list of references look at the reference section in the package documentation.
Data sets used in the book "R Graphics Cookbook" by Winston
    Chang, published by O'Reilly Media.
Analyze the default risk of credit portfolios. Commonly known models, 
		like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
		The portfolio loss distribution can be achieved either by simulation or analytically 
		in case of the classic CreditRisk+ model. Models are only implemented to respect losses
		caused by defaults, i.e. migration risk is not included. The package structure is kept
		flexible especially with respect to distributional assumptions in order to quantify the
		sensitivity of risk figures with respect to several assumptions. Therefore the package
		can be used to determine the credit risk of a given portfolio as well as to quantify
		model sensitivities.
Provides implementation of the generic composite similarity measure
    (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The
    implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations
    of the structural similarity (SSIM) and the composite similarity measure based
    on means, standard deviations, and correlation coefficient (CMSC), are included.
Geographical detectors for measuring spatial stratified heterogeneity,
             as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and 
             Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the
             optimal discretization of continuous data, four primary functions of 
             geographical detectors, comparison of size effects of spatial unit and 
             the visualizations of results. 
             To use the package and to refer the 
             descriptions of the package, methods and case datasets, please cite 
             Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>.
             The model has been applied in factor exploration of road performance and 
             multi-scale spatial segmentation for network data, as described in 
             Yongze Song (2018) <doi:10.3390/rs10111696> and 
             Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users
    define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and 
    resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, 
    applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, 
    exporting data cubes as 'netCDF' or 'GeoTIFF' files, and plotting.  The package implements lazy evaluation and 
    multithreading. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. 
    See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.
R's 'sf' package ships with self-contained 'GDAL'
    executables, including a bare bones interface to several
    'GDAL'-related utility programs collectively known as the 'GDAL
    utilities'. For each of those utilities, this package provides an
    R wrapper whose formal arguments closely mirror those of the
    'GDAL' command line interface. The utilities operate on data
    stored in files and typically write their output to other
    files. Therefore, to process data stored in any of R's more
    common spatial formats (i.e. those supported by the 'sp', 'sf',
    and 'raster' packages), first write them to disk, then process
    them with the package's wrapper functions before reading the
    outputted results back into R.
Wrappers for the Geospatial Data Abstraction Library (GDAL)
    Utilities.
Various R programming tools for data manipulation, including:
   - medical unit conversions ('ConvertMedUnits', 'MedUnits'),
   - combining objects ('bindData', 'cbindX', 'combine', 'interleave'),
   - character vector operations ('centerText', 'startsWith', 'trim'),
   - factor manipulation ('levels', 'reorder.factor', 'mapLevels'),
   - obtaining information about R objects ('object.size', 'elem', 'env',
     'humanReadable', 'is.what', 'll', 'keep', 'ls.funs',
     'Args','nPairs', 'nobs'),
   - manipulating MS-Excel formatted files ('read.xls',
     'installXLSXsupport', 'sheetCount', 'xlsFormats'),
   - generating fixed-width format files ('write.fwf'),
   - extricating components of date & time objects ('getYear', 'getMonth',
     'getDay', 'getHour', 'getMin', 'getSec'),
   - operations on columns of data frames  ('matchcols', 'rename.vars'),
   - matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'),
   - operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'),
   - operations on data frames ('frameApply', 'wideByFactor'),
   - value of last evaluated expression ('ans'), and
   - wrapper for 'sample' that ensures consistent behavior for both
     scalar and vector arguments ('resample').
Contains functions for 'specific' Multiple Correspondence Analysis, 
	Class Specific Analysis, Multiple Factor Analysis, 'standardized' MCA, computing and plotting structuring factors and concentration ellipses, 
	inductive tests and others tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>). It also provides functions
	for the translation of logit models coefficients into percentages (Deauvieau (2010) <doi:10.1177/0759106309352586>), weighted contingency tables, an association 
  measure for contingency tables ("Percentages of Maximum Deviation from Independence", aka PEM, see Cibois (1993) <doi:10.1177/075910639304000103>) and some tools to measure bivariate associations between variables
  (phi, Cram<c3><a9>r V, correlation coefficient, eta-squared...).
Functions to explore datasets from the Global Biodiversity Information Facility (GBIF - <https://www.gbif.org/>) using a Shiny interface.
Provides a set of tools to help the de-fragmentation process. It works by prioritizing the different sections of linear infrastructures (e.g. roads, power-lines) to increase the available amount of a given resource.
The GDELT data set is over 60 GB now and growing 100 MB a month.
    The number of source articles has increased over time and unevenly across
    countries. This package makes it easy to download a subset of that data,
    then normalize that data to facilitate valid time series analysis.
Functions for performing graphical difference testing.     
             Differences are generated between raster images.
             Comparisons can be performed between different package
             versions and between different R versions.
A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) <DOI:10.18637/jss.v093.i14>, Ma and de la Torre (2019) <DOI:10.1111/emip.12262>, Ma (2019) <DOI:10.1007/978-3-030-05584-4_29> and de la Torre and Akbay (2019) <DOI:10.14689/ejer.2019.80.9>.
Provides classes and functions to calculate various distance measures and routes in heterogeneous geographic spaces represented as grids. The package implements measures to model dispersal histories first presented by van Etten and Hijmans (2010) <doi:10.1371/journal.pone.0012060>. Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. The distances implemented in the package are used in geographical genetics, accessibility indicators, and may also have applications in other fields of geospatial analysis.
A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models.
Manage and analyze high-dimensional SNP data from chips with multiple densities.
To address the problem of insecurity of 'UDP'-based 'DNS' requests,
    Google Public 'DNS' offers 'DNS' resolution over an encrypted 'HTTPS'
    connection. 'DNS'-over-'HTTPS' greatly enhances privacy and security
    between a client and a recursive resolver, and complements 'DNSSEC'
    to provide end-to-end authenticated DNS lookups. Functions that enable
    querying individual requests that bulk requests that return detailed
    responses and bulk requests are both provided. Support for reverse
    lookups is also provided. See
    <https://developers.google.com/speed/public-dns/docs/dns-over-https> 
    for more information.
Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation
    includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02>
    for the identification of both the number of lags to be used
    in the generalized dynamic principal components as well as the number of components required
    for a given reconstruction accuracy.
Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.
Useful tools for writing vector graphics devices.
Interfaces GAMS data (*.gdx) files with 'data.table's using the GAMS R package 'gdxrrw'. The 'gdxrrw' package is available on the GAMS wiki: <https://support.gams.com/doku.php?id=gdxrrw:interfacing_gams_and_r>.
Some tools for developing general equilibrium models and some general equilibrium models. These models can be used for teaching economic theory and are built by the methods of new structural economics (see <https://www.nse.pku.edu.cn/> and LI Wu, 2019, ISBN: 9787521804225, General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press). The model form and mathematical methods can be traced back to von Neumann, J. (1945, A Model of General Economic Equilibrium. The Review of Economic Studies, 13. pp. 1-9) and Kemeny, J. G., O. Morgenstern and G. L. Thompson (1956, A Generalization of the von Neumann Model of an Expanding Economy, Econometrica, 24, pp. 115-135) et al. By the way, J. G. Kemeny is a co-inventor of the computer language BASIC.
Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).
Geometrically Designed Spline ('GeDS') Regression is a non-parametric geometrically 
  motivated method for fitting variable knots spline predictor models in one or two independent 
  variables, in the context of generalized (non-)linear models. 'GeDS' estimates the number and 
  position of the knots and the order of the spline, assuming the response variable has a 
  distribution from the exponential family. A description of the method can be found in
  Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2017) <https://openaccess.city.ac.uk/18460>.
Provides adaptive association tests for SNP level, gene level and pathway level analyses.
Population-averaged models have been increasingly used in the design and analysis of 
             cluster randomized trials (CRTs). To facilitate the applications of population-averaged 
             models in CRTs, the package implements the generalized estimating equations (GEE) and 
             matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal 
             mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the 
             general GEE/MAEE approach, the package also implements a fast cluster-period GEE method 
             specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives 
             a simple and efficient estimating equations approach based on the cluster-period means to 
             estimate the intervention effects as well as correlation parameters. In addition, the package 
             also provides functions for generating correlated binary data with specific mean vector and 
             correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or 
             the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.
Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) <doi:10.1016/j.jmva.2013.09.003>.
GEE estimation of the parameters in mean structures with possible
    correlation between the outcomes. User-specified mean link and variance
    functions are allowed, along with observation weighting. The 'M' in the name
    'geeM' is meant to emphasize the use of the Matrix package, which allows for an
    implementation based fully in R.
Causal mediation analysis for a single exposure/treatment and a
    single mediator, both allowed to be either continuous or binary. The package
    implements the difference method and provide point and interval estimates as
    well as testing for the natural direct and indirect effects and the mediation
    proportion. Nevo, Xiao and Spiegelman (2019) <doi:10.1515/ijb-2017-0006>.
Generalized estimating equations solver for parameters in
    mean, scale, and correlation structures, through mean link,
    scale link, and correlation link. Can also handle clustered
    categorical responses.
Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.
Provides a general, flexible framework for estimating parameters
    and empirical sandwich variance estimator from a set of unbiased estimating
    equations (i.e., M-estimation in the vein of Stefanski & Boos (2002)
    <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002)
    are published in the corresponding Journal of Statistical Software paper 
    <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample 
    variance corrections.
Functions to compute generalized eigenvalues and eigenvectors,
             the generalized Schur decomposition and
             the generalized Singular Value Decomposition of a matrix pair,
             using Lapack routines.
Methods for fitting macroevolutionary models to phylogenetic trees 
	Pennell (2014) <doi:10.1093/bioinformatics/btu181>.
First major functionality is to compute the bias in misspecified linear gene-environment interaction models. The most 
	generalized function for this objective is GE_bias().  However GE_bias() requires specification of many
	higher order moments of covariates in the model.  If users are unsure about how to calculate/estimate
	these higher order moments, it may be easier to use GE_bias_normal_squaredmis().  This function places
	many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate
	normal distribution) and is thus able to automatically calculate many of the higher order moments automatically,
	necessitating only that the user specify some covariances.  There are also functions to solve for the bias 
	through simulation and non-linear equation solvers, these can be used to check your work. Second major functionality
	is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample
	performance than other inference procedures for testing GxE interaction.  More details on these functions 
	are available in Sun, Carroll, Christiani, and Lin, "Testing for Gene-Environment Interaction Under Exposure Misspecification"
	(In Revision).
For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>).
Implements several extensions of the elastic net regularization
    scheme. These extensions include individual feature penalties for the L1 term,
    feature-feature penalties for the L2 term, as well as translation coefficients
    for the latter.
Reads data files from the 'Gem infrasound logger' for analysis and converts to segy format (which is convenient for reading with traditional seismic analysis software). The Gem infrasound logger is a low-cost, lightweight, low-power instrument for recording infrasound in field campaigns; email the maintainer for more information.
Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package 'gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.
Simulate and analyze multistate models with general hazard
    functions. gems provides functionality for the preparation of hazard functions
    and parameters, simulation from a general multistate model and predicting future
    events. The multistate model is not required to be a Markov model and may take
    the history of previous events into account. In the basic version, it allows
    to simulate from transition-specific hazard function, whose parameters are
    multivariable normally distributed.
Network meta-analyses (mixed treatment comparisons) in the Bayesian
    framework using JAGS. Includes methods to assess heterogeneity and
    inconsistency, and a number of standard visualizations.
    van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>;
    van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.
One can find single-stage and two-stage designs for a phase II 
    single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>.
Contains an engine for spatially-explicit eco-evolutionary mechanistic models with a modular implementation and several support functions. It allows exploring the consequences of ecological and macroevolutionary processes across realistic or theoretical spatio-temporal landscapes on biodiversity patterns as a general term.
A collection of functions for processing 'Gen5' 2.06 exported data.
    'Gen5' is an essential data analysis software for BioTek plate readers <https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>. This package contains functions for data cleaning, 
    modeling and plotting using exported data from 'Gen5' version 2.06. It exports
    technically correct data defined in (Edwin de Jonge and Mark van der Loo 
    (2013) <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>) for customized analysis. It 
    contains Boltzmann fitting for general kinetic analysis.  
    See <https://www.github.com/yanxianUCSB/gen5helper> for more information,
    documentation and examples.
R based genetic algorithm for binary and floating point
        chromosomes.
Defines classes and methods that can be used
  to implement genetic algorithms for feature selection.  The idea is
  that we want to select a fixed number of features to combine into a
  linear classifier that can predict a binary outcome, and can use a
  genetic algorithm heuristically to select an optimal set of features.
genasis package contains methods for air pollution assessment. Concerned on persistent organic pollutants, the package allows to compute trends of their concentrations, compare different datasets and estimate relations between values from active and passive air samplers.
Provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.
Set of functions to create datasets using a correlation matrix.
Infers state-recorded gender categories from first names and dates of birth using historical
    datasets. By using these datasets instead of lists of male and female names,
    this package is able to more accurately infer the gender of a name, and it
    is able to report the probability that a name was male or female. GUIDELINES:
    This method must be used cautiously and responsibly. Please be sure to see the
    guidelines and warnings about usage in the 'README' or the package documentation.
    See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.
Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
Segmentation and classification procedures for data from the 'Activinsights GENEActiv' <https://www.activinsights.com/products/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing.
    Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.
Functions and analytics for GENEA-compatible accelerometer data into R objects. 
             See topic 'GENEAread' for an introduction to the package. 
             See <https://www.activinsights.com/products/geneactiv/> for more details on the GENEActiv device.
Creates visualisations in two and three dimensions of simulated
    data based on detected segments or raw accelerometer data.
An implementation of the advanced gene 
             search in R. This package has basic 
             annotation information. Also, it contains 
             a relative intact gene database which was
             obtained from the Human Gene Database
             <https://www.genecards.org>. It allows 
             users to search gene symbol or alias 
             and convert gene interested to the 
             consistent gene symbols. It also
             provides users with gene WIKI 
             introduction.
Functions for clustering time-course gene expression and reconstructing of gene regulatory network based on Dynamic Bayesian Network.
The GeneCycle package implements the approaches of Wichert
        et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki 
        et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al.
        (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically 
        expressed genes from gene expression time series data.
A function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optional) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL80, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293,  GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>.
This package implements several generalized F-statistics.
        The current version includes a generalized F-statistic based on
        the flexible isotonic/monotonic regression or order restricted
        hypothesis testing.
A pipeline with high specificity and sensitivity in extracting 
  proteins from the RefSeq database (National Center for Biotechnology 
  Information). Manual identification of gene families is highly 
  time-consuming and laborious, requiring an iterative process of manual and 
  computational analysis to identify members of a given family. The pipelines 
  implements an automatic approach for the identification of gene families 
  based on the conserved domains that specifically define that family. See 
  Die et al. (2018) <doi:10.1101/436659> for more information and examples.
"geneListPie" package is for mapping a gene list to
        function categories defined in GOSlim or Kegg. The results can
        be plotted as a pie chart to provide a quick view of the genes
        distribution of the gene list among the function categories.
        The gene list must contain a list of gene symbols. The package
        contains a set of pre-processed gene sets obtained from Gene
        Ontology and MSigDB including human, mouse, rat and yeast. To
        provide a high level concise view, only GO slim and kegg are
        provided. The gene sets are regulared updated. User can also
        use customized gene sets. User can use the R Pie() or Pie3D()
        function for plotting the pie chart. Users can also choose to
        output the gene function mapping results and use external
        software such as Excel(R) for ploting.
Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.
Analyzes gene expression
  (time series) data with focus on the inference of gene networks.
  In particular, GeneNet implements the methods of Schaefer and 
  Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
  for learning large-scale gene association networks (including
  assignment of putative directions).
Belief propagation methods in genotype-phenotype networks (Conditional Gaussian and Discrete Bayesian Networks) to propagate phenotypic evidence through the network.
Functions for Genetic Epi Methods Developed at MSKCC
Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Since causal paths from data are important for all sciences, the
    package provides many sophisticated functions. causeSummBlk()
    gives easy-to-interpret causal paths.  Let Z denote control variables and compare 
    two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X,Z)+e2. Our criterion Cr1 
    says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent"
    than in Y and causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These
    inequalities between many absolute values are quantified by four orders of 
    stochastic dominance. Our third criterion Cr3 for the causal path X to Y
    requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|.
    The function parcorBMany() reports generalized partials between the first
    variable and all others.  The package provides several R functions including
    get0outliers() for outlier detection, bigfp() for numerical integration by the
    trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
    canonRho() for generalized canonical correlations, depMeas() measures nonlinear
    dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns
    is easiest to use. Several functions whose names begin with 'boot' provide bootstrap
    statistical inference including a new bootGcRsq() test for "Granger-causality" 
    allowing nonlinear relations. See Vinod (2019) <doi:10.1080/03610918.2015.1122048>.
Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal).
A statistical hypothesis test for conditional independence. It performs nonlinear regressions on the conditioning variable and then tests for a vanishing covariance between the resulting residuals. It can be applied to both univariate random variables and multivariate random vectors. Details of the method can be found in Rajen D. Shah and Jonas Peters (2018) <arXiv:1804.07203>.
Functions for the hyperbolic and related distributions.
  Density, distribution and quantile functions and random number generation
  are provided for the hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, normal inverse
	Gaussian distribution and generalized inverse Gaussian distribution,
	including fitting of these distributions to data. Linear models with
        hyperbolic errors may be fitted using hyperblmFit.
Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.
Perform the Blinder-Oaxaca decomposition for generalized linear 
   model with bootstrapped standard errors. The twofold and threefold 
   decomposition are given, even the generalized linear model output in each group.
Allows users to quickly and easily generate fake data containing
    Personally Identifiable Information (PII) through convenience functions.
In order to reduce potential package dependencies
    and conflicts, generics provides a number of commonly used S3
    generics.
Estimate gender from names in Spanish and Portuguese. 
    Works with vectors and dataframes. The estimation works not only
    for first names but also full names. The package relies on a
    compilation of common names with it's most frequent associated 
    gender in both languages which are used as look up tables for gender
    inference.
A tool for finding an ensemble gene-signature by a steepest ascending algorithm partially supervised by survival time data.
Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>).
  Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.
Classes and methods for handling genetic data. Includes
        classes to represent genotypes and haplotypes at single markers
        up to multiple markers on multiple chromosomes. Function
        include allele frequencies, flagging homo/heterozygotes,
        flagging carriers of certain alleles, estimating and testing
        for Hardy-Weinberg disequilibrium, estimating and testing for
        linkage disequilibrium, ...
Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
Implementation of spatial graph-theoretic genetic gravity models.
    The model framework is applicable for other types of spatial flow questions.
    Includes functions for constructing spatial graphs, sampling and summarizing
    associated raster variables and building unconstrained and singly constrained
    gravity models.
Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').
Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate  Hidden Markov Models (HMM). The goodness-of-fit test is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Nasri et al (2020) <doi:10.1029/2019WR025122>.
Includes the reference implementation of Genie - a hierarchical
    clustering algorithm that links two point groups in such a way that
    an inequity measure (namely, the Gini index) of the cluster sizes
    does not significantly increase above a given threshold.
    This method most often outperforms many other data segmentation approaches
    in terms of clustering quality as tested on a wide range of benchmark
    datasets. At the same time, Genie retains the high speed of the single
    linkage approach, therefore it is also suitable for analysing larger data sets.
    For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>).
    For an even faster and more feature-rich implementation, including,
    amongst others, noise point detection, see the 'genieclust' package.
A retake on the Genie algorithm - a robust
    hierarchical clustering method
    (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).
    Now faster and more memory efficient; determining the whole hierarchy
    for datasets of 10M points in low dimensional Euclidean spaces or
    100K points in high-dimensional ones takes only 1-2 minutes.
    Allows clustering with respect to mutual reachability distances
    so that it can act as a noise point detector or a robustified version of
    'HDBSCAN*' (that is able to detect a predefined number of
    clusters and hence it does not dependent on the somewhat
    fragile 'eps' parameter).
    The package also features an implementation of economic inequity indices
    (the Gini, Bonferroni index) and external cluster validity measures
    (partition similarity scores; e.g., the adjusted Rand, Fowlkes-Mallows,
    adjusted mutual information, pair sets index).
    See also the 'Python' version of 'genieclust' available on 'PyPI', which
    supports sparse data, more metrics, and even larger datasets.
Implements readers and writers for file formats associated with genetics data.  Reading and writing plink BED/BIM/FAM formats is fully supported, including a lightning-fast BED reader and writer implementations.  Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles plink and eigenstrat tables (FAM, BIM, IND, and SNP files).  There are also "make" functions for FAM and BIM tables with default values to go with simulated genotype data.
Easily access song lyrics in a tidy way.
Provides tools to interact nicely with the 'Genius' API 
    <https://docs.genius.com/>. 
    Search hosted content, extract associated metadata and retrieve lyrics with ease.
Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations.
Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive 'RStudio' plot for better guessing dynamically of initial values for ease of included optimization and simulating.
Generalized meta-analysis is a technique for estimating parameters associated with a multiple regression model through meta-analysis of studies which may have information only on partial sets of the regressors. It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) <arXiv:1708.03818>.
Using the DNA sequence and gene annotation files provided in 
            'ENSEMBL' <https://www.ensembl.org/index.html>, 
            the functions implemented in the package try to find the DNA sequences and 
            protein sequences of any given genomic loci, and to find the genomic coordinates 
            and protein sequences of any given protein locations, which are the frequent
            tasks in the analysis of genomic and proteomic data.
Calculates Agresti's (1980) <https://www.jstor.org/stable/2530495> generalized odds ratios.
  For a randomly selected pair of observations
  from two groups, calculates the odds that
  the second group will have a higher scoring outcome
  than that of the first group.
  Package provides hypothesis testing for if this odds
  ratio is significantly different to 1 (equal chance).
Evaluates likelihood ratio tests for alleged ancestry. Implements the methods of Tvedebrink et al (2018) <doi:10.1016/j.tpb.2017.12.004>.
Individual-based simulations forward in time,
    simulating how patterns in ancestry along the genome change after
    admixture. Full description can be found in Janzen (2020)
    <doi:10.1101/2020.10.19.343491>.
Plot values of markers(SNPs, expression, genes, RNA,...) for all chromosomes.
Local structure in genomic data often induces dependence between observations taken at different genomic locations.  Ignoring this dependence leads to underestimation of the standard error of parameter estimates.  This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.
Implements the genomic mating approach in the recently published article: Akdemir, D., & Sanchez, J. I. (2016). Efficient Breeding by Genomic Mating. Frontiers in Genetics, 7. <DOI:10.3389/fgene.2016.00210>.
Circular genomic permutation approach uses GWAS results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All SNPs in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values. The SNPs annotation and pathways annotations can be performed within the package or provided by the user.
A loose collection of tools for the analysis of expression and genotype data, currently with the main focus on (e)QTL and MDR analysis.
A collection of I/O tools for handling the most commonly used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.
Draws gene or genome maps and comparisons between these, in a 
   publication-grade manner. Starting from simple, common files, it will 
   draw postscript or PDF files that can be sent as such to journals.
A gaussian copula based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.
Functions for whole-genome sequencing studies, including genome-wide scan, candidate region scan and single window test.
We implement a common genotyping workflow with a standardized software interface. 'genotypeR' designs genotyping markers from vcf files, outputs markers for multiplexing suitability on various platforms (Sequenom and Illumina GoldenGate), and provides various QA/QC and analysis functions. We developed this package to analyze data in Stevison LS, SA Sefick, CA Rushton, and RM Graze. 2017. Invited Review: Recombination rate plasticity: revealing mechanisms by design. Philosophical Transactions Royal Society London B Biol Sci 372:1-14. <DOI:10.1098/rstb.2016.0459>, and have published it here Sefick, S.A., M.A. Castronova, and L.S. Stevison. 2017. GENOTYPER: An integrated R packages for single nuleotide polymorphism genotype marker design and data analysis. Methods in Ecology and Evolution 9: 1318-1323. <DOI: 10.1111/2041-210X.12965>.
It provides an interesting solution for handling a high number 
	of segmentation variables in partial least squares structural equation 
	modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, 
	and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient 
	test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) 
	to detect the path coefficients responsible for the identified differences), 
	the hybrid multi-group approach (Lamberti (2021) 
	<doi:10.1007/s11135-021-01096-9>) and the classical multi-group 
	approaches (parametric and permutation approaches). The package also 
	includes an extension of the "Pathmox" algorithm to the case of 
	linear regression models.
Power and sample size calculations for genetic association studies allowing 
    for misspecification of the model of genetic susceptibility.
    Power and/or sample size can be calculated for logistic (case/control study design) 
    and linear (continuous phenotype) regression models, using additive, dominant, 
    recessive or degree of freedom coding of the genetic covariate while assuming 
    a true dominant, recessive or additive genetic effect. In addition, power and 
    sample size calculations can be performed for gene by environment interactions.  
    These methods are extensions of Gauderman (2002) 
    <doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973>
    and are described in: 
    Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations 
    for Genetic Association Studies in the Presence of Genetic Model Misspecification. 
    American Society of Human Genetics. 
    October 2018, San Diego.
The genridge package introduces generalizations of the standard univariate
	ridge trace plot used in ridge regression and related methods.  These graphical methods
	show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
	coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
	both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
	predictors.
Performs search for global minimum of a very complex non-linear
    objective function with a very large number of optima.
Implementation of the Generalized Score Matching estimator in Yu et al. (2019) <http://jmlr.org/papers/v20/18-278.html> for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) <doi:10.1214/16-EJS1126>, with the addition of a diagonal multiplier.
For educational purposes to demonstrate the importance of multiple regression. The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results.
Define and compute with generalized spherical distributions - multivariate probability
 laws that are specified by a star shaped contour (directional behavior) and a radial component.
 The methods are described in Nolan (2016) <doi:10.1186/s40488-016-0053-0>.
Generation of survival data with one (binary)
        time-dependent covariate.  Generation of survival data arising
        from a progressive illness-death model.
The GenSVM classifier is a generalized multiclass support vector
	machine (SVM). This classifier aims to find decision boundaries that
	separate the classes with as wide a margin as possible. In GenSVM, the
	loss function is very flexible in the way that misclassifications are
	penalized.  This allows the user to tune the classifier to the dataset
	at hand and potentially obtain higher classification accuracy than
	alternative multiclass SVMs.  Moreover, this flexibility means that
	GenSVM has a number of other multiclass SVMs as special cases. One of
	the other advantages of GenSVM is that it is trained in the primal
	space, allowing the use of warm starts during optimization.  This
	means that for common tasks such as cross validation or repeated model
	fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den
	Burg and P.J.F. Groenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.
Implement a coherent and flexible protocol for animal color tagging. 'GenTag' provides a simple computational routine with low CPU usage to create color sequences for animal tag. First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications.
Make it easy to create simplified trial summary (TS) domain
  based on FDA FDA guide
  <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.
Defines window or bin boundaries for the analysis of genomic data.
    Boundaries are based on the inflection points of a cubic smoothing spline
    fitted to the raw data. Along with defining boundaries, a technique to
    evaluate results obtained from unequally-sized windows is provided.
    Applications are particularly pertinent for, though not limited to, genome
    scans for selection based on variability between populations (e.g. using
    Wright's fixations index, Fst, which measures variability in subpopulations
    relative to the total population).
Functions to fit geostatistical data. The data can be
        continuous, binary or count data and the models implemented are
        flexible. Conjugate priors are assumed on some parameters while
        inference on the other parameters can be done through a full
        Bayesian analysis of by empirical Bayes methods.
Make geographic box plot as detailed in Willmott et al. (2007).
Easy access to official spatial data sets of Brazil as 'sf' objects 
             in R. The package includes a wide range of geospatial data available
             at various geographic scales and for various years with harmonized
             attributes, projection and topology.
Tools for solving common geocaching puzzle types, and other
    Geocaching-related tasks.
Stores small spatial datasets used to teach basic spatial analysis
    concepts. Datasets are based off of the 'GeoDa' software workbook and data
    site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin
    and team at the University of Chicago. Datasets are stored as 'sf' objects.
Given expression data this package calculate a multivariate
    geometrical characterization of the differential expression and can also
    perform gene-set enrichment.
Spatial stratified heterogeneity (SSH), referring to the within strata are more similar than the between strata, a model with global parameters would be confounded if input data is SSH. Note that the "spatial" here can be either geospatial or the space in mathematical meaning. Geographical detector is a novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2) test the power of determinant X of a dependent variable Y according to the consistency between their spatial distributions; and (3) investigate the interaction between two explanatory variables X1 and X2 to a dependent variable Y (Wang et al 2014 <doi:10.1080/13658810802443457>, Wang, Zhang, and Fu 2016 <doi:10.1016/j.ecolind.2016.02.052>).
The geographic dimension plays a fundamental role 
    in multidimensional systems. To define a geographic dimension 
    in a star schema, we need a table with attributes corresponding 
    to the levels of the dimension. Additionally, we will also need 
    one or more geographic layers to represent the data using this 
    dimension. The goal of this package is to support the definition 
    of geographic dimensions from layers of geographic information 
    related to each other. It makes it easy to define relationships 
    between layers and obtain the necessary data from them.
Dependency-free, ultra fast calculation of geodesic
    distances.  Includes the reference nanometre-accuracy geodesic
    distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by
    the 'sf' package, as well as Haversine and Vincenty distances. Default
    distance measure is the "Mapbox cheap ruler" which is generally more
    accurate than Haversine or Vincenty for distances out to a few hundred
    kilometres, and is considerably faster. The main function accepts one
    or two inputs in almost any generic rectangular form, and returns
    either matrices of pairwise distances, or vectors of sequential
    distances.
Methods for calculating gradient surface metrics for
    continuous analysis of landscape features.
Draw geospatial objects by clicks on the map.
    This packages can help data analyst who want to check
    their own geospatial hypothesis but has no ready-made geospatial objects.
Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included. The k-sphere is a k-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (k+1)-dimensional space. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y> and Kim, H. J., Adluru, N., Collins, M. D., Chung, M. K., Bendin, B. B., Johnson, S. C., Davidson, R. J. and Singh, V. (2014) <doi:10.1109/CVPR.2014.352>.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Download 'OpenStreetMap' data from 'geofabrik' servers <https://download.geofabrik.de/>. This approach uses 
    only the direct link downloads. Besides, this package
    does not import any external package.
Provides geofaceting functionality for 'ggplot2'. Geofaceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation.
Kriging based methods are used for predicting functional data 
             (curves) with spatial dependence.
Tools for reading Finnish open geospatial data in R.
A Kriging method for  functional datasets with spatial dependency.
    This functional Kriging method avoids the need to estimate the
    trace-variogram, and the curve is estimated by minimizing a quadratic
    form. The curves in the functional dataset are smoothed using Fourier
    series. The functional Kriging of this package is a modification of the
    method proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>.
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided.
The American Community Survey (ACS) <https://www.census.gov/programs-surveys/acs> 
    offers geodatabases with geographic information and associated 
    data of interest to researchers in the area. The goal of this 
    package is to generate geomultistar objects from those geodatabases 
    automatically, once the focus of attention is selected. 
    Multidimensional queries with geographic information can be easily 
    defined on these objects.
Turn irregular polygons (such as geographical regions) into regular or hexagonal grids.
    This package enables the generation of regular (square) and hexagonal grids through the package 
    'sp' and then assigns the content of the existing polygons to the new grid using 
    the Hungarian algorithm, Kuhn (1955) (<doi:10.1007/978-3-540-68279-0_2>). 
    This prevents the need for manual generation of hexagonal grids or regular grids 
    that are supposed to reflect existing geography.
Tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries.
Classes for 'GeoJSON' to make working with 'GeoJSON' easier.
    Includes S3 classes for 'GeoJSON' classes with brief summary output,
    and a few methods such as extracting and adding bounding boxes,
    properties, and coordinate reference systems; working with 
    newline delimited 'GeoJSON'; linting through the 'geojsonlint' 
    package; and serializing to/from 'Geobuf' binary 'GeoJSON' 
    format.
Convert data to 'GeoJSON' or 'TopoJSON' from various R classes,
    including vectors, lists, data frames, shape files, and spatial classes.
    'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos',
    but rather aims to be a high level client to simplify conversions of data
    from and to 'GeoJSON' and 'TopoJSON'.
Tools for linting 'GeoJSON'. Includes tools for interacting with the
    online tool <http://geojsonlint.com>, the 'Javascript' library 'geojsonhint'
    (<https://www.npmjs.com/package/geojsonhint>), and validating against a
    'GeoJSON' schema via the 'Javascript' library
    (<https://www.npmjs.com/package/is-my-json-valid>). Some tools work locally
    while others require an internet connection.
Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://tools.ietf.org/pdf/rfc7946.pdf>. The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' <https://github.com/dropbox/json11>. Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages.
Converts Between GeoJSON and simple feature objects.
Processes gridded datasets found on the U.S. Geological Survey
    Geo Data Portal web application or elsewhere, using a web-enabled workflow
    that eliminates the need to download and store large datasets that are reliably
    hosted on the Internet. The package provides access to several data subset and
    summarization algorithms that are available on remote web processing servers (Read et al. (2015) <doi:10.1111/ecog.01880>).
Provides basic functions for global
    positioning based on light intensity measurements over time.
    Positioning process includes the determination of sun events, a
    discrimination of residency and movement periods, the
    calibration of period-specific data and, finally, the
    calculation of positions.
Set of routines for making Map Projections (forward and inverse), Topographic Maps, Perspective plots, Geological Maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Set of data for use in package GEOmap.  Includes world
        map, USA map, Coso map, Japan Map, ETOPO5
Provides eigenvector-based (geometric) forecast
    combination methods; also includes simple approaches (simple average,
    median, trimmed and winsorized mean, inverse rank method) and regression-based
    combination. Tools for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity).
The Genomic Observatory Metadatabase (GeOMe Database) is an open access repository for
    geographic and ecological metadata associated with sequenced samples. This package is used to retrieve
    GeOMe data for analysis. See <http://www.geome-db.org> for more information regarding GeOMe.
Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution.
Provides facilities to handle reading and writing of geographic metadata 
 defined with OGC/ISO 19115, 11119 and 19110 geographic information metadata standards,
 and encoded using the ISO 19139 (XML) standard. It includes also a facility to check
 the validity of ISO 19139 XML encoded metadata.
Provides tools that generate and process fully accessible and tidy
    geometric shapes. The package improves interoperability of spatial and 
    other geometric classes by providing getters and setters that produce 
    identical output from various classes.
Geometry shapes in 'R' are typically represented by matrices (points, lines), with more complex 
  shapes being lists of matrices (polygons). 'Geometries' will convert various 'R' objects into these shapes. 
  Conversion functions are available at both the 'R' level, and through 'Rcpp'.
Makes the 'Qhull' library <http://www.qhull.org>
    available in R, in a similar manner as in Octave and MATLAB. Qhull
    computes convex hulls, Delaunay triangulations, halfspace
    intersections about a point, Voronoi diagrams, furthest-site
    Delaunay triangulations, and furthest-site Voronoi diagrams. It
    runs in 2D, 3D, 4D, and higher dimensions. It implements the
    Quickhull algorithm for computing the convex hull. Qhull does not
    support constrained Delaunay triangulations, or mesh generation of
    non-convex objects, but the package does include some R functions
    that allow for this.
Network visualization in the 'ggplot2' framework. Network
    functionality is provided in a single 'ggplot2' layer by calling the geom 'net'.
    Layouts are calculated using the 'sna' package, example networks are included.
Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://api.mongodb.com/python/current/#> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.
Read, manipulate, and digitize landmark data, generate shape
    variables via Procrustes analysis for points, curves and surfaces, perform
    shape analyses, and provide graphical depictions of shapes and patterns of
    shape variation.
Multidimensional systems allow complex queries to be 
    carried out in an easy way. The geographical dimension, 
    together with the temporal dimension, plays a fundamental 
    role in multidimensional systems. Through this package, vector 
    layers can be associated to the attributes of geographic 
    dimensions, so that the results of multidimensional queries 
    can be obtained directly as vector layers. The multidimensional 
    structures on which we can define the queries can be created 
    from a flat table or imported directly using functions from 
    this package.
The web service at <https://www.geonames.org/> provides a number of spatial data queries, including 
    administrative area hierarchies, city locations and some country postal code queries. A (free) username
    is required and rate limits exist.
Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and exposte it to OGC CSW.
Provides classes and methods for handling networks or 
  graphs whose nodes are geographical (i.e. locations in the globe).
  The functionality includes the creation of objects of class geonetwork
  as a graph with node coordinates, the computation of network measures,
  the support of spatial operations (projection to different Coordinate
  Reference Systems, handling of bounding boxes, etc.) and the plotting of
  the geonetwork object combined with supplementary cartography for spatial 
  representation.
Codes for analyzing various problems of geophysics, continuum mechanics and gravity models.
Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.
Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.
Provides functions for efficiently fitting linear models with 
         spatially correlated errors by robust and Gaussian (Restricted) 
         Maximum Likelihood and for computing robust and customary point 
         and block external-drift Kriging predictions, along with utility
         functions for variogram modelling in ad hoc geostatistical analyses, 
         model building, model evaluation by cross-validation, (conditional) 
         simulation of Gaussian processes, unbiased back-transformation of 
         Kriging predictions of log-transformed data.
Provides an R API to the Open Source Geometry Engine
  ('GEOS') library (<https://trac.osgeo.org/geos/>) and a vector format 
  with which to efficiently store 'GEOS' geometries. High-performance functions 
  to extract information from, calculate relationships between, and
  transform geometries are provided. Finally, facilities to import 
  and export geometry vectors to other spatial formats are provided.
Provides an R interface to the GeoServer REST API, allowing to upload 
 and publish data in a GeoServer web-application and expose data to OGC Web-Services. 
 The package currently supports all CRUD (Create,Read,Update,Delete) operations
 on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes,
 layers, styles, as well as vector data upload operations. For more information about 
 the GeoServer REST API, see <http://docs.geoserver.org/stable/en/user/rest/>.
Function for adding the geological timescale to bivariate plots.
Find the smallest circle that contains all longitude and latitude input points. From the generated center and radius, variable side polygons can be created, navigation based on bearing and distance can be applied, and more. Based on a modified version of Welzl's algorithm for smallest circle. Distance calculations are based on the haversine formula. Calculations for distance, midpoint, bearing and more are derived from <https://www.movable-type.co.uk>.
R binds 'GeoSpark' <http://geospark.datasyslab.org/> extending 'sparklyr' 
    <https://spark.rstudio.com/> R package to make distributed 'geocomputing' easier. Sf is a
    package that provides [simple features] <https://en.wikipedia.org/wiki/Simple_Features> access
    for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring 
    the same simple features access like sf but running on Spark distributed system.
Provides S4 classes and data import, preprocessing, graphing, 
    manipulation and export methods for geo-Spectral datasets (datasets with space/time/spectral 
    dimensions). These type of data are frequently collected within earth observation projects 
    (remote sensing, spectroscopy, bio-optical oceanography, mining, agricultural, atmospheric, 
    environmental or similar branch of science).
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis 
        functions (optimization, prediction and cross-validation), summary
        statistics from cross-validation, pocket plot, and design of
        optimal sampling networks through sequential and simultaneous
        points methods.
Spatio-temporal: Inverse Distance Weighting (IDW) and radial basis functions; optimization, prediction, summary statistics from leave-one-out cross-validation, adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
A collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as 'compositions', 'robCompositions', 'sp', 'gstat' and 'RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at <https://tinyurl.com/UCLgeostats/>.
Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) 
    provide us with a wealth of data, such as RNA-seq, DNA Methylation, 
    and Copy number variation data. It's easy to download data from TCGA using the 
    gdc tool, but processing these data into a format suitable for bioinformatics 
    analysis requires more work. This R package was developed to handle these data.
A compilation of functions for performing calculations and
    creating plots that commonly arise in geotechnical engineering and soil
    mechanics. The types of calculations that are currently included are:
    (1) phase diagrams and index parameters, (2) grain-size distributions, 
    (3) plasticity, (4) soil classification, (5) compaction, (6) groundwater,
    (7) subsurface stresses (geostatic and induced), (8) Mohr circle analyses,
    (9) consolidation settlement and rate, (10) shear strength, (11) bearing
    capacity, (12) lateral earth pressures, (13) slope stability, and (14)
    subsurface explorations. Geotechnical engineering students, educators,
    researchers, and practitioners will find this package useful.
The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure.There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.
It analyzes raster maps and other information as input/output
    files from the Hydrological Distributed Model GEOtop. It contains functions
    and methods to import maps and other keywords from geotop.inpts file. Some
    examples with simulation cases of GEOtop 2.x/3.x are presented in the package.
    Any information about the GEOtop Distributed Hydrological Model source code
    is available on www.geotop.org. Technical details about the model are
    available in Endrizzi et al, 2014
    (<http://www.geosci-model-dev.net/7/2831/2014/gmd-7-2831-2014.html>).
Provides functions and methods for: splitting large raster objects
             into smaller chunks, transferring images from a binary format into raster 
             layers, transferring raster layers into an 'RData' file, calculating the 
             maximum gap (amount of consecutive missing values) of a numeric vector, 
             and fitting harmonic regression to periodic time series. The methods 
             implemented for harmonic regression are based on G. Roerink, M. Menenti 
             and W. Verhoef (2000) <doi:10.1080/014311600209814>.
The toolbox have functions to load and process geographic information for Uruguay. 
        And extra-function to get address coordinates and orthophotos through the uruguayan 'IDE' API <https://www.gub.uy/infraestructura-datos-espaciales/tramites-y-servicios/servicios/servicio-direcciones-geograficas>.
Simpler processing of digital elevation model and GPS trace data for use with the 'rayshader' package.
Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.
Encode and decode the Google Encoded Polyline Algorithm Format (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Data package to disseminate the  'GermaParl' corpus of parliamentary debates of 
  the German Bundestag prepared in the 'PolMine Project'. The package includes a small subset of
  the corpus for demonstration and testing purposes. The package includes functionality to 
  download the full corpus and supplementary data from the open science repository 'Zenodo'.
A collection of different indices and visualization techniques for evaluate the seed germination process in ecophysiological studies (Lozano-Isla et al. 2019) <doi:10.1111/1440-1703.1275>.
Provides functions to compute various germination indices such as
    germinability, median germination time, mean germination time, mean
    germination rate, speed of germination, Timson's index, germination value,
    coefficient of uniformity of germination, uncertainty of germination
    process, synchrony of germination etc. from germination count data. Includes
    functions for fitting cumulative seed germination curves using
    four-parameter hill function and computation of associated parameters. See
    the vignette for more, including full list of citations for the methods
    implemented.
