A set of analytical tools useful in analysing ecological and geographical data sets, both ancient and modern. The package includes functions for estimating species richness (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta diversity, species area curves and geographic distances and areas.
Functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database.
Simulating taxonomy and fossil data on phylogenetic trees under mechanistic
    models of speciation, preservation and sampling.
A shiny application based on 'FossilSim'. Used for simulating
    tree, taxonomic and fossil data under mechanistic models of speciation,
    preservation and sampling.
A tool to use a principal component analysis on radially averaged
  two dimensional Fourier spectra to characterize image texture. The method
  within the context of ecology was first described by Couteron et al. (2005)
  <doi:10.1111/j.1365-2664.2005.01097.x> and expanded upon by
  Solorzano et al. (2018) <doi:10.1117/1.JRS.12.036006>
  using a moving window approach.
Interface to 'Palantir Foundry', including
    reading and writing structured or unstructured datasets, and more
    <https://www.palantir.com/platforms/foundry/>.
The four-gamete test is based on the infinite-sites model which assumes that the probability of the same mutation occurring twice (recurrent or parallel mutations) and the probability of a mutation back to the original state (reverse mutations) are close to zero. Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility.
Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation.
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> 
    four parameter IRT model with lower and upper asymptotes using Bayesian
    formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.
Implements parsimonious hidden Markov models for four-way data via expectation-
    conditional maximization algorithm, as described in Tomarchio et al. (2020) <arXiv:2107.04330>.
    The matrix-variate normal distribution is used as emission distribution. For each hidden
    state, parsimony is reached via the eigen-decomposition of the covariance matrices of the
    emission distribution. This produces a family of 98 parsimonious hidden Markov models.
Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.
Computes the power and sample size (PASS) required to test for the
    difference in the mean function between two groups under a repeatedly measured longitudinal 
    or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> 
    for details of the PASS formula and computational details. The details of the testing
    procedure for univariate and multivariate response are presented in
    Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) 
    <arXiv:2302.05612> respectively.
Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Standardisation of cluster validation statistics by random clusterings and 
  comparison between many clustering methods and numbers of clusters based on
  this.  
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.
Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.
Functions to represent functional objects under a Reproducing Kernel Hilbert Space (RKHS) framework as described 
  in Muñoz & González (2010). Autoregressive Hilbertian Model for functional time series using RKHS and predictive confidence bands construction 
  as proposed in Hernández et al (2021).
Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    'The R Inferno' <https://www.burns-stat.com/pages/Tutor/R_inferno.pdf>).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster 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 factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional data sets.
Tools to help text files importation. It can return 
 the number of lines; print the first and last lines; convert 
 encoding. Operations are made without reading the entire file 
 before starting, resulting in good performances with large files. 
 This package provides an alternative to a simple use of the 
 'head', 'tail', 'wc' and 'iconv' programs that are not always 
 available on machine where R is installed.
Given the values of sampled units and selection probabilities 
             the desraj function  in the package computes the estimated value of the total
             as well as estimated variance.
This data contains a large variety of information on players and their
  current attributes on Fantasy Premier League
  <https://fantasy.premierleague.com/>. In particular, it contains a
  `next_gw_points` (next gameweek points) value for each player
  given their attributes in the current week. Rows represent player-gameweeks,
  i.e. for each player there is a row for each gameweek. This
  makes the data suitable for modelling a player's next gameweek points, given
  attributes such as form, total points, and cost at the current gameweek.
  This data can therefore be used to create Fantasy Premier League bots that
  may use a machine learning algorithm and a linear programming solver
  (for example) to return the best possible transfers and team to pick for
  each gameweek, thereby fully automating the decision making process in
  Fantasy Premier League. This function simply supplies the required data
  for such a task.
Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and box plots. See <https://lrberge.github.io/fplot/> for more information.
Read and process a large delimited file block by
    block. A block consists of all the contiguous rows that have the same value
    in the first field. The result can be returned as a list or a data.table,
    or even directly printed to an output file.
A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles.
  The 'fpop'  package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017
  <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated 
  in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error 
  for a penalty linear in the number of changepoints.
Weighted-L2 FPOP Maidstone et al. (2017) <doi:10.1007/s11222-016-9636-3> and pDPA/FPSN Rigaill (2010) <arXiv:1004.0887> algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) <doi:10.1016/j.sigpro.2004.11.012> and the 'capsushe' package.
A collection of functions to optimize portfolios and to analyze them from different points of view.
Returns the noncentrality parameter of the noncentral F
        distribution if probability of type I and type II error,
        degrees of freedom of the numerator and the denominator are
        given.  It may be useful for computing minimal detectable
        differences for general ANOVA models.  This program is
        documented in the paper of A. Baharev, S. Kemeny, On the
        computation of the noncentral F and noncentral beta
        distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
        the book "Forecasting: principles and practice" by Rob J
        Hyndman and George Athanasopoulos. All packages required to run
        the examples are also loaded.
All data sets required for the examples and exercises
  in the book "Forecasting: principles and practice" (2nd ed, 2018)
  by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>.
  All packages required to run the examples are also loaded.
All data sets required for the examples and exercises in the book
    "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos
    <https://OTexts.com/fpp3/>.  All packages required to run the examples are also
    loaded.
Efficient approximation of first passage time densities for diffusion processes based on the First Passage Time Location (FPTL) function.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, see Filzmoser and Viertl (2004) <doi:10.1007/s001840300269>, (2) testing fuzzy hypotheses based on crisp data, see Parchami et al. (2010) <doi:10.1007/s00362-008-0133-4>, and (3) testing fuzzy hypotheses based on fuzzy data, see Parchami et al. (2012) <doi:10.1007/s00362-010-0353-2>. In all cases, the fuzziness of data or / and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
A collection of functions for calculating Floristic Quality
    Assessment (FQA) metrics using regional FQA databases that have been
    approved or approved with reservations as ecological planning models
    by the U.S. Army Corps of Engineers (USACE). For information on FQA
    see Spyreas (2019) <doi:10.1002/ecs2.2825>. These databases are stored
    in a sister R package, 'fqadata'. Both packages were developed for the
    USACE by the U.S. Army Engineer Research and Development Center’s
    Environmental Laboratory.
Contains regional Floristic Quality Assessment databases that
    have been approved or approved with reservations by the U.S. Army
    Corps of Engineers (USACE). Paired with the 'fqacalc' R package, these
    data sets allow for Floristic Quality Assessment metrics to be
    calculated. For information on FQA see Spyreas (2019)
    <doi:10.1002/ecs2.2825>. Both packages were developed for the USACE by
    the U.S.  Army Engineer Research and Development Center's
    Environmental Laboratory.
A "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.
Methods to "add" two tables; also an alternative
     interpretation of named vectors as generalized tables, so that
     c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3).  Uses
     'disordR' discipline (Hankin, 2022, <arxiv:2210.03856>).
     Extraction and replacement methods are provided.  The underlying
     mathematical structure is the Free Abelian group, hence the name.
     To cite in publications please use Hankin (2023)
     <arxiv:2307:13184>.
Maximum likelihood estimation of the parameters of a fractionally
   differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989);
   including inference and basic methods.  Some alternative algorithms to estimate "H".
Calculate numerical asymptotic distribution functions of likelihood ratio 
    statistics for fractional unit root tests and tests of cointegration rank. 
    For these distributions, the included functions calculate critical values 
    and P-values used in unit root tests, cointegration tests, and rank tests 
    in the Fractionally Cointegrated Vector Autoregression (FCVAR) model.
    The functions implement procedures for tests described in the following articles:
    Johansen, S. and M. Ø. Nielsen (2012) <doi:10.3982/ECTA9299>,
    MacKinnon, J. G. and M. Ø. Nielsen (2014) <doi:10.1002/jae.2295>.