Simple git client for R based on 'libgit2' with support for SSH and 
    HTTPS remotes. All functions in 'gert' use basic R data types (such as vectors
    and data-frames) for their arguments and return values. User credentials are
    shared with command line 'git' through the git-credential store and ssh keys
    stored on disk or ssh-agent.
Fit a variety of component-based structural equation models.
Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.
Reproducible, programmatic retrieval of datasets from the
    GESIS Data Archive.  The GESIS Data Archive <https://search.gesis.org>  
    makes available thousands of invaluable datasets, but researchers using
    these datasets are caught in a bind.  The archive's terms and conditions
    bar dissemination of downloaded datasets to third parties, but to ensure 
    that one's work can be reproduced, assessed, and built upon by others, one
    must provide access to the raw data one has employed.  The 'gesisdata'
    package cuts this knot by providing registered users with programmatic,
    reproducible access to GESIS datasets from within 'R'.
Provides a suite of function-building tools centered around a
  (forward) composition operator, %>>>%, which extends the semantics of the
  'magrittr' %>% operator and supports 'tidyverse' quasiquotation. It enables
  you to construct composite functions that can be inspected and transformed as
  list-like objects. In conjunction with %>>>%, a compact function constructor,
  fn(), and a function that performs partial application, partial(), are also
  provided. Both support quasiquotation.
Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).
The Gene Expression State Transformation (GESTr) models
        the states of expression of genes across a compendium of
        samples in order to provide a universal scale of gene
        expression for all genes. TranSAM is a modification of the SAM
        approach designed to utilise GESTr-transformed gene expression
        data.
Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) <doi:10.1515/em-2015-0005> and Dukes and Vansteelandt <doi:10.1093/aje/kwx347>. The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories. The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable.
Implementation of global envelopes for a set of general d-dimensional vectors T
    in various applications. A 100(1-alpha)% global envelope is a band bounded by two
    vectors such that the probability that T falls outside this envelope in any of the d
    points is equal to alpha. Global means that the probability is controlled simultaneously
    for all the d elements of the vectors. The global envelopes can be used for graphical
    Monte Carlo and permutation tests where the test statistic is a multivariate vector or
    function (e.g. goodness-of-fit testing for point patterns and random sets, functional
    analysis of variance, functional general linear model, n-sample test of correspondence
    of distribution functions), for central regions of functional or multivariate data (e.g.
    outlier detection, functional boxplot) and for global confidence and prediction bands
    (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See
    Myllymäki and Mrkvička (2020) <arXiv:1911.06583>,
    Myllymäki et al. (2017) <doi: 10.1111/rssb.12172>,
    Mrkvička et al. (2017) <doi: 10.1007/s11222-016-9683-9>,
    Mrkvička et al. (2020) <doi: 10.14736/kyb-2020-3-0432>,
    Mrkvička et al. (2019) <doi: 10.1007/s11009-019-09756-y>,
    Mrkvička et al. (2019) <arXiv:1902.04926>,
    Mrkvička et al. (2016) <doi: 10.1016/j.spasta.2016.04.005>, and
    Myllymäki et al. (2020) <doi: 10.1016/j.spasta.2020.100436>.
Dynamically retrieve data from the web to render HTML tables
    on inspection in R Markdown HTML documents.
Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the 'memoise' package and
    , multicore/multisession with 'furrr' and format of output data (long/wide).
Provides functions that automate downloading and importing
    University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0
    climatology data, facilitates the calculation of minimum temperature and
    maximum temperature and formats the data into a tidy data frame as a
    'tibble' or a list of 'raster' 'stack' objects for use.  'CRU' 'CL' v. 2.0
    data are a gridded climatology of 1961-1990 monthly means released in 2002
    and cover all land areas (excluding Antarctica) at 10 arcminutes
    (0.1666667 degree) resolution.  For more information see the description of
    the data provided by the University of East Anglia Climate Research Unit,
    <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Reads annual financial reports including assets, liabilities, dividends history, stockholder composition and much more from Bovespa's DFP, FRE and FCA systems <http://www.bmfbovespa.com.br/en_us/products/listed-equities-and-derivatives/equities/listed-companies.htm>.
 These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing.
The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design.
    The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.
Full access to the Geth command line interface for running full Ethereum nodes. With 
    gethr it is possible to carry out different tasks such as mine ether, transfer funds, create 
    contacts, explore block history, etc. The package also provides access to all the available APIs. 
    The officially exposed by Ethereum blockchains (eth, shh, web3, net) and some provided directly 
    by Geth (admin, debug, miner, personal, txpool). For more details on Ethereum, access the project 
    website <https://www.ethereum.org/>. For more details on the Geth client, access the project 
    website <https://github.com/ethereum/go-ethereum/wiki/geth/>.
Get Landsat 8 Data from Amazon Web Services ('AWS')
    public data sets (<https://registry.opendata.aws/landsat-8/>).
    Includes functions for listing images and fetching them, and handles
    caching to prevent unnecessary additional requests.
Tool for import and process data from 'Lattes' curriculum platform (<http://lattes.cnpq.br/>). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in 'Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. 'getLattes' package allows work with 'Lattes' data exported to XML format.
A simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.
Hydrologic models often require users to collect and format input meteorological data. This package contains functions for sourcing, formatting, and
    editing meteorological data for hydrologic models.
Quantifying systematic heterogeneity in meta-analysis using R.
    The M statistic aggregates heterogeneity information across multiple
    variants to, identify systematic heterogeneity patterns and their direction
    of effect in meta-analysis. It's primary use is to identify outlier studies,
    which either show "null" effects or consistently show stronger or weaker
    genetic effects than average across, the panel of variants examined in a
    GWAS meta-analysis. In contrast to conventional heterogeneity metrics
    (Q-statistic, I-squared and tau-squared) which measure random heterogeneity
    at individual variants, M measures systematic (non-random)
    heterogeneity across multiple independently associated variants. Systematic
    heterogeneity can arise in a meta-analysis due to differences in the study
    characteristics of participating studies. Some of the differences may
    include: ancestry, allele frequencies, phenotype definition, age-of-disease
    onset, family-history, gender, linkage disequilibrium and quality control
    thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical
    statistical theory, documentation and examples.
Package designed to be used with Rscript to write
    ``#!'' shebang scripts that accept short and long flags/options.
    Many users will prefer using instead the packages optparse or argparse
    which add extra features like automatically generated help option and usage,
    support for default values, positional argument support, etc.
This is a command-line argument parser which wraps the 
    powerful Perl module Getopt::Long and with some adaptations for easier use
	in R. It also provides a simple way for variable interpolation in R.
A micro-package for reading "passwords", i.e.  reading
    user input with masking, so that the input is not displayed as it 
    is typed.  Currently we have support for 'RStudio', the command line
    (every OS), and any platform where 'tcltk' is present.
Allows get address and port 
	of the free proxy server, from one of two services
	<http://gimmeproxy.com/> or <https://getproxylist.com/>. 
	And it's easy to redirect your Internet connection through
	a proxy server.
Imports time series data from the 'Quandl' database <https://www.quandl.com>. The package uses the  'json api' at <https://www.quandl.com/tools/api>, local caching ('memoise' package) and the tidy format by default. 
   Also allows queries of databases, allowing the user to see which time series are available for each database id. In short, it is an alternative to package 'Quandl', with faster data importation in the tidy/long format.
The GetR package calculates Guttman error trees, which can
        be used to find homogeneous subgroups regarding Guttman errors.
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03>.
An implementation of SPRE (standardised predicted random-effects)
    statistics in R to explore heterogeneity in genetic association meta-
    analyses, as described by Magosi et al. (2019) 
    <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision 
    weighted residuals that indicate the direction and extent with which 
    individual study-effects in a meta-analysis deviate from the average 
    genetic effect. Overly influential positive outliers have the potential 
    to inflate average genetic effects in a meta-analysis whilst negative 
    outliers might lower or change the direction of effect. See the 'getspres' 
    website for documentation and examples 
    <https://magosil86.github.io/getspres/>.
Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <http://www.tesouro.fazenda.gov.br/tesouro-direto-balanco-e-estatisticas>.
A function to retrieve the system timezone on Unix systems
 which has been found to find an answer when 'Sys.timezone()' has failed.
 It is based on an answer by Duane McCully posted on 'StackOverflow', and
 adapted to be callable from R. The package also builds on Windows, but
 just returns NULL.
A novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.
Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.
Perform statistical tests to unveil genomic clusters,
        produces garphical interpretations of the statistical results
        in pdf files, perform a Gene Ontology analysis and produces
        graphic results in pdf files
Generates experiments - simulating structured or experimental data as: 
             completely randomized design, randomized block design, latin square design, 
             factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; 
             Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; 
             Montgomery, 2001, ISBN:0471316490).
Download geyser eruption and observation data from the GeyserTimes
  site (<https://geysertimes.org>) and optionally store it locally. The vignette
  shows the a simple analysis of downloading, accessing, and summarizing
  the data.
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.
Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL '<https://oops.uni-oldenburg.de/id/eprint/4290>').
Supports analyses using the Global Forest Change dataset released
    by Hansen et al. gfcanalysis was originally written for the Tropical Ecology 
    Assessment and Monitoring (TEAM) Network. For additional details on the 
    Global Forest Change dataset, see: Hansen, M. et al. 2013. "High-Resolution 
    Global Maps of 21st-Century Forest Cover Change." Science 342 (15 
    November): 850-53. The forest change data and more information on the 
    product is available at <http://earthenginepartners.appspot.com>.
Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
    Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.
The philosophy in the package is described in Stasny (1988) <doi:10.2307/1391558> and Gutiérrez, A., Trujillo, L. & Silva, N. (2014), <ISSN:1492-0921> to estimate the gross flows under complex surveys using a Markov chain approach with non response.
Focuses on data collecting, analyzing and visualization in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites such as MEP (Ministry of Environmental Protection of China, <http://www.mep.gov.cn>), water 
  related projects identification and environmental data visualization.
Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.
Fiducial framework to perform inference on the quantiles for a generalized Pareto distribution model and on the parameters of the Pareto exceedance distribution, assuming the exceedance threshold is a known or unknown parameter. Reference: Damian V. Wandler & Jan Hannig (2012) <doi:10.1007/s10687-011-0127-9>.
Fiducial framework for linear regression models allowing normal, Student, Cauchy, or logistic error terms. Only low-dimensional models are possible, such as the simple linear regression model, or the one-way ANOVA model with two factor levels. Reference: Hannig, Lai & Lee (2014) <doi:10.1016/j.csda.2013.03.003>.
Simulation of the generalized fiducial distribution for normal linear mixed models with interval data. Fiducial inference is somehow similar to Bayesian inference, in the sense that it is based on a distribution that represents the uncertainty about the parameters, like the posterior distribution in Bayesian statistics. It does not require a prior distribution, and it yields results close to frequentist results. Reference: Cisewski and Hannig (2012) <doi:10.1214/12-AOS1030>.
Fiducial framework for the logistic regression model. The fiducial distribution of the parameters of the logistic regression is simulated, allowing to perform statistical inference on any parameter of interest. The algorithm is taken from Jessi Cisewski's PhD thesis: Jessi Cisewski (2012), "Generalized fiducial inference for mixed linear models".
Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.
Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.
Software to implement methodology to preform automatic response category
  combinations in multinomial logistic regression. There are functions for both cross validation
  and AIC for model selection.  The method provides regression coefficient estimates
  that may be useful for better understanding the true probability distribution of
  multinomial logistic regression when category probabilities are similar. These methods are not
  recommended for a large number of predictor variables.
Download 'Google' fonts and generate 'CSS' to use in 'rmarkdown' documents and 
  'shiny' applications. Some popular fonts are included and ready to use.
Implements the parametric g-formula algorithm of Robins (1986) 
    <doi:10.1016/0270-0255(86)90088-6>. The g-formula can be used to estimate 
    the causal effects of hypothetical time-varying treatment interventions on 
    the mean or risk of an outcome from longitudinal data with time-varying 
    confounding. This package allows: 1) binary or continuous/multi-level 
    time-varying treatments; 2) different types of outcomes (survival or 
    continuous/binary end of follow-up); 3) data with competing events or 
    truncation by death and loss to follow-up and other types of censoring 
    events; 4) different options for handling competing events in the case of 
    survival outcomes; 5) a random measurement/visit process; 6) joint 
    interventions on multiple treatments; and 7) general incorporation of a 
    priori knowledge of the data structure.
Penalized parametric change-point detection by functional pruning dynamic programming algorithm. 
  The successive means are constrained using a graph structure with edges of types null, up, down, std or abs. To each edge we can associate some additional properties: a minimal gap size, a penalty, some robust parameters (K,a). The user can also constrain the inferred means to lie between some minimal and maximal values. Data is modeled by a quadratic cost with possible use of a robust loss, biweight and Huber (see edge parameters K and a). Other losses are also available with log-linear representation or a log-log representation.
It is not very easy to define segments for y-axis in a 'ggplot2' plot. gg.gap() function in this package can carry it out.
Extra geoms and scales for 'ggplot2', including geom_cloud(),
  a Normal density cloud replacement for errorbars;
  transforms ssqrt_trans and pseudolog10_trans, which are loglike but 
  appropriate for negative data; interp_trans() and warp_trans() which
  provide scale transforms based on interpolation;
  and an infix compose operator for scale transforms.
Alluvial plots use variable-width ribbons and stacked bar plots to
    represent multi-dimensional or repeated-measures data with categorical or
    ordinal variables; see Riehmann, Hanfler, and Froehlich (2005)
    <doi:10.1109/INFVIS.2005.1532152> and Rosvall and Bergstrom (2010)
    <doi:10.1371/journal.pone.0008694>.
    Alluvial plots are statistical graphics in the sense of Wilkinson (2006)
    <doi:10.1007/0-387-28695-0>; they share elements with Sankey diagrams and
    parallel sets plots but are uniquely determined from the data and a small
    set of parameters. This package extends Wickham's (2010)
    <doi:10.1198/jcgs.2009.07098> layered grammar of graphics to generate
    alluvial plots from tidy data.
The R package 'ggplot2' is a plotting system based on the grammar of graphics.
    'GGally' extends 'ggplot2' by adding several functions
    to reduce the complexity of combining geometric objects with transformed data.
    Some of these functions include a pairwise plot matrix, a two group pairwise plot
    matrix, a parallel coordinates plot, a survival plot, and several functions to
    plot networks.
A compendium of new geometries, coordinate systems, statistical 
    transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, 
    univariate average shifted histograms, a new map coordinate system based on the 
    'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of 
    geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly'
    compatibility and the 'StateFace' open source font 'ProPublica'. Further new 
    functionality includes lollipop charts, dumbbell charts, the ability to encircle
    points and coordinate-system-based text annotations.
Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.
The grammar of graphics as implemented in the 'ggplot2' package has
    been successful in providing a powerful API for creating static 
    visualisation. In order to extend the API for animated graphics this package
    provides a completely new set of grammar, fully compatible with 'ggplot2' 
    for specifying transitions and animations in a flexible and extensible way.
Plots a symmetric matrix with three different fill
    aesthetics for the top-left and bottom-right triangles and along the
    diagonal. It operates within the Grammar of Graphics paradigm implemented
    in 'ggplot2'.
Provides two methods of plotting categorical scatter plots such
    that the arrangement of points within a category reflects the density of
    data at that region, and avoids over-plotting.
When comparing discrete data mini bubble plots allow displaying 
   more information than traditional bubble plots via colour, shape or labels. 
   Exact overlapping coordinates will be transformed so they surround the
   original point circularly without overlapping. This is implemented as a 
   position_surround() function for 'ggplot2'.
Saves a 'ggplot' object into multiple files, each with a layer 
    added incrementally. Generally to be used in presentation slides.
    Flexible enough to allow different file types for the final complete plot,
    and intermediate builds.
A geom for ggplot to create bump plots. Can be good to use for showing rank over time.
Streamline the creation of common charts by taking care of a lot of
    data preprocessing and plot customization for the user. Provides a
    high-level interface to create plots using 'ggplot2'.
Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data
    to human-like faces.
Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.
A flexible interface for ggplot2::theme(), potentially saving 50% of your typing.
The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for reordering the
    correlation matrix and displays the significance level on the plot. It also
    includes a function for computing a matrix of correlation p-values.
Tidy, analyze, and plot directed acyclic graphs
    (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses
    the 'DAGitty' web tool (<http://dagitty.net>) for creating and
    analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty'
    objects using 'ggplot2' and 'ggraph', as well as common analytic and
    graphical functions, such as determining adjustment sets and node
    relationships.
Activate dark mode on your favorite 'ggplot2' theme 
             with dark_mode() or use the dark versions of 
             'ggplot2' themes, including dark_theme_gray(),
             dark_theme_minimal(), and others. When a dark theme 
             is applied, all geom color and geom fill defaults 
             are changed to make them visible against a dark 
             background. To restore the defaults to their original 
             values, use invert_geom_defaults().
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 and may be cumbersome to 
    apply to models that yield a continuous result.
  Decision curve analysis is a suitable method for evaluating 
    alternative diagnostic and prognostic strategies that has advantages over 
    other commonly used measures and techniques.
  This method was described by Andrew J. Vickers (2006) <doi:10.1177/0272989X06295361>.
Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment 
    made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>),
    the seasonal adjustment software officially recommended to the members of the European Statistical System and
    the European System of Central Banks.
This is a set of tools for dendrograms and
    tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
    clearly separate data from the presentation.
    Unfortunately the plot method for dendrograms plots
    directly to a plot device without exposing the data.
    The 'ggdendro' package resolves this by making available
    functions that extract the dendrogram plot data. The package
    provides implementations for 'tree', 'rpart', as well as diana and agnes
    (from 'cluster') diagrams.
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for
    visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as 
    frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as 
    bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include
    but are not limited to: points with multiple uncertainty intervals, 
    eye plots (Spiegelhalter D., 1999) <doi:10.1111/1467-985X.00120>,
    density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>,
    quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>,
    complementary cumulative distribution function 
    barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>,
    and fit curves with multiple uncertainty ribbons.
Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.
Create biplots for GGE (genotype plus genotype-by-environment) and
    GGB (genotype plus genotype-by-block-of-environments) models. 
    See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.
Provides a series of aliases to commonly used but difficult 
    to remember 'ggplot2' sequences.
Description: A GUI with which to construct and interact with GGE biplots.
Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within 'GGEBiplotGUI'.
Interactively edit 'ggplot2' layer and theme aesthetics definitions.
Compute marginal effects from statistical models and returns the 
    result as tidy data frames. These data frames are ready to use with the 
    'ggplot2'-package. Marginal effects can be calculated for many different 
    models. Interaction terms, splines and polynomial terms are also supported. 
    The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a 
    generic plot()-method to plot the results using 'ggplot2'.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.
Extension of 'ggplot2' providing layers, scales and preprocessing functions
    useful to represent behavioural variables that are recorded over multiple animals and days.
    This package is part of the 'rethomics' framework <http://rethomics.github.io/>.
Collection of functions and layers to enhance 'ggplot2'. The 
    flagship function is 'ggMarginal()', which can be used to add marginal
    histograms/boxplots/density plots to 'ggplot2' scatterplots.
Implements the functionality of the 'fanplot' package as 'geoms' 
  for 'ggplot2'. Designed for summarising MCMC samples from a posterior 
  distribution, where a visualisation is desired for several values of a 
  continuous covariate. Increasing posterior intervals of the sampled quantity 
  are mapped to a continuous colour scale.
Provides 'ggplot2' geoms to fit text into a box by growing, shrinking
    or wrapping the text.
A 'ggplot2' extension that provides tools for automatically
    creating scales to focus on subgroups of the data plotted 
    without losing other information.
The aim of 'ggplot2' is to aid in visual data investigations. This
    focus has led to a lack of facilities for composing specialised plots.
    'ggforce' aims to be a collection of mainly new stats and geoms that fills
    this gap. All additional functionality is aimed to come through the official
    extension system so using 'ggforce' should be a stable experience.
Provides a formula interface to 'ggplot2' graphics.
Unified plotting tools for statistics commonly used, such as GLM,
    time series, PCA families, clustering and survival analysis. The package offers
    a single plotting interface for these analysis results and plots in a unified
    style using 'ggplot2'.
The function gggap() streamlines the creation of segments on
    the y-axis of 'ggplot2' plots which is otherwise not a trivial task to
    accomplish.
Provides a 'ggplot2' geom and helper functions for drawing gene
  arrow maps.
Creates a reproducible 'ggplot2' object by storing the data and calls.
Moon charts are like pie charts except that the proportions are
    shown as crescent or gibbous portions of a circle, like the lit and unlit
    portions of the moon. As such, they work best with only one or two groups.
    'gggibbous' extends 'ggplot2' to allow for plotting multiple moon charts in
    a single panel and does not require a square coordinate system.
Utilities for Creating Guitar Tablature using tidyverse packages.
A 'ggplot2' extension that does a variety of little
    helpful things.  The package extends 'ggplot2' facets through
    customisation, by setting individual scales per panel, resizing panels
    and providing nested facets.  Also allows multiple colour and fill
    scales per plot. Also hosts a smaller collection of stats, geoms and axis 
    guides.
Reproduce the halfnorm() function found in the 'faraway' package 
    using the 'ggplot2' API.
A 'ggplot2' extension for easy plotting of half-half geom combinations. Think half boxplot and half jitterplot, or half violinplot and half dotplot.
Make it easier to explore data with highlights.
Supports image files and graphic objects to be visualized in
    'ggplot2' graphic system.
Provides two new layer types for displaying image data as layers
  within the Grammar of Graphics framework. Displays images using either a
  rectangle interface, with a fixed bounding box, or a point interface using a
  central point and general size parameter. Images can be given as local
  JPEG or PNG files, external resources, or as a list column containing
  raster image data.
Visualise the results of F test to compare two variances, Student's t-test, test of equal or given proportions, Pearson's chi-squared test for count data and test for association/correlation between paired samples.
Extensions to 'ggplot2' providing low-level debug tools: statistics
    and geometries echoing their data argument. Layer manipulation: deletion,
    insertion, extraction and reordering of layers. Deletion of unused variables 
    from the data object embedded in "ggplot" objects.
A 'ggplot2' extension that enables visualization of
    IP (Internet Protocol) addresses and networks. The address space is
    mapped onto the Cartesian coordinate system using a space-filling
    curve. Offers full support for both IPv4 and IPv6 (Internet Protocol
    versions 4 and 6) address spaces.
A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from  'Actigraph' <https://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format and has either no header or a two column header. Also the package allows for external function embedding.
Create interactive 'ggplot2' graphics using 'htmlwidgets'.
Collection of functions to enhance 'ggplot2' and 'ggiraph'. Provides functions for exploratory plots.
    All plot can be a 'static' plot or an 'interactive' plot using 'ggiraph'.
Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
Allows for easy creation of diagnostic plots for linear models using the Grammar of Graphics.  
  Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.
Visualize sequences in (modified) logo plots. The design choices
    used by these logo plots allow sequencing data to be more easily analyzed.
    Because it is integrated into the 'ggplot2' geom framework, these logo plots
    support native features such as faceting.
Pass a data frame and mapping aesthetics to ggloop() in order
    to create a list of 'ggplot2' plots. The way x-y and dots are paired together
    is controlled by the remapping arguments. Geoms, themes, facets, and other
    features can be added with the special %L+% (L-plus) operator.
Provides statistical transformations for plotting empirical 
    ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and 
    generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.
Tools for marginalization, conditioning and fitting by maximum likelihood.
A collection of functions to visualize spatial data and models
    on top of static maps from various online sources (e.g Google Maps and Stamen
    Maps). It includes tools common to those tasks, including functions for
    geolocation and routing.
Tools for assessing and diagnosing convergence of
    Markov Chain Monte Carlo simulations, as well as for graphically display
    results from full MCMC analysis. The package also facilitates the graphical
    interpretation of models by providing flexible functions to plot the
    results against observed variables, and functions to work with
    hierarchical/multilevel batches of parameters
    (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>, 
  including the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>, 
  seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>,
  exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>, 
  smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>,
  logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>,
  Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>,
  and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions
  for computing variable inclusion probabilities, multiple regression coefficients, and 
  statistical inference <doi:10.1214/15-EJS1031>.
Mosaic plots in the 'ggplot2' framework. Mosaic
    plot functionality is provided in a single 'ggplot2' layer by calling
    the geom 'mosaic'.
Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.
Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).
Supports visualizing multiple sequence alignment of DNA and protein sequences using 'ggplot2'. It supports a number of colour schemes, including Chemistry, Clustal, Shapely, Taylor and Zappo. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as aligning a phylogenetic tree produced by 'ggtree' with multiple sequence alignment.
Graph estimation in Gaussian Graphical Models, following the method
  developed by C. Giraud, S. Huet and N. Verzelen (2012) <doi:10.1515/1544-6115.1625>.
  The main functions return the adjacency matrix of an undirected graph
  estimated from a data matrix.
Create plots that combine a phylogeny and frequency dynamics.
    Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo".
    Inspired by similar plots in publications of the labs of RE Lenski and JE
    Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose
    code this package exploits).
It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.
Geometries to plot network objects with 'ggplot2'.
Use multiple fill and colour scales in 'ggplot2'.
Uses 'ggplot2' to create normally distributed
    violin plots with specified means and standard deviations. This
    function can be useful in showing hypothetically normal distributions
    and confidence intervals.
Provides a theme, a discrete color palette, and continuous scales
    to make 'ggplot2' look like 'gnuplot'. This may be helpful if you use both
    'ggplot2' and 'gnuplot' in one project.
Allows plotting data on bathymetric maps using 'ggplot2'. Plotting
  oceanographic spatial data is made as simple as feasible, but also flexible
  for custom modifications. Data that contain geographic information from 
  anywhere around the globe can be plotted on maps generated by the basemap()
  function using 'ggplot2' layers separated by the '+' operator. The package
  uses spatial shapefiles stored in the 'ggOceanMapsData' package, geospatial 
  packages for R to manipulate, and the 'ggspatial' package to help to plot 
  these shapefiles.
A funny coding challenge to reproduce the game Pac-Man using 'ggplot2' and 'gganimate'.
    It provides a pre-defined moves set for Pac-Man and the ghosts for the first level of the
    game Pac-Man as well as polygon datasets to draw ghosts in 'ggplot2'.
Facilitates the creation of page layout
    visualizations in which words are represented as rectangles with sizes
    relating to the length of the words. Which then is divided in lines
    and pages for easy overview of up to quite large texts.
Create hammock plots, parallel sets, and common angle plots
    with 'ggplot2'.
Simple parliament plots using 'ggplot2'. Visualize election results as points in the architectural layout of the legislative chamber.
Extends 'ggplot2' functionality to the 'partykit' package. 'ggparty' provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class 'party'.
Implements methods to plot periodic data in any arbitrary range on the fly.
Provides the ggplot binning layer stat_summaries_hex(),
    which functions similar to its singular form,
    but allows the use of multiple statistics per bin.
    Those statistics can be mapped to multiple bin aesthetics.
A system for 'declaratively' creating graphics,
    based on "The Grammar of Graphics". You provide the data, tell 'ggplot2'
    how to map variables to aesthetics, what graphical primitives to use,
    and it takes care of the details.
A dataset about movies. This was previously contained in ggplot2,
   but has been moved its own package to reduce the download size of ggplot2.
An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package.
    You can learn each steps of making plot by clicking your mouse without coding.
    You can get resultant code for the plot.
Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.
Convert plot function call (using expression or formula) to 'grob' or 'ggplot' object that compatible to the 'grid' and 'ggplot2' ecosystem. With this package, we are able to e.g. using 'cowplot' to align plots produced by 'base' graphics, 'ComplexHeatmap', 'eulerr', 'grid', 'lattice', 'magick', 'pheatmap', 'vcd' etc. by converting them to 'ggplot' objects.
Convenience functions for smooth conversion from 'ggplot' to 'plotly' where the conversion using ggplotly() usually gives an unexpected labels. The package ease the process of making a 'plotly' figures generated from 'ggplot2' object more aesthetic in terms of labels and customizability.
Extensions to 'ggplot2' respecting the grammar of graphics
    paradigm. Specialization of method ggplot(): accept and convert on the fly 
    time series data. Geom: "table", "plot" and "grob" add insets to plots
    using native data coordinates, while "table_npc", "plot_npc" and "grob_npc"
    do the same using "npc" coordinates through new aesthetics "npcx" and "npcy". 
    Statistics: locate and tag peaks and valleys; count observations in different 
    quadrants of a plot; select observations based on 2D density; label with the 
    equation of a polynomial fitted with lm() or other types of models; labels 
    with P-value, R^2 or adjusted R^2 or information criteria for fitted models; 
    label with ANOVA table for fitted models; label with summary for fitted 
    models. Model fit classes for which suitable methods are provided by package
    'broom' are supported. Scales and stats to build volcano and quadrant plots
    based on outcomes, fold changes, p-values and false discovery rates.
At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling 
  activities in order to reduce the overall effort required for generating such plots. 
  For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, 
  called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient 
  for publication and submissions using few lines of code.
A cross between a 2D density plot and a scatter plot,
    implemented as a 'ggplot2' geom. Points in the scatter plot are 
    colored by the number of neighboring points. This is useful to
    visualize the 2D-distribution of points in case of overplotting.
A 'ggplot2' extension for implementing parliament charts and several other useful visualizations.
Tools for working with polygons with holes in 'ggplot2', with a
    new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding'
    rules.
Provides various themes, palettes, and other functions
    that are used to customise ggplots to look like they were made in 'GraphPad 
    Prism'. The 'Prism'-look is achieved with theme_prism() and 
    scale_fill|colour_prism(), axes can be changed with custom guides like 
    guide_prism_minor(), and significance indicators added with add_pvalue().
The 'ggplot2' package is excellent and flexible for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.
Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, 
    and adds the resulting p-values to the plot with an annotation bar.
    Visualizing group differences are frequently performed by boxplots, bar plots, etc.
    Statistical test results are often needed to be annotated on these plots. 
    This package provides a convenient function that works on 'ggplot2' objects, 
    performs the desired statistical test between groups of interest and annotates the test results on the plot.
Plot single and faceted type quality control charts
  for 'ggplot'.
A quantile-quantile plot can be used to compare a sample of p-values 
  to the uniform distribution.  But when the dataset is big (i.e. > 1e4 p-values),
  plotting the quantile-quantile plot can be slow.  geom_QQ uses all the data to
  calculate the quantiles, but thins it out in a way that focuses on points near zero 
  before plotting to speed up plotting and decrease file size, when vector graphics are stored.
Quickly and easily perform exploratory data analysis by uploading your
     data as a 'csv' file. Start generating insights using 'ggplot2' plots and
     'table1' tables with descriptive stats, all using an easy-to-use point and click 
     'Shiny' interface.
An extension of 'ggplot2' to provide quiver plots to visualise vector fields. 
    This functionality is implemented using a geom to produce a new graphical layer, which
    allows aesthetic options. This layer can be overlaid on a map to improve visualisation
    of mapped data.
Graphic elements for exploring Random Forests using the 'randomForest' or
    'randomForestSRC' package for survival, regression and classification forests and
    'ggplot2' package plotting.
The grammar of graphics as implemented in ggplot2 is a poor fit for
    graph and network visualizations due to its reliance on tabular data input.
    ggraph is an extension of the ggplot2 API tailored to graph visualizations
    and provides the same flexible approach to building up plots layer by layer.
Intended for both technical and non-technical users to create
    interactive data visualizations through a web browser GUI without writing any
    code.
Given a group of genomes and their relationship with each other, the package clusters the genomes and selects the most representative members of each cluster. Additional data can be provided to the prioritize certain genomes. The results can be printed out as a list or a new phylogeny with graphs of the trees and distance distributions also available. For detailed introduction see: Thomas H Clarke, Lauren M Brinkac, Granger Sutton, and Derrick E Fouts (2018), GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models, Bioinformatics, bty300, <doi:10.1093/bioinformatics/bty300>.
Rasterize only specific layers of a 'ggplot2' plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without loosing vector properties of the scale-sensitive information.
Provides text and label geoms for 'ggplot2' that help to avoid
    overlapping text labels. Labels repel away from each other and away from the
    data points.
An R package for creating panels of diagnostic plots for residuals from a model 
    using ggplot2 and plotly to analyze residuals and model assumptions from a variety of 
    viewpoints. It also allows for the creation of interactive diagnostic plots.
The Graphical Group Ridge 'GGRidge' package package classifies ridge regression predictors in disjoint groups of conditionally correlated variables and derives different penalties (shrinkage parameters) for these groups of predictors. It combines the ridge regression method with the graphical model for high-dimensional data (i.e. the number of predictors exceeds the number of cases) or ill-conditioned data (e.g. in the presence of multicollinearity among predictors). The package reduces the mean square errors and the extent of over-shrinking of predictors as compared to the ridge method.
Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
The risk plot may be one of the most commonly used figures in 
    tumor genetic data analysis. We can conclude the following two points: 
    Comparing the prediction results of the model with the real survival situation 
    to see whether the survival rate of the high-risk group is lower than that of the 
    low-level group, and whether the survival time of the high-risk group is 
    shorter than that of the low-risk group. The other is to compare the heat 
    map and scatter plot to see the correlation between the predictors and the 
    outcome.
Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
A collection of 'ggplot2' color palettes inspired by
    plots in scientific journals, data visualization libraries,
    science fiction movies, and TV shows.
Provides 'ggplot2' 'stats' that estimate seasonally adjusted series 
    and rolling summaries such as rolling average on the fly for time series.
The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.
A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), 
    geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more 
    aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a 
    neon glow around lines to get a steampunk style.
The grammar of graphics as shown in 'ggplot2' has provided
  an expressive API for users to build plots. 'ggside' extends 'ggplot2' 
  by allowing users to add graphical information about one of the main panel's
  axis using a familiar 'ggplot2' style API with tidy data. This package is
  particularly useful for visualising metadata on a discrete axis, or summary
  graphics on a continuous axis such as a boxplot or a density distribution.
Enrich your 'ggplots' with group-wise comparisons.
    This package provides an easy way to indicate if two groups are
    significantly different.  Commonly this is shown by a bracket on top
    connecting the groups of interest which itself is annotated with the
    level of significance (NS, *, **, ***).  The package provides a single
    layer (geom_signif()) that takes the groups for comparison and the
    test (t.test(), wilcox.text() etc.) as arguments and adds the
    annotation to the plot.
Adds north symbols (18 options) and scale bars in kilometers,
    meters, nautical miles, or statue miles, to maps in geographic
    or metric coordinates created with 'ggplot2' or 'ggmap'.
The 'ggplot2' package provides a powerful set of tools 
  for visualising and investigating data. The 'ggsoccer' package provides a 
  set of functions for elegantly displaying and exploring soccer event data
  with 'ggplot2'. Providing extensible layers and themes, it is designed to
  work smoothly with a variety of popular sports data providers.
An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. 
 Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014.
 <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AL%3A2015%3A012%3ATOC>. 
 Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided.
 The structure files can be adopted for localization or for insurance companies who use Internal Models (IM).
 Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts.
 With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment).
The aim of this package is to offer more variability of graphics based on the self-organizing maps.
Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of spatial objects.
Additional annotations, stats, geoms and scales for plotting 
    "light" spectra with 'ggplot2', together with specializations of ggplot()
    and autoplot() methods for spectral data and waveband definitions
    stored in objects of classes defined in package 'photobiology'. Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'. This package is now superseded by 'ggplot2' itself
    which now has full native support for horizontal layouts. It
    remains available for backward compatibility.
To create the regular polygon layer for easily discernible shapes, 
             we developed the package, it can be easily used if you know the 'ggplot2'.
Extension of 'ggplot2', 'ggstatsplot' creates
    graphics with details from statistical tests included in the plots
    themselves. It provides an easier API to generate information-rich plots
    for statistical analysis of continuous (violin plots, scatterplots,
    histograms, dot plots, dot-and-whisker plots) or categorical (pie and
    bar charts) data. Currently, it supports the most common types of
    statistical approaches and tests: parametric, nonparametric, robust, and Bayesian
    versions of t-test/ANOVA, correlation analyses, contingency table
    analysis, meta-analysis, and regression analyses.
Provides an extension to 'ggplot2' (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two 
    types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean.
    These plots contain multiple user-defined confidence areas with varying colours, 
    defined by the underlying t-distribution used to compute standard confidence intervals for 
    the mean of the normal distribution when the variance is unknown.
    Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the 
    shape (horizontal width) is defined by the probability density function of the t-distribution.
Offers various swiss maps as data frames and 'ggplot2' objects and gives the
    possibility to add layers of data on the maps. Data are publicly available
    from the swiss federal statistical office.
Extends the functionality of 'ggplot2', providing the capability
    to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally,
    'ggtern' has implemented several NEW geometries which are unavailable to the
    standard 'ggplot2' release. For further examples and documentation, please
    proceed to the 'ggtern' website.
A 'ggplot2' extension that enables the rendering of
    complex formatted plot labels (titles, subtitles, facet labels,
    axis labels, etc.). Text boxes with automatic word wrap are also
    supported.
Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
Some extra themes, geoms, and scales for 'ggplot2'.
    Provides 'ggplot2' themes and scales that replicate the look of plots
    by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata',
    'Excel', and 'The Wall Street Journal', among others.
    Provides 'geoms' for Tufte's box plot and range frame.
Provides additional display mediums for time series visualisations, such as calendar heat map, steamgraph, marimekko, etc.
An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and 'GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available.
Replace the standard x-axis in 'ggplots' with a combination matrix
  to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize
  the overlap of sets as an alternative to Venn diagrams. 
  This package provides a simple way to produce such plots using 'ggplot2'. 
  In addition it can convert any categorical axis into a combination
  matrix axis.
An easy-to-use way to draw pretty venn diagram by 'ggplot2'.
Easy-to-use functions to generate 2-4 sets Venn plot in publication quality. 
  'ggVennDiagram' is the first software that can automatically fill different colors to each part of a Venn diagram.
A collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.
An implementation of an interactive grammar of graphics, taking the
    best parts of 'ggplot2', combining them with the reactive framework of
    'shiny' and drawing web graphics using 'vega'.
Easy creation and manipulation of Voronoi diagrams using 'deldir' with visualization in 'ggplot2'.
Provides a word cloud text geom for 'ggplot2'. Texts
    are placed so that they do not overlap as in 'ggrepel'.  The algorithm
    used is a variation around the one of 'wordcloud2.js'.
The 'ggplot2' package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid 'ggplot2' commands.
Minimal client to access the 'GitHub' 'API'.
Haplotype calling from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. The package was first described in Bioinformatics by Utsunomiya et al. (2016, <doi:10.1093/bioinformatics/btw356>). Since the v2 release, the package provides functions for unsupervised and supervised detection of ancestry tracks. The methods implemented in these functions were described in an article published in Methods in Ecology and Evolution by Utsunomiya et al. (2020, <doi:10.1111/2041-210X.13467>).
Functions are provided for quantifying evolution and selection on complex traits. 
              The package implements effective handling and analysis algorithms scaled for 
              genome-wide data and calculates a composite statistic, denoted Ghat, which is used 
              to test for selection on a trait. The package provides a number of simple examples 
              for handling and analysing the genome data and visualising the output and results. 
              Beissinger et al., (2018) <doi:10.1534/genetics.118.300857>.
A statistical hypothesis test for conditional independence.
    Given residuals from a sufficiently powerful regression, it tests whether 
    the covariance of the residuals is vanishing. It can be applied to both
    discretely-observed functional data and multivariate data. 
    Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas
    Peters (2020) <arXiv:2101.07108>.
Colour palettes inspired by Studio Ghibli <https://en.wikipedia.org/wiki/Studio_Ghibli> 
    films, ported to R for your enjoyment.
Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.
A 'GraphQL' client, with an R6 interface for initializing
    a connection to a 'GraphQL' instance, and methods for constructing
    queries, including fragments and parameterized queries. Queries
    are checked with the 'libgraphqlparser' C++ parser via the
    'gaphql' package.
Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.
Detailed functionality for working
        with the univariate and multivariate Generalized Hyperbolic
        distribution and its special cases (Hyperbolic (hyp), Normal
        Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
        and Gaussian distribution). Especially, it contains fitting
        procedures, an AIC-based model selection routine, and functions
        for the computation of density, quantile, probability, random
        variates, expected shortfall and some portfolio optimization
        and plotting routines as well as the likelihood ratio test. In
        addition, it contains the Generalized Inverse Gaussian
        distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. 
        Quantitative risk management: Concepts, techniques and tools. 
        Princeton University Press, Princeton (2005).
Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG).
    To learn how to use it, check the vignettes for a quick tutorial.
    Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300>
    together with those relevant references from the one listed below.
    The package is based on the research developed at the Chair of Systems Design, ETH Zurich.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <arXiv:1607.02441>.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>.
    Casiraghi, G., (2017) <arxiv:1702.02048>
    Casiraghi, G., Nanumyan, V. (2018) <arXiv:1810.06495>.
    Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>
    Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>.
Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets (Schmid et al. (2016) <doi:10.1093/bioinformatics/btw030>).
Build a map of path-based geometry, this is a simple description of the number
 of parts in an object and their basic structure. Translation and restructuring operations for 
 planar shapes and other hierarchical types require a data model with a record of the underlying
 relationships between elements. The gibble() function creates a geometry map, a simple record of 
 the underlying structure in path-based hierarchical types. There are methods for the planar shape 
 types in the 'sf' and 'sp' packages and for types in the 'trip' and 'silicate' packages.
This package provides two generic functions for performing
        Markov chain sampling in a naive way for a user-defined target
        distribution, which involves only continuous variables. The
        function "gibbs_met" performs Gibbs sampling with each
        1-dimensional distribution sampled with Metropolis update using
        Gaussian proposal distribution centered at the previous state.
        The function "met_gaussian" updates the whole state with
        Metropolis method using independent Gaussian proposal
        distribution centered at the previous state. The sampling is
        carried out without considering any special tricks for
        improving efficiency. This package is aimed at only routine
        applications of MCMC in moderate-dimensional problems.
Gibbs sampler for one-way linear mixed-effects models
        (ANOVA, ANCOVA) with homoscedasticity of errors and uniform
        priors.
Provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <http://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.
Implements categorical principal component analysis ('PRINCALS'), multiple correspondence analysis ('HOMALS'), monotone regression analysis ('MORALS'). It replaces the 'homals' package.
Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. 
    Converts images to GIF animations using pngquant's efficient cross-frame 
    palettes and temporal dithering with thousands of colors per frame.
Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.
A framework and functions to create 'MOODLE' quizzes. 'GIFTr' takes dataframe of questions of
    four types: multiple choices, numerical, true or false and short answer questions, and exports a text 
    file formatted in 'MOODLE' GIFT format. You can prepare a spreadsheet in any software and import 
    it into R to generate any number of questions with 'HTML', 'markdown' and 'LaTeX' support.
Wrapping command line functions into R functions, which in turn run the command line functions.
Generator and density function for the
  Generalized Inverse Gaussian (GIG) distribution.
Provides a simple to use, intuitive, and
  extensible interface to several stochastic simulation
  algorithms for generating simulated trajectories of finite
  population continuous-time model. Currently it implements
  Gillespie's exact stochastic simulation algorithm (Direct
  method) and several approximate methods (Explicit tau-leap,
  Binomial tau-leap, and Optimized tau-leap). The package also
  contains a library of template models that can be run as demo
  models and can easily be customized and extended. Currently the
  following models are included, 'Decaying-Dimerization' reaction
  set, linear chain system, logistic growth model, 'Lotka'
  predator-prey model, Rosenzweig-MacArthur predator-prey model,
  'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model.
  Pineda-Krch et al. (2008) <doi:10.18637/jss.v025.i12>.
A fast, scalable, and versatile framework for
    simulating large systems with Gillespie's Stochastic Simulation
    Algorithm ('SSA').  This package is the spiritual successor to the
    'GillespieSSA' package originally written by Mario Pineda-Krch.
    Benefits of this package include major speed improvements (>100x),
    easier to understand documentation, and many unit tests that try to
    ensure the package works as intended.
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.
Automated identification and estimation of group- and
    individual-level relations in time series data.
Supplemental tools for the 'gimme' R package. It contains an interactive graphical user interface, allowing for the flexible specification of a variety of both basic and advanced options. It will expand to include a variety of tools for navigating output.
This is a set of functions to retrieve information about GIMMS
    NDVI3g files currently available online; download (and re-arrange, in the 
    case of NDVI3g.v0) the half-monthly data sets from NASA Ames Ecological 
    Forecasting Lab (ECOCAST); import downloaded files from ENVI binary 
    (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the 
    widespread 'raster' package; conduct quality control; and generate monthly 
    composites (e.g., maximum values) from the half-monthly input data. As a 
    special gimmick, a method is included to conveniently apply the Mann-Kendall 
    trend test upon 'Raster*' images, optionally featuring trend-free 
    pre-whitening to account for lag-1 autocorrelation.
Performs image segmentation in fruit or seeds pictures in order to measure physical features in a high-throughput manner for genome-wide association (GWAS) and genomic selection programs.
An implementation of a new Gini covariance and correlation to measure dependence between a categorical and numerical variables. Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) <arXiv:1809.09793>.
Gini-based coefficients and plot of the ordinary and generalized curve of maximum inequality in the presence of weighted and negative attributes.
A workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. You can find atmospheric delay data here: <http://ceg-research.ncl.ac.uk/v2/gacos>.This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework.
    References: 
            Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.
Maximum likelihood estimation under relational models, with or without the overall effect.
An interface to the 'API' of 'Giphy', a popular index-based search 
    engine for 'GIFs' and animated stickers (see <http://giphy.com/faq> and 
    <https://github.com/Giphy/GiphyAPI> for more information about 'Giphy' and 
    its 'API') . This package also provides a 'RStudio Addin', which can help 
    users easily search and download 'GIFs' and insert them to a 'rmarkdown' 
    presentation.
Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general 
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices. 
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the 
 likelihood using MCMC samplers for large lattices.
Tools to download data from the GISCO 
    (Geographic Information System of the Commission) Eurostat database
    <https://ec.europa.eu/eurostat/web/gisco>. Global and European map data available.
    This package is in no way officially related to or endorsed by Eurostat.
Some mapping and spatial data manipulation tools - in particular
    drawing choropleth maps with nice looking legends,  and aggregation of point
    data to polygons.
Work with 'GitHub' 'gists' from 'R' (e.g., 
    <https://en.wikipedia.org/wiki/GitHub#Gist>, 
    <https://docs.github.com/en/github/writing-on-github/creating-gists/>). A 'gist'
    is simply one or more files with code/text/images/etc. This package allows
    the user to create new 'gists', update 'gists' with new files, rename files,
    delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists',
    open a 'gist' in your default browser, get embed code for a 'gist', list
    'gist' 'commits', and get rate limit information when 'authenticated'. Some
    requests require authentication and some do not. 'Gists' website: 
    <https://gist.github.com/>.
Interface to the 'libgit2' library, which is a pure C
    implementation of the 'Git' core methods. Provides access to 'Git'
    repositories to extract data and running some basic 'Git'
    commands.
The git2rdata package is an R package for writing and reading
    dataframes as plain text files.  A metadata file stores important
    information.  1) Storing metadata allows to maintain the classes of
    variables.  By default, git2rdata optimizes the data for file storage.
    The optimization is most effective on data containing factors.  The
    optimization makes the data less human readable.  The user can turn
    this off when they prefer a human readable format over smaller files.
    Details on the implementation are available in vignette("plain_text",
    package = "git2rdata").  2) Storing metadata also allows smaller row
    based diffs between two consecutive commits.  This is a useful feature
    when storing data as plain text files under version control.  Details
    on this part of the implementation are available in
    vignette("version_control", package = "git2rdata").  Although we
    envisioned git2rdata with a git workflow in mind, you can use it in
    combination with other version control systems like subversion or
    mercurial.  3) git2rdata is a useful tool in a reproducible and
    traceable workflow.  vignette("workflow", package = "git2rdata") gives
    a toy example.  4) vignette("efficiency", package = "git2rdata")
    provides some insight into the efficiency of file storage, git
    repository size and speed for writing and reading.  Please cite using
    <doi:10.5281/zenodo.1485309>.
Query, set, delete credentials from the 'git' credential
    store. Manage 'GitHub' tokens and other 'git' credentials. This package
    is to be used by other packages that need to authenticate to 'GitHub'
    and/or other 'git' repositories.
'Gitea' is a community managed, lightweight code hosting solution 
  were projects and their respective git repositories can be managed 
  <https://gitea.io>. This package gives an interface to the 'Gitea' API to 
  access and manage repositories, issues and organizations directly in R.
An Rstudio addin for version control that allows users to clone
    repositories, create and delete branches, and sync forks on GitHub, GitLab, etc.
    Furthermore, the addin uses the GitLab API to allow instructors to create
    forks and merge requests for all students/teams with one click of a button.
Provides an helpful way to install packages hosted on GitHub.
Simple interface to query gitignore.io to fetch
    gitignore templates that can be included in the .gitignore file. More
    than 450 templates are currently available.
Provides R functions to access the API of the project and
    repository management web application gitlab. For many common tasks (repository
    file access, issue assignment and status, commenting) convenience wrappers
    are provided, and in addition the full API can be used by specifying request
    locations. Gitlab is open-source software and can be self-hosted or used on
    gitlab.com.
Provides helpers to add 'Git' links to 'shiny'
    applications, 'rmarkdown' documents, and other 'HTML' based resources.
    This is most commonly used for 'GitHub' ribbons.
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.  Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Functions for the g-and-k and generalised g-and-h distributions.
Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
A simple way to interact with and extract data from
  the official 'Google Knowledge Graph' API <https://developers.google.com/knowledge-graph/>.
Imports the 'gkmSVM' v2.0 functionalities into R <http://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
    Users should note that the suggested packages 'rtracklayer', 'GenomicRanges', 'BSgenome', 'BiocGenerics', 
    'Biostrings', 'GenomeInfoDb', 'IRanges', and 'S4Vectors' are all BioConductor packages <https://bioconductor.org>.
A fully distributed glacier surface mass balance model developed for the simulation of accumulation and ablation processes on debris-free as well as debris-covered glaciers.
Functions capable of performing  efficient design matrix free  penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The generic glamlasso() function solves the penalized maximum likelihood estimation (PMLE) problem in a pure generalized linear array model (GLAM) as well as in a GLAM containing a non-tensor component. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the followings models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. Furthermore this package also contains two  functions  that can be used to fit special cases of GLAMs, see glamlassoRR() and glamlassoS(). The procedure underlying these functions is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>.
Generate data frames for all the variables with some summaries and also some plots for numerical variables.
    Several functions from the 'tidyverse' and 'GGally' packages are used.
Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to
    search job statistics, employer statistics, and job progression,
    where 'Glassdoor' provides a breakdown of other jobs a person did
    after their current one.
Estimation of a sparse inverse covariance matrix using a lasso (L1)
             penalty. Facilities are provided for estimates along a path of values
	     for the regularization parameter.
A fast and improved implementation of the graphical LASSO.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Fits latent class analysis (LCA) including group variable and covariates. 
   The group variable can be handled either by multilevel LCA described in Vermunt (2003) <DOI:10.1111/j.0081-1750.2003.t01-1-00131.x> or standard LCA at each level of group variable.
   The covariates can be incorporated in the form of logistic regression (Bandeen-Roche et al. (1997) <DOI:10.1080/01621459.1997.10473658>).
Enables calculation of image textures (Haralick 1973) 
    <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices 
    (GLCMs). Supports processing images that cannot fit in memory.
The generalised lambda distribution, or Tukey lambda distribution, 
  provides a wide variety of shapes with one functional form.   
  This package provides random numbers, quantiles, probabilities, 
  densities and density quantiles for four different types of the distribution,
  the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden
  and Loots 2009) and FM5 - see documentation for details.
  It provides the density function, distribution function, and Quantile-Quantile 
  plots.  
  It implements a variety of estimation methods for the distribution, 
  including diagnostic plots. 
  Estimation methods include the starship (all 4 types), 
  method of L-Moments for the GPD and FKML types, and a 
  number of methods for only the FKML type.  
  These include maximum likelihood, maximum product of spacings, 
  Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.
Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile regression is a competitive flexible model compared to standard/quantile regression.  The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.
Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
    Huang (2014) <doi:10.1080/01621459.2013.824892>.
    Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
    Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
Functions evaluate global and individual tests for direct effects in Mendelian randomization studies.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
Routines for log-linear models of incomplete contingency tables,
             including some latent class models, via EM and Fisher scoring
             approaches. Allows bootstrapping. See Espeland and Hui (1987)
             <doi:10.2307/2531553> for general approach.
Analysis of multivariate data using generalized linear latent variable models (gllvm). 
      Estimation is performed using either Laplace approximation method or variational approximation method implemented via TMB (Kristensen et al., (2016), <doi:10.18637/jss.v070.i05>). 
      For details see Niku et al. (2019a) <doi:10.1371/journal.pone.0216129> and Niku et al. (2019b) <doi:10.1111/2041-210X.13303>.
Provides two functions that generate source code implementing the predict 
  function of fitted glm objects. In this version, code can be generated for either 'C' 
  or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm 
  predictive models into production. The source code generated by this package implements 
  two function/methods. One of such functions implements the equivalent to 
  predict(type="response"), while the second implements predict(type="link"). Source code 
  is written to disk as a .c or .java file in the specified path. In the case of c, an .h 
  file is also generated.
Functions to calculate predicted values and the difference between
    the two cases with confidence interval for lm() [linear model], glm() [general linear model], glm.nb() [negative binomial model],
	polr() [ordinal logistic model], multinom() [multinomial model] and tobit() [tobit model] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive  sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
Find all hierarchical models of specified generalized linear
    model with information criterion (AIC, BIC, or AICc) within specified
    cutoff of minimum value.  Alternatively, find all such graphical models.
    Use branch and bound algorithm so we do not have to fit all models.
Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.
In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative distribution function for the linkage, and Z, the design matrix.
A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) <https://hal.inria.fr/inria-00074164>) associated with a Gibbs sampler which purpose is to learn a constrained representation for logistic regression that is called quantization (Ehrhardt et al. (2019) <arXiv:1903.08920>). Continuous features are discretized and categorical features' values are grouped to produce a better logistic regression model. Pairwise interactions between quantized features are dynamically added to the model through a Metropolis-Hastings algorithm (Hastings, W. K. (1970) <doi:10.1093/biomet/57.1.97>).
This package contains functions to perform generalized
        linear mixed Dirichlet models using posterior simulation.
Provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference.
Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 
    'partykit'. The fitting algorithm is described in more detail in Fokkema,
    Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).
Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple
   and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values,
   less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint().
   See Wilks (1938) <doi:10.1214/aoms/1177732360> for the LRT chi-square approximation.
   See Rao (1948) <doi:10.1017/S0305004100023987> for Rao's score test.
   See Wald (1943) <doi:10.2307/1990256> for Wald's test.
We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.
Efficient algorithms for fitting regularization paths for
        linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Fits generalized linear mixed models for a single grouping factor under
    maximum likelihood approximating the integrals over the random effects with an 
    adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) 
    <doi:10.1080/10618600.1995.10474663>.
Performs bootstrap resampling for most models that update() works for. There
    are two primary functions: bootstrap_model() performs block resampling if random effects
    are present, and case resampling if not; bootstrap_ci() converts output from
    bootstrap model runs into confidence intervals and p-values. By default,
    bootstrap_model() calls bootstrap_ci().
    Package motivated by Humphrey and Swingley (2018) <arXiv:1805.08670>.
Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.
Implements Bayesian spatial and spatiotemporal
    models that optionally allow for extreme spatial deviations through
    time. 'glmmfields' uses a predictive process approach with random
    fields implemented through a multivariate-t distribution instead of
    the usual multivariate normal.  Sampling is conducted with 'Stan'.
    References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.
Binomial and Poisson regression for clustered data, fixed
        and random effects with bootstrapping.
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. 
    Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.
Conduct inference about generalized linear mixed models, with a
    choice about which method to use to approximate the likelihood. In addition
    to the Laplace and adaptive Gaussian quadrature approximations, which are
    borrowed from 'lme4', the likelihood may be approximated by the sequential
    reduction approximation, or an importance sampling approximation. These
    methods provide an accurate approximation to the likelihood in some
    situations where it is not possible to use adaptive Gaussian quadrature.
Fit linear and generalized linear mixed models with various
    extensions, including zero-inflation. The models are fitted using maximum
    likelihood estimation via 'TMB' (Template Model Builder). Random effects are
    assumed to be Gaussian on the scale of the linear predictor and are integrated
    out using the Laplace approximation. Gradients are calculated using automatic
    differentiation.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model,  multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead.  The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal
 response dataset for fitting continuation ratio models for datasets  where the number of covariates exceeds the sample size or when
 there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.
Contains all the data and functions used in Generalized
    Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create
    all models, tables, and plots are included for each data set.
A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
Implements a generalized version of principal components analysis
    (GLM-PCA) for dimension reduction of non-normally distributed data such as
    counts or binary matrices.
    Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.
    Townes FW (2019) <arXiv:1907.02647>.
Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
Have you ever struggled to find "good data" for a generalized linear model? Would you like
    to test how quickly statistics converge to parameters, or learn how picking different
    link functions affects model performance? This package creates ideal data for both common
    and novel generalized linear models so your questions can be empirically answered.
It provides an extremely efficient procedure for fitting the entire truncated lasso regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. The algorithm uses the difference of convex technique. The detail of the algorithm is described in Shen, Pan and Zhu (2012) <doi:10.1080/01621459.2011.645783>. The package is inherited from a popular R package 'glmnet' and many functions in 'glmnet' can be directly used in 'glmtlp'. You can learn more details by the online manual (<http://wuchong.org/glmtlp.html>).
A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user's criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://github.com/adimajo/manuscrit_these/releases/>.
Automated model selection and model-averaging. Provides a
        wrapper for glm and other functions, automatically generating
        all possible models (under constraints set by the user) with
        the specified response and explanatory variables, and finding
        the best models in terms of some Information Criterion (AIC,
        AICc or BIC). Can handle very large numbers of candidate
        models. Features a Genetic Algorithm to find the best models
        when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
             including parametric links and heteroskedastic latent variables.
permutation based global test with deviance as test statistic
Initial metabolic networks often inaccurately predict in-silico growth or non-growth if compared to in-vivo data. This package refines metabolic network models by making networks changes (i.e., removing, adding, changing reversibility of reactions; adding and removing biomass metabolites) and simultaneously matching sets of experimental growth and non-growth data (e.g., KO-mutants, mutants grown under different media conditions,...)
Implementation of three different Gene set analysis (GSA) algorithms for combining the individual pvalues of a set of genetic variats (SNPs) in a gene level pvalue. The implementation includes the selection of the best inheritance model for each SNP.
Second-order summary statistics K- and pair-correlation functions describe
    interactions in point pattern data. This package provides computations to estimate those
    statistics on inhomogeneous point processes, using the methods of
    in T Shaw, J Møller, R Waagepetersen, 2020 <arXiv:2004.00527>.
It provides more configurations on the option values such as validation
    and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown" or "free") objects in R expressions
    by code inspection using various strategies (ordered, liberal, or
    conservative).  The objective of this package is to make it as simple as
    possible to identify global objects for the purpose of exporting them in
    parallel, distributed compute environments.
Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
Tools for the generalized logistic distribution (Type I,
             also known as skew-logistic distribution), encompassing
	     basic distribution functions (p, q, d, r, score), maximum
	     likelihood estimation, and structural change methods.
Provides a framework for creating plots with glowing points.
R Interface to C API of GLPK, depends on GLPK Version >= 4.42.
Functions to conduct four generalized logrank tests and a score test under a proportional hazards model
Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An implementation of interpreted string literals, inspired by
  Python's Literal String Interpolation <https://www.python.org/dev/peps/pep-0498/> and Docstrings
  <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted String Literals
  <https://docs.julialang.org/en/v1.3/manual/strings/#Triple-Quoted-String-Literals-1>.
Ease the transition between R vectors and markdown
    text. With 'gluedown' and 'rmarkdown', users can create traditional
    vectors in R, glue those strings together with the markdown syntax,
    and print those formatted vectors directly to the document. This
    package primarily uses GitHub Flavored Markdown (GFM), an offshoot of
    the unambiguous CommonMark specification by John MacFarlane (2019)
    <https://spec.commonmark.org/>.
Provides standardized root mean residual global fit index for generalized latent variable models.
Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.
Performs genomic mediation
    analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale
    mediation analysis and adaptively selects potential confounding variables to
    adjust for each mediation test from a pool of candidate confounders. The package
    is tailored for but not limited to genomic mediation analysis (e.g., cis-gene
    mediating trans-gene regulation pattern where an eQTL, its cis-linking gene
    transcript, and its trans-gene transcript play the roles as treatment, mediator
    and the outcome, respectively), restricting to scenarios with the presence of
    cis-association (i.e., treatment-mediator association) and random eQTL (i.e.,
    treatment).
An interface to the 'Gmail' 'RESTful' API.  Allows
    access to your 'Gmail' messages, threads, drafts and labels.
Contains many functions useful for univariate outlier detection, permutation-based t-test, permutation-based chi-square test, visualization of residuals, and bootstrap 'Cramer V', plotting of the results of the 'Mann-Whitney' and 'Kruskal-Wallis' test, calculation of 'Brainerd-Robinson' similarity coefficient and subsequent clustering, validation of logistic regression models, optimism-corrected AUC, robust 'Bland-Altman' plot, calculation of posterior probability for different chronological relationships between two Bayesian radiocarbon phases, point pattern analysis, clustering of spatial features.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
Simulation of correlation matrices possibly constrained by a given undirected or acyclic directed graph. In particular, the package provides functions that implement the simulation methods described in Córdoba et al. (2018) <doi:10.1007/978-3-030-03493-1_13> and Córdoba et al. (2020) <doi:10.1016/j.ijar.2020.07.007>.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described
    multiple test procedures.
A template for a geometallurgical database and a fast and easy
    interface for accessing it is provided in this package.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response (Dag et al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).
Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.
    Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms
    that performs gradually complicated polynomial models and selecting the best solution by an external criterion.
    In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and
    selecting an optimal structure of model or network.
    The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.
    An introduction of GMDH algorithms:
    Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292>
    Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.
Fast algorithms  for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
Some methods for the inference and clustering of univariate and 
             multivariate functional data, using a generalization of Mahalanobis
             distance, along with some functions useful for the analysis of functional data.
             For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
Support for geostatistical analysis of multivariate data, 
         in particular data with restrictions, e.g. positive amounts data, 
         compositional data, distributional data, microstructural data, etc. 
         It includes descriptive analysis and modelling for such data, both 
         from a two-point Gaussian perspective and multipoint perspective.
         The methods mainly follow Tolosana-Delgado, Mueller and van den
         Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
Gaussian mixture graphical models include Bayesian networks and
    dynamic Bayesian networks (their temporal extension) whose local probability
    distributions are described by Gaussian mixture models. They are powerful
    tools for graphically and quantitatively representing nonlinear dependencies
    between continuous variables. This package provides a complete framework to
    create, manipulate, learn the structure and the parameters, and perform
    inference in these models. Most of the algorithms are described in the PhD
    thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.
Tools for making the descriptive "Table 1" used in medical
    articles, a transition plot for showing changes between categories
    (also known as a Sankey diagram), flow charts by extending the grid package,
    a method for variable selection based on the SVD, Bézier lines with arrows
    complementing the ones in the 'grid' package, and more.
High performance trainers for parameterizing and clustering weighted data. The Gaussian mixture (GM) module includes the conventional EM (expectation maximization) trainer, the component-wise EM trainer, the minimum-message-length EM trainer by Figueiredo and Jain (2002) <doi:10.1109/34.990138>. These trainers accept additional constraints on mixture weights and covariance eigen ratios. The K-means (KM) module offers clustering with the options of (i) deterministic and stochastic K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii) Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse representation of data input. The package improved the typical implementations of GM and KM algorithms in various aspects. It is carefully crafted in multithreaded C++ for modeling large data in industry use.
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Likelihood-based boosting approaches for generalized mixed models are provided.
An implementation of maximum simulated likelihood method for the
    estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>.
    Specifically, it allows estimating models with continuous heterogeneity
    such as the mixed multinomial logit and the generalized multinomial logit.
    It also allows estimating models with discrete heterogeneity such as the
    latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Make 2D and 3D plots of linear programming (LP), 
    integer linear programming (ILP), or mixed integer linear programming (MILP) models 
    with up to three objectives. Plots of both the solution and criterion space are possible.
    For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models 
    (see vignettes for an overview). The package also contains an function for checking if a point
    is inside the convex hull.
Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).
Functions for graph matching via nodes' degree profiles are provided in this package. The models we can handle include Erdos-Renyi random graphs and stochastic block models(SBM). More details are in the reference paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <arXiv:2006.03284>.
A collection of tools to create, use and maintain modularized model code written in the modeling 
    language 'GAMS' (<https://www.gams.com/>). Out-of-the-box 'GAMS' does not come with support for modularized
    model code. This package provides the tools necessary to convert a standard 'GAMS' model to a modularized one
    by introducing a modularized code structure together with a naming convention which emulates local
    environments. In addition, this package provides tools to monitor the compliance of the model code with
    modular coding guidelines.
Integrates game theory and ecological theory to construct 
    social-ecological models that simulate the management of populations and 
    stakeholder actions. These models build off of a previously developed 
    management strategy evaluation (MSE) framework to simulate all aspects of 
    management: population dynamics, manager observation of populations, manager
    decision making, and stakeholder responses to management decisions. The 
    newly developed generalised management strategy evaluation (GMSE) 
    framework uses genetic algorithms to mimic the decision-making process of 
    managers and stakeholders under conditions of change, uncertainty, and 
    conflict. Simulations can be run using gmse(), gmse_apply(), and
    gmse_gui() functions.
Two-Step Lasso (TS-Lasso) and compound minimum methods to recover 
             the abundance of missing peaks in mass spectrum analysis. TS-Lasso is an 
             imputation method that handles various types of missing peaks simultaneously. This package 
             provides the procedure to generate missing peaks (or data) for simulation study, 
             as well as a tool to estimate and visualize the proportion of missing at random.
Interface between the GMT map-making software and R, enabling the
  user to manipulate geographic data within R and call GMT commands to draw and
  annotate maps in postscript format. The gmt package is about interactive data
  analysis, rapidly visualizing subsets and summaries of geographic data, while
  performing statistical analysis in the R console.
Unconstrained and constrained maximum likelihood estimation of structural and reduced form 
    Gaussian mixture vector autoregressive (GMVAR) model, quantile residual tests, graphical diagnostics,
    simulations, forecasting, and estimation of generalized impulse response function.
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>,
    Savi Virolainen (2020) <arXiv:2007.04713>.
Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots.
A collection of functions to perform statistical tests of the following methods: Detrended Fluctuation Analysis, RHODCCA coefficient,<doi:10.1103/PhysRevE.84.066118>, DMC coefficient, SILVA-FILHO et al. (2021) <doi:10.1016/j.physa.2020.125285>, Delta RHODCCA coefficient, Guedes et al. (2018) <doi:10.1016/j.physa.2018.02.148> and <doi:10.1016/j.dib.2018.03.080> , Delta DMCA coefficient and Delta DMC coefficient.
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure.  Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05>.
Provide functions to compute standard and generalized Nash Equilibria. 
 Optimization methods are available nonsmooth reformulation, fixed-point formulation, 
 minimization problem and constrained-equation reformulation. 
 See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.
Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures.
A variety of functions to fit linear and nonlinear
    regression with a large selection of distributions.
Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others.  Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.
Tools to set up, train, store, load, investigate and analyze
             generative neural networks. In particular, functionality for
	     generative moment matching networks is provided.
	     See Hofert, Prasad, Zhu (2018) <arXiv:1811.00683>
	     for more details.
Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>.
    It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.
Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.
Provides tools for interacting with the 'geographic name resolution service' ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The 'GNRS' is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The 'GNRS' resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the 'GNRS' attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The 'GNRS' works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the 'GNRS' does not attempt to match state or county.
Homogenize GNSS (Global Navigation Satellite System) time-series. The general model is a segmentation in the mean model including a periodic function and considering monthly variances,  see Quarello (2020) <arXiv:2005.04683>.
Read data files readable by 'gnumeric' into 'R'. Can read
        whole sheet or a range, from several file formats, including
        the native format of 'gnumeric'. Reading is done by using
        'ssconvert' (a file converter utility included in the 'gnumeric'
        distribution <http://projects.gnome.org/gnumeric/>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.
Harness the power of 'quanteda', 'data.table' & 'stringi' to quickly generate 'tm' DocumentTermMatrix
             and TermDocumentMatrix data structures.
Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.
Implementation of several goodness-of-fit tests for functional
     data. Currently, mostly related with the functional linear model with
     functional/scalar response and functional/scalar predictor. The package
     allows for the replication of the data applications considered in
     García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga
     (2019) <arXiv:1909.07686>.
We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.
Tests of goodness-of-fit based on a kernel smoothing of the data.
Consider a goodness-of-fit (GOF) problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze martingale transformation method - which was proposed by Khmaladze (1981) <DOI:10.1137/1126027> - provides asymptotic distribution free test for the GOF problem. This package contains one function: KhmaladzeTrans(). In this version, KhmaladzeTrans() provides test statistic and critical value of GOF test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) <DOI:10.1007/s00180-020-00971-7> and tests for other distributions will be available at the later version.
An interactive document on  the topic of goodness of fit analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.
Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, 'Weibull', 'Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
	     for continuous univariate distributions, using
	     efficient algorithms.
Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.
Implementation of the GO-GARCH model class
A tool for identifying multiple word key terms in free text with application to Gene Ontology labels.
An opinionated framework for building a
    production-ready 'Shiny' application. This package contains a series
    of tools for building a robust 'Shiny' application from start to
    finish.
A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.
Allows the user to animate text within 'rmarkdown' documents and 'shiny' applications. 
    The animations are activated using the 'Animate.css' library. See <https://animate.style/> for more information.
Contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms
             Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.
Association analysis between categorical
    variables using the Goodman and Kruskal tau measure. This asymmetric association
    measure allows the detection of asymmetric relations between categorical
    variables (e.g., one variable obtained by re-grouping another).
Give advice about good practices when building R packages.
    Advice includes functions and syntax to avoid, package structure,
    code complexity, code formatting, etc.
Interact with the Google Analytics 
  APIs <https://developers.google.com/analytics/>, including 
  the Core Reporting API (v3 and v4), Management API, User Activity API
  and Multi-Channel Funnel API.
Create R functions that interact with OAuth2 Google APIs 
    <https://developers.google.com/apis-explorer/> easily,
    with auto-refresh and Shiny compatibility.
Tools to easily enable R scripts in the Google Cloud Platform.
  Utilise cloud services such as Cloud Run <https://cloud.google.com/run/> for R over HTTP, 
  Cloud Build <https://cloud.google.com/cloud-build/> for Continuous Delivery 
  and Integration services and 
  Cloud Scheduler <https://cloud.google.com/scheduler/> for scheduled scripts.
Interact with Google Cloud Storage <https://cloud.google.com/storage/> 
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> project.
Interact with the 'Google Cloud Vision' <https://cloud.google.com/vision/>
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> project.
Interact with the 'Google Compute Engine' API in R. Lets you create, 
  start and stop instances in the 'Google Cloud'.  Support for preconfigured instances, 
  with templates for common R needs.
GET and POST data to Google Forms; an API to Google Forms,
    allowing users to POST data securely to Google Forms without needing authentication or permissioning.
Allows you to retrieve information from the 'Google Knowledge Graph' API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The 'Knowledge Graph Search' API lets you find entities in the 'Google Knowledge Graph'. The API uses standard 'schema.org' types and is compliant with the 'JSON-LD' specification.
Call 'Google Cloud' machine learning APIs for text and speech tasks.
  Call the 'Cloud Translation' API <https://cloud.google.com/translate/> for detection 
  and translation of text, the 'Natural Language' API <https://cloud.google.com/natural-language/> to 
  analyse text for sentiment, entities or syntax, the 'Cloud Speech' API 
  <https://cloud.google.com/speech/> to transcribe sound files to text and 
  the 'Cloud Text-to-Speech' API <https://cloud.google.com/text-to-speech/> to turn text 
  into sound files.
Interact with Google's Cloud Natural Language API
    <https://cloud.google.com/natural-language/> (v1) via R. The API has
    four main features, all of which are available through this
    R package: syntax analysis and part-of-speech tagging, entity
    analysis, sentiment analysis, and language identification.
Encodes simple feature ('sf') objects and coordinates, and decodes polylines 
    using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Allows printing documents from R through 'Google Cloud Print' API.
  See <https://developers.google.com/cloud-print/docs/overview> for more information
  about 'Google Cloud Print'.
Provides a collection of functions to set up 'Google Public Data Explorer'
  <https://www.google.com/publicdata/> data visualization tool with your own data,
  building automatically the corresponding DataSet Publishing Language file, or
  DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to
  be published in 'Public Data Explorer'.
This is a wrapper for the command line tool 'googler', which can be
    found at the following URL: <https://github.com/jarun/googler>.
Interact with Google Sheets through the Sheets API
    v4 <https://developers.google.com/sheets/api>. "API" is an acronym for
    "application programming interface"; the Sheets API allows users to
    interact with Google Sheets programmatically, instead of via a web
    browser. The "v4" refers to the fact that the Sheets API is currently
    at version 4. This package can read and write both the metadata and
    the cell data in a Sheet.
Interact with the 'Google Tag Manager' API <https://developers.google.com/tag-manager/api/v2>, enabling scripted deployments and updates across multiple tags, triggers, variables and containers.
R interface to Google's chart tools, allowing users
    to create interactive charts based on data frames. Charts
    are displayed locally via the R HTTP help server. A modern
    browser with an Internet connection is required and for some
    charts a Flash player. The data remains local and is not
    uploaded to Google.
Provides a mechanism to plot a 'Google Map' from 'R' and overlay
    it with shapes and markers. Also provides access to 'Google Maps' APIs,
    including places, directions, roads, distances, geocoding, elevation and
    timezone.
Implementation of multilayered visualizations for enhanced
    graphical representation of functional analysis data. It combines and integrates
    omics data derived from expression and functional annotation enrichment
    analyses. Its plotting functions have been developed with an hierarchical
    structure in mind: starting from a general overview to identify the most
    enriched categories (modified bar plot, bubble plot) to a more detailed one
    displaying different types of relevant information for the molecules in a given
    set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models.
Implements the generalized order-restricted information criterion
    approximation (GORICA) by Kuiper, Hoijtink, & Silvapulle (2012)
    <doi:10.1016/j.jspi.2012.03.007>. The GORICA is an AIC-like information
    criterion that
    can be utilized to evaluate informative hypotheses specifying
    directional relationships between model parameters in terms of (in)equality
    constraints. The GORICA is applicable not only to
    normal  linear models, but also to generalized linear models (GLMs),
    generalized linear mixed models (GLMMs), and structural equation models
    (SEMs). In addition, the GORICA can be utilized in the context of
    contingency tables for which (in)equality constrained hypotheses do not
    necessarily contain linear restrictions on cell probabilities, but instead
    often contain non-linear restrictions on cell probabilities.
Add a scroll back to top 'Font Awesome' icon 
    <https://fontawesome.com/> in 'rmarkdown' documents and 'shiny'
    apps thanks to 'jQuery GoTop' <https://scottdorman.blog/jquery-gotop/>.
A suite of custom R Markdown formats and templates
    for authoring web pages styled with the GOV.UK Design System.
This package purposes to deal with public survey data of
        Japanese government via their Application Programming Interface
        (http://statdb.nstac.go.jp/)
Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.
It provides an effective, efficient, and fast way to explore the Gene Ontology (GO). 
             Given a set of genes, the package contains functions to assess the GO and obtain the 
             terms associated with the genes and the levels of the GO terms. The package provides 
             functions for the three different GO ontology. We discussed the methods explicitly in 
             the following article <doi:10.1038/s41598-020-73326-3>.
A collection of tools which extract a model documentation from 'GAMS' code and comments. 
             In order to use the package you need to install 'pandoc' and 'pandoc-citeproc' 
             first (<https://pandoc.org/>).
Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.
Gaussian process regression models, a.k.a. Kriging models, are
    applied to global multi-objective optimization of black-box functions.
    Multi-objective Expected Improvement and Step-wise Uncertainty Reduction
    sequential infill criteria are available. A quantification of uncertainty
    on Pareto fronts is provided using conditional simulations.
Gradient Projection Algorithm Rotation for Factor Analysis. See ?GPArotation.Intro for more details.
An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2020) <arXiv:2004.02653> for more information on the methodology.
Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/>.
General polygon clipping routines for R based on Alan Murta's C library.
Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
This package computes the bootstrap goodness-of-fit test
        for the generalized Pareto distribution by Villasenor-Alva and
        Gonzalez-Estrada (2009). The null hypothesis includes heavy and
        non-heavy tailed gPd's. A function for fitting the gPd to data
        using the parameter estimation methods proposed in the same
        article is also provided.
Functionalities for modelling functional data with 
    multidimensional inputs, multivariate functional data, and non-separable 
    and/or non-stationary covariance structure of function-valued processes. 
    In addition, there are functionalities for functional regression models where 
    the mean function depends on scalar and/or functional covariates and 
    the covariance structure depends on functional covariates. 
    The development version of the package can be found on 
    <https://github.com/gpfda/GPFDA-dev>.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods.
    Includes utilities for public key encryption, creating and verifying digital signatures,
    and managing your local keyring. Note that some functionality depends on the version of 
    GnuPG that is installed on the system. On Windows this package can be used together with
    'GPG4Win' which provides a GUI for managing keys and entering passphrases.
Functions for fitting and doing predictions with
    Gaussian process models using Vecchia's (1988) approximation. 
    Package also includes functions for reordering input locations, 
    finding ordered nearest neighbors (with help from 'FNN' package), 
    grouping operations, and conditional simulations.
    Covariance functions for spatial and spatial-temporal data
    on Euclidean domains and spheres are provided. The original 
    approximation is due to Vecchia (1988) 
    <http://www.jstor.org/stable/2345768>, and the reordering and
    grouping methods are from Guinness (2018) 
    <doi:10.1080/00401706.2018.1437476>.
    Model fitting employs a Fisher scoring algorithm described
    in Guinness (2019) <arXiv:1905.08374>.
Compute the GPIC index as described in Pham (2020)
    <doi:10.35542/osf.io/ajz5v>.
A small package for calculating lots of different price indexes, and by extension quantity indexes. Provides tools to build and work with any type of generalized bilateral index (of which most price indexes are), along with a few important indexes that don't belong to the generalized family. Implements and extends many of the methods in Balk (2008, ISBN:978-1-107-40496-0) and ILO, IMF, OECD, Eurostat, UN, and World Bank (2004, ISBN:92-2-113699-X) for bilateral price indexes.
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Convert the chip ID of GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> 
    to GeneBank Accession and ENTREZID <http://www.ncbi.nlm.nih.gov/gene>.
Implements the most common Gaussian process (GP) models using Laplace and 
  expectation propagation (EP) approximations, maximum marginal likelihood 
  (or posterior) inference for the hyperparameters, and sparse approximations 
  for larger datasets.
Provides functions for estimating a generalized partial
	     linear model, a semiparametric variant of the generalized linear model
	     (GLM) which replaces the linear predictor by the sum of a linear
	     and a nonparametric function.
Various R programming tools for plotting data, including:
  - calculating and plotting locally smoothed summary function as
    ('bandplot', 'wapply'),
  - enhanced versions of standard plots ('barplot2', 'boxplot2',
    'heatmap.2', 'smartlegend'),
  - manipulating colors ('col2hex', 'colorpanel', 'redgreen',
    'greenred', 'bluered', 'redblue', 'rich.colors'),
  - calculating and plotting two-dimensional data summaries ('ci2d',
    'hist2d'),
  - enhanced regression diagnostic plots ('lmplot2', 'residplot'),
  - formula-enabled interface to 'stats::lowess' function ('lowess'),
  - displaying textual data in plots ('textplot', 'sinkplot'),
  - plotting a matrix where each cell contains a dot whose size
    reflects the relative magnitude of the elements ('balloonplot'),
  - plotting "Venn" diagrams ('venn'),
  - displaying Open-Office style plots ('ooplot'),
  - plotting multiple data on same region, with separate axes
    ('overplot'),
  - plotting means and confidence intervals ('plotCI', 'plotmeans'),
  - spacing points in an x-y plot so they don't overlap ('space').
Combining a generalized linear model with an additional tree part 
          on the same scale. A four-step procedure is proposed to fit the model and test 
          the joint effect of the selected tree part while adjusting on confounding factors. 
          We also proposed an ensemble procedure based on the bagging to improve prediction 
          accuracy and computed several scores of importance for variable selection.
Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.
Tools for studying genotype-phenotype maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al (2013) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Frontier in Genetics 4:216 <doi:10.3389/fgene.2013.00216>.
Platform dedicated to the Global Modelling technique. Its aim
    is to obtain ordinary differential equations of polynomial form directly
    from time series. It can be applied to single or multiple time series under
    various conditions of noise, time series lengths, sampling, etc. This platform
    is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO),
    UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE.
    The developments were funded by the French program Les Enveloppes Fluides
    et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The
    French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects
    Crops'IChaos and Musc & SlowFast).
Estimation of the spectrum of Lyapunov Exponents and the Kaplan-Yorke 
  dimension of any low-dimensional model of polynomial form.
  It can be applied, for example, to systems such as the chaotic
  Lorenz-1963 system or the hyperchaotic Rossler-1979 system.
  It can also be applied to dynamical models in Ordinary Differential Equations (ODEs) directly obtained
  from observational time series using the 'GPoM' package.
  The approach used is semi-formal,
  the Jacobian matrix being estimated automatically from the polynomial equations.
  Two methods are made available; one introduced by Wolf et al. 
  (1985) <doi:10.1016/0167-2789(85)90011-9>
  and the other one introduced by Grond et al. (2003) <doi:10.1016/S0960-0779(02)00479-4>.
  The package is provided with an interface for a more intuitive usage,
  it can also be run without the interface.
  This platform is developed at the Centre d'Etudes Spatiales de la Biosphere (CESBIO),
  UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE.
  The developments were funded by the French program Les Enveloppes Fluides 
  et l'Environnement (LEFE, MANU, projects GloMo, SpatioGloMo and MoMu). The 
  French programs Defi InFiNiTi (CNRS) and PNTS (CNRS) are also acknowledged 
  (projects Crops'I Chaos and Musc & SlowFast).
Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.
Provides an implementation of Gaussian process panel modeling (GPPM). 
              GPPM is described in Karch (2016; <DOI:10.18452/17641>) and Karch, Brandmaier & Voelkle (2018; <DOI:10.17605/OSF.IO/KVW5Y>).
              Essentially, GPPM is Gaussian process based modeling of longitudinal panel data.
              'gppm' also supports regular Gaussian process regression (with a focus on flexible model specification), and multi-task learning.
Implements a Gaussian process (GP)-based ranking method
    which can be used to rank multiple time series according to their
    temporal activity levels. An example is the case when expression
    levels of all genes are measured over a time course and the main
    concern is to identify the most active genes, i.e. genes which
    show significant non-random variation in their expression levels.
    This is achieved by computing Bayes factors for each time series
    by comparing the marginal likelihoods under time-dependent and
    time-independent GP models. Additional variance information from
    pre-processing of the observations is incorporated into the GP
    models, which makes the ranking more robust against model
    overfitting. The package supports exporting the results to
    'tigreBrowser' for visualisation, filtering or ranking.
A Bayesian statistical model for estimating child (under-five age group) and adult (15-60 age group) mortality.  The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by 'rstan' package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) <doi:10.1016/S0140-6736(12)61719-X>, Wang H, Liddell CA, Coates MM, et al. (2014) <doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian, Mehdipour et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.
This package has been deprecated and will not be updated. 
    New users should use the package 'gprofiler2' (<https://CRAN.R-project.org/package=gprofiler2>)
    for up-to-date data and improved functionality.
    Functional enrichment analysis, gene identifier conversion and
    mapping homologous genes across related organisms via the 'g:Profiler' toolkit
    (<https://biit.cs.ut.ee/gprofiler/>).
A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (<https://biit.cs.ut.ee/gprofiler>). 
    The main tools are: 
    (1) 'g:GOSt' - functional enrichment analysis and visualization of gene lists; 
    (2) 'g:Convert' - gene/protein/transcript identifier conversion across various namespaces;
    (3) 'g:Orth' - orthology search across species;
    (4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions, genes and variant effects
    This package is an R interface corresponding to the 2019 update of 'g:Profiler' and provides access to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the package 'gProfileR' for accessing older versions from the 'g:Profiler' toolset.
Implements the generalized propensity score cumulative distribution
    function proposed by Greene (2017)
    <https://digitalcommons.library.tmc.edu/dissertations/AAI10681743/>.
    A single scalar balancing score is calculated for any generalized propensity
    score vector with three or more treatments. This balancing score is used for
    propensity score matching and stratification in outcome analyses when analyzing
    either ordinal or multinomial treatments.