Aim is to provide fractional Brownian vector field generation algorithm, Hurst parameter estimation method and fractional kriging model for multivariate data modeling.
Functions for fitting data to a quiescent growth model,
        i.e. a growth process that involves members of the population
        who stop dividing or propagating.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data <doi:10.1214/11-STS370>.
The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) <doi:10.1002/sim.6497>.
Various functions for performing fractal and multifractal analysis including performing fractal regression. Please refer to Peng and colleagues (1994) <doi:10.1103/physreve.49.1685>, Kantelhardt and colleagues (2002)<doi:10.1016/S0378-4371(02)01383-3>, and Likens and colleagues (2019) <doi:10.1016/j.physa.2019.121580>.
Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) <doi:10.1007/BF02065874>.
Turn numeric,data.frame,matrix into fraction form.
The main function of this package allows numerical vector objects to
  be displayed with their values in vulgar fractional form.  This is convenient if
  patterns can then be more easily detected.  In some cases replacing the components
  of a numeric vector by a rational approximation can also be expected to remove
  some component of round-off error.  The main functions form a re-implementation
  of the functions 'fractions' and 'rational' of the MASS package, but using a
  radically improved programming strategy.
Provides functions for converting decimals to a
    matrix of numerators and denominators or a character vector of
    fractions.  Supports mixed or improper fractions, finding common
    denominators for vectors of fractions, limiting denominators to powers
    of ten, and limiting denominators to a maximum value.  Also includes
    helper functions for finding the least common multiple and greatest
    common divisor for a vector of integers.  Implemented using C++ for
    maximum speed.
A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 <doi:10.1016/j.jclinepi.2013.10.019>; Lin, 2021 <doi:10.1111/jep.13428>), conventional pairwise meta-analyses (Atal et al., 2019 <doi:10.1016/j.jclinepi.2019.03.012>), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 <doi:10.1016/j.jclinepi.2020.07.003>). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is detailed in Lin and Chu (2022 <doi:10.1371/journal.pone.0268754>).
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).
Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.
Implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.
The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers,
    Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>,
    Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.
Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.
Tools to support sensible statistics for functional response analysis.
Provides a friendly interface for modifying data frames with a sequence of piped commands built upon the 'tidyverse' Wickham et al., (2019) <doi:10.21105/joss.01686> . The majority of commands wrap 'dplyr' mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via 'tidyselect'.
Point and interval estimation in dual frame surveys. In contrast
    to classic sampling theory, where only one sampling frame is considered,
    dual frame methodology assumes that there are two frames available for
    sampling and that, overall, they cover the entire target population. Then,
    two probability samples (one from each frame) are drawn and information
    collected is suitably combined to get estimators of the parameter of
    interest.
With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.
Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.
Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.
Generating fractional binomial random variables and computing density, cumulative distribution, and quantiles of fractional binomial distributions. (Lee, J. (2023) <arXiv:2209.01516>.)
An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. 
    FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
    representing the reasoning of human experts in a set of IF-THEN rules, to
    handle real-life problems in, e.g., control, prediction and inference, data
    mining, bioinformatics data processing, and robotics. FRBSs are also known
    as fuzzy inference systems and fuzzy models. During the modeling of an
    FRBS, there are two important steps that need to be conducted: structure
    identification and parameter estimation. Nowadays, there exists a wide
    variety of algorithms to generate fuzzy IF-THEN rules automatically from
    numerical data, covering both steps. Approaches that have been used in the
    past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
    methods, genetic algorithms, squares methods, etc. Furthermore, in this
    version we provide a universal framework named 'frbsPMML', which is adopted
    from the Predictive Model Markup Language (PMML), for representing FRBS
    models. PMML is an XML-based language to provide a standard for describing
    models produced by data mining and machine learning algorithms. Therefore,
    we are allowed to export and import an FRBS model to/from 'frbsPMML'.
    Finally, this package aims to implement the most widely used standard
    procedures, thus offering a standard package for FRBS modeling to the R
    community.
Contains the core functions associated with Fast
        Regularized Canonical Correlation Analysis. Please see the following for
        details: Raul Cruz-Cano, Mei-Ling Ting Lee, 
        Fast regularized canonical correlation analysis, 
        Computational Statistics & Data Analysis, 
        Volume 70, 2014, Pages 88-100, 
        ISSN 0167-9473 <doi:10.1016/j.csda.2013.09.020>.
Provides implementation of statistical methods for random objects 
    lying in various metric spaces, which are not necessarily linear spaces. 
    The core of this package is Fréchet regression for random objects with 
    Euclidean predictors, which allows one to perform regression analysis 
    for non-Euclidean responses under some mild conditions. 
    Examples include distributions in 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'.
Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) <arXiv:2110.11074>.
The free algebra in R with non-commuting indeterminates.
     Uses 'disordR' discipline
     (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>).  To cite the
     package in publications please use Hankin (2022)
     <doi:10.48550/ARXIV.2211.04002>.
Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) 
    <doi:10.1093/biomet/asaa016>. This package relies on the optimization software 'MOSEK', 
    <https://www.mosek.com>.
An R client for the 'freecurrencyapi.com' currency conversion API. The API requires registration of an API key. You can find the full API documentation at <https://freecurrencyapi.com/docs> .
Implements the formulae required to calculate freedom
    from disease according to Cameron and Baldock (1998)
    <doi:10.1016/S0167-5877(97)00081-0>. These are the
    methods used at the Swedish national veterinary institute (SVA) to
    evaluate the performance of our nation animal disease
    surveillance programmes.
The free group in R; juxtaposition is represented by a
  plus.  Includes inversion, multiplication by a scalar,
  group-theoretic power operation, and Tietze forms.  To cite the
  package in publications please use Hankin (2022)
  <doi:10.48550/ARXIV.2212.05883>.
Provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
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.
A collection of functions for linear and non-linear regression 
  modelling. It implements a wrapper for several regression models available 
  in the base and contributed packages of R.
Functions for finding smooth interpolating curves connecting
    a series of points in the plane.  Curves may be open or closed,
    that is, with the first and last point of the curve at the initial point.
Download data sets from Kenneth's French finance data library site <http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>, reads all the data subsets from the file. Allows R users to collect the data as
    'tidyverse'-ready data frames.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
Generates a frequency distribution. The frequency
    distribution includes raw frequencies, percentages in each category, and
    cumulative frequencies. The frequency distribution can be stored as a data
    frame.
Input has to be in the form of vectors of lower class limits and upper class limits and frequencies; the output will give a cumulative frequency distribution table with cumulative frequency plot.
Implementation of dynamic principal component
    analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. 
    These frequency domain methods for dimensionality reduction of multivariate time series
    were introduced by David Brillinger in his book Time Series (1974). We follow implementation
    guidelines as described in Hormann, Kidzinski and Hallin (2016),
    Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
Implementations of functional dynamic principle components analysis. Related graphic tools and frequency domain methods.
  These methods directly use multivariate dynamic principal components implementation,
  following the guidelines from Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
New approaches to parallel coordinates plots for
   multivariate data visualization, including applications to clustering,
   outlier hunting and regression diagnostics.  Includes general functions
   for multivariate nonparametric density and regression estimation, 
   using parallel computation.
Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.
Tools for generating an informative type of line graph, the frequency profile, 
    which allows single behaviors, multiple behaviors, or the specific behavioral patterns 
    of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
Quickly make tables of descriptive statistics (i.e., counts, 
    percentages, confidence intervals) for categorical variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to Microsoft Word ® with minimal pain.
Generate 'SPSS'/'SAS' styled frequency tables. Frequency tables are 
    generated with variable and value label attributes where applicable with optional
    html output to quickly examine datasets.
Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
Implement frequent-directions algorithm for efficient matrix sketching. 