Applies sequential clustering algorithm to animal location data based on user-defined parameters 
    and appends results to the dataframe. Plots interactive cluster maps and provides a summary dataframe
    with attributes for each cluster commonly used as covariates in subsequent modeling efforts. Additional
    functions provide individual keyhole markup language plots for quick assessment, and export 
    global positioning system exchange format files for navigation purposes.
The gptk package implements a general-purpose toolkit for Gaussian
  process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc).
  Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.
An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.
Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.
Server implementation of 'GraphQL' <http://graphql.github.io/graphql-spec/>,
    a query language originally created by Facebook for describing data requirements on complex application
    data models.  Visit <http://graphql.org> to learn more about 'GraphQL'.
Genomic biology is not limited to the confines of the canonical B-
    forming DNA duplex, but includes over ten different types of other secondary
    structures that are collectively termed non-B DNA structures. Of these non-B
    DNA structures, the G-quadruplexes are highly stable four-stranded structures
    that are recognized by distinct subsets of nuclear factors. This package
    provide functions for predicting intramolecular G quadruplexes. In addition, 
    functions for predicting other intramolecular nonB DNA structures are included.
Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.
Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.
An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. 
	The variants of gradient descent algorithm are :
	Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load.
	Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically.
	Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average.
	Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning.
	Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning.
	Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
	Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
	RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
	Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.
	Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient.
	Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time.
	Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information.
	Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.
Provides functions for matching student-answers to teacher answers for a variety of data types.
After being given the location of your students' submissions and a test file, the function runs each .R file, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test.
Widget for an interactive selection and modification of a color gradient. 'gradientPickerD3' allows addition, removement and replacement of color ticks. List of numeric values will automatically translate in their corresponding tick position within the numeric range. App returns a data.frame containing tick values, colors and the positions in percent (0.0 to 1.0) for each color tick in the gradient. The original JS 'jquery-gradient-picker' was implemented by Matt Crinklaw-Vogt (nick: tantaman) <https://github.com/tantaman/>. Widget and JS modifications were done by CD. Peikert.
Probability propagation in graphical independence networks, also
    known as Bayesian networks or probabilistic expert systems. Documentation
    of the package is provided in vignettes included in the package and in
    the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>).
    See 'citation("gRain")' for details.
Data aggregation via moving window or direct methods. Aggregate a 
    fine-resolution raster to a grid. The moving window method smooths the surface 
    using a specified function within a moving window of a specified size and shape 
    prior to aggregation. The direct method simply aggregates to the grid using the 
    specified function.
Given a landscape resistance surface, creates minimum planar graph
    (Fall et al. (2007) <doi:10.1007/s10021-007-9038-7>) and grains of connectivity
    (Galpern et al. (2012) <doi:10.1111/j.1365-294X.2012.05677.x>) models that can be
    used to calculate effective distances for landscape connectivity at multiple scales.
    Documentation is provided by several vignettes, and a paper
    (Chubaty, Galpern & Doctolero (2020) <doi:10.1111/2041-210X.13350>).
A native R implementation of grammatical evolution (GE).
    GE facilitates the discovery of programs that can achieve a desired goal.
    This is done by performing an evolutionary optimisation over a population
    of R expressions generated via a user-defined context-free grammar (CFG)
    and cost function.
Repository based tools for department and analysis level
    reproducibility. 'GRANBase' allows creation of custom branched, continuous
    integration-ready R repositories, including incremental testing of only packages
    which have changed versions since the last repository build.
Provides the classes and methods for GRANRepository
    objects that are used within the 'GRAN' build framework for R packages.
    This is primarily used by the 'GRANBase' package and repositories
    that are created by it.
Contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne' and Montanari, 2018 <arXiv:1803.00374>.
Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for
   summarizing the consensus behavior of biological pathways in the form of a
   template, and for quantifying the extent to which individual samples deviate
   from the template. GRAPE templates are based only on the relative rankings
   of the genes within the pathway and can be used for classification of tissue
   types or disease subtypes. GRAPE can be used to represent gene-expression
   samples as vectors of pathway scores, where each pathway score indicates the
   departure from a given collection of reference samples. The resulting pathway-
   space representation can be used as the feature set for various applications,
   including survival analysis and drug-response prediction. 
   Users of GRAPE should use the following citation:
   Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize 
   tissue-specific functionality from gene expression profiles. 
   BMC Bioinformatics, 18:317 (June 2017).
Turn arbitrary functions into binary operators.
gRapfa is for modelling discrete longitudinal data using acyclic probabilistic finite automata (APFA). The package contains functions for constructing APFA models from a given data using penalized likelihood methods. For graphical display of APFA models, gRapfa depends on 'igraph package'. gRapfa also contains an interface function to Beagle software that implements an efficient model selection algorithm.
Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. Based on the 'vis.js' Graph3d module and the 'htmlwidgets' R package.
Build graphs for landscape genetics analysis. This set of 
	functions can be used to import and convert spatial and genetic data 
	initially in different formats, import landscape graphs created with 
	'GRAPHAB' software (Foltete et al., 2012) <doi:10.1016/j.envsoft.2012.07.002>, 
	make diagnosis plots of isolation by distance relationships in order to 
	choose how to build genetic graphs, create graphs with a large range of 
	pruning methods, weight their links with several genetic distances, plot 
	and analyse graphs,	compare them with other graphs. It uses functions from 
	other packages such as 'adegenet' 
	(Jombart, 2008) <doi:10.1093/bioinformatics/btn129> and 'igraph' (Csardi
	et Nepusz, 2006) <https://igraph.org/>. It also implements methods 
	commonly used in landscape genetics to create graphs, described by Dyer et 
	Nason (2004) <doi:10.1111/j.1365-294X.2004.02177.x> and Greenbaum et 
	Fefferman (2017) <doi:10.1111/mec.14059>, and to analyse distance data 
	(van Strien et al., 2015) <doi:10.1038/hdy.2014.62>.
A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R 
  interface for 'GraphFrames' <https://graphframes.github.io/>. 'GraphFrames' is a package
  for 'Apache Spark' that provides a DataFrame-based API for working with graphs. Functionality
  includes motif finding and common graph algorithms, such as PageRank and Breadth-first
  search.
Provides a quick and easy access to the 'GraphHopper' Directions API.
  'GraphHopper' <https://www.graphhopper.com/> itself is a routing engine based on 'OpenStreetMap' data.
  API responses can be converted to simple feature (sf) objects in a convenient way.
Statistical methodology for sparse multivariate extreme value models. Methods are
             provided for exact simulation and statistical inference for multivariate Pareto distributions
             on graphical structures as described in the paper 'Graphical Models for Extremes' by
             Engelke and Hitz (2018) <arXiv:1812.01734>.
Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
A fast C++ implementation for computing various graph kernels including (1) simple kernels between vertex and/or edge label histograms, (2) graphlet kernels, (3) random walk kernels (popular baselines), and (4) the Weisfeiler-Lehman graph kernel (state-of-the-art).
Several new layout algorithms to visualize networks are provided which are not part of 'igraph'. 
    Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>. 
    Some more specific algorithms allow to emphasize hidden group structures in networks or focus on specific nodes.
Provides a not-so-comprehensive list of methods for estimating graphon,
    a symmetric measurable function, from a single or multiple of observed networks. 
    For a detailed introduction on graphon and popular estimation techniques, 
    see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>.
    It also contains several auxiliary functions for generating sample networks
    using various network models and graphons.
Histogram principal components analysis is the generalization of the PCA. Histogram data are adapted to design complex and big data which histograms used as variables (big data adapter). Functions implemented provides numerical and graphical tools of an extension of PCA. Sun Makosso Kallyth (2016) <doi:10.1002/sam.11270>. Sun Makosso Kallyth and Edwin Diday (2012) <doi:10.1007/s11634-012-0108-0>.
Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL syntax
    and exports the AST in JSON format.
Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.
Functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph'  structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate  correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.
Allows building an edge table from data frame of tweets, 
  also provides function to build nodes and another create a temporal graph.
Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Create graticule lines and labels for maps. Control the creation
    of lines by setting their placement (at particular meridians and parallels)
    and extent (along parallels and meridians). Labels are created independently of
    lines.
Generates time series based on mixture autoregressive models. Kang,Y.,Hyndman,R.,Li,F.(2020)<doi:10.1002/sam.11461>.
Utilities to cost and evaluate Australian tax policy, including fast
    projections of personal income tax collections, high-performance tax and 
    transfer calculators, and an interface to common indices from the Australian
    Bureau of Statistics.  Written to support Grattan Institute's Australian 
    Perspectives program, and related projects. Access to the Australian Taxation
    Office's sample files of personal income tax returns is assumed.
Provides tools for systematically exploring large quantities of 
             temporal data across cyclic temporal granularities
             (deconstructions of time) by visualizing probability distributions.
             Cyclic time granularities can be circular, quasi-circular or 
             aperiodic. 'gravitas' computes cyclic
             single-order-up or multiple-order-up granularities, check the
             feasibility of creating plots for any two cyclic granularities
             and recommend probability distributions plots for exploring
             periodicity in the data.
A wrapper of different standard estimation methods for gravity models. 
  This package provides estimation methods for log-log models and multiplicative models.
The 'gRbase' package provides graphical modelling features
    used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements
    graph algorithms including (i) maximum cardinality search (for marked
    and unmarked graphs).
    (ii) moralization, (iii) triangulation, (iv) creation of junction tree.
    'gRbase' facilitates array operations,
    'gRbase' implements functions for testing for conditional independence.
    'gRbase' illustrates how hierarchical log-linear models may be
    implemented and describes concept of graphical meta
    data. 
    The facilities of the package are documented in the book by Højsgaard,
    Edwards and Lauritzen (2012,
    <doi:10.1007/978-1-4614-2299-0>) and in the paper by 
    Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>).
    Please see 'citation("gRbase")' for citation details. 
    NOTICE  'gRbase' requires that the packages graph,
    'Rgraphviz' and 'RBGL' are installed from 'bioconductor'; for
    installation instructions please refer to the web page given below.
Estimation, model selection and other aspects of
    statistical inference in Graphical Gaussian models with edge and
    vertex symmetries (Graphical Gaussian models with colours).
    Documentation about 'gRc' is provided in the paper by Hojsgaard and
    Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by
    Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).
We implement two main functions.
    The first function uses a given grouped and/or
    right-censored grouping scheme and empirical data to infer parameters,
    and implements chi-square goodness-of-fit tests.
    The second function searches for the global optimal grouping
    scheme of grouped and/or right-censored count responses in surveys.
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
Computes experimental designs for a
    two-arm experiment with covariates via a number of methods. 
    (0) complete randomization and randomization with forced-balance.
    (1) Greedily optimizing a
    balance objective function via pairwise switching. This optimization 
    provides lower variance for the treatment effect estimator (and higher 
    power) while preserving a design that is close to complete randomization.
    We return all iterations of the designs for use in a permutation test.
    (2) The second is via numerical optimization 
    (via 'gurobi' which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>) 
    a la Bertsimas and Kallus. 
    (3) rerandomization, 
    (4) Karp's method for one covariate, 
    (5) exhaustive enumeration to find the 
    optimal solution (only for small sample sizes)
    (6) Binary pair matching using the 'nbpMatching' library
    (7) Binary pair matching plus (1) to further optimize balance
    (8) Binary pair matching plus (3) to further optimize balance
    (9) Hadamard designs
    We also allow for three objective functions:
    Mahalanobis distance,
    Sum of absolute differences standardized and
    Kernel distances via the 'kernlab' library.
These are GreedyExperimentalDesign Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without GreedyExperimentalDesign.
An implementation of functions to display Greek letters on the 'RStudio' (include subscript and superscript indexes) and 'RGui' (without subscripts and only with superscript 1, 2 or 3; because 'RGui' doesn't support printing the corresponding Unicode characters as a string: all subscripts ranging from 0 to 9 and superscripts equal to 0, 4, 5, 6, 7, 8 or 9). The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the 'swirlify' package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the 'clisymbols' package.
Implements a method of iteratively collapsing the rows of a
    contingency table, two at a time, by selecting the pair of categories whose
    combination yields a new table with the smallest loss of chi-squared, as
    described by Greenacre, M.J. (1988) <doi:10.1007/BF01901670>. The result is
    compatible with the class of object returned by the 'stats' package's
    hclust() function and can be used similarly (plotted as a dendrogram,
    cut, etc.). Additional functions are provided for automatic cutting and
    diagnostic plotting.
Methods for manipulating regression models and for describing these in a style adapted for medical journals. 
  Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model 
  estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. 
  In addition to the descriptive methods, there are add-ons for the robust covariance matrix provided by the 'sandwich'
  package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for
  time-splitting a dataset when modeling non-proportional hazards in Cox regressions.
Fit linear mixed-effects models using restricted (or residual)
    maximum likelihood (REML) and with generalized inverse matrices to specify
    covariance structures for random effects. In particular, the package is
    suited to fit quantitative genetic mixed models, often referred to as
    'animal models'. Implements the average information algorithm as the main
    tool to maximize the restricted log-likelihood, but with other algorithms
    available.
We define generalized multipartite networks  as the joint observation of several networks implying some common pre-specified groups of individuals. The aim is to fit an adapted version of  the popular stochastic block model to multipartite networks, as described in Bar-hen, Barbillon and Donnet (2020) <arXiv:1807.10138>.
Allows the user to incorporate multiple sources of co-data 	
	(e.g., 	previously obtained p-values, published gene lists, and annotation) in the	estimation of a logistic regression model to enhance predictive performance and 	feature selection, as described in Münch, Peeters, van der Vaart, and van de Wiel 	(2018) <arXiv:1805.00389>.
Contains many functions useful for
    monitoring and reporting the results of clinical trials and other
    experiments in which treatments are compared.  LaTeX is
    used to typeset the resulting reports, recommended to be in the
    context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
    by 'greport' for high-level graphics.
Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'.
  greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on.
  See the website for more information, including tutorials, examples, package documentation, and the greta forum.
The social network literature features numerous methods for assigning
    value to paths as a function of their ties. 'gretel' systemizes these approaches,
    casting them as instances of a generalized path value function indexed by 
    a penalty parameter. The package also calculates probabilistic path value and
    identifies optimal paths in either value framework. Finally, proximity 
    matrices can be generated in these frameworks that capture high-order connections 
    overlooked in primitive adjacency sociomatrices. Novel methods are described
    in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. 
    More traditional methods are also implemented, as described in Yang, Knoke (2001) 
    <doi:10.1016/S0378-8733(01)00043-0>.
It allows running 'gretl' (<http://gretl.sourceforge.net/index.html>) program from R Markdown. 'gretl' ('Gnu' Regression, 'Econometrics', and Time-series Library) is a statistical software for Econometric analysis.  This package serves as a 'gretl' Knit-Engine for 'knitr' package. Write all your 'gretl' commands in R Markdown chunk.
Convert 'Ensembl' gene identifiers from Genotype-Tissue
    Expression (GTEx) data to identifiers in other annotation systems,
    including 'Entrez', 'HGNC', and 'UniProt'.
Implements functions and instruments for regression model building and its
             application to forecasting. The main scope of the package is in variables selection
             and models specification for cases of time series data. This includes promotional
             modelling, selection between different dynamic regressions with non-standard
             distributions of errors, selection based on cross validation, solutions to the fat
             regression model problem and more. Models developed in the package are tailored
             specifically for forecasting purposes. So as a results there are several methods
             that allow producing forecasts from these models and visualising them.
Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.
A pluggable package for forest-based statistical estimation and inference.
    GRF currently provides methods for non-parametric least-squares regression,
    quantile regression, survival regression and treatment effect estimation (optionally using instrumental
    variables), with support for missing values.
Functions for rendering Bezier
             curves (Pomax, 2018) <https://pomax.github.io/bezierinfo/>
             in 'grid'.  
             There is support for both quadratic and cubic Bezier curves.
             There are also functions for calculating points on curves,
             tangents to curves, and normals to curves.
Functions for drawing scene trees representing 
              scenes that have been drawn using grid graphics.
Provides a number of user-level functions to work with "grid"
    graphics, notably to arrange multiple grid-based plots on a page, and draw
    tables.
Functions for performing polygon geometry with 'grid' grobs.
             This allows complex shapes to be defined by combining simpler
             shapes.
Functions to convert a page of plots drawn with the 
  'graphics' package into identical output drawn with the 'grid' package.
  The result looks like the original 'graphics'-based plot, but consists
  of 'grid' grobs and viewports that can then be manipulated with 
  'grid' functions (e.g., edit grobs and revisit viewports).
Functions for drawing node-and-edge graphs that have been 
              laid out by graphviz.  This provides an alternative 
              rendering to that provided by the 'Rgraphviz' package, with
              two main advantages:  the rendering provided by 'gridGraphviz'
              should be more similar to what 'graphviz' itself would draw;
              and rendering with 'grid' allows for post-hoc customisations
              using the named viewports and grobs that 'gridGraphviz'
              produces.
Discretize multivariate continuous data using a grid
    that captures the joint distribution via preserving clusters in
    the original data (Wang et al. 2020). Joint grid discretization
    is applicable as a data transformation step to prepare data for
    model-free inference of association, function, or causality.
Simulation tool to facilitate determination of
    required sample size to achieve category saturation
    for studies using multiple repertory grids in conjunction with
    content analysis.
Functions to export graphics drawn with package grid to SVG
  format.  Additional functions provide access to SVG features that
  are not available in standard R graphics, such as hyperlinks, 
  animation, filters, masks, clipping paths, and gradient and pattern fills.
Provides support for rendering of formatted text using 'grid' graphics. Text can be
    formatted via a minimal subset of 'Markdown', 'HTML', and inline 'CSS' directives, and it can be
    rendered both with and without word wrap.
Provides the following types of models: Models for contingency
    tables (i.e. log-linear models) Graphical Gaussian models for multivariate
    normal data (i.e. covariance selection models) Mixed interaction models.
    Documentation about 'gRim' is provided by vignettes included in this
    package and the book by Højsgaard, Edwards and Lauritzen (2012,
    <doi:10.1007/978-1-4614-2299-0>); see 'citation("gRim")' for details.
Functions for converting, importing, and drawing PostScript 
              pictures in R plots.
Functions for importing external vector images and
  drawing them as part of 'R' plots.  This package is different from the
  'grImport' package because, where that package imports 'PostScript'
  format images, this package imports 'SVG' format images.  Furthermore,
  this package imports a specific subset of 'SVG', so external images
  must be preprocessed using a package like 'rsvg' to produce 'SVG'
  that this package can import.  'SVG' features that are not supported
  by 'R' graphics, e.g., gradient fills, can be imported and then
  exported via the 'gridSVG' package.
Calculates optical parameters of crystals like the optical
    axes, the axis angle 2V, and the direction of the principal axes of
    the indicatrix from extinction angles measured on a spindle stage 
    mounted on a polarisation microscope stage. Details of the method can be found in Dufey (2017) <arXiv:1703.00070>.
Set of functions designed to solve inverse problems. The direct problem is used to calculate a cost function to be minimized.
  Here are listed some papers using Inverse Problems solvers and sensitivity analysis:
  (Jader Lugon Jr.; Antonio J. Silva Neto 2011) <doi:10.1590/S1678-58782011000400003>.
  (Jader Lugon Jr.; Antonio J. Silva Neto; Pedro P.G.W. Rodrigues 2008) <doi:10.1080/17415970802082864>.
  (Jader Lugon Jr.; Antonio J. Silva Neto; Cesar C. Santana 2008) <doi:10.1080/17415970802082922>.
Uses simple Bayesian conjugate prior update rules to calculate 
    the win probability of each option, value remaining in the test, and 
    percent lift over the baseline for various marketing objectives.
    References: 
    Fink, Daniel (1997) "A Compendium of Conjugate Priors" <https://www.johndcook.com/CompendiumOfConjugatePriors.pdf>.
    Stucchio, Chris (2015) "Bayesian A/B Testing at VWO" <http://cdn2.hubspot.net/hubfs/310840/VWO_SmartStats_technical_whitepaper.pdf>.
Quantitative genetics tool supporting the modelling of multivariate 
 genetic variance structures in quantitative data. It allows fitting different 
 models through multivariate genetic-relationship-matrix (GRM) 
 structural equation modelling (SEM) in unrelated individuals, 
 using a maximum likelihood approach. Specifically, 
 it combines genome-wide genotyping information, as captured by GRMs, 
 with twin-research-based SEM techniques, 
 St Pourcain et al. (2017) <doi:10.1016/j.biopsych.2017.09.020>, 
 Shapland et al. (2020) <doi:10.1101/2020.08.14.251199>.
The program GRNN implements the algorithm proposed by
        Specht (1991).
Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.
A tool which allows users the ability to intuitively create 
  flexible, reproducible portable document format reports comprised of 
  aesthetically pleasing tables, images, plots and/or text.
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) <doi:10.18637/jss.v065.i01>.
Computes Gromov-Hausdorff type l^p distances for labeled metric spaces. These distances were introduced in V.Liebscher, Gromov meets Phylogenetics - new Animals for the Zoo of Metrics on Tree Space <arXiv:1504.05795>  for phylogenetic trees, but may apply to a diversity of scenarios.
Make R scripts that rely on packages reproducible, by ensuring that
    every time a given script is run, the same version of the used packages are
    loaded (instead of whichever version the user running the script happens to
    have installed). This is achieved by using the new command
    groundhog.library() instead of the base command library(), and including a
    date in the call. The date is used to call on the same version of the
    package every time (the most recent version available on CRAN at that date).
Learn group Bayesian Networks using hierarchical Clustering. This package implements the inference of group Bayesian networks based on hierarchical Clustering, and the adaptive refinement of the grouping regarding an outcome of interest.
Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).
Methods for dividing data into groups. 
    Create balanced partitions and cross-validation folds. 
    Perform time series windowing and general grouping and splitting of data. 
    Balance existing groups with up- and downsampling.
Regression models for grouped and coarse data, under the
        Coarsened At Random assumption.
Collection of functions to run statistical tests
    across all combinations of multiple grouping variables.
These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).
Contains an implementation of an independent component analysis (ICA) for grouped data. The main function groupICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding for 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/groupICA/>.
The 'groupr' package provides a more powerful version of grouped
  tibbles from 'dplyr'. It allows groups to be marked inapplicable,
  which is a simple but widely useful way to express structure in a dataset.
  It also provides powerful pivoting and other group manipulation functions.
An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.
A graphical user interface to compute group sequential designs
    based on normally distributed test statistics, particularly critical
    boundaries, power, drift, and confidence intervals of such designs. All
    computations are based on the alpha spending approach by Lan-DeMets with
    various alpha spending functions being available to choose among.
Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
Fits weighted quantile sum (WQS) regressions for one or more chemical groups with continuous or binary outcomes. Wheeler D, Czarnota J.(2016) <doi:10.1289/isee.2016.4698>.
Functional denoising and functional ANOVA through wavelet-domain 
  Markov groves. Fore more details see: Ma L. and Soriano J. (2016) 
  Efficient functional ANOVA through wavelet-domain Markov groves. 
  <arXiv:1602.03990v2 [stat.ME]>.
Estimates a collection of time-indexed functions under
    either of Gaussian process (GP) or intrinsic Gaussian Markov
    random field (iGMRF) prior formulations where a Dirichlet process
    mixture allows sub-groupings of the functions to share the same
    covariance or precision parameters.  The GP and iGMRF formulations
    both support any number of additive covariance or precision terms,
    respectively, expressing either or both of multiple trend and
    seasonality.
Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.
Fits the logistic equation to
    microbial growth curve data (e.g., repeated absorbance measurements
    taken from a plate reader over time). From this fit, a variety of
    metrics are provided, including the maximum growth rate,
    the doubling time, the carrying capacity, the area under the logistic
    curve, and the time to the inflection point. Method described in 
    Sprouffske and Wagner (2016) <doi:10.1186/s12859-016-1016-7>.
A compilation of nonlinear growth models used in many areas
Assists in producing longitudinal or profile plots of measured 
    traits. These allow checks to be made for anomalous data and growth patterns 
    in the data to be explored. Smoothing of growth trends for individual plants 
    using smoothing splines is available for removing transient effects. There 
    are tools for diagnosing the adequacy of trait smoothing, either using this 
    package or other packages, such as those that fit nonlinear growth models. 
    A range of per-unit (pot, plant, plot) growth traits can be extracted from 
    longitudinal data, including single time-point smoothed trait values and 
    their growth rates, interval growth rates and other growth statistics, such 
    as maximum growth. The package is particularly suited to preparing data from 
    high-throughput phenotyping facilities, such as imaging data from a Lemna-Tec Scananalyzer 3D 
    (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/> for more 
    information). The package 'growthPheno' can also be installed from 
  <http://chris.brien.name/rpackages/>.
A nonparametric empirical Bayes method for recovering
        gradients (or growth velocities) from observations of smooth
        functions (e.g., growth curves) at isolated time points.
A collection of methods to determine growth rates from
    experimental data, in particular from batch experiments and
    plate reader trials.
Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.
Fits user-specified (GLM-) models with group lasso penalty.
Implements the simple and computationally efficient standardization scheme for group lasso models with categorical predictors described in Detmer, Cebral, Slawski (2019) <arXiv:1805.06915>.
Efficient algorithms for fitting the regularization path of linear
  regression, GLM, and Cox regression models with grouped penalties.  This
  includes group selection methods such as group lasso, group MCP, and
  group SCAD as well as bi-level selection methods such as the group
  exponential lasso, the composite MCP, and the group bridge.
Group SLOPE is a penalized linear regression method that is used
    for adaptive selection of groups of significant predictors in a
    high-dimensional linear model.
    The Group SLOPE method can control the (group) false discovery rate at a
    user-specified level (i.e., control the expected proportion of irrelevant
    among all selected groups of predictors).
Methods include converting series of event names to strings, finding common patterns
    in a group of strings, discovering featured patterns when comparing two groups of strings as well
    as the number and starting position of each pattern in each string, obtaining transition matrix, 
    computing transition entropy, statistically comparing the difference between two groups of strings,
    and clustering string groups. Event names can be any action names or labels such as events in log
    files or areas of interest (AOIs) in eye tracking research.
Methodology for testing nonlinearity in the conditional mean function
    in low- or high-dimensional generalized linear models, and the significance of
    (potentially large) groups of predictors. Details on the algorithms can be
    found in the paper by Jankova, Shah, Buehlmann and Samworth (2019)
    <arXiv:1908.03606>.
Alternative implementations of some base R functions, including sort, order, and match.  Functions are simplified but can be faster or have other advantages.
Computational resources for test proposed by Gibbons, Ross, Shanken (1989)<DOI:10.2307/1913625>.
	It also has the functions for the power analysis and the choice of the optimal level of significance.
	The optimal level is determined by minimizing the expected loss from hypothesis testing.
Functions to generate and analyze data for psychology experiments based on the General Recognition Theory.
Offers functions for the comparison of Gutenberg-Richter 
	b-values. Several functions in GRTo are helpful for the assessment of the
	quality of seismicity catalogs.
A tool to sensitivity analysis using SOBOL (Sobol, 1993) and AMA (Dell'Oca et al. 2017 <doi:10.5194/hess-21-6219-2017>) indices.
            It allows to identify the most sensitive parameter or parameters of a model.
Provides the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) by combining marginal p-values using the Fisher method. See Fisher, R.A. (1925,ISBN:0-05-002170-2) Statistical Methods for Research Workers.
GSAgm is an R package that completes a self-contained gene set analysis (GSA) for RNA-seq and SNP data using the Gamma Method.
This package contains utility functions used by the Genome Analysis Toolkit (GATK) to load tables and plot data. The GATK is a toolkit for variant discovery in high-throughput sequencing data.
Computation of Quantitative Trait Loci hits in the selected gene set. Performing gene set validation with Quantitative Trait Loci information. Performing gene set enrichment analysis with available Quantitative Trait Loci data and computation of statistical significance value from gene set analysis. Obtaining the list of Quantitative Trait Loci hit genes along with their overlapped Quantitative Trait Loci names.
Write SARIMA models in (finite) AR representation and simulate 
	generalized multiplicative seasonal autoregressive moving average (time) series 
	with Normal / Gaussian, Poisson or negative binomial distribution. 
	The methodology of this method is described in Briet OJT, Amerasinghe PH, and 
	Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.
Group Sequential Operating Characteristics for Clinical,
        Bayesian two-arm Trials with known Sigma and Normal Endpoints.
Given an adjacency matrix drawn from a Generalized Stochastic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.
Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>.
    It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.
Design and analysis of group sequential designs for negative
    binomial outcomes, as described by T Mütze, E Glimm, H Schmidli, T Friede (2018) <doi:10.1177/0962280218773115>.
The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.
Derives group sequential designs and describes their properties.
Robust Estimation of Multivariate Location and Scatter in the
        Presence of Cellwise and Casewise Contamination and Missing Data.
R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).
Provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.
Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.
Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.
Conducts a semi-gSEM statistical analysis (semi-supervised generalized structural equation modeling) on a data frame of coincident observations of multiple predictive or intermediate variables and a final continuous, outcome variable, via two functions sgSEMp1() and sgSEMp2(), representing fittings based on two statistical principles. Principle 1 determines all sensible univariate relationships in the spirit of the Markovian process. The relationship between each pair of variables, including predictors and the final outcome variable, is determined with the Markovian property that the value of the current predictor is sufficient in relating to the next level variable, i.e., the relationship is independent of the specific value of the preceding-level variables to the current predictor, given the current value. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the relationship of the first-level predictors (such as Time and irradiance etc) to the outcome variable (such as, module degradation or yellowing)  is fit by a supervised additive model. Then each significant intermediate variable is taken as the new outcome variable and the other variables (except the final outcome variable) as the predictors in investigating the next-level multivariate relationship by a supervised additive model. This fitting process is continued until all sensible models are investigated.
calculate equivalence and futility boundaries based on the exact bivariate $t$ test statistics for group sequential designs in studies with equivalence hypotheses.
Simple package to download Google Sheets using just the sharing
    link. Spreadsheets can be downloaded as a data frame, or as plain text to parse
    manually. Google Sheets is the new name for Google Docs Spreadsheets <https://www.google.com/sheets/about>.
Global Soil Information Facilities - tools (standards and functions) and sample datasets for global soil mapping.
A set of high efficient functions to decode identifiers of National 
  Football League players.
An R wrapper for some of the functionality of the
 Gnu Scientific Library.
Contains published data sets for global benthic d18O data for 
    0-5.3 Myr <doi:10.1029/2004PA001071> and global sea levels based 
    on marine sediment core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.
Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.
Tools rewritten in C for various smoothing tasks
Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.
Provides automated downloading, parsing, cleaning, unit conversion
    and formatting of Global Surface Summary of the Day ('GSOD') weather data
    from the from the USA National Centers for Environmental Information
    ('NCEI').  Units are converted from from United States Customary System
    ('USCS') units to International System of Units ('SI').  Stations may be
    individually checked for number of missing days defined by the user, where
    stations with too many missing observations are omitted.  Only stations with
    valid reported latitude and longitude values are permitted in the final
    data.  Additional useful elements, saturation vapour pressure ('es'), actual
    vapour pressure ('ea') and relative humidity ('RH') are calculated from the
    original data using the improved August-Roche-Magnus approximation (Alduchov
    & Eskridge 1996) and included in the final data set.  The resulting metadata
    include station identification information, country, state, latitude,
    longitude, elevation, weather observations and associated flags.  For
    information on the 'GSOD' data from 'NCEI', please see the 'GSOD'
    'readme.txt' file available from,
    <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.
Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".
A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. Version 1.0.0 was released on April 12, 2017. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.
A comprehensive package for structural multivariate
        function estimation using smoothing splines.
We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.
Multivariate time series analysis based on Generalized Space-Time Autoregressive Model by Ruchjana et al.(2012) <doi:10.1063/1.4724118>.
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars.
Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.  See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data <arXiv:1810.05973>.
The gsubfn function is like gsub but can take a replacement 
   function or certain other objects instead of the replacement string.
   Matches and back references are input to the replacement function and 
   replaced by the function output.   gsubfn can be used to split strings 
   based on content rather than delimiters and for quasi-perl-style string 
   interpolation. The package also has facilities for translating formulas 
   to functions and allowing such formulas in function calls instead of 
   functions.  This can be used with R functions such as apply, sapply,
   lapply, optim, integrate, xyplot, Filter and any other function that 
   expects another function as an input argument or functions like cat
   or sql calls that may involve strings where substitution is desirable.
   There is also a facility for returning multiple objects from functions
   and a version of transform that allows the RHS to refer to LHS used in
   the same transform.
Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C
    library, version 3.05-4 (commit '5b4d959e54031f9e972f3e863f63e67fa4f5bfec', dated 2017-08-07,
    available at <https://github.com/TEOS-10/GSW-C>, which stems from
    'Matlab' and other code written by members of Working Group 127 of
    'SCOR'/'IAPSO' (Scientific Committee on Oceanic Research /
    International Association for the Physical Sciences of the Oceans).
Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.
Provides causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. This version supports unbalanced panels and implements the matrix completion method. Main reference: Yiqing Xu (2017) <doi:10.1017/pan.2016.2>.
Build display tables from tabular data with an easy-to-use set of
    functions. With its progressive approach, we can construct display tables
    with a cohesive set of table parts. Table values can be formatted using any
    of the included formatting functions. Footnotes and cell styles can be 
    precisely added through a location targeting system. The way in which 'gt'
    handles things for you means that you don't often have to worry about the
    fine details.
Provides tools to measure the reliability of an Information Retrieval test collection.
  It allows users to estimate reliability using Generalizability Theory and map those estimates onto
  well-known indicators such as Kendall tau correlation or sensitivity.
Tools to make it easier to work with "tables" of
    'grobs'. The 'gtable' package defines a 'gtable' grob class that specifies a
    grid along with a list of grobs and their placement in the grid. Further the
    package makes it easy to manipulate and combine 'gtable' objects so that 
    complex compositions can be build up sequentially.
This package provides functions to calculate the
        efficiencies (expected tests per unit) of hierarchical and
        matrix group testing procedures.  Efficiencies can be
        calculated in the presense of correlated responses under
        multiple arrangements of clustesrs.  Efficiencies can also be
        evaluated in the presense of test error.
Generalized Turnbull's estimator proposed by Dehghan and Duchesne
    (2011).
Four graph-based tests are provided for testing whether two samples are from the same distribution.  It works for both continuous data and discrete data.
Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in 'data.table' format. It also has some functions to subset GTFS data in time and space and to convert both representations to simple feature format.
Use GTFS (General Transit Feed Specification) data
    for routing from nominated start and end stations, and for extracting
    isochrones from nominated start station.
Utility functions to read, manipulate, analyse and
    write transit feeds in the General Transit Feed Specification (GTFS)
    data format.
Estimates variance components, generalizability coefficients,
    universe scores, and standard errors when observed scores contain variation from
    one or more measurement facets (e.g., items and raters).
Functions to assist in R programming, including:
  - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion',
    'getDependencies', 'keywords', 'scat'),
  - calculate the logit and inverse logit transformations ('logit', 'inv.logit'),
  - test if a value is missing, empty or contains only NA and NULL values ('invalid'),
  - manipulate R's .Last function ('addLast'),
  - define macros ('defmacro'),
  - detect odd and even integers ('odd', 'even'),
  - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'),
  - perform a binary search ('binsearch'),
  - sort strings containing both numeric and character components ('mixedsort'),
  - create a factor variable from the quantiles of a continuous variable ('quantcut'),
  - enumerate permutations and combinations ('combinations', 'permutation'),
  - calculate and convert between fold-change and log-ratio ('foldchange',
    'logratio2foldchange', 'foldchange2logratio'),
  - calculate probabilities and generate random numbers from Dirichlet distributions
    ('rdirichlet', 'ddirichlet'),
  - apply a function over adjacent subsets of a vector ('running'),
  - modify the TCP\_NODELAY ('de-Nagle') flag for socket objects,
  - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'),
  - generate significance stars from p-values ('stars.pval'),
  - convert characters to/from ASCII codes ('asc', 'chr'),
  - convert character vector to ASCII representation ('ASCIIfy').
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. This package provides with an implementation of the Game-Theoretically OPtimal (GTOP) reconciliation method proposed in van Erven and Cugliari (2015), which is guaranteed to only improve any given set of forecasts. This opens up new possibilities  for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level.
An interface for retrieving and displaying the information
        returned online by Google Trends is provided. Trends (number of
        hits) over the time as well as geographic representation of the
        results can be displayed.
Creates presentation-ready tables summarizing data
    sets, regression models, and more. The code to create the tables is
    concise and highly customizable. Data frames can be summarized with
    any function, e.g. mean(), median(), even user-written functions.
    Regression models are summarized and include the reference rows for
    categorical variables. Common regression models, such as logistic
    regression and Cox proportional hazards regression, are automatically
    identified and the tables are pre-filled with appropriate column
    headers.
Quantitative trait loci mapping and genome wide association analysis are used to find candidate molecular marker or region associated with phenotype based on linkage analysis and linkage disequilibrium. Gene expression quantitative trait loci mapping is used to find candidate molecular marker or region associated with gene expression. In this package, we applied the method in Liu W. (2011) <doi:10.1007/s00122-011-1631-7> and Gusev A. (2016) <doi:10.1038/ng.3506> to genome and transcriptome wide association study, which is aimed at revealing the association relationship between phenotype and molecular markers, expression levels, molecular markers nested within different related expression effect and expression effect nested within different related molecular marker effect. F test based on full and reduced model are performed to obtain p value or likelihood ratio statistic. The best linear model can be obtained by stepwise regression analysis.
Datos de nombres inscritos en Chile
    entre 1920 y 2019, de acuerdo al Servicio de Registro Civil. 
    Este paquete incluye todos los nombres con al menos 15 
    ocurrencias anuales.
    English: Chilean baby names registered in the Civil Registry
    Service. This package contains all names used at least 15 
    times per year, from 1920 to 2019.
Access to 'The Guardian' newspaper's open API
  <https://open-platform.theguardian.com/>, containing all articles published 
  in 'The Guardian' from 1999 to the present, including article text, metadata,
  tags and contributor information. An API key and registration is required.
Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and statistical and 
	graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
	development of statistical and graphic methods for multivariate data in a geo-spatial context of historical interest.
Adjust Estimates of Learning for Guessing. The package provides 
    standard guessing correction, and a latent class model that leverages
    informative pre-post transitions. For details of the latent class model,
    see <http://gsood.com/research/papers/guess.pdf>.
Make an empirical guess on the time and memory complexities of an algorithm or a function.
    Tests multiple, increasing size random samples of your data and tries to fit various complexity functions o(n), o(n2), o(log(n)), etc.
    Based on best fit, it predicts the full computation time on your whole dataset. Results are plotted with 'ggplot2'.
Provides simple "base R"-like functions, 
    such as %notin% (mirroring %in%), se (standard error, mirroring e.g. sd()), quick rounding of a mixed-class object (mirroring round()), count[ing] (mirroring e.g. sum(), mean(), etc.), and an aggregate function (mirroring aggregate()) that can also do pivoting.
A nice GUI for financial DErivatives in R.
A collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and  the guilds sampling formula conditioned on guild size (Janzen et al. 2015).
Create a user-friendly plotting GUI for R.
    In addition, one purpose of creating the R package is to facilitate third-party software to call R for drawing, for example, 'Phoenix WinNonlin' software calls R to draw the drug concentration versus time curve.
Show graphically the results of profiling R functions by tracking their execution time.
Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).
Generalized UniFrac distances for comparing microbial communities. Permutational multivariate analysis of variance using multiple distance matrices.
For plant physiologists, converts conductance (e.g. stomatal conductance) to different units: m/s, mol/m^2/s, and umol/m^2/s/Pa.
Statistical analysis of monthly background checks of gun purchases for the New York Times 
 story "What Drives Gun Sales: Terrorism, Obama and Calls for Restrictions" at 
 <http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html?> is provided.
Provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.
Download and process public domain works in the Project
    Gutenberg collection <http://www.gutenberg.org/>. Includes metadata for
    all Project Gutenberg works, so that they can be searched and retrieved.
Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood.
Perform the estimation and inference of Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.
Several tools for Global Value Chain ('GVC') analysis are
    implemented.
Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.
Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.
Generate Manhattan, Q-Q, and PCA plots from GWAS and PCA results using 'ggplot2'.
Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <DOI:10.1038/s41598-019-50229-6>.
This package contains a function to do exact
        Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
        as typically obtained in a Genome Wide Association Study
        (GWAS).
Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).
Performs variable selection with data from Genome-wide association studies (GWAS) combining, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors, as described in Sanyal et al. (2018).
When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report.
This is a Shiny application intended to provide better understanding of how geometrically-weighted degree terms function in exponential random graph models of networks. It contains just one user function, gwdegree(), which launches the Shiny application.
Computes a elliptical regression model or a geographically weighted regression model with elliptical errors using Fisher's score algorithm. Provides diagnostic measures, residuals and analysis of variance. Cysneiros, F. J. A., Paula, G. A., and Galea, M. (2007) <doi:10.1016/j.spl.2007.01.012>.
This package provides functions to calculate the
        significance threshold for controlling the type I error rate in
        mixed-model association mapping analyses.
Application of multi-site models for daily precipitation and temperature data. 
  This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland.
  It applies fitting procedures and provides weather generators described in the following references:
  - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.5194/hess-22-655-2018>.
  - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.1007/s00704-018-2404-x>.
Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country.
Based on calculations of 758 women this package calculates
        positive predictive values (PPV) and negative predictive values
        (NPV) for inadequate and excessive gestational weight gain
        (GWG) for different prevalences for different BMI categories.
Firstly, both functions of the univariate Poisson dispersion index (DI) for count data and the univariate exponential variation index (VI) for nonnegative continuous data are performed. Next, other functions of univariate indexes such the binomial dispersion index (DIb), the negative binomial dispersion index (DInb) and the inverse Gaussian variation index (VIiG) are given. Finally, we are computed some multivariate versions of these functions such that the generalized dispersion index (GDI) with its marginal one (MDI) and the generalized variation index (GVI) with its marginal one (MVI) too.
Re-implementation of the 'gWidgets' API. The API is defined in this
    package. A second, toolkit-specific package is required to use it. There
    are three in development: 'gWidgets2RGtk2', 'gWidgets2Qt', and 'gWidgets2tcltk'.
Implements the 'gWidgets2' API for 'RGtk2.'
Port of the 'gWidgets2' API for the 'tcltk' package.
Fit a geographically weighted logistic elastic net regression. Detailed explanations can be found in Yoneoka et al. (2016): New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening <doi:10.1038/srep26582>.
Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
Implements a geographically weighted non-negative principal components analysis, which consists of the fusion of geographically weighted and sparse non-negative principal components analyses <doi:10.17608/k6.auckland.9850826.v1>.
Implements a geographically weighted partial correlation which is an extension from gwss() function in the 'GWmodel' package (Percival and Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).
Fits Weighted Quantile Sum (WQS) regression  (Carrico et al. (2014) <doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin et al. (2019) <doi:10.1080/03610918.2019.1577971>) and a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.
Statistical functions to fit, validate and describe a Generalized
    Waring Regression Model (GWRM).
Computes the sample probability value (p-value)
    for the estimated coefficient from a standard
    genome-wide univariate regression.  It computes the exact
    finite-sample p-value under the assumption that the measured
    phenotype (the dependent variable in the regression) has
    a known Bernoulli-normal mixture distribution.
    Finite-sample genome-wide regression p-values (Gwrpv) with
    a non-normally distributed phenotype (Gregory Connor and
    Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).
Fits geographically weighted regression (GWR) models and
        has tools to diagnose and remediate collinearity in the GWR
        models. Also fits geographically weighted ridge regression
        (GWRR) and geographically weighted lasso (GWL) models.
Shiny application for the analysis of groundwater
    monitoring data, designed to work with simple time-series data for
    solute concentration and ground water elevation, but can also plot
    non-aqueous phase liquid (NAPL) thickness if required. Also provides
    the import of a site basemap in GIS shapefile format.
Melds genome-wide association tests with structural
    equation modeling (SEM) using 'OpenMx'. This package contains
    low-level C/C++ code to rapidly read genetic data encoded in U.K.
    Biobank or 'plink' formats. Prebuilt modeling options include one and
    two factor models. Alternately, analyses may utilize arbitrary,
    user-provided SEMs.  See Verhulst, Maes, & Neale (2017)
    <doi:10.1007/s10519-017-9842-6> for details. An updated manuscript is
    in preparation.
The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.
Tools to run genome-wide association study (GWAS) and
  genome-wide by environment interaction study (GWEIS) scans using the genetic
  data stored in a binary dosage file. The user provides a data frame with
  the subject's covariate data and the information about the binary dosage
  file returned by the BinaryDosage::getbdinfo() routine.
Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
OpenAI Gym is a open-source Python toolkit for developing and comparing
    reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API,
    and enables access to an ever-growing variety of environments.
    For more details on OpenAI Gym, please see here: <https://github.com/openai/gym>.
    For more details on the OpenAI Gym API specification, please see here:
    <https://github.com/openai/gym-http-api>.
R interface for 'H2O', the scalable open source machine learning
    platform that offers parallelized implementations of many supervised and
    unsupervised machine learning algorithms such as Generalized Linear
    Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests,
    Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes,
    Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA,
    Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Interface to 'H2O4GPU' <https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.
Implements the sample size methods for hierarchical 2x2 factorial trials under a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations.
A Haar-Fisz algorithm for Poisson intensity estimation
Simple tools for converting columns to new data types. Intuitive functions for columns with missing values.
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Use the <https://hacker-news.firebaseio.com/v0/> API through R. Retrieve
    posts, articles and other items in form of convenient R objects.
Move between data frames and lists more efficiently with precision 
  splitting via 'dplyr' verbs. Easily cast variables to different data types. Keep 
  rows with NAs. Shift row values.
Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. Any genomic marker can be targeted to assess likely required specimen sample sizes. The method is well-suited to assess sampling sufficiency for DNA barcoding initiatives.
Generates Hadamard matrices using different construction methods. For those who want to generate Hadamard matrix, a generic function, Hadamard_matrix() is provided. For those who want to generate Hadamard matrix using a particular method, separate functions are available. See Horadam (2007, ISBN:9780691119212) Hadamard Matrices and their applications, Princeton University Press for more information on Hadamard Matrices.
Functions for processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by 
             Mass Spectrometry experiments (HDX-MS). 'HaDeX' introduces a new standardized and reproducible workflow 
             for the analysis of the HDX-MS data, including novel uncertainty intervals. Additionally, it covers 
             data exploration, quality control and generation of publication-quality figures. All functionalities 
             are also available in the in-built 'Shiny' app.
Provides a framework for writing map/reduce scripts for
        use in Hadoop Streaming. Also facilitates operating on data in
        a streaming fashion, without Hadoop.
Toolkit to perform statistical analyses of correlation
   functions generated from Lattice Monte Carlo simulations. In
   particular, a class 'cf' for correlation functions and
   methods to analyse those are defined. This includes (blocked)
   bootstrap (based on the 'boot' package) and jackknife, but also an
   automatic determination of integrated autocorrelation
   times. 'hadron' also provides a very  general function
   bootstrap.nlsfit() to bootstrap a non-linear least squares fit.
   More specific functions are provided to extract hadronic quantities
   from Lattice Quantum Chromodynamics simulations, a particular Monte
   Carlo simulation,(see e.g. European Twisted Mass Collaboration, P. Boucaud et
   al. (2008) <doi:10.1016/j.cpc.2008.06.013>). Here, to determine
   energy eigenvalues of hadronic states, specific fitting routines
   and in particular the generalised eigenvalue method (see
   e.g. B. Blossier et al. (2009) <doi:10.1088/1126-6708/2009/04/094>
   and M. Fischer et al. (2020)
   <https://inspirehep.net/literature/1792113>) are implemented.
   In addition, input/output and plotting routines are available.
Analysis of plant pathogen pathotype survey data.  Functions
  provided calculate distribution of susceptibilities, distribution of
  complexities with statistics, pathotype frequency distribution, as well as
  diversity indices for pathotypes.  This package is meant to be a direct
  replacement for Herrmann, Löwer and Schachtel's (1999)
  <doi:10.1046/j.1365-3059.1999.00325.x> Habgood-Gilmour Spreadsheet, 'HaGiS',
  previously used for pathotype analysis.
Read data from the City of Portland's 'HYDRA' <http://or.water.usgs.gov/precip/> rainfall datasets within R.
A scalable implementation of the highly adaptive lasso algorithm,
  including routines for constructing sparse matrices of basis functions of the
  observed data, as well as a custom implementation of Lasso regression tailored
  to enhance efficiency when the matrix of predictors is composed exclusively of
  indicator functions. For ease of use and increased flexibility, the Lasso
  fitting routines invoke code from the 'glmnet' package by default. The highly
  adaptive lasso was first formulated and described by MJ van der Laan (2017)
  <doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance
  given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>.
Conditional density estimation is a longstanding and challenging
    problem in statistical theory, and numerous proposals exist for optimally
    estimating such complex functions. Algorithms for nonparametric estimation
    of conditional densities based on a pooled hazard regression formulation and
    semiparametric estimation via conditional hazards modeling are implemented
    based on the highly adaptive lasso, a nonparametric regression function for
    efficient estimation with fast convergence under mild assumptions. The
    pooled hazards formulation implemented was first described by Díaz and
    van der Laan (2011) <doi:10.2202/1557-4679.1356>.
There are growing concerns on flow data in diverse fields including trade, migration, knowledge diffusion, disease spread, and transportation. The package is an effective visual support to learn the pattern of flow which is called halfcircle diagram. The flow between two nodes placed on the center line of a circle is represented using a half circle drawn from the origin to the destination in a clockwise direction. Through changing the order of nodes, the halfcircle diagram enables users to examine the complex relationship between bidirectional flow and each potential determinants. Furthermore, the halfmeancenter function, which calculates (un) weighted mean center of half circles, makes the comparison easier.
Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.
Converts among many citation formats, including 'BibTeX',
    'Citeproc', 'Codemeta', 'RDF XML', 'RIS', 'Schema.org', and
    'Citation File Format'. A low level 'R6' class is provided, as well
    as stand-alone functions for each citation format for both read
    and write.
An S4 implementation of Eq. (3) and Eq. (7) by
    David J. Hand and Robert J. Till (2001) <DOI:10.1023/A:1010920819831>.
Several handy plots for quickly looking at the relationship between two numeric vectors of equal length. Quickly visualize scatter plots, residual plots, qq-plots, box plots, confidence intervals, and prediction intervals.
The haversine is a function used to calculate the distance between a pair of 
    latitude and longitude points while accounting for the assumption that the points 
    are on a spherical globe. This package provides a fast, dataframe compatible, 
    haversine function. For the first publication on the haversine calculation see 
    Joseph de Mendoza y Ríos (1795) <https://books.google.cat/books?id=030t0OqlX2AC> (In Spanish).
Provides functions to download data from the 
  <http://www.data.parliament.uk/> APIs. Because of the structure of the API, 
  there is a named function for each type of available data for ease of use, 
  as well as some functions designed to retrieve specific pieces of commonly 
  used data. Functions for each new API will be added as and when they become
  available.
The following R functions are used for inference of trait
        associations with haplotypes and other covariates in
        generalized linear models.  The functions are developed
        primarily for data collected in cohort or cross-sectional
        studies. They can accommodate uncertain haplotype phase and
        handle missing genotypes at some SNPs.
Inference of chromosome-length haplotypes using a few haploid 
	gametes of an individual. The gamete genotype data may be generated from various platforms 
	including genotyping arrays and sequencing even with low-coverage. Hapi simply takes 
	genotype data of known hetSNPs in single gamete cells as input and report the high-resolution 
	haplotypes as well as confidence of each phased hetSNPs. The package also includes a module 
	allowing downstream analyses and visualization of identified crossovers in the gametes.
The package provides a set of functions whose aim is to
        propose 4 methods of QTL detection. HAPimLD is an
        interval-mapping method designed for unrelated individuals with
        no family information that makes use of linkage disequilibrium.
        HAPimLDL is an interval-mapping method for design of half-sib
        families. It combines linkage analysis and linkage
        disequilibrium. HaploMax is based on an analysis of variance
        with a dose haplotype effect. HaploMaxHS is based on an
        analysis of variance with a sire effect and a dose haplotype
        effect in half-sib family design.  Fundings for the package
        development were provided to the LDLmapQTL project by the ANR
        GENANIMAL program and APIS-GENE.
Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. 'Haplin' estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. 'Haplin' also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. 'Haplin' allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in Gjessing, HK and Lie, RT (2006) <doi:10.1111/j.1529-8817.2005.00218.x>.
Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.
A set of utilities for querying 
             'HaploReg' <https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php>, 
             'RegulomeDB' <https://www.regulomedb.org/regulome-search/> 
             web-based tools. The package connects to 
             'HaploReg', 'RegulomeDB' searches and downloads results, without 
             opening web pages, directly from R environment. 
             Results are stored in a data frame that can be directly used in various 
             kinds of downstream analyses.
Reconstruction of founder haplotype blocks from time series data.
Simulate haplotypes through meioses. Allows specification
        of population parameters.
Function to  identify  haplotypes
  within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL. This function 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, haplotyper groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, haplotyper calculates such
  probability from relative frequencies.
Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel coding methods (only simple indel coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapses identical DNA sequences into haplotypes or inferring haplotypes using user provided absolute pairwise character difference matrix.  This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks.
Software creation is a pretty big deal!  A repository's initial commit date can be thought of as its birthday.  Next time, drop in and wish a developer (any Github username) a happy birthday of their repo(s).  Or maybe just toast to the upcoming anniversary of your own software!  The software life cycle is too short not to celebrate!
There are two interesting games in this package, one is 2048 games(for windows), using up and down to control the direction until there is a 2048 figure. And the other is 'what to eat today',preparing for people who choose difficulties, including most of the delicious Cantonese cuisine.
Package for haplotype-based genotype simulations. Haplotypes are
        generated such that their allele frequencies and linkage
        disequilibrium coefficients match those estimated from an input
        data set.
Building modeling packages is hard. A large amount
    of effort generally goes into providing an implementation for a new
    method that is efficient, fast, and correct, but often less emphasis
    is put on the user interface. A good interface requires specialized
    knowledge about S3 methods and formulas, which the average package
    developer might not have.  The goal of 'hardhat' is to reduce the
    burden around building new modeling packages by providing
    functionality for preprocessing, predicting, and validating input.
Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908;  Weinberg, 1908) <doi:10.1126/science.28.706.49> for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included (Graffelman & Weir, 2016) <doi: 10.1038/hdy.2016.20>, including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.
Estimation, simulation, and forecasting using the HAR model from Corsi(2009) <DOI:10.1093/jjfinec/nbp001> and extensions.
The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended.
Fits the first harmonics in a Fourier expansion to one or more time series. Trend elimination can be performed.  Computed values include estimates of amplitudes and phases, as well as confidence intervals and p-values for the null hypothesis of Gaussian noise.
Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr'
    implements some function to manipulate distance matrices and phylogenetic trees
    to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse'
    tools.
Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise.
The goal of 'HARtools' is to provide a simple set of functions
    to read/parse, write and visualise HTTP Archive ('HAR') files in R.
Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information.
Implements a data structure similar to hashes
        in Perl and dictionaries in Python but with a purposefully R
        flavor.  For objects of appreciable size, access using hashes
        outperforms native named lists and vectors.
An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.
Apply an adaptation of the SuperFastHash algorithm to any R
    object. Hash whole R objects or, for vectors or lists, hash R objects to obtain
    a set of hash values that is stored in a structure equivalent to the input. See
    <http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash
    algorithm.
Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set.
Import foreign statistical formats into R via the
    embedded 'ReadStat' C library,
    <https://github.com/WizardMac/ReadStat>.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.
Damage Functions (DFs), also known as
    Vulnerability Functions, associate the physical damage
    to a building or a structure (and also its contents and
    inventory) from natural disasters to financial damage.
    The Federal Emergency Management Agency (FEMA) in USA
    developed several thousand DFs and these serve as a
    benchmark in natural catastrophe modeling, both in
    academia and industry. However, these DFs and their
    documentation are buried within the HAZUS software are
    not easily accessible for analysis and visualization.
    This package provides more than 1300 raw DFs used by FEMA's
    HAZUS software and also functionality to extract and
    visualize DFs specific to the flood hazard. The vignette
    included with this package demonstrates its use.
Fit an array of decision-making tasks with computational models in
    a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of
    various computational models with a single line of coding
    (Ahn et al., 2017) <doi:10.1162/CPSY_a_00002>.
Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.
Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
A package for building hierarchical block matrices from association matrices and for performing multi-scale analysis. It specifically targets chromatin contact maps, generated from high-throughput chromosome conformation capture data, such as 5C and Hi-C, and provides methods for detecting movements and for computing chain hierarchy and region communicability across scales.
Contains functions for fitting hierarchical versions of
        EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
        our gamma signal detection model to recognition memory
        confidence-ratings data.
Functions to compute small area estimates based on a basic
        area or unit-level model. The model is fit using restricted
        maximum likelihood, or in a hierarchical Bayesian way. In the
        latter case numerical integration is used to average over the
        posterior density for the between-area variance.  The output
        includes the model fit, small area estimates and corresponding
        MSEs, as well as some model selection measures. Additional
        functions provide means to compute aggregate estimates and
        MSEs, to minimally adjust the small area estimates to
        benchmarks at a higher aggregation level, and to graphically
        compare different sets of small area estimates.
This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.
Texts for H.C. Andersens fairy tales, ready for
    text analysis. Fairy tales in German, Danish, English, Spanish and
    French.
A new diagnostic check for model adequacy in regression
        and generalized linear models is implemented.
This package calculates the interval estimates for the parameters of 
 linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
 bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
 the percentile bootstrap and percentile bootstrap double. It is possible to calculate
 consistent estimates of the covariance matrix of the parameters of linear regression models
 with heteroskedasticity of unknown form. The package also provides function to calculate
 consistently the covariance matrix of the parameters of linear models with heteroskedasticity
 of unknown form.
Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) <arXiv:1810.01509>. The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities.
By binding R functions and the 'Highmaps' <https://www.highcharts.com.cn/products/highmaps> chart library, 'hchinamap' package provides a simple way to map China and its provinces. The map of China drawn by this package contains complete Chinese territory, especially the Nine-dotted line, South Tibet, Hong Kong, Macao and Taiwan.
Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.
Estimation of parameters in 3-segment (i.e. 2 change-point)
    regression models with heteroscedastic variances is provided based on both
    likelihood and hybrid Bayesian approaches, with and without continuity
    constraints at the change points.
This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. 
    We implement an effective solution to recover the above hidden compact representation under the likelihood framework. 
    Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.
Given a database of previous treatment/placebo estimates, their standard errors and sample sizes,
      the program calculates a significance criteria and power estimate that takes into account the among
      trial variation.
A novel searching scheme for tuning parameter in high-dimensional 
             penalized regression. We propose a new estimate of the regularization
             parameter based on an estimated lower bound of the proportion of false 
             null hypotheses (Meinshausen and Rice (2006) <doi:10.1214/009053605000000741>).
             The bound is estimated by applying the empirical null distribution of the higher 
             criticism statistic, a second-level significance testing, which is constructed
             by dependent p-values from a multi-split regression and aggregation method
             (Jeng, Zhang and Tzeng (2019) <doi:10.1080/01621459.2018.1518236>). An estimate 
             of tuning parameter in penalized regression is decided corresponding to the lower 
             bound of the proportion of false null hypotheses. Different penalized 
             regression methods are provided in the multi-split algorithm.
Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Binary segmentation methods for detecting and estimating 
	multiple change-points in the mean or second-order structure 
	of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.
Perform mediation analysis in the presence of high-dimensional
    mediators based on the potential outcome framework. High dimensional
    Bayesian mediation (HDBM), developed by Song et al (2018)
    <doi:10.1101/467399>, relies on two Bayesian sparse linear mixed models to
    simultaneously analyze a relatively large number of mediators for a
    continuous exposure and outcome assuming a small number of mediators are
    truly active. This sparsity assumption also allows the extension of
    univariate mediator analysis by casting the identification of active
    mediators as a variable selection problem and applying Bayesian methods
    with continuous shrinkage priors on the effects.
Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.
Discriminant analysis and data clustering methods for high
    dimensional data, based on the assumption that high-dimensional data live in
    different subspaces with low dimensionality proposing a new parametrization of
    the Gaussian mixture model which combines the ideas of dimension reduction and
    constraints on the model.
Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <http://jmlr.org/papers/v18/16-342.html>.
Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations.
A procedure that fits derivative curves based on a sequence of quotient differences.  In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves.  In a non-hierarchical setting the package produces a single derivative curve.
Hard drive data: Class of data allowing the easy importation/manipulation of out of memory data sets. The data sets are located on disk but look like in-memory, the syntax for manipulation is similar to 'data.table'. Operations are performed "chunk-wise" behind the scene.
Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features.  The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods.
Cross-validated linear discriminant calculations determine
  the optimum number of features. Test and training scores from
  successive cross-validation steps determine, via a principal
  components calculation, a low-dimensional global space onto which test
  scores are projected, in order to plot them. Further functions are
  included that are intended for didactic use. The  package implements,
  and extends, methods described in J.H. Maindonald and C.J. Burden (2005)
  <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.
Tools to discover hydrological data, accessing catalogues and databases from various data providers. The package is described in Vitolo (2017) "hddtools: Hydrological Data Discovery Tools" <doi:10.21105/joss.00056>.
'HDF5' is a data model, library and file format for storing 
    and managing large amounts of data. This package provides a nearly
    feature complete, object oriented  wrapper for the 'HDF5' API
    <https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html> using R6 classes. 
    Additionally, functionality is added so that 'HDF5' objects behave very 
    similar to their corresponding R counterparts.
Provides an interface to 'HDFql' <http://www.hdfql.com/> 
    and helper functions for reading data from and writing data to 'HDF5' files. 'HDFql' 
    provides a high-level language for managing 'HDF5' data that is platform independent.
    For more information, see the reference manual 
    <http://www.hdfql.com/resources/HDFqlReferenceManual.pdf>.
Test the significance of coefficients in high dimensional generalized linear models.
Implementation of multiple approaches to perform inference in high-dimensional models.
A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.
We provide an R tool for nonparametric plug-in estimation of Highest Density Regions (HDRs) in the directional setting. Concretely, circular and spherical regions can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2020) <arXiv:2009.08915>. This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences.
The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2020). "Statistical inference for high-dimensional vector autoregression with measurement error", arXiv preprint <arXiv:2009.08011>.
Mimics the lm() function found in the package stats to fit
        high dimensional regression models with point estimates,
        standard errors, and p-values. Methods for printing and
        summarizing the results are given.
Testing homogeneity of k multivariate distributions is a classical and challenging problem in
             statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size.
             We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. 
             These tests are easy to implement and distribution-free in finite sample situations. Under appropriate 
             regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the 
             dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale 
             approach, where the results for different number of partitions are aggregated judiciously. Details are in 
             Biplab Paul, Shyamal K De and Anil K Ghosh (2020); Soham Sarkar and Anil K Ghosh (2019) 
             <doi:10.1109/TPAMI.2019.2912599>; William M Rand (1971) <doi:10.1080/01621459.1971.10482356>;  
             Cyrus R Mehta and Nitin R Patel (1983) <doi:10.2307/2288652>; Joseph C Dunn (1973) 
             <doi:10.1080/01969727308546046>; Sture Holm (1979) <doi:10.2307/4615733>; 
             Yoav Benjamini and Yosef Hochberg (1995) <doi: 10.2307/2346101>.
Implementation of selected high-dimensional statistical and
    econometric methods for estimation and inference. Efficient estimators and
    uniformly valid confidence intervals for various low-dimensional causal/
    structural parameters are provided which appear in high-dimensional
    approximately sparse models. Including functions for fitting heteroscedastic
    robust Lasso regressions with non-Gaussian errors and for instrumental variable
    (IV) and treatment effect estimation in a high-dimensional setting. Moreover,
    the methods enable valid post-selection inference and rely on a theoretically
    grounded, data-driven choice of the penalty.
    Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
Penalized regression for generalized linear models for
  measurement error problems (aka. errors-in-variables). The package
  contains a version of the lasso (L1-penalization) which corrects
  for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). 
  It also contains an implementation of the Generalized Matrix Uncertainty 
  Selector, which is a version the (Generalized) Dantzig Selector for the 
  case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
A multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package 'HDMT' we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) <doi:10.1080/01621459.2020.1765785>.
Creates nomogram visualizations for penalized Cox regression
    models, with the support of reproducible survival model building,
    validation, calibration, and comparison for high-dimensional data.
An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers.
In high-dimensional settings:
	Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model.
	Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors.
	Adjust the shrinkage bias in the predicted PC scores.
	Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.
Algorithms for lasso and fused-lasso problems: implementation of
    the lars algorithm for lasso and fusion penalization and EM-based
    algorithms for (logistic) lasso  and fused-lasso penalization.
Implementation of MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model (hdpGLM) presented in the paper Ferrari (2020) Modeling Context-Dependent Latent Heterogeneity, Political Analysis <DOI:10.1017/pan.2019.13>.
Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.
Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.
High dimensional testing procedures on mean, covariance and white noises.
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
A machine learning toolbox tailored to healthcare data.
Provides a shiny interface for a free, open-source managerial
    accounting-like system for health care practices. This package allows
    health care administrators to project revenue with monthly adjustments
    and procedure-specific boosts up to a 3-year period. Granular data
    (patient-level) to aggregated data (department- or hospital-level) can
    all be used as valid inputs provided historical volume and revenue
    data is available. For more details on managerial accounting
    techniques, see Brewer et al. (2015, ISBN:9780078025792).
Scrape data from Patient Forum <https://patient.info/forums> by entering urls. It will return a data frame containing text, user names, like counts, reply counts, etc.
Hospital data analysis workflow tools, modeling, and automations. This library
    provides many useful tools to review common administrative hospital data. Some 
    of these include average length of stay, readmission rates, average net pay
    amounts by service lines just to name a few. The aim is to provide a simple
    and consistent verb framework that takes the guesswork out of everything.
Provides data for functions typically used in the 'healthyR' package.
Hospital time series data analysis workflow tools, modeling, and automations. 
    This library provides many useful tools to review common administrative time 
    series hospital data. Some of these include average length of stay, and 
    readmission rates. The aim is to provide a simple and consistent verb 
    framework that takes the guesswork out of everything.
The 'healthyverse' is a set of packages that work in
    harmony because they share common data representations and 'API'
    design. This package is designed to make it easy to install and load
    multiple 'healthyverse' packages in a single step.
The heatex package calculates heat storage in the body and
        the components of heat exchange (conductive, convective,
        radiative, and evaporative) between the body and the
        environment during physical activity based on the principles of
        partitional calorimetry. The program enables heat exchange
        calculations for a range of environmental conditions when
        wearing various clothing ensembles.
Allows heatmap matrix to have non-identical X- and
        Y-dimensions.  Allows multiple tracks of annotation for
        RowSideColors and ColSideColors.
An improved heatmap package. Completely
    compatible with the original R function 'heatmap',
    and provides more powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
    binary dependent variables, and calculates the related heatmap fit
    statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.
Create interactive cluster 'heatmaps' that can be saved as a stand-
    alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
    available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to
    show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical
    method for visualizing high-dimensional data, in which a table of numbers
    are encoded as a grid of colored cells. The rows and columns of the matrix
    are ordered to highlight patterns and are often accompanied by 'dendrograms'.
    'Heatmaps' are used in many fields for visualizing observations, correlations,
    missing values patterns, and more. Interactive 'heatmaps' allow the inspection
    of specific value by hovering the mouse over a cell, as well as zooming into
    a region of the 'heatmap' by dragging a rectangle around the relevant area.
    This work is based on the 'ggplot2' and 'plotly.js' engine. It produces
    similar 'heatmaps' to 'heatmap.2' with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), the ability to zoom from
    the 'dendrogram' panes, and the placing of factor variables in the sides of the
    'heatmap'.
The different methods of defining and detecting extreme events, known as heatwaves or cold-spells in both air and water temperature data are encompassed within this package. These detection algorithms may be used on non-temperature data as well however, this is not catered for explicitly here as no use of this technique in the literature currently exists.
Functions to perform robust estimation considering heavy-tailed distributions.
    Currently, the package includes routines for linear regression, linear mixed-effect
    models, Grubbs' model, multivariate location and scatter estimation, multivariate
    regression, penalized splines, random variate generation and some support functions.
Maximum likelihood estimation by an EM algorithm of Heckman-type sample selection Normal or Student-t models. The reference is Lachos, Prates and Dey (2020) <arXiv:2006.08036>.
Helper functions designed to make
    dynamically generating R Markdown documents easier by providing a
    simple and tidy way to create report pieces, shape them to your data,
    and combine them for exporting into a single R Markdown document.
Hedgehog will eat all your bugs.
  'Hedgehog' is a property-based testing package in the spirit
  of 'QuickCheck'. With 'Hedgehog', one can test properties
  of their programs against randomly generated input, providing
  far superior test coverage compared to unit testing. One of the
  key benefits of 'Hedgehog' is integrated shrinking of
  counterexamples, which allows one to quickly find the cause of
  bugs, given salient examples when incorrect behaviour occurs.
An implementation of the modelling and reporting features described 
    in reference textbook and guidelines (Briggs, Andrew, et al. Decision 
    Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011;
    Siebert, U. et al. State-Transition Modeling. Medical Decision Making 
    32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, 
    heterogeneity analysis, time dependency on state-time and model-time 
    (semi-Markov and non-homogeneous Markov models), etc.
Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.
The main focus is on preprocessing and data visualization of machine learning models performances. 
  Some functions allow to fill in gaps in time series using linear interpolation on panel data, some functions 
  permit to draw lift effect and lift curve in order to benchmark machine learning models or you can even find 
  the optimal number of clusters in agglomerative clustering algorithm.
Create publication-quality, 2-dimensional visualizations of alpha-helical peptide
  sequences. Specifically, allows the user to programmatically generate
  helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of
  alpha-helical oligopeptides. See Wadhwa RR, et al. (2018)
  <doi:10.21105/joss.01008> for more information.
Empirical value of the Hellinger correlation, a measure of dependence between
	     two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) <arXiv:1810.10276v4>.
Base R's default setting for 'stringsAsFactors' within
    'data.frame()' and 'as.data.frame()' is supposedly the most often complained
    about piece of code in the R infrastructure. The 'hellno' package provides
    an explicit solution without changing R itself or having to mess around with
    options. It tries to solve this problem by providing alternative
    'data.frame()' and 'as.data.frame()' functions that are in fact simple
    wrappers around base R's 'data.frame()' and 'as.data.frame()' with
    'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE )
    by default.
A dummy package to demonstrate how to interface to a jar
        file that resides inside an R package.
Access to large host-parasite data is often hampered by the 
  availability of data and difficulty in obtaining it in a programmatic way
  to encourage analyses. 'helminthR' provides a programmatic interface to the 
  London Natural History Museum's host-parasite database, one of the largest 
  host-parasite databases existing currently <https://www.nhm.ac.uk/research-curation/scientific-resources/taxonomy-systematics/host-parasites/>. The package allows the user
  to query by host species, parasite species, and geographic location.
Contains miscellaneous functions useful for managing 'NetCDF' files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Tools for accessing various open data sources in the Helsinki
    region in Finland. Current data sources include
    the Real Estate Department (<http://ptp.hel.fi/avoindata/>),
    Service Map API (<http://api.hel.fi/servicemap/v1/>),
    Linked Events API (<http://api.hel.fi/linkedevents/v0.1/>),
    Helsinki Region Infoshare statistics API (<https://dev.hel.fi/stats/>).
An implementation of several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs). 'HEMDAG' package: 1) reconciles flat predictions with the topology of the ontology; 2) can enhance the predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; 3) provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; 4) is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (as FunCat), since trees are DAGs; 5) scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; 6) provides several utility functions to process and analyze graphs; 7) provides several performance metrics to evaluate HEMs algorithms. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
Provides HE plot and other functions for visualizing hypothesis
    tests in multivariate linear models. HE plots represent sums-of-squares-and-
    products matrices for linear hypotheses and for error using ellipses (in two
    dimensions) and ellipsoids (in three dimensions). The related 'candisc' package
    provides visualizations in a reduced-rank canonical discriminant space when
    there are more than a few response variables.
Constructs paths to your project's files.
    Declare the relative path of a file within your project with 'i_am()'.
    Use the 'here()' function as a drop-in replacement for 'file.path()',
    it will always locate the files relative to your project root.
Interface to the 'HERE' REST APIs <https://developer.here.com/develop/rest-apis>:
  (1) geocode and autosuggest addresses or reverse geocode POIs using the 'Geocoder' API;
  (2) route directions, travel distance or time matrices and isolines using the 'Routing', 'Matrix Routing' and 'Isoline Routing' APIs;
  (3) request real-time traffic flow and incident information from the 'Traffic' API;
  (4) find request public transport connections and nearby stations from the 'Public Transit' API;
  (5) request intermodal routes using the 'Intermodal Routing' API;
  (6) get weather forecasts, reports on current weather conditions, astronomical
  information and alerts at a specific location from the 'Destination Weather' API.
  Locations, routes and isolines are returned as 'sf' objects.
Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
A novel statistical method based on expectation maximisation (EM)
  algorithm and genetic segregation analysis to identify heritable DNA
  methylation marks. Details about the method can be found in
  Joo et al. (2018) <doi:10.1038/s41467-018-03058-6>.
Statistical framework to analyze heritability of gene expression 
    based on next-generation sequencing data and simulating sequencing reads. 
    Variance partition coefficients (VPC) are computed using linear mixed effects 
    and generalized linear mixed effects models. Compound Poisson and negative 
    binomial models are included. Reference: Rudra, Pratyaydipta, et al. "Model based heritability scores for high-throughput sequencing data." BMC bioinformatics 18.1 (2017): 143.
Probability functions and other utilities for the generalized Hermite distribution.
Facilitates estimation of full univariate and bivariate 
  probability density functions and cumulative distribution functions along with
  full quantile functions (univariate) and Spearman's rank correlation 
  (bivariate) using Hermite series based estimators. These estimators are 
  particularly useful in the sequential setting (both stationary and 
  non-stationary) and one-pass batch estimation setting for large data sets. 
  Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, 
  Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential Estimation of Nonparametric Correlation using Hermite Series Estimators." arXiv Preprint (2020) <arXiv:2012.06287>.
An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) <doi:10.1111/rssb.12007>.    A hero is a specific type of sandwich.  Dictionary.com (2018) <https://www.dictionary.com> describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes.
A modular and computationally efficient R package for  
  parameterizing, simulating, and analyzing health economic simulation 
  models. The package supports cohort discrete time state transition models 
  (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>,
  N-state partitioned survival models (Glasziou et al. 1990)
  <doi:10.1002/sim.4780091106>, and individual-level continuous 
  time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>,
  encompassing both Markov (time-homogeneous and time-inhomogeneous) and 
  semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is 
  quantified with standard graphical and tabular summaries of a probabilistic 
  sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, 
  <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table
  make individual-patient simulation, probabilistic sensitivity analysis, 
  and incorporation of patient heterogeneity fast.
Distribution free heteroscedastic tests for functional data. 
    The following tests are included in this package: test of no main treatment or contrast
    effect and no simple treatment effect given in 
    Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>,
    no main time effect, and no interaction effect based on 
    original observations given in Wang and Akritas (2010a) 
    <doi:10.1080/10485250903171621>
    and tests based on ranks given in Wang and Akritas (2010b)
    <doi:10.1016/j.jmva.2010.03.012>.
The goal of this package is to user-friendly realizing Gaussian 
             graphical model-based heterogeneity analysis.
             Recently, several Gaussian graphical model-based heterogeneity 
             analysis techniques have been developed. A common methodological limitation 
             is that the number of subgroups is assumed to be known a priori, which 
             is not realistic. In a very recent study (Ren et al., 2021), a novel approach 
             based on the penalized fusion technique is developed to fully 
             data-dependently determine the number and structure of subgroups in 
             Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing 
             the Gaussian graphical model technique in more practical settings. Beyond 
             Ren et al. (2021), more estimations and functions are added, so 
             that the package is self-contained and more comprehensive and can 
             provide "more direct" insights to practitioners (with the 
             visualization function). Reference: 
             Ren, M., Zhang S., Zhang Q. and Ma S. (2021). Gaussian Graphical 
             Model-based Heterogeneity Analysis via Penalized Fusion. 
             Biometrics, <doi:10.1111/biom.13426>.
Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) <doi:10.3102/1076998618795124> and Reardon, Shear, Castellano and Ho (2017) <doi:10.3102/1076998616666279>.  It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) <doi:10.1111/1467-9868.00135>.
Functions for the fitting and summarizing of heteroscedastic t-regression.
Computes diagnostics for linear regression when treatment effects are heterogeneous.
    The output of 'hettreatreg' represents ordinary least squares (OLS) 
    estimates of the effect of a binary treatment as a weighted average of the average treatment effect 
    on the treated (ATT) and the average treatment effect on the untreated (ATU). 
    The program estimates the OLS weights on these parameters, computes the associated model diagnostics, 
    and reports the implicit OLS estimate of the average treatment effect (ATE). 
    See Sloczynski (2019), <http://people.brandeis.edu/~tslocz/Sloczynski_paper_regression.pdf>.
Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <arXiv:1412.5000>,
    and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <arXiv:1605.06566>
    for testing whether there is unexplained variation in treatment effects across observations, and for characterizing
    the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions
    implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.
Structural handling of Finnish identity numbers (persons and companies); extract information, check ID validity and diagnostics.
Implements various heuristics like Take The Best and
    unit-weight linear, which do two-alternative choice: which of
    two objects will have a higher criterion?  Also offers functions
    to assess performance, e.g. percent correct across all row pairs
    in a data set and finding row pairs where models disagree.
    New models can be added by implementing a fit and predict function--
    see vignette.
Provides the heuristics miner algorithm for process discovery 
   as proposed by Weijters et al. (2011) <doi:10.1109/CIDM.2011.5949453>. The
   algorithm builds a causal net from an event log created with the 'bupaR' 
   package. Event logs are a set of ordered sequences of events for which 
   'bupaR' provides the S3 class eventlog(). The discovered causal nets 
   can be visualised as 'htmlwidgets' and it is possible to annotate them with
   the occurrence frequency or processing and waiting time of process 
   activities.
Binning and plotting functions for hexagonal bins.
Helper functions for creating reproducible hexagon sticker purely in R.
Simple and integrated tool that automatically extracts and folds all hairpin sequences from raw genome-wide data. It predicts the secondary structure of several overlapped segments, with longer length than the mean length of sequences of interest for the species under processing, ensuring that no one is lost nor inappropriately cut.
Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.
Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.
A data only package containing commercial domestic flights that
    departed Houston (IAH and HOU) in 2011.
Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) <https://www.jstor.org/stable/2346105?seq=1>. We provide an implementation (Ronnegard, Alam and Shen 2010) <https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf> following Lee, Nelder and Pawitan (2006) <ISBN: 9781420011340> with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) <https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf>.
This data-only package was created for distributing data used in the examples of the 'hglm' package.
The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization
  constants of unnormalized probability distributions by utilizing holonomic 
  systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method
  to find maximal likelihood estimates by utilizing the HGM.
Contains functions for
 identifying and correcting HGNC human gene symbols and MGI mouse gene symbols 
 which have been converted to date format by Excel, withdrawn, or aliased.
 Also contains functions for reversibly converting between HGNC
 symbols and valid R names.
Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.
A handy collection of utility functions designed to aid in package development, plotting and scientific research.
    Package development functionalities includes among others tools such as cross-referencing package imports with the description file,
    analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub.
    Some of the other functionalities include automatic package installation and loading, plotting points without overlap,
    creating nice breaks for plots, overview tables and many more handy utility functions.
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Heller-Heller-Gorfine tests are a set of powerful statistical
    tests of multivariate k-sample homogeneity and independence (Heller et. al., 2013, <doi:10.1093/biomet/ass070>). For the univariate
    case, the package also offers implementations of the 'MinP DDP' and 'MinP ADP'
    tests by Heller et. al. (2016), which are consistent against all continuous alternatives but are
    distribution-free, and are thus much faster to apply.
Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
    age-structured spatio-temporal model for infectious disease counts. The
    approach is illustrated in a case study on norovirus gastroenteritis in
    Berlin, 2011-2015, by age group, city district and week, using additional
    contact data from the POLYMOD survey. This package contains the data and
    code to reproduce the results from the paper, see 'demo("hhh4contacts")'.
Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in a space. This package allows for intuitive and straightforward computation of HHI scores, requiring placement of objects of interest directly into the function. The package also includes a plot function for quick visual display of an HHI time series using any measure of time (year, quarter, month, etc.). For usage, please cite the Journal of Open Source Software paper associated with the package: Waggoner, Philip D. (2018) <doi:10.21105/joss.00828>.
Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.
Simulation from distributions supported by nested
        hyperplanes, using the algorithm described in Petris &
        Tardella, "A geometric approach to transdimensional Markov
        chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4,
        (2003).  Also random direction multivariate Adaptive Rejection
        Metropolis Sampling.
We propose a multiple logistic regression model to assess the influences of genomic features such as DNA-binding proteins and functional elements on topological domain borders.
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
             It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical
             clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
             geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance
             thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
             with preliminary big data support, different clustering methods, hybrid hierarchical clustering,
             multivariate clustering (MVC), cluster validation, visualization of regionalization results, and
             exporting region map and mean timeseries into NetCDF-4 file.
             The technical details are described in the paper by Hamada S. Badr, Benjamin F. Zaitchik, and
             Amin K. Dezfuli (2015) <doi:10.1007/s12145-015-0221-7>.
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.
Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies.  Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Performs linear discriminant analysis in high dimensional
        problems based on reliable covariance estimators for problems
        with (many) more variables than observations. Includes routines
        for classifier training, prediction, cross-validation and
        variable selection.
Partitioning of the independent and joint contributions of each
    variable in a multivariate data set, to a linear regression by hierarchical
    decomposition of goodness-of-fit measures of regressions using all subsets 
    of predictors in the data set. (i.e., model (1), (2), ..., (N), (1,2), ...,
    (1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of
    each predictor is provided by using a randomisation test.
Functions for performing hierarchical analysis of distance
    sampling data, with ability to use an areal spatial ICAR model on
    top of user supplied covariates to get at variation in abundance
    intensity.  The detection model can be specified as a function of
    observer and individual covariates, where a parametric model is
    supposed for the population level distribution of covariate values.
    The model uses data augmentation and a reversible jump MCMC
    algorithm to sample animals that were never observed.  Also
    included is the ability to include point independence (increasing
    correlation multiple observer's observations as a function of
    distance, with independence assumed for distance=0 or first
    distance bin), as well as the ability to model species
    misclassification rates using a multinomial logit formulation on data
    from double observers.  There is also the the ability to
    include zero inflation, but this is only recommended for cases where
    sample sizes and spatial coverage of the survey are high.
Pure set data visualization approaches are often limited in
    scalability due to the combinatorial explosion of distinct set families as
    the number of sets under investigation increases. hierarchicalSets applies
    a set centric hierarchical clustering of the sets under investigation and
    uses this hierarchy as a basis for a range of scalable visual
    representations. hierarchicalSets is especially well suited for collections
    of sets that describe comparable comparable entities as it relies on the
    sets to have a meaningful relational structure.
Implementation of the convex banding procedure (using a
    hierarchical group lasso penalty) for covariance estimation that is
    introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
    Matrix. Accepted for publication in JASA.
Bipartite graph-based hierarchical clustering, developed for pharmacogenomic 
    datasets and datasets sharing the same data structure. The goal is to 
    construct a hierarchical clustering of groups of samples based on 
    association patterns between two sets of variables. In the context of
    pharmacogenomic datasets, the samples are cell lines, and the two sets of 
    variables are typically expression levels and drug sensitivity values. 
    For this method, sparse canonical correlation analysis from 
    Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> 
    is first applied to extract association patterns for each group of samples. 
    Then, a nuclear norm-based dissimilarity measure is used to construct a 
    dissimilarity matrix between groups based on the extracted associations. 
    Finally, hierarchical clustering is applied.
Hierarchical group-wise partitioning of phenotypic diversity into 
    within-group (alpha), among-group (beta), and pooled-total (gamma) 
    components using Hill numbers.
    Turnover and overlap are also calculated as standardized alternatives to
    beta diversity. Hierarchical bootstrapping is used to approximate 
    uncertainty around each diversity component.
Miscellaneous R functions for calculating and decomposing hierarchical diversity metrics, including hierarchical allele richness, hierarchical exponential Shannon entropy (true diversity of order q=1), hierarchical heterozygosity and genetic differentiation (Jaccard dissimilarity, Delta D,Fst and Jost's D). In addition,a new approach to identify population structure based on the homogeneity of multivariate variances of Shannon differentiation is presented. This package allows you to analyse spatial structured genetic data or species data under a unifying framework (Gaggiotti, O. E. et al, 2018, Evol Appl, 11:1176-1193; <DOI:10.1111/eva.12593>), which partitions diversity and differentiation into any hierarchical levels. It helps you easily structure and format your data. In summary,it implements the analyses of true diversity profiles (q=0,1,2), hierarchical diversities and differentiation decomposition, visualization of population structure, as well as the estimation of correlation between geographic distance and genetic differentiation.