  (Edo Liberty (2013) <doi:10.1145/2487575.2487623>).
A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the median survival time) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. Though The survival endpoint is often encountered in phase II trials, the existing SSD methods cannot deal with the survival endpoint. Furthermore, the existing SSD won’t control the type I error rate.  The proposed designs can “partially” control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.
Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
Customize 'Bootstrap' and 'Bootswatch' themes, like colors, fonts, grid layout, 
  to use in 'Shiny' applications, 'rmarkdown' documents and 'flexdashboard'.
Procedure for solving the maximin problem for identical design across heterogeneous data groups. Particularly efficient when the design matrix is either orthogonal or has tensor structure. Orthogonal wavelets can be specified for 1d, 2d or 3d data simply by name. For tensor structured design the tensor components (two or three) may be supplied. The package also provides an efficient implementation of the generic magging estimator.
A simple way to unload none-base packages and remove all global
    variables.
Regular and non-regular Fractional Factorial 2-level designs 
        can be created. Furthermore, analysis tools for Fractional
        Factorial designs with 2-level factors are offered (main
        effects and interaction plots for all factors simultaneously,
        cube plot for looking at the simultaneous effects of three
        factors, full or half normal plot, alias structure in a more
        readable format than with the built-in function alias).
Catalogues of resolution IV regular
        fractional factorial designs in 128 runs are provided 
        for up to 33 2-level factors. The catalogues are complete, 
        excluding resolution IV designs without 5-letter words, 
        because these do not add value for a search for unblocked 
        clear designs. The previous package version
        1.0 with complete catalogues up to 24 runs (24 runs and a
        namespace added later) can be downloaded from the authors
        website.
Computes relative importance of main and interaction effects. Also, sum of the modified generalized weights is computed. Ibrahim et al. (2022) <doi:10.1134/S1064229322080051>.
Read and write Frictionless Data Packages. A 'Data Package'
    (<https://specs.frictionlessdata.io/data-package/>) is a simple container
    format and standard to describe and package a collection of (tabular) data. 
    It is typically used to publish FAIR 
    (<https://www.go-fair.org/fair-principles/>) and open datasets.
The complete scripts from the American sitcom Friends in tibble 
    format. Use this package to practice data wrangling, text analysis and 
    network analysis.
Miscellaneous utilities, tools and helper
    functions for finding and searching files on disk, searching for and
    removing R objects from the workspace.
    Does not import or depend on any third party package, but on core R 
    only (i.e. it may depend on packages with priority 'base').
A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework.  Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale et al. <arXiv:2110.02507> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.
When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz/regression/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.
Fast, numerically robust computation of weighted moments via 'Rcpp'. 
   Supports computation on vectors and matrices, and Monoidal append of moments. 
   Moments and cumulants over running fixed length windows can be computed, 
   as well as over time-based windows.
   Moment computations are via a generalization of Welford's method, as described
   by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.
Maximum Likelihood Estimation of
   Stochastic Frontier Production and Cost Functions.
   Two specifications are available:
   the error components specification with time-varying efficiencies
   (Battese and Coelli, 1992, <doi:10.1007/BF00158774>)
   and a model specification in which the firm effects are directly 
   influenced by a number of variables (Battese and Coelli, 1995,
   <doi:10.1007/BF01205442>).
It calculates the alpha-quantile proposed by Daouia and Simar (2007) <doi:10.1016/j.jeconom.2006.07.002> and order-m efficiency score in multi-dimension proposed by Daouia and Gijbels (2011) <doi:10.1016/j.jeconom.2010.12.002> and computes several summaries and representation of the associated frontiers in 2d and 3d.
An R API to MET Norway's 'Frost' API <https://frost.met.no/index.html> 
    to retrieve data as data frames. The 'Frost' API, and the underlying data, is 
    made available by the Norwegian Meteorological Institute (MET Norway). The data
    and products are distributed under the 
    Norwegian License for Open Data 2.0 (NLOD) <https://data.norge.no/nlod/en/2.0>
    and Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.
Emulates a 'Forth' programming environment with added features to interface between R and 'Forth'. Implements most of the functionality described in the original "Starting Forth" textbook <https://www.forth.com/starting-forth/>.
Functions for automatically performing 
             a reanalysis series
             on a data set using CNA, and for calculating the fit-robustness
             of the resulting models, as described in 
             Parkkinen and Baumgartner (2021) <doi:10.1177/0049124120986200>.
Climate is a critical component limiting growing range of plant species, which
    also determines cultivar adaptation to a region. The evaluation of climate influence on
    fruit production is critical for decision-making in the design stage of orchards and 
    vineyards and in the evaluation of the potential consequences of future climate. Bio-
    climatic indices and plant phenology are commonly used to describe the suitability of 
    climate for growing quality fruit and to provide temporal and spatial information about 
    regarding ongoing and future changes. 'fruclimadapt' streamlines the assessment of 
    climate adaptation and the identification of potential risks for grapevines and fruit 
    trees. Procedures in the package allow to i) downscale daily meteorological variables
    to hourly values (Forster et al (2016) <doi:10.5194/gmd-9-2315-2016>),
    ii) estimate chilling and forcing heat accumulation (Miranda et al (2019)
    <https://ec.europa.eu/eip/agriculture/sites/default/files/fg30_mp5_phenology_critical_temperatures.pdf>),
    iii) estimate plant phenology (Schwartz (2012) <doi:10.1007/978-94-007-6925-0>), iv) 
    calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr 
    et al (2017) <doi:10.3354/cr01532>), v) estimate the incidence of weather-related disorders 
    in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi)
    estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)).
A cross-platform interface to file system operations, built
    on top of the 'libuv' C library.
A variety of simple fish stock assessment methods.
The datasets to support the Fish Stock Assessment ('FSA') package.
A flexible and interfaced framework for importing, processing and ploting Applied Biosystems data files. Application to Reverse-Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA) gene-expression profiling and classification is illustrated in Mareschal, Ruminy et al (2015) <doi:10.1016/j.jmoldx.2015.01.007>. Gene-fusion detection and Sanger sequencing are illustrated in Mareschal, Palau et al (2021) <doi:10.1182/bloodadvances.2020002517>. Examples are provided for genotyping applications as well.
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 comprehensive and computationally efficient
    software package for robust statistics in regression, multivariate
    and categorical data analysis. The current R version implements tools
    for regression: (forward search, S- and MM-estimation, least trimmed
    squares (LTS) and least median of squares (LMS)), for multivariate analysis
    (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression.
    The distinctive feature of our package is the possibility of
    monitoring the statistics of interest as a function of breakdown point,
    efficiency or subset size, depending on the estimator. This is
    accompanied by a rich set of graphical features, such as dynamic
    brushing, linking, particularly useful for exploratory data analysis.
Functions for selecting attributes from a given
    dataset. Attribute subset selection is the process of identifying and
    removing as much of the irrelevant and redundant information as
    possible.
'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection 
 algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
 In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf>
 with a sparse matrix support.
Routines for estimation or simultaneous estimation and variable selection of
  several functional semiparametric models with scalar response, such as the functional single-index model, 
  the semi-functional partial linear model or the semi-functional partial linear single-index model. 
  In addition, it includes algorithms for dealing with scalar covariates with linear effect coming from the discretization
  of a curve in the cases of the linear model, the multi-functional partial linear model
  and the multi-functional partial linear single-index model.
Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from <http://sourceforge.net/projects/formscanner/>. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects.
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/fskx-food-safety-knowledge-exchange-format/>
    using the 'R' programming environment. Furthermore, it enables users
    to run simulations contained in the 'FSK' files and visualize the results.
Wrapper functions that interface with 'FSL' 
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with 'FSL'
    completely in R, where you pass R objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' if desired.
Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.
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>.
Support for fuzzy spatial objects, their operations, and fuzzy spatial inference models based on Spatial Plateau Algebra. 
    It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. 
    It mainly implements underlying concepts defined in the following research papers: 
    (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" <doi:10.1109/FUZZ-IEEE.2018.8491565>; 
    (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" <doi:10.1109/FUZZ-IEEE.2019.8858878>; 
    (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" <doi:10.1109/FUZZ48607.2020.9177620>;
    (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" <doi:10.1109/FUZZ-IEEE.2017.8015707>;
    (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" <doi:10.1109/FUZZ-IEEE55066.2022.9882658>.
Social Relations Analysis with roles ("Family SRM") are computed,
    using a structural equation modeling approach. Groups ranging from three members
    up to an unlimited number of members are supported and the mean structure can
    be computed. Means and variances can be compared between different groups of
    families and between roles.
An optimizer of Fused-Sparse Structural Equation Models, which is 
 the state of the art jointly fused sparse maximum likelihood function 
 for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 
 <doi:10.1101/466623>).
Provides three methods proposed by Shang and Apley (2019) <doi:10.1080/00224065.2019.1705207> to generate fully-sequential space-filling designs inside a unit hypercube. A 'fully-sequential space-filling design' means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design.
Multithreaded serialization of compressed data frames using the 'fst' format. The
    'fst' format allows for full random access of stored data and a wide range of compression
    settings using the LZ4 and ZSTD compressors.
Provides efficient methods to compute local and genome wide
    genetic distances (corresponding to the so called Hudson Fst
    parameters) through moment method, perform chromosome segmentation
    into homogeneous Fst genomic regions, and selection sweep detection
    for multi-population comparison. When multiple profile segmentation is
    required, the procedure can be parallelized using the future package.
Has two functions to help with calculating feature selection stability. 'Lump' is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length.
    'ASM' is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection.
    The calculation for 'asm' uses the Adjusted Stability Measure proposed in: 'Lustgarten', 'Gopalakrishnan', & 'Visweswaran' (2009)<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815476/>.
The 'fstlib' library provides multithreaded serialization of compressed data frames using the
    'fst' format. The 'fst' format allows for random access of stored data and compression with the 'LZ4' and 'ZSTD'
    compressors.
A program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.
Functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.
Calculation and plotting of instantaneous unavailabilities of basic events along with the top event of fault trees are issues important in reliability analysis of complex systems. Here, a fault tree is provided in terms of its minimal cut sets, along with reliability and maintainability distribution functions of the basic events. All the methods are derived from Horton (2002, ISBN: 3-936150-21-4), Niloofar and Lazarova-Molnar (2022).
A wrapper for the API of the Danish Parliament. It makes it 
    possible to get data from the API easily into a data frame. Learn more at 
    <http://www.ft.dk/dokumenter/aabne_data>.
Build display tables easily by extending the functionality of the
    'flextable' package. Features include spanning header, grouping rows,
    parsing markdown and so on.
A collection of functions for trading and rebalancing financial
	instruments. It implements various technical indicators to analyse time series such
	as moving averages or stochastic oscillators.
Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) <DOI:10.1093/bib/bbz041>. 'iFeature' 'Z. Chen et al.' (2018) <DOI:10.1093/bioinformatics/bty140>. <https://CRAN.R-project.org/package=rDNAse>. 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) <DOI:10.1093/bioinformatics/btw564>. 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) <DOI:10.1016/j.ygeno.2018.01.005>. 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) <DOI:10.1073/pnas.92.19.8700>. 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) <DOI:10.1038/srep13859>.
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
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.
Provides utilities to facilitate handling of Fude Polygon data 
    downloadable from the Ministry of Agriculture, Forestry and Fisheries 
    website <https://open.fude.maff.go.jp>.
Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package 'fuel' implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.
Fuel economy data from the EPA, 1985-2015,
    conveniently packaged for consumption by R users.
As in music, a fugue statistic repeats a theme in small variations.  Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes.  The main functions in the package are sen() and senCI().  For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.
Provides core functions and utilities for packages and other code
    developed by Jordan Mark Barbone.
We facilitate the analysis of full factorial mating designs with mixed-effects models. There are now six vignettes containing detailed examples.
Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). 
  References: Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". <doi:10.13140/RG.2.2.20415.94885/1>  ,
              Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. <doi:10.1177/1745691620902426>.
This is a collection of R games and other funny stuff, such as the
    classic Mine sweeper and sliding puzzles.
Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, 
    protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' 
    from gene set over-expression analysis. Performs clustering of pathway based on similarity 
    of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) 
    when up and down regulated genes are known. The methods are described in a paper currently
    submitted by Orecchioni et al, 2020 in Nanoscale.
The FunCC algorithm allows to apply the FunCC algorithm to simultaneously cluster the rows and the columns of a data matrix whose inputs are functions.
Compute alpha and beta contributional diversity metrics,
    which is intended for linking taxonomic and functional microbiome
    data. See 'GitHub' repository for the tutorial:
    <https://github.com/gavinmdouglas/FuncDiv/wiki>. Citation: Gavin M.
    Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023)
    <doi:10.1093/bioinformatics/btac809>.
Provides functional control charts 
    for statistical process monitoring of functional data, 
    using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507> and 
    Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>.
    The package is thoroughly illustrated in the paper of 
    Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.
YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. 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>,
 an adapted functional chi-squared test (Kumar & Song 2022) 
 <doi:10.1093/bioinformatics/btac206>, 
 and an exact functional test (Zhong & Song 2019)
 <doi:10.1109/TCBB.2018.2809743> (Nguyen et al. 2020)
 <doi:10.24963/ijcai.2020/372>. The normalized functional
 chi-squared test was used by Best Performer 'NMSUSongLab'
 in HPN-DREAM (DREAM8) Breast Cancer Network Inference
 Challenges (Hill et al. 2016) <doi:10.1038/nmeth.3773>. A
 function index (Zhong & Song 2019)
 <doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018)
 <doi:10.1109/BIBM.2018.8621502> derived from the
 functional test statistic offers a new effect size measure
 for the strength of functional dependency, a better
 alternative to conditional entropy in many aspects. For
 continuous data, these tests offer an advantage over
 regression analysis when a parametric functional form
 cannot be assumed; for categorical data, they provide a
 novel means to assess directional dependency not possible
 with symmetrical Pearson's chi-squared or Fisher's exact
 tests.
Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers).  Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank.  FuncMap should be useful for comparing packages at a high level for their overall design.  Plus, it's just plain fun.  The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package.  Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.
A collection of functions which fit functional neural network models. In
            other words, this package will allow users to build deep learning models 
            that have either functional or scalar responses paired with functional and 
            scalar covariates. We implement the theoretical discussion found 
            in Thind, Multani and Cao (2020) <arXiv:2006.09590> through the help of a main fitting and 
            prediction function as well as a number of helper functions to assist with 
            cross-validation, tuning, and the display of estimated functional weights.
An implementation of revised functional regression models for multiple genetic variation data, such as single nucleotide polymorphism (SNP) data, which provides revised functional linear regression models, partially functional interaction regression analysis with penalty-based techniques and corresponding drawing functions, etc.(Ruzong Fan, Yifan Wang, James L. Mills, Alexander F. Wilson, Joan E. Bailey-Wilson, and Momiao Xiong (2013) <doi:10.1002/gepi.21757>).
Curry, Compose, and other higher-order functions
Find functions in an unstructured directory and explore their dependencies. 
    Sourcing of R source files is performed without side-effects: from R scripts that have 
    executable code and function definitions only functions are sourced.
S4 classes for univariate and multivariate functional data with
    utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description 
    of the package functionalities and its interplay with the MFPCA package for 
    multivariate functional principal component analysis 
    <https://CRAN.R-project.org/package=MFPCA>.