Estimates hierarchical F-statistics from haploid or
    diploid genetic data with any numbers of levels in the hierarchy, following the
    algorithm of Yang (Evolution(1998), 52:950).
    Tests via randomisations the significance
    of each F and variance components, using the likelihood-ratio statistics G
    (Goudet et al. (1996) <https://www.genetics.org/content/144/4/1933>).
    Estimates genetic diversity statistics
    for haploid and diploid genetic datasets in various formats, including inbreeding and
    coancestry coefficients, and population specific F-statistics following
    Weir and Goudet (2017) <https://www.genetics.org/content/206/4/2085>.
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
Implementation of the convex hierarchical testing (CHT) procedure
    introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing
    of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.
Uses likelihood to estimate ancestry and heterozygosity.
        Evaluates simple hybrid classifications (parentals, F1, F2,
        backcrosses). Estimates genomic clines.
A wrapper for the 'Highcharts' library including
    shortcut functions to plot R objects. 'Highcharts'
    <http://www.highcharts.com/> is a charting library offering
    numerous chart types with a simple configuration syntax.
Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.
Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].
Implements the high-dimensional two-sample test 
    proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>. 
    It also implements the test proposed by Srivastava, Katayama, 
    and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests 
    are particularly suitable to high dimensional data from two populations 
    for which the classical multivariate Hotelling's T-square test fails due 
    to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm 
    tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>, 
    referred to as zwl_test() in this package, provide a reliable and powerful test.
Computes the scores and ranks candidates according to voting rules electing the highest median grade.
    Based on "Tie-breaking the highest median: alternatives to the majority judgment", A. Fabre, Social Choice & Welfare (forthcoming as of 2020). The paper is available here: <https://github.com/bixiou/highest_median/raw/master/Tie-breaking%20Highest%20Median%20-%20Fabre%202019.pdf>.
    Functions to plot the voting profiles can be found on github: <https://github.com/bixiou/highest_median/blob/master/packages_functions_data.R>.
Provide functionality to manage, clean and match highfrequency
    trades and quotes data, calculate various liquidity measures, estimate and
    forecast volatility, detect price jumps and investigate microstructure noise and intraday
    periodicity.
Syntax highlighter for R code based on the results
    of the R parser. Rendering in HTML and latex markup. Custom Sweave
    driver performing syntax highlighting of R code chunks.
A tool to format R markdown with CSS ids for HTML output. 
    The tool may be most helpful for those using markdown to create reproducible
    documents. The biggest limitations in formatting is the knowledge of CSS
    by the document authors.
Provides various tests for comparing high-dimensional mean vectors in two sample populations.
Provides syntax highlighting for R source code. Currently it
    supports LaTeX and HTML output. Source code of other languages is supported
    via Andre Simon's highlight package (<http://www.andre-simon.de>).
Functions for determining and evaluating high-risk zones and
    simulating and thinning point process data, as described in 'Determining
    high risk zones using point process methodology - Realization by building
    an R package' Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> 
    and 'Determining high-risk zones for unexploded World War II bombs by using point 
    process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.
Can be used to carry out extraction, normalization, quality control (QC), candidate hits identification and visualization for plate based assays, in drug discovery. The package methods were applied in H. W. Choi et al. "Identification of Novel Mast Cell Activators Using Cell-Based High-Throughput Screening", SLAS Discovery 24(6), 2019. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
Environmental health studies are increasingly measuring multiple pollutants
    to characterize the joint health effects attributable to exposure mixtures.
    However, the underlying dose-response relationship between toxicants and
    health outcomes of interest may be highly nonlinear, with possible nonlinear
    interaction effects. Hierarchical integrative group least absolute shrinkage
    and selection operator (HiGLASSO), developed by Boss et al (2020)
    <arXiv:2003.12844>, is a general framework to identify noteworthy nonlinear 
    main and interaction effects in the presence of group structures among a set
    of exposures.
Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm,
    a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD).
    In addition, this method attaches a confidence interval to assess the uncertainty of its predictions.
    See Su and Zhu (2018) <arXiv:1802.04876> for details.
Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires
    some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid,
    the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve
    each sample can be visualized as a simple density plot, and the distance between samples can be calculated from
    the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences
    between samples can identified using a simple bootstrap procedure.
Makes it easy to extract and combine variables from the HILDA (Household, Income and Labour Dynamics in Australia) survey maintained by the Melbourne Institute <https://melbourneinstitute.unimelb.edu.au/hilda>.
Tools for analysing, comparing, visualising and partitioning diversity based on Hill numbers.
  'hilldiv' is an R package that provides a set of functions to assist analysis of diversity for
  diet reconstruction, microbial community profiling or more general ecosystem characterisation
  analyses based on Hill numbers, using OTU/ASV tables and associated phylogenetic trees as
  inputs. The package includes functions for (phylo)diversity measurement, (phylo)diversity
  profile plotting, (phylo)diversity comparison between samples and groups, (phylo)diversity
  partitioning and (dis)similarity measurement. All of these grounded in abundance-based and
  incidence-based Hill numbers.
  The statistical framework developed around Hill numbers encompasses many of the most
  broadly employed diversity (e.g. richness, Shannon index, Simpson index),
  phylogenetic diversity (e.g. Faith's PD, Allen's H, Rao's quadratic entropy) and
  dissimilarity (e.g. Sorensen index, Unifrac distances) metrics. This enables the most
  common analyses of diversity to be performed while grounded in a single statistical
  framework. The methods are described in Jost et al. (2007) <DOI:10.1890/06-1736.1>,
  Chao et al. (2010) <DOI:10.1098/rstb.2010.0272> and Chiu et al. (2014)
  <DOI:10.1890/12-0960.1>; and reviewed in the framework of molecularly characterised
  biological systems in Alberdi & Gilbert (2019) <DOI:10.1111/1755-0998.13014>.
Generate occupancy patterns based on arrival and departure timestamps
Calculate taxonomic, functional and phylogenetic diversity measures 
    through Hill Numbers proposed by Chao, Chiu and Jost (2014) 
    <doi:10.1146/annurev-ecolsys-120213-091540>.
Estimation of heritability with confidence intervals in linear mixed models.
Allows to estimate and test high-dimensional mediation effects based on sure independent screening and minimax concave penalty techniques. A joint significance test is used for mediation effect. Haixiang Zhang, Yinan Zheng, Zhou Zhang, Tao Gao, Brian Joyce, Grace Yoon, Wei Zhang, Joel Schwartz, Allan Just, Elena Colicino, Pantel Vokonas, Lihui Zhao, Jinchi Lv, Andrea Baccarelli, Lifang Hou, Lei Liu (2016) <doi:10.1093/bioinformatics/btw351>.
H-index and h-alpha are a bibliometric indicators. This package 
  provides functions to simulate how these indicators may develop over time for
  a given set of researchers and to visualize the simulation data. 
  The implementation is based on the 'STATA' ado h-index and is described in 
  more detail in Bornmann et al. (2019) <arXiv:1905.11052>.
H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories.
Can be used for paternity and maternity assignment and outperforms 
  conventional methods where closely related individuals occur in the pool of 
  possible parents. The method compares the genotypes of offspring with any 
  combination of potentials parents and scores the number of mismatches of these 
  individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). 
  It elaborates on a prior exclusion method based on the Homozygous Opposite Test 
  (HOT; Huisman 2017 <doi:10.1111/1755-0998.12665>) by introducing the 
  additional exclusion criterion HIPHOP (Homozygous Identical Parents, 
  Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential 
  parents are excluded if they have more mismatches than can 
  be expected due to genotyping error and mutation, and thereby one can identify the 
  true genetic parents and detect situations where one (or both) of the true 
  parents is not sampled. Package 'hiphop' can deal with (a) the case where there 
  is contextual information about parentage of the mother (i.e. a female has been 
  seen to be involved in reproductive tasks such as nest building), but paternity 
  is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, 
  because there is no contextual information on which female laid eggs and which 
  male fertilized them (e.g. polygynandrous mating system where multiple females 
  and males deposit young in a common nest, or organisms with external fertilisation 
  that breed in aggregations).
    For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L.,
       Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. 
       (in revision). HIPHOP: improved paternity assignment among close relatives 
       using a simple exclusion method for bi-allelic markers. 
       Molecular Ecology Resources, DOI to be added upon acceptance.
Based on hierarchical modeling, this package provides a
        few practical functions to find and present the optimal designs
        for a pooled NGS design.
Read hierarchical fixed width files like those commonly used by 
    many census data providers. Also allows for reading of data in chunks,
    and reading 'gzipped' files without storing the full file in memory.
Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses
    by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion.
    The main idea is to use changing intensity ratios of ion pairs from peak list generated with 'xcms' as candidates and evaluate those against
    base peak chromatograms and spectra information within the raw measurement data automatically.
    The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.
Implementation of a class of hierarchical item response
  theory (IRT) models where both the mean and the variance of latent preferences
  (ability parameters) may depend on observed covariates. The current
  implementation includes both the two-parameter latent trait model for binary data and the
  graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM)
  algorithm. Asymptotic standard errors are derived from the observed information
  matrix.
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
The 'HistData' package provides a collection of small data sets
    that are interesting and important in the history of statistics and data
    visualization. The goal of the package is to make these available, both for
    instructional use and for historical research. Some of these present interesting
    challenges for graphics or analysis in R.
In the framework of Symbolic Data Analysis, a relatively new
    approach to the statistical analysis of multi-valued data, we consider
    histogram-valued data, i.e., data described by univariate histograms. The
    methods and the basic statistics for histogram-valued data are mainly based
    on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
    between quantile functions. The package contains unsupervised classification
    techniques, least square regression and tools for histogram-valued data and for
    histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi:10.1007/s11634-014-0176-4>.
Using the MDL principle, it is possible to estimate
	parameters for a histogram-like model. The package contains
	the implementation of such an estimation method.
Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).
Provides a number of utility functions useful for manipulating large histograms.  This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning.  It also provides a protocol buffer representations of the default R histogram class to allow histograms over large data sets to be computed and manipulated in a MapReduce environment.
These sample data sets are intended for historians
    learning R. They include population, institutional, religious,
    military, and prosopographical data suitable for mapping,
    quantitative analysis, and network analysis.
Automatically tracks and makes programmatically available code
    evaluation history in R sessions and dynamic documents.
Hierarchical inference testing (HIT) for (generalized) linear models with 
    correlated covariates applicable to high-dimensional settings.
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
The functions in this package produce a complete set of mortality rates as a function of a combination of HIV prevalence and either life expectancy at birth (e0), child mortality (5q0), or child mortality with adult mortality (45q15)
Hadoop InteractiVE facilitates distributed 
             computing via the MapReduce paradigm through R and Hadoop. An easy to use 
             interface to Hadoop, the Hadoop Distributed File System (HDFS), 
	     and Hadoop Streaming is provided.
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>.
This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.
Datasets related to Hong Kong, including information on the 2019 elected District Councillors (<https://www.districtcouncils.gov.hk> and <https://dce2019.hk01.com/>) and traffic collision data from the Hong Kong Department of Transport (<https://www.td.gov.hk/>). All
    of the data in this package is available in the public domain.
Several procedures around a particular hierarchical model for extreme value: the HKEVP of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>. Simulation, estimation and spatial extrapolation of this model are available for extreme value data. A special case of this process is also handled: the Latent Variable Model of Davison et al. (2012) <DOI:10.1214/11-STS376>.
A set of functions helps to retrieve data from HKEX (Hong Kong Stock Exchange), see <https://www.hkex.com.hk/> for more information. In addition, a function generates insert SQL statements from a dataframe.
Methods to make inference about the Hurst-Kolmogorov and the AR(1) process.
Provides access to datasets published by 'Hlídač státu' <https://www.hlidacstatu.cz/>, 
    a Czech watchdog, via their API.
A suite of diagnostic tools for hierarchical
    (multilevel) linear models. The tools include
    not only leverage and traditional deletion diagnostics (Cook's
    distance, covratio, covtrace, and MDFFITS) but also 
    convenience functions and graphics for residual analysis. Models
    can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package.
Implements Hierarchical Latent Space Network Model (HLSM) for ensemble of networks as described in Sweet, Thomas & Junker (2013). <DOI:10.3102/1076998612458702>.
For estimation of a variable of interest using two sources of auxiliary information available in a nested structure. For reference see Saarela et al. (2016)<doi:10.1007/s13595-016-0590-1> and Saarela et al. (2018) <doi:10.3390/rs10111832>.
Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models.  All of the code is written in R.  Theoretical references are listed below:.
    Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, 
    Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>, 
    Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>,
    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, 
    Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, 
    Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.
Utilities for reading data from the Human Mortality Database (<https://www.mortality.org>), Human Fertility Database (<https://www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<http://www.fertilitydata.org/>), The Japanese Mortality Database (<http://www.ipss.go.jp/p-toukei/JMD>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
Classification performance metrics that are derived from the ROC
    curve of a classifier. The package includes the H-measure performance metric
    as described in <http://link.springer.com/article/10.1007/s10994-009-5119-5>,
    which computes the minimum total misclassification cost, integrating over any
    uncertainty about the relative misclassification costs, as per a user-defined
    prior. It also offers a one-stop-shop for other scalar metrics of performance,
    including sensitivity, specificity and many others, and also offers plotting
    tools for ROC curves and related statistics.
Provides weighted lasso framework for high-dimensional mixed data graph estimation.  
    In the graph estimation stage, the graph structure is estimated by maximizing the conditional 
    likelihood of one variable given the rest. We focus on the conditional loglikelihood of each variable 
    and fit separate regressions to estimate the parameters, much in the spirit of the neighborhood 
    selection approach proposed by Meinshausen-Buhlmann for the Gaussian Graphical Model and by Ravikumar
    for the Ising Model. Currently, the discrete variables can only take two values. In the future, method 
    for general discrete data and for visualizing the estimated graph will be added. 
    For more details, see the linked paper.
Runs single level and multilevel imputation models as described in Speidel, Drechsler and Jolani (2020) <doi:10.18637/jss.v095.i09>. The user just has to pass the data to the main function and, optionally, his analysis model. Basically the package then translates this analysis model into commands to impute the data according to it with functions from 'mice', 'MCMCglmm' or routines build for this package.
Contains many functions useful for data
	analysis, high-level graphics, utility operations, functions for
	computing sample size and power, simulation, importing and annotating datasets,
	imputing missing values, advanced table making, variable clustering,
	character string manipulation, conversion of R objects to LaTeX and html code,
	and recoding variables.
A simple implementation of HMLasso (Lasso with High Missing rate).
  Takada, M., Fujisawa, H., & Nishikawa, T. (2019) <arXiv:1811.00255>.
Easy to use library to setup, apply and make inference
        with discrete time and discrete space Hidden Markov Models
Fits hidden Markov models with discrete non-parametric 
	observation distributions to data sets.  The observations may
	be univariate or bivariate. Simulates data from such models.
	Finds most probable underlying hidden states, the most
	probable sequences of such states, and the log likelihood
	of a collection of observations given the parameters of
	the model.  Auxiliary predictors are accommodated in the
        univariate setting.
Asymmetric hidden Markov model (HMM-A) learning.
   HMM-As are similar to regular HMMs, but use Bayesian Networks (BNs) in 
   their emission distribution. The HMM-As can therefore offer more problem 
   insight [Bueno et al. 2017, <doi:10.1016/j.ijar.2017.05.011>].
The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
R functions to estimate and perform goodness of fit test for several
    Markov regime switching and mixture bivariate copula models. 
    The goodness of fit test is based on a Cramer von Mises statistic and 
    uses the Rosenblatt transform and parametric bootstrap to estimate the p-value. 
    The estimation of the copula parameters are based on the pseudo-maximum likelihood
    method using pseudo-observations defined as normalized ranks.
Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) <arXiv:1710.09663> for more details.
Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte.
Functions for specifying and fitting marginal models for contingency tables proposed 
	by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al. 
	(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
 	tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables 
	is described by a marginal model. 
	Inequality constraints on the parameters are allowed and can be tested.
Provide functions to make estimate the number of states for a hidden Markov 
    model (HMM) using marginal likelihood method proposed by the authors.
    See the Manual.pdf file a detail description of all functions, and a detail tutorial.
Analysing time-series accelerometer data to quantify length and 
             intensity of physical activity using hidden Markov models. 
             It also contains the traditional cut-off point method.
             Witowski V, Foraita R, Pitsiladis Y, Pigeot I, 
             Wirsik N (2014)<doi:10.1371/journal.pone.0114089>.
Using Dirichlet-Multinomial distribution to provide several functions for formal hypothesis testing, power and sample size calculations for human microbiome experiments.
Tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.
Hierarchical Modelling of Species Communities (HMSC) is
   a model-based approach for analyzing community ecological data. 
   This package implements it in the Bayesian framework with Gibbs
   Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020)
   <doi:10.1111/2041-210X.13345>).
Tracks elapsed clock time using a `hms::hms()` scalar, 
    which if running has an attribute named start 
    that specifies the system time when the timer was started.
    The elapsed time is the value of the scalar 
    plus the difference between the current system time 
    and the system time when the timer was started.
Perform association test between a group of variable and the outcome.
Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.
Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions  of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).
Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.
Suite of tools for managing cached files, targeting
    use in other R packages. Uses 'rappdirs' for cross-platform paths.
    Provides utilities to manage cache directories, including targeting
    files by path or by key; cached directories can be compressed and
    uncompressed easily to save disk space.
Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://www.esrl.noaa.gov/gmd/ccgg/trends/data.html>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/data/seaice_index/archives>, CSIRO <http://www.cmar.csiro.au/sealevel/sl_data_cmar.html>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/>, Vostok Paleo carbon dioxide and temperature data: <https://cdiac.ess-dive.lbl.gov/trends/co2/vostok.html>.
Simulates hands and tournaments of Texas Holdem, 
	the most popular form of poker.
	For examples of probability problems involving Texas Holdem 
	and a brief explanation of the game see Schoenberg, F. (2011).
	An Introduction to Probability with Texas Hold'em Examples.
	Taylor and Francis, New York, ISBN-13: 978-1439827680.
Contains trading holiday and half-day data that is automatically registered when loaded.
Provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets
    with groups of variables with different degrees of variance, covariance, and effect size.
Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
A collection of functions useful for the analysis of
    building physics experiments.
A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.
Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (2016) <arXiv:1611.06739v2>.
A wrapper for the homologene database by the National Center for
    Biotechnology Information ('NCBI'). It allows searching for gene homologs across 
    species. Data in this package can be found at <ftp://ftp.ncbi.nih.gov/pub/HomoloGene/build68/>.
    The package also includes an updated version of the homologene database where 
    gene identifiers and symbols are replaced with their latest (at the time of
    submission) version and functions to fetch latest annotation data to keep updated.
Homomorphic computations in R for privacy-preserving applications. Currently only
             the Paillier Scheme is implemented.
A collection of homogeneity tests described in: Viglione
        A., Laio F., Claps P. (2007) ``A comparison of homogeneity
        tests for regional frequency analysis'', Water Resources
        Research, 43, W03428, doi:10.1029/2006WR005095.  More on
        Regional Frequency Analysis can be found in package nsRFA.
Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
Calculates horizon elevation angle and sky view factor from a digital terrain model (Dozier and Frew, 1990).
Supplement for the book "Handbook of Regression Methods" by D. S. Young.  Some datasets used in the book are included and documented.  Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. 
    Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.
Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression described in Nalenz & Villani (2017) <arXiv:1702.05008>.
Contains functions for applying the horseshoe prior to high-
    dimensional linear regression, yielding the posterior mean and credible
    intervals, amongst other things. The key parameter tau can be equipped with
    a prior or estimated via maximum marginal likelihood estimation (MMLE).
    The main function, horseshoe, is for linear regression. In addition, there
    are functions specifically for the sparse normal means problem, allowing
    for faster computation of for example the posterior mean and posterior
    variance. Finally, there is a function available to perform variable
    selection, using either a form of thresholding, or credible intervals.
Provides the posterior estimates of the regression coefficients when horseshoe prior is specified. 
             The regression models considered here are logistic model for binary response and 
             log normal accelerated failure time model for right censored survival response. 
             The linear model analysis is also available for completeness. 
             All models provide deviance information criterion and widely applicable information criterion. 
             See <doi:10.1111/rssc.12377> Maity et. al. (2019) <doi:10.1111/biom.13132> Maity et. al. (2020).
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
A set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.
Contains data for software hotspot analysis, along with a function performing the analysis itself.
The hotspots package is designed to look within a set of measured values of a variable and identify values that are disproportionately high based on both the deviance of any given value from a statistical distribution and its similarity to other values. Because this relative magnitude of each value is taken into account, a value that is a statistical outlier may not always be a hot spot if other values are similarly large.
Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016.  Additional data sets containing geographical information and links to Wikipedia are also included.
A wrapper around a CSS library called 'Hover.css', intended for use 
  in 'shiny' applications.
When testing multiple hypotheses simultaneously, this
        package provides functionality to calculate a lower bound for
        the number of correct rejections (as a function of the number
        of rejected hypotheses), which holds simultaneously -with high
        probability- for all possible number of rejections.  As a
        special case, a lower bound for the total number of false null
        hypotheses can be inferred.  Dependent test statistics can be
        handled for multiple tests of associations. For independent
        test statistics, it is sufficient to provide a list of
        p-values.
Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.
By binding R functions and the 'Highcharts' <http://www.highcharts.com/> charting library, 'hpackedbubble' package provides a simple way to draw split packed bubble charts.
This package provides all the functions necessary to
        estimate the 8 parameters of the Heligman Pollard mortality
        model using a Bayesian Melding procedure with IMIS as well as
        to convert those parameters into age-specifc probabilities of
        death and a corresponding life table
Probabilistic models describing the behavior 
	of workload and queue on a High Performance Cluster and computing GRID 
	under FIFO service discipline basing on modified Kiefer-Wolfowitz 
	recursion. Also sample data for inter-arrival times, service times, 
	number of cores per task and waiting times of HPC of Karelian 
	Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
	Functions provided to import/export workload traces in Standard Workload Format (swf).
	Stability condition of the model may be verified either exactly, or approximately.
Compute house price indexes and series using a variety of different methods and
    models common through the real estate literature.  Evaluate index 'goodness' based
    on accuracy, volatility and revision statistics. Background on basic model construction
    for repeat sales models can be found at: Case and Quigley (1991) 
    <https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html> and for hedonic pricing models at: 
    Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>. The package author's working paper on the 
    random forest approach to house price indexes can be found at: <http://www.github.com/andykrause/hpi_research>.
An implementation of high-probability lower bounds for the total variance distance 
             as introduced in Michel & Naef & Meinshausen (2020) <arXiv:2005.06006>. An estimated 
             lower-bound (with high-probability) on the total variation distance between two probability distributions from which
             samples are observed can be obtained with the function HPLB.
Collection of functions for manipulating sets of HPO terms and
    plotting them with a various options.
This package contains some miscellaneous convenience functions, 
	to create a matrix of dummy columns from a factor, 
	to determine whether x lies in range [a,b], 
	to add a rectangular bracket to an existing plot, 
	and to convert frequencies between Hz, semitones, mel and Bark. 
	This package also contains an example data set of a stratified sample
	of 80 talkers of Dutch.
Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.
Transform and analyze workforce data in meaningful ways for human resources (HR) analytics. Get started with workforce planning using a simple Shiny app.
A compilation of extra 'ggplot2' themes, scales and utilities, including a 
    spell check function for plot label fields and an overall emphasis on typography. 
    A copy of the 'Google' font 'Roboto Condensed' <https://github.com/google/roboto/> 
    is also included along with a copy of the 'IBM' 'Plex Sans' <https://github.com/IBM/type>,
    'Titillium Web' <https://fonts.google.com/specimen/Titillium+Web>, and
    'Public Sans' <https://github.com/uswds/public-sans/> fonts
    are also included to support their respective typography-oriented themes.
Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).
Estimates the log hazard ratio associated with a binary exposure using a Cox PH model weighted by the propensity score. 
  Propensity model is estimated using a simple logistic regression. 
  Variance estimation takes into account the propensity score estimation step with the method proposed by Hajage et al. (2018) <doi:10.1002/bimj.201700330>. 
  Both the average treatment effect on the overall (ATE) or the treated (ATT) population can be estimated. 
  For the ATE estimation, both unstabilized and stabilized weights can be used.
Methods for testing main and interaction effects in possibly
    high-dimensional parametric or nonparametric repeated measures in factorial designs for univariate or multivariate data.
    The observations of the subjects are assumed to be multivariate normal if using the parametric test.
    The nonparametric version tests with regard to nonparametric relative effects (based on pseudo-ranks).
    It is possible to use up to 2 whole- and 3 subplot factors.
The analysis of high-resolution melting (HRM) curves provides cost-efficient post-PCR distinction among amplicons differing by as little as a single base pair, based on their thermal denaturation (melting) properties. HRM curve analysis has been used mainly for genotyping and detection of mutations. Beside genotyping, the method has been used to differentiate viral, bacterial and fungal pathogens, and it has a potential for plant genotyping in breeding and as an alternative to DNA barcoding for taxonomic assignment of species. This package offers functions that facilitate drawing denaturation, melting, and difference curves in HRM curve analysis in R. Lukas Beule was supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the Bonares-SIGNAL project (funding codes: 031A562A and 031B0510A). Please cite Schiwek et al. (2020) <doi:10.3390/pathogens9040270> if you use this package in your work.
The book "Semiparametric Regression with R" by J. Harezlak, D. Ruppert & M.P. Wand (2018, Springer; ISBN: 978-1-4939-8851-8) makes use of datasets and scripts to explain semiparametric regression concepts. Each of the book's scripts are contained in this package as well as datasets that are not within other R packages. Functions that aid semiparametric regression analysis are also included.
Methods of homogenous segmentation for spatial lines data, such as pavement 
             performance indicators and traffic volumes. Three methods are available for 
             homogenous segmentation, including cumulative difference approach, minimization 
             coefficient of variation, and spatial heterogeneity based method.
A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
Functions, data sets, analyses and examples from the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2006). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the 
  second edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2008). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  the package contains Sweave code for producing slides for selected
  chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the 
  third edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Torsten Hothorn and Brian S.
  Everitt, Chapman & Hall/CRC, 2014). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  Sweave source code for slides of selected chapters is included in 
  this package (see HSAUR3/inst/slides). The publishers web page is 
  '<http://www.crcpress.com/product/isbn/9781482204582>'.
The theoretical covariance between pairs of markers is calculated
    from either paternal haplotypes and maternal linkage disequilibrium (LD) or 
    vise versa. A genetic map is required. Grouping of markers is based on the 
    correlation matrix and a representative marker is suggested for each group.
    Employing the correlation matrix, optimal sample size can be derived for 
    association studies based on a SNP-BLUP approach.
    The implementation relies on paternal half-sib families and biallelic 
    markers. If maternal half-sib families are used, the roles of sire/dam are 
    swapped. Multiple families can be considered.
    Wittenburg, Bonk, Doschoris, Reyer (2019) "Design of Experiments for 
    Fine-Mapping Quantitative Trait Loci in Livestock Populations" 
    <doi:10.1101/2019.12.17.879106>.
    Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a 
    maximally informative set of single-nucleotide polymorphisms for association
    analyses using linkage disequilibrium" <doi:10.1086/381000>.
Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.
We explore sparsity and homogeneity of regression coefficients incorporating prior constraint information. A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints. We develop an modified alternating direction method of multipliers algorithm (ADMM) to obtain the estimators.
User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer et al. 2006 <doi:10.1890/04-0609>). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Density (probability), distribution, inverse
	distribution (quantile) and random data generation
	functions for the hse ("hope springs eternal") distribution.
	Maximum likelihood estimation of parameters.  Plotting of
	the probability mass function, from specified parameters or
	from a maximum likelihood fit, and of the likelihood surface.
	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.
	Goodness of fit tests.
Canonical correlation analysis that extracts nonlinear
        correlation through the use of Hilbert Schmidt Independence
        Criterion and Centered Kernel Target Alignment.
Implementation of the block coordinate descent procedure for
    solving the proximal function of latent group Lasso, highlighted by
    decomposing a DAG into several non-overlapping path graphs, and getting
    closed-form solution for each path graph. The procedure was introduced
    as Algorithm 4 in Yan and Bien (2017) <doi:10.1214/17-STS622>
    "Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations", and the
    closed-form solution for each path graph is solved in Algorithm 3 of
    that paper.
A package for computation of hidden semi markov models
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data.
Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.
Paternal recombination rate and maternal linkage disequilibrium 
    (LD) are estimated for pairs of biallelic markers such as single nucleotide 
    polymorphisms (SNPs) from progeny genotypes and sire haplotypes. At least 
    one sire has to be double heterozygous at the investigated pairs of SNPs.
    The implementation relies on paternal half-sib families. If maternal half-
    sib families are used, the roles of sire/dam are swapped. Multiple families 
    can be considered.
    Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of 
    recombination rate and maternal linkage disequilibrium in half-sibs" 
    <doi:10.3389/fgene.2018.00186>.
    Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium 
    in half-sib families" <doi:10.1534/genetics.111.137521>.
Linear and logistic regression models penalized with hierarchical
  shrinkage priors for selection of biomarkers (or more general variable
  selection), which can be fitted using Stan (Carpenter et al. (2017)
  <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized
  horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>),
  as well as the projection predictive selection approach to recover a sparse
  set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
  <doi:10.1214/20-EJS1711>).
Provides bindings to the National Geodetic Survey (NGS) Horizontal
 Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay,
 Chris Pearson, and Jarir Saleh of NGS. HTDP is a utility that allows users to
 transform positional coordinates across time and between spatial reference
 frames. See <https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml> for more
 information.
A collection of reweighted marginal hypothesis tests for clustered data, based
    on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) <doi:10.1111/1541-0420.00005>.
    The tests in this collection are clustered analogs to well-known hypothesis tests
    in the classical setting, and are appropriate for data with cluster- and/or group-size 
    informativeness. The syntax and output of functions are modeled after common, 
    recognizable functions native to R. Methods used in the package refer to 
    Gregg, M., Datta, S., and Lorenz, D. (2020) <doi:10.1177/0962280220928572>, 
    Nevalainen, J., Oja, H., and Datta, S. (2017) <doi:10.1002/sim.7288>
    Dutta, S. and Datta, S. (2015) <doi:10.1111/biom.12447>, 
    Lorenz, D., Datta, S., and Harkema, S. (2011) <doi:10.1002/sim.4368>, 
    Datta, S. and Satten, G. (2008) <doi:10.1111/j.1541-0420.2007.00923.x>, 
    Datta, S. and Satten, G. (2005) <doi:10.1198/016214504000001583>.
Efficient Bayesian multinomial logistic regression based on heavy-tailed
  (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters
  is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and
  Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed
  description of the method: Li and Yao (2018), 
  Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.
Convert a html document to simple plain texts by removing all html tags.  This package utilizes regular expressions to strip off html tags. It also offers gettxt() and browse() function, which enables you to get or browse texts at a certain web page.
Provides a 'Shiny' app allowing to convert 'HTML' code to 'R' code (e.g. '<span>Hello</span>' to 'tags$span("Hello")'), for usage in a 'Shiny' UI.
Generates HTML tag strings for HTML5 elements included in Mozilla's documentation of HTML5. 
    Attributes are passed as parameters. If the attribute name is a reserved R word, the attribute is suffixed with _attr (ex: for_attr). 
    To declare a DOCTYPE, wrap html with function html_doc().
    Mozilla's documentation for HTML5 is available here: <https://developer.mozilla.org/en-US/docs/Web/HTML/Element>.
    Elements marked as obsolete are not included.
Simple tools for scraping webpages, extracting common html tags and parsing contents to a tidy, tabular format.  Tools help with extraction of page titles, links, images, rss feeds, social media handles and page metadata.
Tables with state-of-the-art layout elements such as row spanners,
    column spanners, table spanners, zebra striping, and more. While allowing
    advanced layout, the underlying css-structure is simple in order to maximize
    compatibility with word processors such as 'MS Word' or 'LibreOffice'. The package
    also contains a few text formatting functions that help outputting text
    compatible with HTML/LaTeX.
HTML documents can be beautiful and pristine. They can also be
    wretched, evil, malformed demon-spawn. Now, you can tidy up that HTML and XHTML
    before processing it with your favorite angle-bracket crunching tools, going beyond
    the limited tidying that 'libxml2' affords in the 'XML' and 'xml2' packages and
    taming even the ugliest HTML code generated by the likes of Google Docs and Microsoft
    Word. It's also possible to use the functions provided to format or "pretty print"
    HTML content as it is being tidied. Utilities are also included that make it  
    possible to view formatted and "pretty printed" HTML/XML
    content from HTML/XML document objects, nodes, node sets and plain character HTML/XML
    using 'vkbeautify' (by Vadim Kiryukhin) and 'highlight.js' (by Ivan Sagalaev).
    Also (optionally) enables filtering of nodes via XPath or viewing an HTML/XML document
    in "tree" view using 'XMLDisplay' (by Lev Muchnik). See
    <https://github.com/vkiryukhin/vkBeautify> and 
    <http://www.levmuchnik.net/Content/ProgrammingTips/WEB/XMLDisplay/DisplayXMLFileWithJavascript.html> 
    for more information about 'vkbeautify' and 'XMLDisplay', respectively.
Facilitates automated HTML report creation, in particular
        framed HTML pages and dynamically sortable tables.
A framework for creating HTML widgets that render in various
    contexts including the R console, 'R Markdown' documents, and 'Shiny'
    web applications.
Historical regression trees are an extension of standard trees, 
	producing a non-parametric estimate of how the response depends on 
	all of its prior realizations as well as that of any time-varying predictor 
	variables. The method applies equally to regularly as well as irregularly 
	sampled data. The package implements random forest and boosting ensembles 
	based on historical regression trees, suitable for longitudinal data. 
	Standard error estimation and Z-score variable importance is also implemented.
Provides methods for analysing and forecasting hierarchical and 
    grouped time series. The available forecast methods include bottom-up,
    top-down, optimal combination reconciliation (Hyndman et al. 2011) 
    <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation
    (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.
A Poisson mixture model is implemented to cluster genes from high-
    throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is
    performed using either the EM or CEM algorithm, and the slope heuristics are
    used for model selection (i.e., to choose the number of clusters).
Functions for the management and treatment of hydrology and 
  meteorology time-series stored in a 'Sqlite' data base.
Functions for analyzing high throughput sequencing 
    stable isotope probing (HTS-SIP) data.
    Analyses include high resolution stable isotope probing (HR-SIP),
    multi-window high resolution stable isotope probing (MW-HR-SIP), 
    and quantitative stable isotope probing (q-SIP).
Generic models and chemical-specific data for simulation and
             statistical analysis of chemical toxicokinetics ("TK") as
             described by Pearce et al. (2017) <doi:10.18637/jss.v079.i04>.
             Chemical-specific in vitro data have been obtained from relatively
             high throughput experiments. Both physiologically-based ("PBTK")
             and empirical (for example, one compartment) "TK" models can be
             parameterized with the data provided for thousands of chemicals,
             multiple exposure routes, and various species. The models consist
             of systems of ordinary differential equations which are solved
             using compiled (C-based) code for speed. A Monte Carlo sampler is
             included, which allows for simulating human biological variability
             (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>)
             and propagating parameter uncertainty. Calibrated methods are
             included for predicting tissue:plasma partition coefficients and
             volume of distribution
             (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>).
             These functions and data provide a set of tools for
             in vitro-in vivo extrapolation ("IVIVE") of high throughput
             screening data (for example, Tox21, ToxCast) to real-world
             exposures via reverse dosimetry (also known as "RTK")
             (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).
In order to improve performance for HTTP API clients, 'httpcache'
    provides simple tools for caching and invalidating cache. It includes the
    HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in
    replacements for those in the 'httr' package. These functions are cache-aware
    and provide default settings for cache invalidation suitable for RESTful
    APIs; the package also enables custom cache-management strategies.
    Finally, 'httpcache' includes a basic logging framework to facilitate the
    measurement of HTTP request time and cache performance.
Find and explain the meaning of 'HTTP' status codes.
    Functions included for searching for codes by full or partial number,
    by message, and get appropriate dog and cat images for many
    status codes.
A graphics device for R that is accessible via network protocols.
    This package was created to make it easier to embed live R graphics in 
    integrated development environments and other applications.
    The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics.
    The device asynchronously serves 'SVG' graphics via 'HTTP' and 'WebSockets'.
A suite of functions to ping 'URLs' and to time
    'HTTP' 'requests'. Designed to work with 'httr'.
Tools for emitting the 'Problem Details' structure defined in
  'RFC' 7807 <https://tools.ietf.org/html/rfc7807> for reporting errors from
  'HTTP' servers in a standard way.
HTTP Request protocols. Implements the GET, POST and multipart POST request.
Testing and documenting code that communicates with remote servers
    can be painful. Dealing with authentication, server state,
    and other complications can make testing seem too costly to
    bother with. But it doesn't need to be that hard. This package enables one
    to test all of the logic on the R sides of the API in your package without
    requiring access to the remote service. Importantly, it provides three
    contexts that mock the network connection in different ways, as well as
    testing functions to assert that HTTP requests were---or were
    not---made. It also allows one to safely record real API responses to use as
    test fixtures. The ability to save responses and load them offline also
    enables one to write vignettes and other dynamic documents that can be
    distributed without access to a live server.
Provides low-level socket and protocol support for handling
    HTTP and WebSocket requests directly from within R. It is primarily
    intended as a building block for other packages, rather than making it
    particularly easy to create complete web applications using httpuv alone.
    httpuv is built on top of the libuv and http-parser C libraries, both of
    which were developed by Joyent, Inc. (See LICENSE file for libuv and
    http-parser license information.)
Useful tools for working with HTTP organised by
    HTTP verbs (GET(), POST(), etc). Configuration functions make it easy
    to control additional request components (authenticate(),
    add_headers() and so on).
Creating effective colour palettes for figures is 
    challenging. This package generates and plot palettes of optimally 
    distinct colours in perceptually uniform colour space, based on 
    'iwanthue' <http://tools.medialab.sciences-po.fr/iwanthue/>. 
    This is done through k-means clustering of CIE Lab colour space, 
    according to user-selected constraints on hue, chroma, and 
    lightness.
Provides a general framework for
        high-dimensional undirected graph estimation. It integrates
        data preprocessing, neighborhood screening, graph estimation,
        and model selection techniques into a pipeline. In
        preprocessing stage, the nonparanormal(npn) transformation is
        applied to help relax the normality assumption. In the graph
        estimation stage, the graph structure is estimated by
        Meinshausen-Buhlmann graph estimation or the graphical lasso,
        and both methods can be further accelerated by the lossy
        screening rule preselecting the neighborhood of each variable
        by correlation thresholding. We target on high-dimensional data
        analysis usually d >> n, and the computation is
        memory-optimized using the sparse matrix output. We also
        provide a computationally efficient approach, correlation
        thresholding graph estimation. Three
        regularization/thresholding parameter selection methods are
        included in this package: (1)stability approach for
        regularization selection (2) rotation information criterion (3)
        extended Bayesian information criterion which is only available
        for the graphical lasso.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
             to discriminate the class labels, visualizing the ROC curve for two or three class labels.
Format quantities of time or bytes into human-friendly strings.
Human names are complicated and nonstandard things. Humaniformat,
    which is based on Anthony Ettinger's 'humanparser' project (https://github.com/
    chovy/humanparser) provides functions for parsing human names, making a best-
    guess attempt to distinguish sub-components such as prefixes, suffixes, middle
    names and salutations.
An almost direct port of the 'python' 'humanize' package <https://github.com/jmoiron/humanize>.
  This package contains utilities to convert values into human readable forms.
Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling,
  in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>).
  The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).
Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.
Low level spell checker and morphological analyzer based on the 
    famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
    or check individual words as well as parse text, latex, html or xml documents.
    For a more user-friendly interface use the 'spelling' package which builds on
    this package to automate checking of files, documentation and vignettes in all
    common formats.
Scraped dataset of the Hurricane Research Division's Hurricane 
    Re-Analysis Project known as HURDAT. Storm details are available for most 
    known hurricanes and tropical storms for the Atlantic and northeastern 
    Pacific ocean (northwestern hemisphere). See <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html> 
    for more information.
When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.
Allows users to create time series of tropical storm
    exposure histories for chosen counties for a number of hazard metrics
    (wind, rain, distance from the storm, etc.). This package interacts
    with data available through the 'hurricaneexposuredata' package, which
    is available in a 'drat' repository. To access this data package, see the 
    instructions at <https://github.com/geanders/hurricaneexposure>. 
    The size of the 'hurricaneexposuredata' package is
    approximately 20 MB. This work was supported in part by grants from the National
    Institute of Environmental Health Sciences (R00ES022631), the National Science
    Foundation (1331399), and a NASA Applied Sciences Program/Public Health Program
    Grant (NNX09AV81G).
Provides utility functions for, and drawing on, the 'data.table' package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of 'utils' and the author.
Provides utility functions that are simply, frequently used, 
    but may require higher performance that what can be obtained from base R.
    Incidentally provides support for 'reverse geocoding', such as matching a point
    with its nearest neighbour in another array. Used as a complement to package
    'hutils' by sacrificing compilation or installation time for higher running 
    speeds. The name is a portmanteau of the author and 'Rcpp'.
Creates styled tables for data presentation. Export to HTML, LaTeX,
  RTF, 'Word', 'Excel', and 'PowerPoint'. Simple, modern interface to manipulate 
  borders, size, position, captions, colours, text styles and number formatting.
  Table cells can span multiple rows and/or columns.
  Includes  a 'huxreg' function for creation of regression tables, and 'quick_*' 
  one-liners to print data to a new document.
HW.pval calculates plain and fully conditional
        root-mean-square, chi-square, and log likelihood-ratio P-values
        for the user-provided genotypic counts to be consistent with
        the Hardy-Weinberg equilibrium model.  For further information
        on the Hardy-Weinberg equilibrium model and the pseudocode,
        refer to the paper "Testing Hardy-Weinberg equilibrium with a
        simple root-mean-square statistic" by Rachel Ward.
Fits models for genotypic disequilibria, as described in
  Huttley and Wilson (2000), Weir (1996) and Weir and Wilson (1986).
  Contrast terms are available that account for first order interactions
  between loci.  Also implements, for a single locus in a single
  population, a conditional exact test for Hardy-Weinberg equilibrium.
Estimation and testing of HWE using Bayesian methods.
        Three models are currently considered: HWE, a model
        parameterized in terms of the allele frequencies and a single
        inbreeding coefficient f, and the saturated model. Testing is
        based on Bayes factors.