Computes six functional diversity indices. These are namely,
  Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness
  (FRic), Functional Richness intersections (FRic_intersect), Functional
  Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008
  <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions
  to compute functional diversity indices
  (preprint: <doi:10.32942/osf.io/dg7hw>).
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>.
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights.
    Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices:
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <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
	for analysis of computer experiments
	with emphasis on treatment of functional inputs that are regularly sampled. This package
	offers: (i) flexible modeling of functional-input regression
	problems through the fairly general Gaussian process model; (ii)
	built-in dimension reduction for functional inputs; (iii)
	heuristic optimization of the structural parameters of the model
	(e.g., active inputs, kernel function, type of distance).
	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>.
Similar to base's unique function, only optimized for working with 
    data frames, especially those that contain date-time columns.
Provides four addons for analyzing trends and
    unit roots in financial time series: (i) functions for the density
    and probability of the augmented Dickey-Fuller Test, (ii) functions 
    for the density and probability of MacKinnon's unit root test 
    statistics, (iii) reimplementations for the ADF and MacKinnon
    Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's
    unit root test suite.
Compare variables of interest between (potentially large
    numbers of) spatial interactions and meta-variables. Spatial variables
    are summarized using K, or other, functions, and projected for use in
    a modified random forest model. The model allows comparison of
    functional and non-functional variables to each other and to noise,
    giving statistical significance to the results. Included are
    preparation, modeling, and interpreting tools along with example
    datasets, as described in VanderDoes et al., (2023)
    <doi:10.1101/2023.07.18.549619>.
Allows generating heatmap-like visualisations for benchmark data 
  frames. Funky heatmaps can be fine-tuned by providing annotations of the 
  columns and rows, which allows assigning multiple palettes or geometries
  or grouping rows and columns together in categories.
  Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.
The funLBM algorithm allows to simultaneously cluster the rows and the columns of a data matrix where each entry of the matrix is a function or a time series.
Fits a functional mediation model with a scalar distal outcome. The method is described in detail by Coffman, Dziak, Litson, Chakraborti, Piper & Li (2021) <arXiv:2112.03960>. The model is similar to that of Lindquist (2012) <doi:10.1080/01621459.2012.695640> although allowing a binary outcome as an alternative to a numerical outcome.  The current version is a minor bug fix in the vignette. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Time-based joins to analyze sequence of events, 
    both in memory and out of memory. after_join() joins two 
    tables of events, while funnel_start() and funnel_step() 
    join events in the same table. With the type argument, you
    can switch between different funnel types, like first-first
    and last-firstafter.
An implementation of methods presented by Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots for comparing institutional performance, for standardised ratios, ratios of counts and proportions with additive overdispersion adjustment.
A set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.
Functional principal component analysis under the Linear Mixed Models representation of smoothing splines. The method utilizes the Demmler-Reinsch basis and assumes error independence. For more details see: F. Rosales (2016) <https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-87F9-6>.
High-order functions for data manipulation : sort or group data, given one
    or more auxiliary functions. Functions are inspired by other pure
    functional programming languages ('Haskell' mainly). The package also 
    provides built-in function operators for creating compact anonymous
    functions, as well as the possibility to use the 'purrr' package syntax.
A small utility which wraps Rscript and provides access to all R
    functions from the shell.
Computes functional rarity indices as proposed by Violle et al.
    (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed
    using both regional and local information. Functional Rarity combines both
    the functional aspect of rarity as well as the extent aspect of rarity.
    'funrar' is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.
Performs functional regression, and some related
    approaches, for intensive longitudinal data (see the book by Walls & Schafer, 
    2006, Models for Intensive Longitudinal Data, Oxford) when such data is not
    necessarily observed on an equally spaced grid of times.  The
    approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
    Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but
    with some modifications to make it more feasible to use with long rather
    than wide, non-rectangular longitudinal datasets with unequal and
    potentially random measurement times.  It also allows easy plotting of the
    correlation between the smoothed covariate and the outcome as a function of
    time, which can add additional insights on how to interpret a functional
    regression.  Additionally, it also provides several permutation tests for
    the significance of the functional predictor.  The heuristic interpretation
    of ``time'' is used to describe the index of the functional predictor, but
    the same methods can equally be used for another unidimensional continuous
    index, such as space along a north-south axis.  Note that most of the functionality
	of this package has been superseded by added features after 2016 in the 'pfr' function by
    Jonathan Gellar, Mathew W. McLean, Jeff Goldsmith, and Fabian Scheipl, in the 
	'refund' package built by Jeff Goldsmith and co-authors and maintained by Julia Wrobel.
	The development of the funreg package in 2015 and 2016 was part of a research 
	project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 
	DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the
    authors and does not necessarily represent the official views of the
    National Institute on Drug Abuse, the National Cancer Institute, or the
    National Institutes of Health.
Estimation of functional spaces based on traits of organisms.
    The package includes functions to impute missing trait values (with or
    without considering phylogenetic information), and to create,
    represent and analyse two dimensional functional spaces based on
    principal components analysis, other ordination methods, or raw
    traits. It also allows for mapping a third variable onto the
    functional space.  See 'Carmona et al. (2021)' 
    <doi:10.1038/s41586-021-03871-y>, 'Puglielli et al.  (2021)'
    <doi:10.1111/nph.16952>, 'Carmona et al. (2021)'
    <doi:10.1126/sciadv.abf2675>, 'Carmona et al. (2019)'
    <doi:10.1002/ecy.2876> for more information.
Helpers for parsing out the R functions
  and packages used in R scripts and notebooks.
Implementation of two sample comparison procedures based on median-based statistical tests 
    for functional data, introduced in Smida et al (2022) <doi:10.1080/10485252.2022.2064997>. 
    Other competitive state-of-the-art approaches proposed by Chakraborty and Chaudhuri (2015) <doi:10.1093/biomet/asu072>,
    Horvath et al (2013) <doi:10.1111/j.1467-9868.2012.01032.x> or Cuevas et al (2004) <doi:10.1016/j.csda.2003.10.021>
    are also included in the package, as well as procedures to run test result comparisons and power analysis 
    using simulations.
Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.
Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.
A collection of toys to do things like generate Collatz 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 for pi), and so on.
Contains four main functions (i.e., four pieces of furniture):
    table1() which produces a well-formatted table of descriptive statistics common as Table 1
    in research articles, tableC() which produces a well-formatted table of correlations,
    tableF() which provides frequency counts, and washer() which 
    is helpful in cleaning up the data. These furniture-themed functions are designed 
    to simplify common tasks in quantitative analysis. Other data summary and cleaning tools 
    are also available.
Implementations of the family of map() functions from 'purrr'
    that can be resolved using any 'future'-supported backend, e.g.
    parallel on the local machine or distributed on a compute cluster.
Implementation of fused Markov graphical model (FMGM; Park and Won, 2022). The functions include building mixed graphical model (MGM) objects from data, inference of networks using FMGM, stable edge-specific penalty selection (StEPS) for the determination of penalization parameters, and the visualization. For details, please refer to Park and Won (2022) <arXiv:2208.14959>.
Use Rmarkdown First method to build your package. Start your
    package with documentation, functions, examples and tests in the same
    unique file. Everything can be set from the Rmarkdown template file
    provided in your project, then inflated as a package. Inflating the
    template copies the relevant chunks and sections in the appropriate
    files required for package development.
Enables high-dimensional penalized regression across heterogeneous 
    subgroups. Fusion penalties are used to share information about the linear 
    parameters across subgroups. The underlying model is described 
    in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.
FusionCharts provides awesome and minimalist functions to make beautiful interactive charts <https://www.fusioncharts.com/>.
Provides the Big Merge Tracker and COSCI algorithms for convex clustering and 
    feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and 
    T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.
The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.
Provides a simple yet powerful logging utility. Based loosely on
    log4j, futile.logger takes advantage of R idioms to make logging a
    convenient and easy to use replacement for cat and print statements.
A scoped options management framework. Used in other packages.
Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results.
The purpose of this package is to provide a lightweight and
    unified Future API for sequential and parallel processing of R
    expression via futures.  The simplest way to evaluate an expression
    in parallel is to use `x %<-% { expression }` with `plan(multisession)`.
    This package implements sequential, multicore, multisession, and
    cluster futures.  With these, R expressions can be evaluated on the
    local machine, in parallel a set of local machines, or distributed
    on a mix of local and remote machines.
    Extensions to this package implement additional backends for
    processing futures via compute cluster schedulers, etc.
    Because of its unified API, there is no need to modify any code in order
    switch from sequential on the local machine to, say, distributed
    processing on a remote compute cluster.
    Another strength of this package is that global variables and functions
    are automatically identified and exported as needed, making it
    straightforward to tweak existing code to make use of futures.
Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.  These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
Implementation of the Future API on top of the 'batchtools' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.
Implementation of the Future API on top of the 'callr' package.  This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine.  Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes.
Backends implementing the 'Future' API, as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API.  The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines.
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.
It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) <doi:10.1007/s40815-020-00936-4>.
An interface for training Fuzzy DBScan with both Fuzzy Core and Fuzzy Border.
    Therefore, the package provides a method to initialize and run the algorithm and a
    function to predict new data w.t.h. of 'R6'.
    The package is build upon the paper "Fuzzy Extensions of the DBScan algorithm"
    from Ienco and Bordogna (2018) <doi:10.1007/s00500-016-2435-0>.
    A predict function assigns new data according to the same criteria as the algorithm itself.
    However, the prediction function freezes the algorithm to preserve the trained
    cluster structure and treats each new prediction object individually.
Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
    See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
    for further details.
Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.
Provides methods to solve Fuzzy Linear Programming Problems with
             fuzzy constraints (following different approaches proposed by 
             Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy
             technological matrix.
Contains functions for operations with fuzzy cognitive maps using t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay and George Metcalfe (2007) <doi:10.1007/s00153-007-0047-1>. System indicators are described by Cox, Earl D. (1995) <isbn:1886801010>. Executable examples are provided in the "inst/examples" folder.
S4 classes and methods
    to deal with fuzzy numbers. They allow for computing any arithmetic
    operations (e.g., by using the Zadeh extension principle),
    performing approximation of arbitrary fuzzy numbers by trapezoidal
    and piecewise linear ones, preparing plots for publications, computing
    possibility and necessity values for comparisons, etc.
One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.
Estimates fuzzy measures of poverty and deprivation. It also estimates the sampling variance of these measures using bootstrap or jackknife repeated replications.
Fuzzy clustering of species in an ecological community as common or
    rare based on their abundance and occupancy. It also includes functions to
    compute confidence intervals of classification metrics and plot results. See
    Balbuena et al. (2020, <doi:10.1101/2020.08.12.247502>).
Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic.
    This toolkit includes with graphical user interface (GUI) and an adaptive neuro-
    fuzzy inference system (ANFIS). This toolkit is a continuation from the previous
    package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA)
    and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. 
    A big thank you to the many people who have contributed to the development/evaluation of the toolbox.
    Please cite the toolbox and the corresponding paper <doi:10.1109/FUZZ48607.2020.9177780> when using it.
    More related papers can be found in the NEWS.
Does fuzzy tests and confidence intervals (following Geyer
    and Meeden, Statistical Science, 2005, <doi:10.1214/088342305000000340>)
    for sign test and Wilcoxon signed rank and rank sum tests.
Estimators for fuzzy linear regression. The functions estimate parameters of 
    fuzzy linear regression models with crisp or fuzzy independent variables (triangular
    fuzzy numbers are supported). Implements multiple methods for parameter estimation and 
    algebraic operations with triangular fuzzy numbers. Includes functions for 
    summarising, printing and plotting the model fit. Calculates predictions from the 
    model and total error of fit. Individual methods are described in
    Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>,
	Hung & Yang (2006) <doi:10.1016/j.fss.2006.08.004>,
	Lee & Tanaka (1999) <doi:10.15807/jorsj.42.98>,
	Nasrabadi, Nasrabadi & Nasrabady (2005) <doi:10.1016/j.amc.2004.02.008>,
	Skrabanek, Marek & Pozdilkova (2021) <doi:10.3390/math9060685>,
	Tanaka, Hayashi & Watada (1989) <doi:10.1016/0377-2217(89)90431-1>,
	Zeng, Feng & Li (2017) <doi:10.1016/j.asoc.2016.09.029>.
The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317)  "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data.
 The main aim of the algorithms implemented in this package  is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample.
 To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. <doi:10.2991/eusflat-19.2019.68>, Grzegorzewski et al. (2020) <doi:10.2991/ijcis.d.201012.003>, Grzegorzewski et al. (2020) <doi:10.34768/amcs-2020-0022>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-030-95929-6_3>,  Romaniuk and Hryniewicz (2019) <doi:10.1007/s00500-018-3251-5>).
 Some additional procedures related to these resampling methods are also provided,
 like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers")
 and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, <doi:10.1016/j.ejor.2015.11.016>)).
 Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions.
Functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, loss or gain), and for data preparation, such as obtaining unique abbreviations of species names, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting absences under specified criteria, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) <doi:10.1111/2041-210X.12372>.
Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective 
 procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, 
 see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details).
 Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk
 (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>)
 together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap.
 The package also includes a real-life data set of epistemic fuzzy triangular numbers.
 The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package.
An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
        from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley 
        in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities.
The aim of the package is to provide some basic functions
 for doing statistics with trapezoidal fuzzy numbers. In particular,
  the package contains several functions for simulating trapezoidal 
  fuzzy numbers, as well as for calculating some central tendency 
  measures (mean and two types of median), some scale measures 
  (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and 
  one diversity index and one inequality index. Moreover, 
  functions for calculating the 1-norm distance, the mid/spr 
  distance and the (phi,theta)-wabl/ldev/rdev distance between 
  fuzzy numbers are included, and a function to calculate the 
  value phi-wabl given a sample of trapezoidal fuzzy numbers.
The aim of the package is to contain the package 'FuzzyStatTra' in 
    Encapsulated Object Oriented Programming using R6. 'FuzzyStatTra' contains Statistical 
    Methods for Trapezoidal Fuzzy Numbers, whose aim is to provide some basic functions 
    for doing statistical analysis with trapezoidal fuzzy numbers. For more details, you 
    can visit the website of the SMIRE+CoDiRE (Statistical Methods with Imprecise Random 
    Elements and Comparison of Distributions of Random Elements) Research Group 
    (<https://bellman.ciencias.uniovi.es/smire+codire/>). The most related paper can be 
    found in References. Now, those functions are organized in specific classes and methods. 
    This object-based approach is an important step in making statistical computing more 
    accessible to users.
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.
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.
An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in
   the 'boot' package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing
   weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020)
   <doi:10.1080/00031305.2020.1731599> for details.
A simple method
    to select the best model or best subset of variables using
    different types of data (binary, Gaussian or Poisson) and
    applying it in different contexts (parametric or non-parametric).
Implementation of fast algorithms for wild cluster bootstrap 
             inference developed in 'Roodman et al' (2019, 'STATA' Journal,
             <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), 
             which makes it feasible to quickly calculate bootstrap test 
             statistics based on a large number of bootstrap draws even for 
             large samples. Multiple bootstrap types as described in 'MacKinnon, 
             Nielsen & Webb' (2022) are supported. 
             Further, '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'). 
             Additionally implements a 'heteroskedasticity-robust' ('HC1') wild 
             bootstrap.