General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.
Provides a way to display word clouds in R. The word cloud is a html widget, so you can use it in interactive documents and 'shiny' applications.
Easy-to-use and versatile functions to output R objects in
        HTML format
Provides methods to test whether time series is consistent
	with white noise.
Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. Functions to cross-validate the hybrid ensemble and plot and summarize the results are also provided. There is also a function to assess the importance of the predictors.
This package is an R implementation of the Hybrid Monte
        Carlo and Multipoint Hybrid Monte Carlo sampling techniques
        described in Liu (2001): "Monte Carlo Strategies in Computing".
Simulates stochastic hybrid models for transmission of infectious
    diseases in dynamic networks. It is a metapopulation model in which each
    node in the network is a sub-population and disease spreads within nodes
    and among them, combining two approaches: stochastic simulation algorithm
    (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based
    approach, respectively. Equations that models spread within nodes are
    customizable and there are two link types among nodes: migration and
    influence (commuting). More information in Fernando S. Marques,
    Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.
Facilities for easy implementation of hybrid Bayesian networks
    using R. Bayesian networks are directed acyclic graphs representing joint
    probability distributions, where each node represents a random variable and
    each edge represents conditionality. The full joint distribution is therefore
    factorized as a product of conditional densities, where each node is assumed
    to be independent of its non-descendents given information on its parent nodes.
    Since exact, closed-form algorithms are computationally burdensome for inference
    within hybrid networks that contain a combination of continuous and discrete
    nodes, particle-based approximation techniques like Markov Chain Monte Carlo
    are popular. We provide a user-friendly interface to constructing these networks
    and running inference using the 'rjags' package. Econometric analyses (maximum
    expected utility under competing policies, value of information) involving
    decision and utility nodes are also supported.
Calculate an optimal embedding of a set of data points into low-dimensional hyperbolic space. This uses the strain-minimizing hyperbolic embedding of Keller-Ressel and Nargang (2019), see <arXiv:1903.08977>.
Functions for basic hydraulic calculations related to 
    water flow in circular pipes both flowing full (under pressure), and 
    partially full (gravity flow), and trapezoidal open channels. For 
    pressure flow this includes friction loss calculations by solving 
    the Darcy-Weisbach equation for head loss, flow or diameter, and 
    plotting a Moody diagram. The Darcy-Weisbach friction factor is 
    calculated using the Colebrook (or Colebrook-White equation), 
    the basis of the Moody diagram, the original citation being 
    Colebrook (1939) <doi:10.1680/ijoti.1939.13150>. For gravity flow, the
    Manning equation is used, again solving for missing parameters. The 
    derivation of and solutions using the Darcy-Weisbach equation and the
    Manning equation are outlined in many fluid mechanics texts such as 
    Finnemore and Franzini (2002, ISBN:978-0072432022). For the Manning equation
    solutions, this package uses modifications of original code from the 'iemisc' 
    package by Irucka Embry.
Package providing tools for hydrological applications and models developed for regional analysis in Northwestern Italy.
Pfafstetter Hydrological Codes 
    as cited in Verdin and Verdin (1999) <doi: 10.1016/S0022-1694(99)00011-6> 
    are decoded for upstream or downstream queries.
Contains one function for drawing Piper diagrams (also
    called Piper-Hill diagrams) of water analyses for major ions.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
Tools to link geographic data with hydrologic network, including lakes, streams and rivers. 
    Includes automated download of U.S. National Hydrography Network and other hydrolayers.
This version 2 of the HydroMe v.1 package estimates the parameters
  in infiltration and water retention models by curve-fitting methods <doi:10.1016/j.cageo.2008.08.011>. The
  models considered are those commonly used in soil science.  It has new models
  for water retention and characteristic curves.
State-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable). hydroPSO can be used as a replacement of the 'optim' R function for (global) optimization of non-smooth and non-linear functions. However, the main focus of hydroPSO is the calibration of environmental and other real-world models that need to be executed from the system console. hydroPSO is model-independent, allowing the user to easily interface any computer simulation model with the calibration engine (PSO). hydroPSO  communicates with the model through the model's own input and output files, without requiring access to the model's source code. Several PSO variants and controlling options are included to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. hydroPSO is parallel-capable, to alleviate the computational burden of complex models with "long" execution time. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian). See Zambrano-Bigiarini and Rojas (2013) <doi:10.1016/j.envsoft.2013.01.004> for more details.
R interface to the Greek National Data Bank for Hydrological and 
    Meteorological Information <http://www.hydroscope.gr/>. It covers 
    Hydroscope's data sources and provides functions to transliterate, 
    translate and download them into tidy dataframes.
Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.
Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling,
    data resampling, gap filler of precipitation, bias correction of forecasting
    data, flexible time series plot, and spatial map generation. It is a good pre-
    processing and post-processing tool for hydrological and hydraulic modellers.
Includes two main high level codes for hyperplane fitting (hyper.fit) and visualising (hyper.plot2d / hyper.plot3d). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
A suite of routines for the hyperdirichlet distribution; supersedes the 'hyperdirichlet' package.
This package provides functions for the hyperbolic and
        related distributions. Density, distribution and quantile
        functions and random number generation are provided for the
        hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, including fitting of
        the hyperbolic to data.
Provides functions and methods for organizing data in hypercubes
  (i.e., a multi-dimensional cube). Cubes are generated from molten data frames.
  Each cube can be manipulated with five operations: rotation (change.dimensionOrder()),
  dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()),
  and rolling up (remove.aggregation()).
Implements various tools for storing and analyzing hypergraphs.
  Handles basic undirected, unweighted hypergraphs, and various ways of 
  creating hypergraphs from a number of representations, and
  converting between graphs and hypergraphs.
Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs.
The Gaussian hypergeometric function for complex numbers.
Evaluates the hypergeometric functions of a matrix argument, which appear in random matrix theory. This is an implementation of Koev & Edelman's algorithm (2006) <doi:10.1090/S0025-5718-06-01824-2>.
Uses support vector machines to identify a perfectly separating hyperplane (linear or curvilinear) between two entities in high-dimensional space. If this plane exists, the entities do not overlap. Applications include overlap detection in morphological, resource or environmental dimensions. More details can be found in: Brown et al. (2020) <doi:10.1111/2041-210X.13363> .
Implements an algorithm for efficient and exact calculation of hypergeometric 
    and binomial probabilities using Chebyshev polynomials, while other algorithm use an 
    approximation when N is large. A useful applications is also considered in this package 
    for the construction of attribute sampling plans which is an important field of statistical
    quality control. The quantile, and the confidence limit for the attribute sampling plan are
    also implemented in this package. The hypergeometric distribution can be represented in 
    terms of Chebyshev polynomials. This representation is particularly useful in the calculation
    of exact values of hypergeometric variables.
Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data.
Comfortable ways to work with hyperspectral data sets.
    I.e. spatially or time-resolved spectra, or spectra with any other kind
    of information associated with each of the spectra. The spectra can be data
    as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
    etc. More generally, any data that is recorded over a discretized variable,
    e.g. absorbance = f (wavelength), stored as a vector of absorbance values
    for discrete wavelengths is suitable.
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
A Bay Area high level phylogenetic analysis package mostly
        using the birth-death process. Analysis of species tree
        branching times and simulation of species trees under a number
        of different time variable birth-death processes.  Analysis of
        gene tree species tree reconciliations and simulations of gene
        trees in species trees.
Compute the confidence interval of the population mean
        with one sample or of the difference of population means of two
        samples from normal distributions or t-distributions.Compute
        the confidence interval of population variance with one sample
        or of the difference of population variances of two samples by
        chi-square tests.Test for population mean or the differences of
        two normal samples under normality with the given null
        hypothesis H0,which depends on the user,so that he can know if
        he can reject H0 or not at the significance level alpha.Do the
        chi-square tests with one or two samples which have multinomial
        distributions by using an approximate chi-square distribution
        when n is large enough.
Interact with the application programming interface for the web
    annotation service 'Hypothes.is' (See <http://hypothes.is> for more
    information.) Allows users to download data about public annotations, and
    create, retrieve, update, and delete their own annotations.
Translation between experimental null hypotheses, hypothesis matrices, and contrast matrices as used in linear regression models. The package is based on the method described in Schad, Vasishth, Hohenstein, and Kliegl (2019) <doi:10.1016/j.jml.2019.104038> and Rabe, Vasishth, Hohenstein, Kliegl, and Schad (2020) <doi:10.21105/joss.02134>.
Construction and analysis of multivalued zero-sum matrix games over the abstract space of probability distributions, which describe the losses in each scenario of defense vs. attack action. The distributions can be compiled directly from expert opinions or other empirical data (insofar available). The package implements the methods put forth in the EU project HyRiM (Hybrid Risk Management for Utility Networks), FP7 EU Project Number 608090. The method has been published in Rass, S., König, S., Schauer, S., 2016. Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions. PLoS ONE 11, e0168583. <doi:10.1371/journal.pone.0168583>, and applied for advanced persistent thread modeling in Rass, S., König, S., Schauer, S., 2017. Defending Against Advanced Persistent Threats Using Game-Theory. PLoS ONE 12, e0168675. <doi:10.1371/journal.pone.0168675>. A volume covering the wider range of aspects of risk management, partially based on the theory implemented in the package is the book edited by S. Rass and S. Schauer, 2018. Game Theory for Security and Risk Management: From Theory to Practice. Springer, <doi:10.1007/978-3-319-75268-6>, ISBN 978-3-319-75267-9.
Methods for addressing uncertainty in risk assessments using hybrid representations of uncertainty (probability distributions, fuzzy numbers, intervals, probability distributions with imprecise parameters). The uncertainty propagation procedure combines random sampling using Monte Carlo method with fuzzy interval analysis of Baudrit et al. (2007) <doi:10.1109/TFUZZ.2006.876720>. The sensitivity analysis is based on the pinching method of Ferson and Tucker (2006) <doi:10.1016/j.ress.2005.11.052>.
Enhance package 'testthat' by allowing tests to be attached to the function/object they test. 
    This allows to keep functional and unit test code together.
Fit, summarize and plot sinusoidal hysteretic processes using:
    two-step simple harmonic least squares, ellipse-specific non-linear least
    squares, the direct method, geometric least squares or linear least squares.
An R API wrapper for the 'Hystreet' project <https://hystreet.com>. 'Hystreet' provides pedestrian counts in different cities in Germany.
A collection of tools for modeling the shape of 1 dimensional clines.
Create customized, web-based dashboards for data presentation, exploration and sharing. 'i2dash' integrates easily into existing data analysis pipelines and can organize scientific findings thematically across different pages and layouts.
Provides functions to work with 'incidence2' objects, including a
  simplified interface for trend fitting and peak estimation. This package is
  part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for 
  outbreak analysis.
In randomized-controlled trials, interim analyses are often planned for possible early 
   trial termination to claim superiority or futility of a new therapy. Blinded data also have 
   information about the potential treatment difference between the groups. We developed a blinded 
   data monitoring tool that enables investigators to predict whether they observe such an unblinded 
   interim analysis results that supports early termination of the trial. Investigators may skip 
   some of the planned interim analyses if an early termination is unlikely. 
   This tool will provide reference information about N: Sample size at interim analysis, and T: Total 
   number of responders at interim analysis for decision on performing an interim analysis.
Contains data frames and functions used in the book "An Introduction to Acceptance Sampling and SPC with R". This book is available electronically at <https://bookdown.org/>. A physical copy will be published by CRC Press.
Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading <DOI:10.1080/19466315.2018.1462727>.
Calculate false ring proportions from data frames of intra annual 
  density fluctuations.
An interface to the algorithms of 'Interpretable AI'
    <https://www.interpretable.ai> from the R programming language.
    'Interpretable AI' provides various modules, including 'Optimal Trees' for
    classification, regression, prescription and survival analysis, 'Optimal
    Imputation' for missing data imputation and outlier detection, and 'Optimal
    Feature Selection' for exact sparse regression. The 'iai' package is an
    open-source project. The 'Interpretable AI' software modules are proprietary
    products, but free academic and evaluation licenses are available.
Provides a monthly summary of Iowa liquor (class E) sales from January 2015
    to October 2020. See the package website for more information, 
    documentation and examples. Data source: 
    Iowa Data portal <https://data.iowa.gov/resource/m3tr-qhgy.csv>.
Synthesizes information across collaborating research. Created specifically for Integrative Analysis of Longitudinal Studies of Aging (IALSA).
Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.
Implements the standard D-Scoring algorithm
    (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT)
    data and includes plotting capabilities for exploring raw IAT data.
Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>.    The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample.    The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent).    Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds).    A sample dataset (titled 'sampledata') is included in this package to practice with.
This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with.
Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).
An implementation of the iterative bootstrap procedure of 
    Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This
    procedure has better bias correction properties than the 
    bootstrap bias correction technique.
Bayesian integrative models of gene expression and comparative genomic hybridization data. The package provides inference on copy number variations and their association with gene expression.
Call wrappers for Istanbul Metropolitan
    Municipality's Open Data Portal (Turkish: İstanbul Büyükşehir
    Belediyesi Açık Veri Portalı) at <https://data.ibb.gov.tr/en/>.
Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) <doi:10.1534/g3.117.300309>.
A collection of several utility functions related to binary incomplete block designs. The package contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. The package also contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Package also allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.
Functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Method to test genetic linkage with covariates by
        regression methods with response IBD sharing for relative
        pairs.  Account for correlations of IBD statistics and
        covariates for relative pairs within the same pedigree.
Simulation of segments shared identical-by-descent (IBD) by 
    pedigree members. Using sex specific recombination rates along the human
    genome (Kong et. al (2010) <doi:10.1038/nature09525>), phased chromosomes
    are simulated for all pedigree members, either by unconditional gene 
    dropping or conditional on a specified IBD pattern. Additional functions 
    provide summaries and further analysis of the simulated genomes.
Simulation of segments shared identical-by-descent (IBD) by 
    pedigree members. Using sex specific recombination rates along the human
    genome (Halldorsson et al. (2019) <doi:10.1126/science.aau1043>), phased chromosomes
    are simulated for all pedigree members. Additional features include calculation
    of realised IBD coefficients and plots of IBD segment distributions.
A data-driven and adaptive hierarchical-scale decomposition method for irregular-lattice field (represented by polygons).
Some basic functions to implement belief functions including:
    transformation between belief functions using the method introduced by
    Philippe Smets <arXiv:1304.1122>, evidence combination, evidence
    discounting, decision-making, and constructing masses. Currently, thirteen
    combination rules and six decision rules are supported. It can also be
    used to generate different types of random masses when working on belief
    combination and conflict management.
Implementation of some (simple) Individual Based Models and methods
  to create new ones, particularly for population dynamics models (reproduction, 
  mortality and movement). The basic operations for the simulations are 
  implemented in Rcpp for speed.
Authentication can be the most difficult part about
  working with a new API. 'ibmAcousticR' facilitates making a
  connection to the 'IBM Acoustic' email campaign management API
  and executing various queries. The 'IBM Acoustic' API 
  documentation is available at
  <https://developer.ibm.com/customer-engagement/docs/>. This
  package is not supported by 'IBM'.
It provides a generic set of tools for initializing a synthetic
         population with each individual in specific disease states, and
         making transitions between those disease states according to the rates
         calculated on each timestep. The new version 1.0.0 has C++ code 
         integration to make the functions run faster. It has also a higher level
         function to actually run the transitions for the number of timesteps
         that users specify. Additional functions will follow for changing
         attributes on demographic, health belief and movement.
Functionality required to efficiently use R with IBM(R) Db2(R) 
        Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in 
        conjunction with IBM Db2 Analytics Accelerator for z/OS. 
        Many basic and complex R operations are pushed down into the database, 
        which removes the main memory boundary of R and allows to make full 
        use of parallel processing in the underlying database.
Simulation of the random evolution of structured population dynamics, called stochastic Individual Based Models (IBMs) (see e.g. Ferrière and Tran (2009) <doi:10.1051/proc/2009033>, Bansaye and Méléard (2015) <doi:10.1007/978-3-319-21711-6>, Boumezoued (2016)).
    The package allows users to simulate the random evolution of a population in which individuals are characterised by their date of birth, a set of attributes, and their potential date of death.
Generates Personality Insights sunburst diagrams
    based on 'IBM Watson' Personality Insights service output.
The package implements a general algorithm to obtain
        iterated bootstrap tests and confidence sets for a
        p-dimensional parameter based on the unstudentized version of
        the Rao statistic.
Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
Model agnostic tool for decomposition of predictions from black boxes.
    Supports additive attributions and attributions with interactions.
    The Break Down Table shows contributions of every variable to a final prediction. 
    The Break Down Plot presents variable contributions in a concise graphical way. 
    This package works for classification and regression models. 
    It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>,
    with new and faster strategies for orderings. 
    It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). 
    The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420>
    This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Provides native R access to Interactive Brokers Trader Workstation API.
Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function.
Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible)
             using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is 
             evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance
             are computed for variable selection. The thresholds values for variable selection are 
             computed using a nonparametric permutation test.
Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.
Implementation of the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.
Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.
Lorenz and concentration curves; Atkinson, Generalized
        entropy and SGini indices (with decomposition)
Calculation of IC50 values, automatic drawing of
        dose-response curves and validation of compound screens on 96-
        and 384-well plates.
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
Imperialist Competitive Algorithm (ICA)
         <http://en.wikipedia.org/wiki/Imperialist_competitive_algorithm>
         is a computational method that is used to solve optimization
	 problems of different types and it is the mathematical model
	 and the computer simulation of human social evolution.  
         The package provides a minimum value for the cost function
	 and the best value for the optimization variables by
	 Imperialist Competitive Algorithm. 
         Users can easily define their own objective function
	 depending on the problem at hand. 
         This version has been successfully applied to solve
	 optimization problems, for continuous functions.
A simple wrapper around the 'ical.js' library executing 
    'Javascript' code via 'V8' (the 'Javascript' engine driving the 'Chrome' 
    browser and 'Node.js' and accessible via the 'V8' R package). 
    This package enables users to parse 'iCalendar' files ('.ics', '.ifb', 
    '.iCal', '.iFBf') into lists and 'data.frames' to ultimately do statistics
    on events, meetings, schedules, birthdays, and the like.
To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as 'iCAMP' (Ning et al 2020) <doi:10.1038/s41467-020-18560-z>. It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) <doi:10.1038/ismej.2015.142> based on neutral theory model (Sloan et al 2006) <doi:10.1111/j.1462-2920.2005.00956.x> and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) <doi:10.1038/ismej.2013.93>. It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for 'QPEN' and added function 'icamp.cate()' to summarize 'iCAMP' results for different categories of taxa (e.g. core versus rare taxa).
Analysis and visualization of experimentally elucidated mutational
    signatures -- the kind of analysis and visualization in Boot et al.,
    "In-depth characterization of the cisplatin mutational signature in 
    human cell lines and in esophageal and liver tumors", Genome Research 2018, 
    <doi:10.1101/gr.230219.117> and
    "Characterization of colibactin-associated mutational signature in an 
    Asian oral squamous cell carcinoma and in other mucosal tumor types",
    Genome Research 2020 <doi:10.1101/gr.255620.119>.
    'ICAMS' stands for In-depth Characterization 
    and Analysis of Mutational Signatures. 'ICAMS' has functions to read in 
    variant call files (VCFs) and to collate the corresponding catalogs of 
    mutational spectra and to analyze and plot catalogs of mutational spectra
    and signatures. Handles both "counts-based" and "density-based" catalogs
    of mutational spectra or signatures.
Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.
Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources
Implements the integrative conditional autoregressive horseshoe model 
    discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. 
    BMC Bioinformatics 21, 11 (2020) <doi:10.1186/s12859-019-3333-0>.
    The model consists in three levels: Metabolic pathways level modeling interdependencies between
    variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify
    potential associations between heterogeneous omic variables via a Horseshoe prior and experimental
    design level to capture experimental design conditions through a mixed-effects model.
    The package also provides functions to simulate data from the model, construct pathway matrices,
    post process and plot model parameters.
Provides user-friendly tools for calibration in survey sampling.
    The package is production-oriented, and its interface is inspired by the famous
    popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to
    'icarus'. In addition to calibration (with linear, raking and logit methods),
    'icarus' features functions for calibration on tight bounds and penalized
    calibration.
Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds model, and probit model) to interval-censored time-to-event data.
Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.
Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.
Provides functions to calculate the requisite sample size for studies where ICC is 
    the primary outcome. Can also be used for calculation of power. In both cases it
    allows the user to test the impact of changing input variables by calculating the outcome
    for several different values of input variables. Based off the work of Zou.
    Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with
    precision and assurance. Statistics in medicine, 31(29), 3972-3981.
Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.
A function and vignettes for computing an intraclass correlation
    described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>.
    This package quantifies the share of variance in a dependent variable that
    is attributed to group heterogeneity in slopes.
Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals.
The function computes the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). The function transforms profiles comprising correlations according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score before entering into the function (McCrae, 2008). In case of missing values, the function will automatically use pairwise deletion. For details, see Furr (2010) <doi:10.1080/00223890903379134> or McCrae (2008) <doi:10.1080/00223890701845104>.
Implements the conditional inference forest approach 
  to modeling interval-censored survival data. It also provides 
  functions to tune the parameters and evaluate the model fit. See 
  Yao et al. (2019) <arXiv:1901.04599>.
Data from the United States Center for Medicare and Medicaid
    Services (CMS) is included in this package. There are ICD-9 and ICD-10
    diagnostic and procedure codes, and lists of the chapter and sub-chapter 
    headings and the ranges of ICD codes they encompass. There are also two 
    sample datasets. These data are used by the 'icd' package for finding
    comorbidities.
Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information <https://www.dimdi.de>, and are redistributed in accordance with their license.
Provides an estimator for generalized linear models with incomplete
    data for discrete covariates. The estimation is based on the EM algorithm by the
    method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.
Categorization and scoring of injury severity typically involves
    trained personnel with access to injured persons or their medical records. 'icdpicr' contains a function
    that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS')
    from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury 
    severity scoring. 'ICDPIC' was originally developed in 'Stata', and 'icdpicr' is an open-access update 
    that accepts both 'ICD-9' and 'ICD-10' codes.
Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression.  2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.
Kernel Estimators for Interval-Censored Data
Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
Tools for correcting biases and calibrating sea ice predictions obtained from dynamic ensemble models. Implements and extends Director et al. (2017) <doi:10.1175/JCLI-D-17-0185.1> This package depends on the 'ncdf4' and 'rgeos' R packages. These packages require installing externally from R Unidata's 'NetCDF' library and Geometry Engine - Open Source ('GEOS'). (See the 'rgeos' and 'ncdf4' packages for details.)
Given two unbiased samples of patient level data on cost and effectiveness
  for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
  bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
  the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
  specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
  changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
  with economic preferences from an ICE map with specified values of lambda, beta and gamma
  parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation
  in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves
  from an ICE map with specified values of lambda, beta and either gamma or eta parameters.
A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq and CITE-Seq. Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550>  and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Estimates the parameters of the semiparametric linear transformation model using imputation method, naive method and regression calibration method when time-to-event is interval-censored and a covariate is measured with error. A right censoring indicator must be available. The methods implemented in this package can be found in Mandal, S., Wang, S. and Sinha, S. (2019+). Analysis of Linear Transformation Models with Covariate Measurement Error and Interval Censoring. (accepted, Statistics In Medicine).
Regression models for interval censored data. Currently supports
    Cox-PH, proportional odds, and accelerated failure time models. Allows for
    semi and fully parametric models (parametric only for accelerated failure
    time models) and Bayesian parametric models. Includes functions for easy visual
    diagnostics of model fits and imputation of censored data.
Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017). "Survival Analysis with Interval-Censored Data: A Practical Approach." ISBN 978-1-4200-7747-6, published by Chapman and Hall/CRC. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.
We consider studies in which information from error-prone
    diagnostic tests or self-reports are gathered sequentially to determine the
    occurrence of a silent event. Using a likelihood-based approach
    incorporating the proportional hazards assumption, we provide functions to
    estimate the survival distribution and covariate effects. We also provide 
    functions for power and sample size calculations for this setting.
    Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) 
    <doi: 10.1214/15-AOAS810>, Xiangdong Gu and Raji Balasubramanian (2016) 
    <doi: 10.1002/sim.6962>, Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes,
    Yunsheng Ma, and Raji Balasubramanian (2020) <doi: 10.1186/s12911-020-01223-w>.
A collection of functions that facilitate computational steps
  related to advice for fisheries management, according to ICES guidelines.
  These include methods for calculating reference points and model diagnostics.
R interface to access the web services of the ICES (International
             Council for the Exploration of the Sea) DATRAS trawl survey
             database <https://datras.ices.dk/WebServices/Webservices.aspx>.
Functions to support the ICES Transparent Assessment Framework
  <https://taf.ices.dk> to organize data, methods, and results used in ICES
  assessments. ICES is an organization facilitating international collaboration
  in marine science.
R interface to access the RECO POX web services of the ICES
  (International Council for the Exploration of the Sea) Vocabularies database
  <https://vocab.ices.dk/services/POX.aspx>.
ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.
Generalized Odds Rate Hazards (GORH) model is a flexible model of fitting survival data, including the Proportional Hazards (PH) model and the Proportional Odds (PO) Model as special cases. This package fit the GORH model with interval censored data.
A minimal wrapper around the NIH's 'iCite' API (<https://icite.od.nih.gov/api>). 
  Given a vector of pubmed IDs, this package returns a dataframe of the information yielded by
  the 'iCite' API. The primary metrics yielded by 'iCite' are measurements of a paper's 
  scientific influence and translational potential, but the API also returns other meta-data 
  from the paper, including author names, publication journal, publication year, paper title, 
  doi, and a number of other citation metrics.
A GUI designed to support the analysis of financial-economic time
    series data.
Integrative clustering of multiple genomic data types
        using a joint latent variable model.
Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression (Pungpapong et. al. (2015) <doi:10.1214/15-EJS1034>, Pungpapong et. al. (2017) <arXiv:1707.08298>).
Sieve semiparametric likelihood methods for analyzing 
 interval-censored failure time data from an outcome-dependent sampling (ODS) 
 design and from a case-cohort design. 
 Zhou, Q., Cai, J., and Zhou, H. (2018) <doi:10.1111/biom.12744>; 
 Zhou, Q., Zhou, H., and Cai, J. (2017)  <doi:10.1093/biomet/asw067>.
Provides easy-to-use and easy-to-access datasets from the
    Index of COmplex Networks (ICON) database available on the University
    of Colorado website. All datasets can be loaded with a single function
    call and new datasets are being slowly added from ICON at
    <https://icon.colorado.edu>. Specific ICON datasets of interest can
    be requested at the BugReports URL <https://github.com/rrrlw/ICON/issues>.
Set of formal methods for studying archaeological iconographic datasets (rock-art, pottery decoration, stelae, etc.) using network and spatial analysis (Alexander 2008 <doi:10.11588/propylaeumdok.00000512>; Huet 2018 <https://hal.archives-ouvertes.fr/hal-02913656>).
Employs triangular tessellation to refine icosahedra
    defined in 3d space. The procedures can be set to provide a grid with a
    custom resolution. Both the primary triangular and their inverted penta-
    hexagonal grids are available for implementation. Additional functions
    are provided to position points (latitude-longitude data) on the grids,
    to allow 2D and 3D plotting, use raster data and shapefiles.
Reproducible, programmatic retrieval of datasets from the
    Inter-university Consortium for Political and Social Research archive.
Provides functions to compute and plot Krippendorff's inter-coder 
    reliability coefficient alpha and bootstrapped uncertainty estimates 
    (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to
    make use of parallel threads where supported.
Algorithms to construct simultaneous confidence intervals for the ranks of means mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.
Implements interval censored recursive forests (ICRF) based on Cho, Jewell, and Kosorok (2020+). 
            ICRF is a variant of random forests where the outcome variable is interval censored survival data.
            It can be used for usual right censored data and current status data as well.
            A recursion technique is used to improve accuracy and smoothed survival curves are provided.
Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests.  The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.
Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009,
            <doi:10.1111/j.1467-9868.2009.00706.x>) and Oja, Sirkia and Eriksson's
            (AJS, 2006, <http://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) method of two different
            scatter matrices to obtain an invariant coordinate system or independent
            components, depending on the underlying assumptions.
Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide.
Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components.
The Iterative Cumulative Sum of Squares (ICSS) algorithm by Inclan/Tiao (1994) <https://www.jstor.org/stable/2290916> detects multiple change points, i.e. structural break points, in the variance of a sequence of independent observations. For series of moderate size (i.e. 200 observations and beyond), the ICSS algorithm offers results comparable to those obtained by a Bayesian approach or by likelihood ration tests, without the heavy computational burden required by these approaches.
Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2016) <arXiv:1612.06118>) using a shiny app.
We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing. This package is based on a diploma thesis Hudak (2017) <https://opac.crzp.sk/?fn=detailBiblioForm&sid=7FE4E0810F66E41DF9A9CA23557F&seo=CRZP-detail-kniha>.
Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.
Provides the necessary tools to estimate average treatment effects with an instrumental variable by re-weighting observations using a model of compliance.
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
Functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.
Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.
Use R to make requests to the US Census Bureau's International Data Base API.
             Results are returned as R data frames.  For more information about the IDB API, visit
             <http://www.census.gov/data/developers/data-sets/international-database.html>.
The digits of the old version (before 2000 year) of 'Chinese ID Card Number' is 15, this package aims to update to the current version of 18 digits. Besides, this package can help check whether the given 'ID' is right or not.
The Integro-Difference Equation model is a linear, dynamical model used to model
   phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011,
   ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the
   heart of the model is the kernel, which dictates how the process evolves from one time
   point to the next. Both process and parameter reduction are used to facilitate computation,
   and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed
   to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted
   by maximum likelihood, and estimation is carried out using an evolution algorithm.
Time series plain text conversion and data visualization. It allows
    to transform IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales)
    daily series from plain text to CSV files or data frames in R. Additionally,
    it is possible to obtain exploratory graphs from times series. IDEAM’s data
    is freely delivered under formal request through the official web page
    <http://www.ideam.gov.co/solicitud-de-informacion>.
Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.
In randomized studies involving severely ill patients, functional
    outcomes are often unobserved due to missed clinic visits, premature
    withdrawal or death. It is well known that if these unobserved functional
    outcomes are not handled properly, biased treatment comparisons can be
    produced. In this package, we implement a procedure for comparing treatments
    that is based on the composite endpoint of both the functional outcome and
    survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>
    and Wang et al. (2020) <DOI:10.18637/jss.v093.i12>. It considers missing data
    imputation with different sensitivity
    analysis strategies to handle the unobserved functional outcomes not due to
    death.
Interactive dendrogram that enables the user to select and
    color clusters, to zoom and pan the dendrogram, and to visualize
    the clustered data not only in a built-in heat map, but also in
    'GGobi' interactive plots and user-supplied plots. 
    This is a backport of Qt-based 'idendro' 
    (<https://github.com/tsieger/idendro>) to base R graphics and 
    Tcl/Tk GUI.
Calculate identity coefficients, based on Mark Abney's C
        code.
In contrast to other methods of modeling spatio-temporal data,
  dynamic spatio-temporal models (DSTMs) directly model the dynamic
  data-generating process.
  'ideq' supports two main classes of DSTMs:
  (1) empirical orthogonal function (EOF) models and
  (2) integrodifference equation (IDE) models.
  EOF models do not directly use any spatial information;
  instead, they make use of observed relationships in the data
  (the principal components) to model the underlying process.
  In contrast, IDE models are based on diffusion dynamics and the process
  evolution is governed by a (typically Gaussian) redistribution kernel.
  Both types have a variety of options for specifying the model components,
  including the process matrix, process error, and observation error.
  The classic reference for DSTMs is
  Noel Cressie and Christopher K. Wikle (2011, ISBN:978-0471692744).
  For IDE models specifically, see
  Christopher K. Wikle and Noel Cressie (1999, <https://www.jstor.org/stable/2673587>)
  and 
  Christopher K. Wikle (2002, <doi:10.1191/1471082x02st036oa>).
An implementation of various methods for estimating intrinsic
    dimension of vector-valued dataset or distance matrix. Most methods implemented
    are based on different notion of fractal dimension such as the capacity
    dimension, the box-counting dimension, and the information dimension.
Provides efficient implementation of the Isolate-Detect
    methodology for the consistent estimation of the number and location of multiple 
    change-points in one-dimensional data sequences from the "deterministic 
    + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and
    Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>.
    Currently implemented scenarios are: piecewise-constant signal with Gaussian
    noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear 
    signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.
Intensity-duration-frequency (IDF) curves are a widely used analysis-tool
              in hydrology to assess extreme values of precipitation
              [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>].
              The package 'IDF' provides functions to estimate IDF parameters for given
              precipitation time series on the basis of a duration-dependent
              generalized extreme value distribution
              [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.
Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids (when not circular). Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangular, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).
Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.
Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".
Contains techniques for mining large and high-dimensional data
    sets by using the concept of Intrinsic Dimension (ID). Here the ID is
    not necessarily an integer. It is extended to fractal dimensions. And
    the Morisita estimator is used for the ID estimation, but other
    tools are included as well.
Explore a range of infectious disease models in a consistent framework. 
    The primary aim of 'idmodelr' is to provide a library of infectious disease models
    for researchers, students, and other interested individuals. These models can be 
    used to understand the underlying dynamics and as a reference point when developing
    models for research. 'idmodelr' also provides a range of utilities. These include: plotting 
    functionality; a simulation wrapper; scenario analysis tooling; an interactive 
    dashboard; tools for handling mult-dimensional models; and both model and parameter
    look up tables. Unlike other modelling packages such as 'pomp' (<https://kingaa.github.io/pomp/>), 'libbi' (<http://libbi.org>)
    and 'EpiModel' (<http://www.epimodel.org>), 'idmodelr' serves primarily as an educational 
    resource. It is most comparable to epirecipes (<http://epirecip.es/epicookbook/chapters/simple>)
    but provides a more consistent framework, an R based workflow, and additional utility tooling. 
    After users have explored model dynamics with 'idmodelr' they may then 
    implement their model using one of these packages in order to utilise the model
    fitting tools they provide.  For newer modellers, this package reduces the barrier
    to entry by containing multiple infectious disease models, providing a consistent
    framework for simulation and visualisation, and signposting towards other, more 
    research focussed, resources.
Modeling of regression effects for transition probabilities in a 
   progressive illness-death model. Azarang, Scheike, and de Una-Alvarez (2017) <doi:10.1002/sim.7245>.
Integrate molecular profiles to discover candidate oncogenic drivers.
Different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region.  In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra, converting humidity measures.
Functions to perform robust
		 nonparametric survival analysis with right censored 
		 data using a prior near-ignorant Dirichlet Process.
		 Mangili, F., Benavoli, A., de Campos, C.P., Zaffalon, M. (2015) <doi:10.1002/bimj.201500062>.
This is a package for estimating the copula mixture model and plotting correspondence curves in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel
Generate random or human readable and pronounceable identifiers.
Method of interactive detector for spatial associations (IDSA). 
             IDSA is used to quantify the power of interactive determinant (PID) 
             between a spatial response variable and explanatory variables. 
             IDSA is developed based on methods of spatial heterogeneity.
Implements various novel and standard
    clustering statistics and other analyses useful for understanding the
    spread of infectious disease.
Convert files to and from IDX format to vectors, matrices and arrays.
    IDX is a very simple file format designed for storing vectors and multidimensional matrices in
    binary format. The format is described on the website from Yann LeCun
    <http://yann.lecun.com/exdb/mnist/>.
iDynoMiCS is a computer program, developed by an international team of researchers, whose purpose is to model and simulate microbial communities in an individual-based way. It is described in detail in the paper "iDynoMiCS: next-generation individual-based modelling of biofilms" by Lardon et al, published in Environmental Microbiology in 2011. The simulation produces results in XML file format, describing the state of each species in each timestep (agent_State), a summary of the species statistics for a timepoint (agent_Sum), the state of each solute grid in each timestep (env_State) and a summary of the solutes for a timestep (env_Sum). This R package provides a means of reading this XML data into R such that the simulation response can be statistically analysed. iDynoMiCS is available from the website iDynoMiCS.org, where a full tutorial on using both the simulation and this R package is provided.
A collection of Irucka Embry's miscellaneous USGS functions
    (processing .exp and .psf files, statistical error functions,
    "+" dyadic operator for use with NA, creating ADAPS and QW
    spreadsheet files, calculating saturated enthalpy). Irucka created these
    functions while a Cherokee Nation Technology Solutions (CNTS) United States
    Geological Survey (USGS) Contractor and/or USGS employee.
A pair of functions for getting and setting the IEEE
        rounding mode for floating point computations.
A collection of Irucka Embry's miscellaneous functions
    (Engineering Economics, Civil & Environmental/Water Resources Engineering,
    Geometry, Statistics, GNU Octave length functions, Trigonometric functions
    in degrees, etc.).
Miscellaneous data sets [Engineering Economics, Environmental/
    Water Resources Engineering, US Presidential Elections].
The eclectic collection includes the following written pieces:
    "The War Prayer" by Mark Twain, "War Is A Racket" by Major General
    Smedley Butler, "The Mask of Anarchy: Written on the Occasion of the
    Massacre at Manchester" by Percy Bysshe Shelley, "Connect the D.O.T.S." by
    Obiora Embry, "Untitled: Climate Strange" by Irucka Ajani Embry, and
    "Untitled: Us versus Them or People Screwing over Other People (as we all
    live on one Earth and there is no "us versus them" in the actual Ultimate
    Reality}" by Irucka Ajani Embry.
Computes characteristics of independent rainfall events (duration, total rainfall depth, and intensity) 
  extracted from a sub-daily rainfall time series based on the inter-event time definition (IETD) method. To have a 
  reference value of IETD, it also analyzes/computes IETD values through three methods: autocorrelation analysis, the 
  average annual number of events analysis, and coefficient of variation analysis. Ideal for analyzing the sensitivity 
  of IETD to characteristics of independent rainfall events.
  Adams B, Papa F (2000) <ISBN: 978-0-471-33217-6>.
  Joo J et al. (2014) <doi:10.3390/w6010045>.
  Restrepo-Posada P, Eagleson P (1982) <doi:10.1016/0022-1694(82)90136-6>.
The package performes Independent Factor Analysis
A novel approach to make inference on the association of covariates with the absolute abundance (AA) of 'microbiome' in an ecosystem. It can be also directly applied to relative abundance (RA) data to make inference on AA (even if AA data is not available) because the ratio of two RA is equal ratio of their AA. This algorithm can estimate and test the associations of interest while adjusting for potential 'confounders'. The estimates of this method have easy interpretation like a typical regression analysis. High-dimensional covariates are handled with regularization and it is implemented by parallel computing. This algorithm finds optimal reference 'taxa/OTU (Operational Taxonomic Unit)/ASV (Amplicon Sequence Bariant)' and uses permutation to control FDR (False Discovery Rate).
This package implements a Bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.
Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'.
    This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
Contains several tools to treat imaging flow cytometry data from 'ImageStream®' and 'FlowSight®' cytometers ('Amnis®', part of 'Luminex®'). Provides an easy and simple way to read, write and subset .rif, .cif and .daf files. Information such as masks, features, regions and populations set within these files can be retrieved. In addition, raw data such as images stored can also be accessed. Users, may hopefully increase their productivity thanks to dedicated functions to extract, visualize and export 'IFC' data. Toy data example can be installed through the 'IFCdata' package of approximately 32 MB, which is available in a 'drat' repository <https://gitdemont.github.io/IFCdata/>. See file 'COPYRIGHTS' and file 'AUTHORS' for a list of copyright holders and authors.
Provides utility functions to deal with Italian fiscal
    code ('codice fiscale').
Compute both static and recursive time series forecasts of machine learning models.
A suite for identifying causal models using relative concordances and identifying causal polymorphisms in case-control genetic association data, especially with large controls re-sequenced data.
Insightful Research Tools is a collection of signal processing,
  image processing, and time series modeling routines written in the C
  language for speed and efficency. The routines were originally developed at
  Insightful for use in S-PLUS.
A dataset of the top colours of photos from Instagram 
 taken in 2014 in the city of Vancouver, British Columbia, Canada.
 It consists of: top colour and counts data. This data was
 obtained using the Instagram API. Instagram is a web photo 
 sharing service. It can be found at: <https://instagram.com>.
 The Instagram API is documented at: <https://instagram.com/developer/>.
Functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments.
  This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>.
  Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.
A collection of statistical tests for genetic association studies.
An implementation of the initial guided analytics for parameter testing and
    controlband extraction framework. Functions are available for continuous and 
    categorical target variables as well as for generating standardized reports of the
    conducted analysis. See <https://github.com/stefan-stein/igate> for more information
    on the technology.
Implements Bayesian linear regression, normal means estimation, and variable selection using the inverse gamma-gamma prior, as introduced by Bai and Ghosh (2018) <arXiv:1710.04369>.
Implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series and lasagna plots.
Tools to extract information from the
    Intergovernmental Organizations ('IGO') Database , version 3, provided
    by the Correlates of War Project <https://correlatesofwar.org/>. See
    also Pevehouse, J. C. et al. (2020), <doi:10.1177/0022343319881175>. 
    Version 3 includes information from 1815 to 2014.
Provides function to read data from the 'Igor Pro' data analysis
    program by Wavemetrics. The data formats supported are 'Igor' packed 
    experiment format (pxp) and 'Igor' binary wave (ibw). See: 
    http://www.wavemetrics.com/ for details. Also includes functions to load 
    special pxp files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp' 
    packages for recording and analysing neuronal data. See 
    http://www.neuromatic.thinkrandom.com/ for details.
Routines for simple graphs and network analysis. It can
  handle large graphs very well and provides functions for generating random
  and regular graphs, graph visualization, centrality methods and much more.
A small collection of various network data sets,
    to use with the 'igraph' package: the Enron email network, various food webs,
    interactions in the immunoglobulin protein, the karate club network,
    Koenigsberg's bridges, visuotactile brain areas of the macaque monkey,
    UK faculty friendship network, domestic US flights network, etc.
Using 'shiny' to demo 'igraph' package makes learning graph theory easy and fun.
Graph matching methods and analysis. The package
  works for both 'igraph' objects and matrix objects. You provide the adjacency
  matrices of two graphs and some other information you might know, choose the
  graph matching method, and it returns the graph matching results. 'iGraphMatch'
  also provides a bunch of useful functions you might need related to graph
  matching.
This program facilitates exporting igraph graphs to the
        SoNIA file format
To integrate multiple GSEA studies, we propose a hybrid strategy,
    iGSEA-AT, for choosing random effects (RE) versus fixed effect (FE) models,
    with an attempt to achieve the potential maximum statistical efficiency as 
    well as stability in performance in various practical situations. In addition
    to iGSEA-AT, this package also provides options to perform integrative GSEA
    with testing based on a FE model (iGSEA-FE) and testing based on a RE model
    (iGSEA-RE). The approaches account for different set sizes when testing a
    database of gene sets. The function is easy to use, and the three approaches
    can be applied to both binary and continuous phenotypes.
Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.
Make complex, interactive heatmaps. 'iheatmapr' includes a modular 
    system for iteratively building up complex heatmaps, as well as the 
    iheatmap() function for making relatively standard heatmaps.
Web scraping the <https://www.dallasfed.org> for
    up-to-date data on international house prices and exuberance
    indicators. Download data in tidy format.
Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.
Efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso.
  Takada, M., Suzuki, T., & Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. AISTATS.
  <http://proceedings.mlr.press/v84/takada18a/takada18a.pdf>.
Computes individual contributions to the overall Gini and 
    Theil's T and Theil's L measures and their decompositions by groups 
    such as race, gender, national origin, with the three functions of 
    iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2019)
    <doi:10.1177/0049124119875961>.
General purpose TIFF file I/O for R users.
    Currently the only such package with read and write support for TIFF
    files with floating point (real-numbered) pixels, and the only package
    that can correctly import TIFF files that were saved from 'ImageJ' and
    write TIFF files than can be correctly read by 'ImageJ'
    <https://imagej.nih.gov/ij/>.  Also supports text image I/O.
Fitting a class of Lee-Carter mortality models using iterative fitting algorithm.
It performs interlaboratory studies (ILS) to detect those
    laboratories that provide non-consistent results when comparing to others. It
    permits to work simultaneously with various testing materials, from standard
    univariate, and functional data analysis (FDA) perspectives. The univariate
    approach based on ASTM E691-08 consist of estimating the Mandel's h and
    k statistics to identify those laboratories that provide more significant
    different results, testing also the presence of outliers by Cochran and Grubbs
    tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey
    tests) to test differences in means corresponding to different laboratories per
    each material. Taking into account the functional nature of data retrieved in
    analytical chemistry, applied physics and engineering (spectra, thermograms,
    etc.). ILS package provides a FDA approach for finding the Mandel's k and h
    statistics distribution by smoothing bootstrap resampling.
Performs mQTL (methylation quantitative-trait locus) mapping in 
    bisulfite sequencing studies by fitting a binomial mixed model and then 
    incorporating the allelic-specific methylation pattern. Based on Fan, Yue; 
    Vilgalys, Tauras P.; Sun, Shiquan; Peng, Qinke; Tung, Jenny; Zhou, Xiang 
    (2019) <doi:10.1101/615039>.
Improve optical character recognition by binarizing images. The package focuses primarily on local adaptive thresholding algorithms. 
    In English, this means that it has the ability to turn a color or gray scale image into a black and white image. This is particularly useful
    as a preprocessing step for optical character recognition or handwritten text recognition.
An implementation of the Canny Edge Detector for detecting edges in images. The package provides an interface to the algorithm available at <https://github.com/Neseb/canny>.
An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). 
    The algorithm is explained at <doi:10.5201/ipol.2016.175>.
An implementation of the "FAST-9" corner detection algorithm explained in the paper 'FASTER and better: A machine learning approach to corner detection' by Rosten E., Porter R. and Drummond T. (2008), available at <arXiv:0810.2434>.
    The package allows to detect corners in digital images.
An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at <doi:10.5201/ipol.2018.229>. 
    The package allows to detect relevant points in images which are characteristic to the digital image.
Facility wrappers around the image processing functionality of
    'dlib'. 'Dlib' <http://dlib.net> is a 'C++' toolkit containing machine learning
    algorithms and computer vision tools. Currently the package allows to find feature
    descriptors of digital images, in particular 'SURF' and 'HOG' descriptors.
An open source library for face detection in images. 
    Provides a pretrained convolutional neural network based on <https://github.com/ShiqiYu/libfacedetection> which can be used to detect faces which have size greater than 10x10 pixels.
An implementation of the Line Segment Detector on digital images described in the paper: "LSD: A Fast Line Segment Detector with a False Detection Control" by Rafael Grompone von Gioi et al (2012). 
    The algorithm is explained at <doi:10.5201/ipol.2012.gjmr-lsd>.
An implementation of the Otsu's Image Segmentation Method described in the paper: "A C++ Implementation of Otsu's Image Segmentation Method". The algorithm is explained at <doi:10.5201/ipol.2016.158>.
Find text lines in scanned images and segment the lines into words.
    Includes implementations of the paper 'Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents' by Surinta O. et al (2014) <doi:10.1109/ICFHR.2014.37> available at <https://github.com/smeucci/LineSegm>,
    an implementation of 'A Statistical approach to line segmentation in handwritten documents' by Arivazhagan M. et al (2007) <doi:10.1117/12.704538>, 
    and a wrapper for an image segmentation technique to detect words in text lines as described in the paper 'Scale Space Technique for Word Segmentation in Handwritten Documents' by Manmatha R. and Srimal N. (1999) paper at <doi:10.1007/3-540-48236-9_3>, wrapper for code available at <https://github.com/arthurflor23/text-segmentation>.
Note that 'imageData' has been superseded by 'growthPheno'. 
    The package 'growthPheno' incorporates all the functionality of 
    'imageData' and has functionality not available in 'imageData', 
    but some 'imageData' functions have been renamed.  
    The 'imageData' package is no longer maintained, but is retained 
    for legacy purposes.
Get image statistics based on processing fluency theory. The
    functions provide scores for several basic aesthetic principles that
    facilitate fluent cognitive processing of images: contrast,
    complexity / simplicity, self-similarity, symmetry, and typicality.
    See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr
    (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.
Temporal fusion of raster image time series. Provides implementation of various fusion algorithms in C++.
Synthesize images into characteristic features for time-series analysis or machine learning applications.  The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity.  However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.
Fast image processing for images in up to 4 dimensions (two spatial
    dimensions, one time/depth dimension, one colour dimension). Provides most
    traditional image processing tools (filtering, morphology, transformations,
    etc.) as well as various functions for easily analysing image data using R. The
    package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image
    processing.
Provides advanced functions for image processing based on the package 'imager'.
Functions for the implementation of Independent Multiple-sample
  Greedy Equivalence Search (IMaGES), a causal inference algorithm for creating
  aggregate graphs and structural equation modeling data for one or more
  datasets. This package is useful for time series data with specific regions of
  interest. This implementation is inspired by the paper "Six problems
  for causal inference from fMRI" by Ramsey, Hanson, Hanson, Halchenko, Poldrack,
  and Glymour (2010) <DOI:10.1016/j.neuroimage.2009.08.065>. The IMaGES algorithm uses a modified BIC score to compute goodness
  of fit of edge additions, subtractions, and turns across all datasets and
  returns a representative graph, along with structural equation modeling data for
  the global graph and individual datasets, means, and standard errors. Functions
  for plotting the resulting graph(s) are provided. This package is built upon the
  'pcalg' package.
Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.
Provides fast application of image filters to data matrices,
    using R and C++ algorithms.
The pearl oyster, Pinctada margaritifera (Linnaeus, 1758), represents the second economic resource of French Polynesia. It is one of the only bivalves expressing a large varied range of inner shell color, & by correlation, of pearl color. This phenotypic variability is partly under genetic control, but also under environmental influence. With ImaginR, it's now possible to delimit the color phenotype of the pearl oyster's inner shell and to characterize their color variations (by the HSV color code system) with pictures.
This is an Automatic Item Generator for Psychological Assessment. Items created with the 'IMak' package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.
Zoom in and out of maps or any supplied lines or points,
        with control for color, poly fill, and aspect.
Class imbalance usually damages the performance of classifiers. Thus, it is
             important to treat data before applying a classifier algorithm. This package
             includes recent resampling algorithms in the literature: (Barua et al. 2014)
             <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>,
             (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014)
             <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014)
             <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to
             perform oversampling.
Provides a set of fast, chainable image-processing operations
             which are applicable to images of two, three or four dimensions,
             particularly medical images.
Provides API access to the <http://imdbapi.net> which maintains metadata
             about movies, games and television shows through a public API.
Theories are one of the most important tools of science. Although psychologists discussed problems of theory in their discipline for a long time, weak theories are still widespread in most subfields. 
    One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. 
    Previously a computational model for formal theory evaluation based on the concept of explanatory coherence was developed (Thagard, 1989, <doi:10.1017/S0140525X00057046>). 
    However, there are possible improvements to this model and it is not available in software that psychologists typically use. 
    Therefore, a new implementation of explanatory coherence based on the Ising model is available in this R-package.
Implements likelihood based methods for mediation analysis.
Search, extract and formulate IMF's datasets.
Explore and download data from the International Monetary Fund's
    data API <http://data.imf.org/>.
Provides ability to create color palettes from image files. 
    It offers control over the type of color palette to derive from an image (qualitative, sequential or divergent) and other palette properties.
    Quantiles of an image color distribution can be trimmed. 
    Near-black or near-white colors can be trimmed in RGB color space independent of trimming brightness or saturation distributions in HSV color space.
    Creating sequential palettes also offers control over the order of HSV color dimensions to sort by.
    This package differs from other related packages like 'RImagePalette' in approaches to quantizing and extracting colors in images to assemble color palettes 
    and the level of user control over palettes construction.
Provides an interface for image recognition using the 'Google Vision API' <https://cloud.google.com/vision/> .  Converts API data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.
A complete API client for the image hosting service Imgur.com, including the an imgur graphics device, enabling the easy upload and sharing of plots.
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.
Interpretability methods to analyze the behavior
    and predictions of any machine learning model.  Implemented methods
    are: Feature importance described by Fisher et al. (2018)
    <arXiv:1801.01489>, accumulated local effects plots described by Apley
    (2018) <arXiv:1612.08468>, partial dependence plots described by
    Friedman (2001) <www.jstor.org/stable/2699986>, individual
    conditional expectation ('ice') plots described by Goldstein et al.
    (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of
    'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>, the
    Shapley Value described by Strumbelj et. al (2014)
    <doi:10.1007/s10115-013-0679-x>, feature interactions described by
    Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
Send emails using the 'mailgun' api. To use this package you will need an account from <https://www.mailgun.com> .
The pathway fingerprint is a method to indicate the profile of significant pathways being influenced by drugs, which may hint drug functions. Through the similarity of pathway fingerprints, the potential relationship between disease and prescription can be found. Ye (2012) <doi: 10.1007/s13238-012-2011-z>.
Implements some item response models for multiple
    ratings, including the hierarchical rater model, 
    conditional maximum likelihood estimation of linear 
    logistic partial credit model and a wrapper function
    to the commercial FACETS program. See Robitzsch and
    Steinfeld (2018) for a description of the functionality
    of the package. 
    See Wang, Su & Qiu (2014; <doi:10.1111/jedm.12045>)
    for an overview of modeling alternatives.
Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods.
Simulate full B-cell and T-cell receptor repertoires using an in silico 
    recombination process that includes a wide variety of tunable parameters to introduce noise and biases. 
    Additional post-simulation modification functions allow the user to implant motifs or codon biases as 
    well as remodeling sequence similarity architecture. The output repertoires contain records of all 
    relevant repertoire dimensions and can be analyzed using provided repertoire analysis functions.
    Preprint is available at bioRxiv (Weber et al., 2019 <doi:10.1101/759795>).
Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).
Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.
A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.
Implements the compartment model from Tokars (2018) 
  <doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of 
  population-wide impact of vaccination against vaccine-preventable 
  diseases such as influenza.
Imputing blockwise missing data by imprecise imputation,
    featuring a domain-based, variable-wise, and case-wise strategy. 
    Furthermore, the estimation of lower and upper bounds for 
    unconditional and conditional probabilities based on the obtained
    imprecise data is implemented.
    Additionally, two utility functions are supplied: one to check 
    whether variables in a data set contain set-valued observations;
    and another to merge two already imprecisely imputed data. 
    The method is described in a technical report by Endres, Fink and
    Augustin (2018, <doi:10.5282/ubm/epub.42423>).
A tool for computing the scores for the Implicit Association Test 
    (IAT; Greenwald, McGhee & Schwartz (1998) <doi:10.1037/0022-3514.74.6.1464>) 
    and the Single Category-IAT (SC-IAT: Karpinski & Steinman 
    (2006) <doi:10.1037/0022-3514.91.1.16>). Functions for preparing the data 
    (both for the IAT and the SC-IAT), plotting the results, and obtaining a 
    table with the scores of implicit measures descriptive statistics are 
    provided.
Convert bookmaker odds into proper probabilities. Seven different
    algorithms are available, including basic normalization, Shin's method 
    (Hyun Song Shin, (1992) <doi:10.2307/2234526>), and others.
'SQL' back-end to 'dplyr' for Apache Impala, the massively
    parallel processing query engine for Apache 'Hadoop'. Impala enables
    low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed
    File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple 
    Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', 
    and Dell 'EMC' 'Isilon'. See <https://impala.apache.org> for more
    information about Impala.
Alternative mechanism for importing objects from packages
    and R modules. The syntax allows for importing multiple objects with a single
    command in an expressive way. The import package bridges some of the gap
    between using library (or require) and direct (single-object) imports.
    Furthermore the imported objects are not placed in the current environment.
Enables 'Python'-like importing/loading of packages or functions
    with aliasing to prevent namespace conflicts.
Import and export data from the most common statistical formats by using 
	     R functions that guarantee the least loss of the data information, giving special
	     attention to the date variables and the labelled ones.
Download and manage data sets of statistical projects and geographic data created by Instituto Nacional de Estadistica y Geografia (INEGI). See <https://www.inegi.org.mx/>.
Provides color palettes from Impressionism and post-Impressionism artworks. This package allows to select colors combinations while looking at the original paintings where colors were sampled from.
Creation of imprecise classification trees. They rely on
    probability estimation within each node by means of either the
    imprecise Dirichlet model or the nonparametric predictive
    inference approach. The splitting variable is selected by the
    strategy presented in Fink and Crossman (2013)
    <http://www.sipta.org/isipta13/index.php?id=paper&paper=014.html>,
    but also the original imprecise information gain of Abellan and
    Moral (2003) <doi:10.1002/int.10143> is covered.
Missing values often occur in financial data due to a variety 
    of reasons (errors in the collection process or in the processing stage, 
    lack of asset liquidity, lack of reporting of funds, etc.). However, 
    most data analysis methods expect complete data and cannot be employed 
    with missing values. One convenient way to deal with this issue without 
    having to redesign the data analysis method is to impute the missing 
    values. This package provides an efficient way to impute the missing 
    values based on modeling the time series with a random walk or an 
    autoregressive (AR) model, convenient to model log-prices and log-volumes 
    in financial data. In the current version, the imputation is 
    univariate-based (so no asset correlation is used). In addition,
    outliers can be detected and removed.
    The package is based on the paper:
    J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of 
    Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on 
    Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.
The package contains a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
This package provides various approaches to handling
        missing values for the MDR analysis to identify gene-gene
        interactions using biallelic marker data in genetic association
        studies
Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.
Implements imputation methods using EM and Data Augmentation for
    multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
Imputes the missing values in time series data with PSF algorithm based approach.
    The details about PSF algorithm are available at:
    <https://cran.r-project.org/package=PSF>.
Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.
Provides a test bench for the comparison of missing data imputation 
    methods in uni-variate time series. Imputation methods are compared using 
    different error metrics. Proposed imputation methods and alternative error 
    metrics can be used.
Imputation (replacement) of missing values 
             in univariate time series. 
             Offers several imputation functions
             and missing data plots. 
             Available imputation algorithms include: 
            'Mean', 'LOCF', 'Interpolation', 
            'Moving Average', 'Seasonal Decomposition', 
            'Kalman Smoothing on Structural Time Series models',
            'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) 
            <doi: 10.32614/RJ-2017-009>.
Method brings less bias and more efficient estimates for AFT models.
Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers)
  evaluate patient images (cases). What does this mean? ... Many imaging studies are designed
  so that every reader reads every case in all modalities, a fully-crossed study. In this case, the
  data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects.
  An MRMC analysis accounts for the variability and correlations from the readers and cases when
  estimating variances, confidence intervals, and p-values. The functions in this package can treat
  arbitrary study designs and studies with missing data, not just fully-crossed study designs.
  The initial package analyzes the reader-average area under the receiver operating characteristic (ROC)
  curve with U-statistics according to Gallas, Bandos, Samuelson,
  and Wagner 2009 <doi:10.1080/03610920802610084>.
  Additional functions analyze other endpoints with U-statistics (binary performance
  and score differences) following the work by Gallas, Pennello, and Myers 2007
  <doi:10.1364/JOSAA.24.000B70>.
  Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.
Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.
Implementation of the information matrix test for generalized partial credit models.
Graphical User Interface (GUI) to some of the functions in the package extRemes version >= 2.0 are included.
Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting the algorithms. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the centers of clusters, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.
A framework for analysing inbreeding and heterozygosity-fitness
    correlations (HFCs) based on microsatellite and SNP markers.
Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. They are useful for census-weights adjustments, or for performing linear regression with integer parameters. This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy.
Handle data in formats used by cancer centers in Sweden, both from 'INCA' 
  (<https://rcc.incanet.se>) and by the older register platform 'Rockan'.
  All variables are coerced to suitable classes based on their format. 
  Dates (from various formats such as with missing month or day, with or 
  without century prefix or with just a week number) are all recognized as
  dates and coerced to the ISO 8601 standard (Y-m-d).
  Boolean variables (internally stored either as 0/1 or "True"/"False"/blanks 
  when exported) are coerced to logical. 
  Variable names ending in '_Beskrivning' and '_Varde' will be character, 
  and 'PERSNR' will be coerced (if possible) to a valid personal identification 
  number 'pin' (by the 'sweidnumbr' package).
  The package also allow the user to interactively choose if a variable should 
  be coerced into a potential format even though not all of its values might 
  conform to the recognized pattern.
  It also contain a caching mechanism in order to temporarily store data sets 
  with its newly decided formats in order to not rerun the identification 
  process each time. 
  The package also include a mechanism to aid the documentation process 
  connected to projects build on data from 'INCA'.
  From version 0.7, some general help functions are also included, 
  as previously found in the 'rccmisc' package.
Offers a pipe-friendly alternative to the 'dplyr'
    functions case_when() and if_else().  These functions accept a vector
    as an optional first argument, allowing conditional statements to be
    built using the 'magrittr' dot operator.  The functions also coerce
    all possible outputs to the same type, meaning you no longer have to
    worry about using specific typed variants of NA or explicitly
    declaring integer outputs.
Data analysis, normalisation and differential expression for Translatome studies by microarrays (T Sbarrato et al. RNA. 2017 Aug 25; <DOI:10.1261/rna.060525.116>).
The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>.
An efficient and incremental approach for calculating 
    the differences in orbit counts when performing single edge modifications 
    in a network. Calculating the differences in orbit counts is much more efficient than
    recalculating all orbit counts from scratch for each time point.
Provides functions and classes to compute, handle and visualise
  incidence from dated events for a defined time interval. Dates can be provided
  in various standard formats. The class 'incidence' is used to store computed
  incidence and can be easily manipulated, subsetted, and plotted. In addition,
  log-linear models can be fitted to 'incidence' objects using 'fit'. This
  package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit
  for outbreak analysis.
Provides functions and classes to compute, handle and visualise 
  incidence from dated events for a defined time interval. Dates can be 
  provided in various standard formats. The class 'incidence2' is used to store
  computed incidence and can be easily manipulated, subsetted, and plotted.
  This package is part of the RECON (<https://www.repidemicsconsortium.org/>) 
  toolkit for outbreak analysis.
Make empirical Bayes incidence curves from reported case data using a specified delay distribution.
Implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values.
    Bhoj, D. S. (1991) <doi:10.1002/bimj.4710330108> "Testing equality of means in the presence of correlation and missing data".
    Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) <doi:10.22237/jmasm/1020254460> "Rank-based procedures for mixed paired and two-sample designs".
    Einsporn, R. L. and Habtzghi, D. (2013) <https://pdfs.semanticscholar.org/89a3/90bafeb2bc41ed4414533cfd5ab84a6b54b6.pdf> "Combining paired and two-sample data using a permutation test".
    Ekbohm, G. (1976) <doi:10.1093/biomet/63.2.299> "On comparing means in the paired case with incomplete data on both responses".
    Lin, P. E. and Stivers, L. E. (1974) <doi:10.1093/biomet/61.2.325> On difference of means with incomplete data".
    Maritz, J. S. (1995) <doi:10.1111/j.1467-842x.1995.tb00649.x> "A permutation paired test allowing for missing values".
Suite of functions to study animal incubation.
    At the core of incR 
    lies an algorithm that allows for the scoring of 
    incubation behaviour. Additionally, several functions 
    extract biologically relevant metrics of incubation such as off-bout number 
    and off-bout duration - for a review of avian incubation studies, 
    see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) 
    edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.
Tools for estimating incidence from biomarker data in cross-
    sectional surveys, and for calibrating tests for recent infection. 
    Implements and extends the method of Kassanjee et al. (2012)
    <doi:10.1097/EDE.0b013e3182576c07>.
Genome-wide gene insertion and deletion rates can be modelled in a maximum 
    likelihood framework with the additional flexibility of modelling potential missing 
    data using the models included within. These models simultaneously estimate insertion 
    and deletion (indel) rates of gene families and proportions of "missing" data for 
    (multiple) taxa of interest. The likelihood framework is utilized for parameter 
    estimation. A phylogenetic tree of the taxa and gene presence/absence patterns 
    (with data ordered by the tips of the tree) are required. See Dang et al.
    (2016) <doi:10.1534/genetics.116.191973> for more details.
Performs three ranking-based nonparametric tests for the
                independence of two continuous variables:
                (1) the classical Hoeffding's D test;
                (2) a refined variant of it, named R;
                (3) the Bergsma-Dassios T* sign covariance.
                The first test is consistent assuming an
                absolutely continuous bivariate distribution,
                i.e., the population coefficient D=0
                iff the variables are independent.
                The latter two are consistent
                under no restriction on the distribution.
                All three statistics are computed
                in time O(n log n) given n iid paired samples.
                The computation of R and T* uses a new algorithm,
                following work of Even-Zohar and Leng (2019),
                see <arXiv:2010.09712>, <arXiv:1911.01414>.
Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://mi.mathnet.ru/eng/ppi797>). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.
Derivation of indexes for benchmarking purposes. A methodology with flexible number of constituents is implemented. Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) <doi:10.1016/j.jempfin.2018.08.004>.
We provide an R tool for teaching in Social Sciences. It allows the computation of index numbers. It is a measure of the evolution of a fixed magnitude for only a product of for several products. It is very useful in Social Sciences. Among others, we obtain simple index numbers (in chain or in serie), index numbers for not only a product or weighted index numbers as the Laspeyres index (Laspeyres, 1864), the Paasche index (Paasche, 1874) or the Fisher index (Lapedes, 1978).
Computes bilateral and multilateral index numbers. 
    It has support for many standard bilateral indexes as well as
    multilateral index number methods such as GEKS, GEKS-Tornqvist 
    (or CCDI), Geary-Khamis and the weighted time product dummy
    (for details on these methods see Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>). 
    It also supports updating of multilateral indexes using 
    several splicing methods.
Calculate Indian Income Tax liability for Financial years of Individual resident aged below 60 years,Senior Citizen,Super Senior Citizen, Firm, Local Authority, Any Non Resident Individual / Hindu Undivided Family / Association of Persons /Body of Individuals / Artificial Judicial Person, Co-operative Society.
Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
Performs a unified approach for obtaining and estimating
    unidimensional Item Response Theory (IRT) Dual Models (DMs), proposed by
    Ferrando (2019 <doi:10.1177/0146621618817779>).
It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence,  and functions for generating and analysing  different types of dependence.
Provides functions to download and parse information from INEGI
    (Official Mexican statistics agency). To learn more about the API, see <https://www.inegi.org.mx/servicios/api_indicadores.html>.
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) <doi:10.1400/209575> Zenga M. (2015) <doi:10.1400/246627> Zenga M., Valli I. (2017) <doi:10.26350/999999_000005> Zenga M., Valli I. (2018) <doi:10.26350/999999_000011>.
Provides simple functions to compute and plot two
    types (sample-size- and coverage-based) rarefaction and extrapolation of species
    diversity (Hill numbers) for individual-based (abundance) data or sampling-unit-
    based (incidence) data. (Hsieh, Ma and Chao 2014) <doi: 10.1111/2041-210X.12613>.
This package contains functions to perform calculations of
        the infine-dimensional model (IDM) and to produce 95%
        confidence intervals around the model elements through
        bootstrapping.
The objective of this package is to perform
    inference using an expressive statistical grammar that coheres with
    the tidy design framework.
Collection of functions to extract inferential values
        (point estimates, confidence intervals, p-values, etc) of a
        fitted model object into a matrix-like object that can be used
        for table/report generation; transform point estimates via the
        delta method.
The InferenceSMR package provides functions to make
        inference about the standardized mortality ratio (SMR) when
        evaluating the effect of a screening program. The package is
        based on methods described in Sasieni (2003) and Talbot et al.
        (2011).
Provides methods for estimating causal effects in the presence of interference described in  B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of
    statistical tests provided in 'stats' package by including additional data types as inputs, expanding and
    restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.
Sampler and post-processing functions for semi-parametric Bayesian infinite factor models, motivated by the Multiplicative Gamma Shrinkage Prior of Bhattacharya and Dunson (2011) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419391/>. Contains component C++ functions for building samplers for linear and 2-way interaction factor models using the multiplicative gamma and Dirichlet-Laplace shrinkage priors. The package also contains post processing functions to return matrices that display rotational ambiguity to identifiability through successive application of orthogonalization procedures and resolution of column label and sign switching. This package was developed with the support of the National Institute of Environmental Health Sciences grant 1R01ES028804-01.
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
Contains a number of infix binary operators that may be useful in day to day practices.
Provides access to core inflation functions. Four different core inflation 
 functions are provided. The well known trimmed means, exclusion and double weighing methods, 
 alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.
This program analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. Reference to software development can be found at <doi:10.1101/2020.10.31.363523>.
Implementation of methods Extremum Surface Estimator (ESE) and 
   Extremum Distance Estimator (EDE) to identify the inflection point of a curve .
   Christopoulos, DT (2014) <arXiv:1206.5478v2 [math.NA]> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
Provides a collection of tools for
        detecting influential cases in generalized mixed effects
        models. It analyses models that were estimated using 'lme4'. The
        basic rationale behind identifying influential data is that
        when single units are omitted from the data, models
        based on these data should not produce substantially different
        estimates. To standardize the assessment of how influential a
        (single group of) observation(s) is, several measures of
        influence are common practice, such as Cook's Distance. 
        In addition, we provide a measure of percentage change of the fixed point 
        estimates and a simple procedure to detect changing levels of significance.
A set of tools for evaluating several measures of case influence for structural equation models.
Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.
Provides functionality to compute various node centrality measures on networks.
    Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
    SNAP library), implementations of Burt's constraint and effective
    network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's
    bridging metric. On Unix systems, the betweenness, Key Players, and
    bridging implementations are parallelized with OpenMP, which may run
    faster on systems which have OpenMP configured.
Contains functions for the classification and ranking of top candidate features, reconstruction of networks from
    adjacency matrices and data frames, analysis of the topology of the network 
    and calculation of centrality measures, and identification of the most
    influential nodes. Also, a function is provided for running SIRIR model, which 
    is the combination of leave-one-out cross validation technique and the conventional SIR model, on a network to unsupervisedly rank the true influence of vertices. Additionally, some functions have been provided for the assessment 
    of dependence and correlation of two network centrality measures as well as 
    the conditional probability of deviation from their corresponding means in opposite direction.
    Fred Viole and David Nawrocki (2013, ISBN:1490523995).
    Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695.
    Adopted algorithms and sources are referenced in function document.
An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.
The main purpose of this package is to generate the structure of the analysis of variance 
            (ANOVA) table of the two-phase experiments. The user only need to input the design and the 
              relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, 
              this package can then quickly generate the structure of the ANOVA table with the 
              coefficients of the variance components for the expected mean squares.
              Thus, the balanced incomplete block design and provides the efficiency
              factors of the fixed effects can also be studied and compared much easily.
Performs exploratory data analysis and variable screening for
    binary classification models using weight-of-evidence (WOE) and information
    value (IV). In order to make the package as efficient as possible, aggregations
    are done in data.table and creation of WOE vectors can be distributed across
    multiple cores. The package also supports exploration for uplift models (NWOE
    and NIV).
Provides companion function for analysing the performance of
    classification models. Also, provides function to optimise probability cut-
    off score based on used specified objectives, Plot 'ROC' Curve in 'ggplot2',
    'AUROC', 'IV', 'WOE' Calculation, 'KS Statistic' etc to aid accuracy improvement
    in binary classification models.
Multiple Imputation for Informative Censoring.
    This package implements two methods. Gamma Imputation
    described in <DOI:10.1002/sim.6274> and Risk Score Imputation
    described in <DOI:10.1002/sim.3480>.
Aids in creating sequence statistics for Butts's 'relevent' software.
This package implements various measures of information theory based on several entropy estimators.
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
Takes a data frame containing latitude and longitude coordinates and downloads images from map servers to determine their file size as a proxy of infrastructure
Facilitates the generation of input files for infraFDTD and processes snapshot output. infraFDTD is a finite-difference model written by Keehoon Kim for simulating infrasound that considers topography and a 1-D atmosphere (see Kim et al., 2015 <doi:10.1002/2015GL064466>).
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated (Rousset et al. 2017 <doi:10.1111/1755-0998.12627>).
Calculation of information utility (i.e., Lindley
        information) quantities for item response models.
Collection of tools for assessment of feature importance and feature effects.
    Key functions are:
    feature_importance() for assessment of global level feature importance,
    ceteris_paribus() for calculation of the what-if plots,
    partial_dependence() for partial dependence plots,
    conditional_dependence() for conditional dependence plots,
    accumulated_dependence() for accumulated local effects plots,
    aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,
    generic print() and plot() for better usability of selected explainers,
    generic plotD3() for interactive, D3 based explanations, and
    generic describe() for explanations in natural language.
    The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Parse simple '.ini' configuration files to an structured list. Users
  can manipulate this resulting list with lapply() functions. This same
  structured list can be used to write back to file after modifications.
R dependency injection framework. Dependency injection allows
    a program design to follow the dependency inversion principle. The user
    delegates to external code (the injector) the responsibility of providing its
    dependencies. This separates the responsibilities of use and construction.
Calculate the injury severity score (ISS)
    based on the dictionary in 'ICDPIC' from <https://ideas.repec.org/c/boc/bocode/s457028.html>. The original code was written in 
    'STATA 11'. The original 'STATA' code was written by David Clark, 
    Turner Osler and David Hahn. I implement the same logic for easier access.
    Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. 
    "ICDPIC: Stata module to provide methods for translating International 
    Classification of Diseases (Ninth Revision) diagnosis codes into standard injury 
    categories and/or scores," Statistical Software Components S457028, 
    Boston College Department of Economics, revised 29 Oct 2010.
Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <http://www.r-inla.org>.
Facilitates spatial modeling using integrated nested Laplace approximation via the 
  INLA package (<http://www.r-inla.org>). Additionally, implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data. See Yuan Yuan, 
  Fabian E. Bachl, Finn Lindgren, David L. Borchers, Janine B. Illian, Stephen T. Buckland, Havard Rue, 
  Tim Gerrodette (2017), <arXiv:1604.06013>.
Implementation of several multivariate areal latent effects for
    'INLA' using the 'rgeneric' latent effect (Palmí-Perales et al., 2019,
    <arXiv:1909.10804>). The 'INLA' package can be downloaded from 
    <https://www.r-inla.org>. In particular, the package includes latent 
    effects ready to use for several multivariate spatial models:
    intrinsic CAR, proper CAR and the M-model (Botella-Rocamora
    et al., 2015, <doi:10.1002/sim.6423>).
Contains data for the U.S. Geological Survey-Idaho National
   Laboratory (USGS-INL) aquifer monitoring networks administrated by the
   Idaho National Laboratory Project Office in cooperation with the U.S.
   Department of Energy.
Functionality to dynamically define R functions and S4 methods
 with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling
 conventions.
Generates Rd files from R source code with comments.
 The main features of the default syntax are that
 (1) docs are defined in comments near the relevant code,
 (2) function argument names are not repeated in comments, and
 (3) examples are defined in R code, not comments.
 It is also easy to define a new syntax.
A collection of functions for creating high-level graphics,
    performing raster-based analysis, processing MODFLOW-based models,
    selecting subsets using a genetic algorithm, creating interactive web maps,
    accessing color palettes, etc. Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory (INL) Project Office.
Infix operators to detect, subset, and replace the elements matched by a given condition.
  The functions have several variants of operator types, including subsets, ranges, regular expressions and others.
  Implemented operators work on vectors, matrices, and lists.
A set of functions to analyse and compare texts, using classical 
  text mining	functions, as well as those from theoretical ecology.
It provides in-place operators for R 
    that are equivalent to '+=', '-=', '*=', '/=' in C++. 
    Those can be applied on integer|double vectors|matrices.
    You have also access to sweep operations (in-place).
Non-parametric resampling-based inference tests for ExPosition.
Collection of functions for quality control (QC) of climatological daily time series (e.g. the ECA&D station data).
A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) 
    assays in pancreatic beta cells or islets.
    The package allows the user to import several sets of experiments from different spreadsheets 
    and to perform subsequent steps: summarise in a tidy format, visualise data quality 
    and compare experimental conditions without omitting to account for technical confounders 
    such as the date of the experiment or the technician.
    Together, insane is a comprehensive method that optimises pre-processing and analyses of 
    GSIS experiments in a friendly-user interface.
    The Shiny App was initially designed for EndoC-betaH1 cell line following method described 
    in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).
Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.
Using embedded sdmx queries, get the data of more than 150 000 insee series from bdm database. Have a look at the detailed sdmx web service page with the following link : <https://www.insee.fr/en/information/2868055>.
A tool to provide an easy, intuitive and consistent
    access to information contained in various R models, like model
    formulas, model terms, information about random effects, data that was
    used to fit the model or data from response variables. 'insight'
    mainly revolves around two types of functions: Functions that find
    (the names of) information, starting with 'find_', and functions that
    get the underlying data, starting with 'get_'.  The package has a
    consistent syntax and works with many different model objects, where
    otherwise functions to access these information are missing.
Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA4' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Functions to compute insolation on complex terrain.
Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.
A collection of utilities for columnwise summary, comparison and visualisation of data frames.  Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage.
Utility functions that implement and automate common sets of validation tasks. 
    These functions are particularly useful to validate inputs, intermediate objects and output 
    values in user-defined functions, resulting in tidier and less verbose functions.
A method to infer modules of co-expressed genes and the
    dependencies among the modules from multiple expression datasets that may
    contain different sets of genes. Please refer to: Extracting a low-dimensional
    description of multiple gene expression datasets reveals a potential driver for
    tumor-associated stroma in ovarian cancer, Safiye Celik, Benjamin A. Logsdon,
    Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins and Su-In
    Lee (2016) <DOI:10.1186/s13073-016-0319-7>.
The function `install_load` checks the local R library(ies) to see
    if the required package(s) is/are installed or not. If the package(s)
    is/are not installed, then the package(s) will be installed along with
    the required dependency(ies). This function pulls source or
    binary packages from the Rstudio-sponsored CRAN mirror and/or
    the USGS GRAN Repository. Lastly, the chosen package(s)
    is/are loaded. The function `load_package` simply loads the provided
    packages. If this package does not fit your needs, then you may want to
    consider these other R packages: 'needs', 'easypackages', 'pacman', 'pak',
    'anyLib', and/or 'librarian'.
R is great for installing software.  Through the 'installr'
    package you can automate the updating of R (on Windows, using updateR())
    and install new software. Software installation is initiated through a
    GUI (just run installr()), or through functions such as: install.Rtools(),
    install.pandoc(), install.git(), and many more. The updateR() command
    performs the following: finding the latest R version, downloading it,
    running the installer, deleting the installation file, copy and updating
    old packages to the new R installation.
Provides an interface to the Instagram API <https://instagram.com/
    developer/>, which allows R users to download public pictures filtered by
    hashtag, popularity, user or location, and to access public users' profile data.
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
Methods for insurance rating. It helps actuaries to implement GLMs within all relevant steps needed to construct 
    a risk premium from raw data. It provides a data driven strategy for the construction of insurance tariff classes. 
    This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. It also provides recipes 
    on how to easily perform one-way, or univariate, analyses on an insurance portfolio. In addition it adds functionality 
    to include reference categories in the levels of the coefficients in the output of a generalized linear regression analysis.
Provides classes and methods for automated
    spatial interpolation.
Includes additional functionality for spatial interpolation in the intamap package, such as bias correction and network optimization.
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves.
Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) <doi:10.1002/sim.7350>. In addition, missing event types or left truncation are considered. We provide the proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
The kernel of this 'Rcpp' based package is an efficient implementation of the generalized gradient projection method for spline function based constrained maximum likelihood estimator for interval censored survival data (Wu, Yuan; Zhang, Ying. Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 <doi:10.1214/12-AOS1016>). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC).
Several integrative data methods in which information of objects from different data sources can be combined are included in the IntClust package. As a single data source is limited in its point of view, this provides more insight and the opportunity to investigate how the variables are interconnected. Clustering techniques are to be applied to the combined information. For now, only agglomerative hierarchical clustering is implemented. Further, differential gene expression and pathway analysis can be conducted on the clusters. Plotting functions are available to visualize and compare results of the different methods.
Provides intdag() for a constrained maximum likelihood estimate of a directed acyclic graph with intervention data. Also available is obsdag() for an estimate with observation data only, based on the method in the paper by Yuan, Shen and Pan (2018) <doi:10.1093/biomet/asy057>.
Provides a systematic framework for
 integrating multiple modalities of assays profiled on the same set of
 samples. The goal is to identify genes that are altered in cancer
 either marginally or consistently across different assays. The
 heterogeneity among different platforms and different samples are
 automatically adjusted so that the overall alteration magnitude can
 be accurately inferred. See Tong and Coombes (2012) 
 <doi:10.1093/bioinformatics/bts561>.
Generates a 'Graphviz' graph of the most significant 3-way 
    interaction gains (i.e. conditional information gains) based on a provided 
    discrete data frame. Various output formats are supported ('Graphviz', SVG, 
    PNG, PDF, PS). For references, see the webpage of Aleks Jakulin 
    <http://stat.columbia.edu/~jakulin/Int/>.
Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.
Electricity is not made equal and it vary in its carbon footprint (or carbon intensity) 
    depending on its source. This package enables to access and query data provided by the 
    Carbon Intensity API (<https://carbonintensity.org.uk/>). National Grid’s Carbon Intensity API 
    provides an indicative trend of regional carbon intensity of the electricity system in Great Britain.
Calculate metrics of change intensity for category, transition and interval levels in categorical maps from sequential intervals. For more information please consult: Aldwaik,Safaa Zakaria and Robert Gilmore Pontius Jr. (2012). "Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition". Landscape and Urban Planning. 106, 103-114. <doi:10.1016/j.landurbplan.2012.02.010>.
Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework.
This package searches for marginal interactions in a
        binary response model. Interact uses permutation methods to
        estimate false discovery rates for these marginal interactions
        and has some, limited visualization capabilities
Produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>].
    It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).
A suite of functions for conducting and interpreting analysis 
  of statistical interaction in regression models that was formerly part of the 
  'jtools' package. Functionality includes visualization of two- and three-way
  interactions among continuous and/or categorical variables as well as 
  calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., 
  Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These
  capabilities are implemented for generalized linear models in addition to the 
  standard linear regression context.
Implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.
An extension of the package 'igraph'. This package create
        possibly to work with 'igraph' objects interactively.
Chi-square tests are computed with corrections.
Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.
Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.
Computes large sample confidence intervals of Liu and Hudgens (2014), exact confidence intervals of Tchetgen Tchetgen and VanderWeele (2012), and exact confidence intervals of Rigdon and Hudgens (2014) for treatment effects on a binary outcome in two-stage randomized experiments with interference.
Computes interference color tables and plots customized Michel-Levy or Raith-Sorensen charts. Automatic interpretation of polarized-light microscopy images is still under development and will come soon.
Functions implemented in this package allow to coerce (i.e.
	convert) network data between classes provided by other R packages.
	Currently supported classes are those defined in packages: network and
	igraph.
Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.
Allows an interactive assessment of the timing of interim analyses. The algorithm simulates both the recruitment and treatment/event phase of a clinical trial based on the package 'interim'.
Converts matrices and lists of matrices into a single vector by interleaving 
    their values. That is, each element of the result vector is filled from the input 
    matrices one row at a time. This is the same as transposing a matrix, then removing the 
    dimension attribute, but is designed to operate on matrices in nested list structures.
Interlinearized glossed texts (IGT) are used in descriptive linguistics for
 representing a morphological analysis of a text through a morpheme-by-morpheme gloss.
 'InterlineaR' provide a set of functions that targets several popular formats of IGT
 ('SIL Toolbox', 'EMELD XML') and that turns an IGT into a set of data frames following
 a relational model (the tables represent the different linguistic units: texts,
 sentences, word, morphems).
 The same pieces of software ('SIL FLEX', 'SIL Toolbox') typically produce dictionaries
 of the morphemes used in the glosses. 'InterlineaR' provide a function for turning
 the LIFT XML dictionary format into a set of data frames following a relational model
 in order to represent the dictionary entries, the sense(s) attached to the entries,
 the example(s) attached to senses, etc.
Bivariate data interpolation on regular and irregular
  grids, either linear or using splines are the main part of this
  package.  It is intended to provide FOSS replacement functions for
  the ACM licensed akima::interp and tripack::tri.mesh functions.
  Currently the piecewise linear interpolation part of akima::interp
  (and also akima::interpp) is implemented in interp::interp, this
  corresponds to the call akima::interp(..., linear=TRUE) which is the
  default setting and covers most of akima::interp use cases in
  depending packages.  A re-implementation of Akimas spline
  interpolation (akima::interp(..., linear=FALSE)) is currently under
  development and will complete this package in a later
  version. Estimators for partial derivatives are already available,
  these are a prerequisite for the spline interpolation.  The basic
  part is currently a GPLed triangulation algorithm (sweep hull
  algorithm by David Sinclair) providing the starting point for the
  piecewise linear interpolator. As side effect this algorithm is also
  used to provide replacements for the basic functions of the tripack
  package which also suffer from the ACM restrictions.  All functions
  are designed to be backward compatible with their akima / tripack
  counterparts.