             Last, the package provides an R binding to 'WildBootTests.jl',
             which provides additional speed gains and functionality, 
             including the 'WRE' bootstrap for instrumental variable models 
             (based on models of type 'ivreg()' from package 'ivreg')
             and hypotheses with q > 1.
Creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the 'fixest' package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <arXiv:2307.00369>.
Fresh biomass determination is the key to evaluating crop genotypes' response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight.
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
The 'fxl' Charting package is used to prepare and design single case design figures that are typically prepared in spreadsheet software. With 'fxl', there is no need to leave the R environment to prepare these works.
Exchange rate regression and structural change tools
             for estimating, testing, dating, and monitoring
	     (de facto) exchange rate regimes.
The goal of this package is to provide an improved version of 
    WA-PLS (Weighted Averaging Partial Least Squares) by including the 
    tolerances of taxa and the frequency of the sampled climate variable. 
    This package also provides a way of leave-out cross-validation that 
    removes both the test site and sites that are both geographically 
    close and climatically close for each cycle, to avoid the risk of 
    pseudo-replication.
In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June
     of the following calendar year. As such, many databases need to represent and 
     validate financial years efficiently. While the use of integer years with a convention that  
     they represent the year ending is common, it may lead to ambiguity with calendar years.
     On the other hand, string representations may be too inefficient and do not easily admit
     arithmetic operations. This package tries to make validation of financial years quicker while
     retaining clarity.
Create and maintain delayed-data packages (ddp's).  Data stored in
  a ddp are available on demand, but do not take up memory until requested.
  You attach a ddp with g.data.attach(), then read from it and assign to it in
  a manner similar to S-PLUS, except that you must run g.data.save() to
  actually commit to disk.
Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters.
 These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors.
 Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.
Full descriptive statistics, physical description of sediment,
            metric or phi sieves.
Interface for 'g3-lollipop' JavaScript library.
    Visualize genetic mutation data using an interactive lollipop diagram in Studio 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.
Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects.
  Inputs are : data frame, RDA (package 'vegan') and PCA (package 'FactoMineR') objects.
  Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters.
The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method.
  By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed.
  This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others.
  First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user.
  By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions.
  The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.
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://gadget-framework.github.io/gadget2/>.
             This is the C++ Gadget2 runtime, making it available for R.
A framework to assist creation of marine ecosystem models,
             generating either 'R' or 'C++' code which can then be optimised using
             the 'TMB' package and standard 'R' tools. Principally designed to
             reproduce gadget2 models in 'TMB', but can be extended beyond
             gadget2's capabilities.
             Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) <doi:10.18637/jss.v070.i05> "TMB: Automatic Differentiation and Laplace Approximation.".
             Begley, J., & Howell, D. (2004) <https://core.ac.uk/download/pdf/225936648.pdf> "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.".
A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).
Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm.  
 For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. 
 This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. 
 Further study is going on for the nonorthogonal cases and generalized linear models. 
 These works are in part supported by the National Natural Foundation of China (No.12171076).
Performs genetic algorithm (Scrucca, L (2013) <doi:10.18637/jss.v053.i04>) 
             assisted genomic best liner unbiased prediction for genomic selection.
             It also provides a binning method in natural population for genomic selection
             under the principle of linkage disequilibrium for dimensional reduction.
Tools for using genetic markers, stable isotope data, and habitat
    suitability data to calculate posterior probabilities of breeding origin of
    migrating birds.
Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators.
  H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.
Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.  The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11.
Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA.
The Global Biodiversity Information Facility 
    ('GBIF', <https://www.gbif.org>) sources data from an international network
    of data providers, known as 'nodes'. Several of these nodes - the "living 
    atlases" (<https://living-atlases.gbif.org>) - maintain their own web 
    services using software originally developed by the Atlas of Living 
    Australia ('ALA', <https://www.ala.org.au>). 'galah' enables the R community 
    to directly access data and resources hosted by 'GBIF' and its partner nodes.
Estimates generalized additive latent and
    mixed models using maximum marginal likelihood, 
    as defined in Sorensen et al. (2023) 
    <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and
    Skrondal (2004)'s unifying framework for multilevel latent variable 
    modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse 
    matrix methods, Laplace approximation, and automatic differentiation. The 
    framework includes generalized multilevel models with heteroscedastic 
    residuals, mixed response types, factor loadings, smoothing splines, 
    crossed random effects, and combinations thereof. Syntax for model 
    formulation is close to 'lme4' (Bates et al. (2015) 
    <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) 
    <doi:10.1080/00273171.2018.1516541>).
Collection of packages for work with API 'Google Ads' <https://developers.google.com/google-ads/api/docs/start>, 
    'Yandex Direct' <https://yandex.ru/dev/direct/>, 'Yandex Metrica' <https://yandex.ru/dev/metrika/>, 
    'MyTarget' <https://target.my.com/help/advertisers/api_arrangement/ru>, 'Vkontakte' <https://vk.com/dev/methods>, 
    'Facebook' <https://developers.facebook.com/docs/marketing-apis/> and 'AppsFlyer' <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>. 
    This packages allows you loading data from ads account and manage your ads materials.
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).
Data sets and scripts used in the book 'Generalized Additive 
             Models: An Introduction with R', Wood (2006,2017) CRC.
Fits unimodal and multimodal gambin distributions to species-abundance distributions
    from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. 
    'gambin' is short for 'gamma-binomial'. The main function is fit_abundances(), which estimates 
    the 'alpha' parameter(s) of the gambin distribution using maximum likelihood. Functions are 
    also provided to generate the gambin distribution and for calculating likelihood statistics.
Simulates a gambling game under the gambler's ruin setup, after asking for the money you have and the money you want to win, along with your win probability in each round of the game.
Boosting models for fitting generalized additive models for
  location, shape and scale ('GAMLSS') to potentially high dimensional
  data.
Contains infrastructure for using mboost::gamboost() in order to estimate multistate models.
Functions and data are provided that support a course
        that emphasizes statistical issues of inference and generalizability.  
        The functions are designed to make it straightforward to illustrate
        the use of cross-validation, the training/test approach, simulation, 
        and model-based estimates of accuracy.  Methods considered are
        Generalized Additive Modeling, Linear and Quadratic Discriminant
        Analysis, Tree-based methods, and Random Forests.
Implementation of various inference and simulation tools to
    apply generalized additive models to bivariate dependence structures and
    non-simplified vine copulas.
Implements the GAMbag, GAMrsm and GAMens ensemble
    classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles
    implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601>
    , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.
Finds adaptive strategies for sequential symmetric 
    games using a genetic algorithm. Currently, any symmetric two by two matrix
    is allowed, and strategies can remember the history of an opponent's play
    from the previous three rounds of moves in iterated interactions between
    players. The genetic algorithm returns a list of adaptive strategies given
    payoffs, and the mean fitness of strategies in each generation.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', <arXiv:1308.5623>.
Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Interface for extra smooth functions including tensor products, neural networks and decision trees.
This is an add-on package to GAMLSS. The purpose of this
        package is to allow users to fit interval response variables in
        GAMLSS models. The main function gen.cens() generates a
        censored version of an existing GAMLSS family distribution.
This is an add on package to 'GAMLSS'. The main purpose of this package is generating and fitting inflated distributions at any desired point (0, 1, 2, ...). The function gen.Kinf() generates K-inflated version of an existing discrete 'GAMLSS' family distribution.
Data used as examples in the current two books on Generalised Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>.
Demos for smoothing and gamlss.family distributions.
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.
Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.
This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.
Interface for extra high-dimensional smooth functions for Generalized Additive Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge, elastic net and least angle regression.
The main purpose of this package is to allow fitting of
        mixture distributions with generalised additive models for location 
        scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.
It allows us to fit Gaussian Markov Random Field within the
    Generalized Additive Models for Location Scale and Shape algorithms.
This is an add on package to GAMLSS. The purpose of this
        package is to allow users to defined truncated distributions in
        GAMLSS models. The main function gen.trun() generates truncated
        version of an existing GAMLSS family distribution.
Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.
Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', using 'lme4' for estimation.
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.
Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2023) <arXiv:2201.00412>.
Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' <https://cran.r-project.org/package=ganGenerativeData>.
Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. In iterated training steps the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data classifying and missing data completion. Reference: Goodfellow et al. (2014) <arXiv:1406.2661v1>.
A network-based gene weighting algorithm for pathway enrichment analysis, using either RNA-seq or microarray data. Zhaoyuan Fang, Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.
This is a dataset package for GANPA, which implements a
        network-based gene weighting approach to pathway analysis. This
        package includes data useful for GANPA, such as a functional
        association network, pathways, an expression dataset and
        multi-subunit proteins.
As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18.
        <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it contains functions for
        sample size calculations of both population-based and family-based designs, probability
        of familial disease aggregation, kinship calculation, statistics in linkage analysis,
        and association analysis involving genetic markers including haplotype analysis with or
        without environmental covariates. Over years, the package has been developed in-between
        many projects hence also in line with the name (gap).
Datasets associated with the 'gap' package. Currently,
             it includes an example data for regional association
             plot (CDKN), an example data for a genomewide association
             meta-analysis (OPG), data in studies of Parkinson's diease (PD),
             ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers
             and Schizophrenia (apoeapoc), cystic fibrosis (cf), a
             Olink/INF panel (inf1), Manhattan plots with (hr1420, mhtdata)
             and without (w4) gene annotations.
Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.
Provides functions to estimate the disparities across categories (e.g. Black and white) that persists if a treatment variable (e.g. college) is equalized. Makes estimates by treatment modeling, outcome modeling, and doubly-robust augmented inverse probability weighting estimation, with standard errors calculated by a nonparametric bootstrap. Cross-fitting is supported. Survey weights are supported for point estimation but not for standard error estimation; those applying this package with complex survey samples should consult the data distributor to select an appropriate approach for standard error construction, which may involve calling the functions repeatedly for many sets of replicate weights provided by the data distributor. The methods in this package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.
Tools to fill missing values in satellite data and to develop new
    gap-fill algorithms. The methods are tailored to data (images) observed
    at equally-spaced points in time. The package is illustrated with MODIS
    NDVI data.
The gap encodes the distance between clusters and improves
    interpretation of cluster heatmaps. The gaps can be of the same
    distance based on a height threshold to cut the dendrogram. Another
    option is to vary the size of gaps based on the distance between
    clusters.
An excerpt of the data available at Gapminder.org. For each
    of 142 countries, the package provides values for life expectancy, GDP
    per capita, and population, every five years, from 1952 to 2007.
Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.
Functions for estimating a GARCHSK model and GJRSK model based on a publication by Leon et,al (2005)<doi:10.1016/j.qref.2004.12.020> and Nakagawa and Uchiyama (2020)<doi:10.3390/math8111990>. These are a GARCH-type model allowing for time-varying volatility, skewness and kurtosis.
Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time, see <https://journal.r-project.org/archive/2021/RJ-2021-057/RJ-2021-057.pdf> for an overview of the package.
Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.
Provides utilities for working with Google APIs
    <https://developers.google.com/apis-explorer>.  This includes
    functions and classes for handling common credential types and for
    preparing, executing, and processing HTTP requests.
An event-Based framework for building 'Shiny' apps.
    Instead of relying on standard 'Shiny' reactive objects, this 
    package allow to relying on a lighter set of triggers, so that 
    reactive contexts can be invalidated with more control.
Methods for estimating univariate long memory-seasonal/cyclical
             Gegenbauer time series processes. See for example (2022) <doi:10.1007/s00362-022-01290-3>.
             Refer to the vignette for details of fitting these processes.
Organize a so-called ragged array as generalized arrays, which
	is simply an array with sub-dimensions denoting the subdivision of
	dimensions (grouping of members within dimensions).
	By the margins (names of dimensions and sub-dimensions) in generalized
	arrays, operators and utility functions provided in this package
	automatically match the margins,
	doing map-reduce style parallel computation
	along margins.  Generalized arrays are also cooperative to R's native
	functions that work on simple arrays.
Simulate, estimate and forecast using univariate and multivariate GAS models 
  as described in Ardia et al. (2019) <doi:10.18637/jss.v088.i06>.
Provides a genetic algorithm for finding variable
    subsets in high dimensional data with high prediction performance. The
    genetic algorithm can use ordinary least squares (OLS) regression models or
    partial least squares (PLS) regression models to evaluate the prediction
    power of variable subsets. By supporting different cross-validation
    schemes, the user can fine-tune the tradeoff between speed and quality of
    the solution.
Functions for greenhouse gas flux calculation from chamber
    measurements.
Estimation, forecasting, and simulation of generalized
    autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013)
    <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>.
    Model specification allows for various data types and distributions,
    different parametrizations, exogenous variables, joint and separate modeling
    of exogenous variables and dynamics, higher score and autoregressive orders,
    custom and unconditional initial values of time-varying parameters, fixed
    and bounded values of coefficients, and missing values. Model estimation is
    performed by the maximum likelihood method and the Hessian matrix.
Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>.  Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.
Provides the standard operations for signal processing on graphs: 
    graph Fourier transform, spectral graph wavelet transform, 
    visualization tools. It also implements a data driven method
    for graph signal denoising/regression, for details see 
    De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. 
    The package also provides an interface to the SuiteSparse Matrix Collection, 
    <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix 
    benchmarks collected from a wide range of applications.
Fits gastric emptying time series from MRI or 'scintigraphic' measurements
   using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with 
   Stan; computes derived parameters such as t50 and AUC.
Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al <doi:10.1159/000488519>.
Allows user to choose/gate a region on the plot and returns points
    within it.
Estimates statistically significant marker combination values within
        which one immunologically distinctive group (i.e., disease case) is more associated than
        another group (i.e., healthy control), successively, using various combinations (i.e.,
        "gates") of markers to examine features of cells that may be different between
        groups. For a two-group comparison, the 'gateR' package uses the spatial relative risk
        function estimated using the 'sparr' package. Details about the 'sparr' package
        methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details
        about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.
        More information about relative risk functions using kernel density estimation can be
        found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Streamlines downloading and cleaning biodiversity data from Integrated Digitized Biocollections (iDigBio) and the Global Biodiversity Information Facility (GBIF).
Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel, and 'Rcpp'
 has been used to speed up calculations.
 For more info about Gaussian process software, see Erickson et al. (2018)
 <doi:10.1016/j.ejor.2017.10.002>.
A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. 'gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence' in the journal Ecology and Evolution.
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 web site below and the accompanying papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", 2022, <arxiv:2202.01553>. L. Davies, "Linear Regression, Covariate Selection and the Failure of Modelling", 2022, <arXiv:2112.08738>.
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.
A statistical disclosure control tool to protect tables by suppression 
    using the Gaussian elimination secondary suppression algorithm. A suggestion is 
    to start by working with functions SuppressSmallCounts() and 
    SuppressDominantCells(). These functions use primary suppression functions for 
    the minimum frequency rule and the dominance rule, respectively. Novel 
    functionality for suppression of disclosive cells is also included. General 
    primary suppression functions can be supplied as input to the general working 
    horse function, GaussSuppressionFromData(). Suppressed frequencies can be 
    replaced by synthetic decimal numbers as described in 
    Langsrud (2019) <doi:10.1007/s11222-018-9848-9>.
R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.
A collection of algorithms and functions 
  for fitting data to a generalized lambda distribution 
  via moment matching methods, and generalized 
  bootstrapping.
Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.
Estimation of the generalized beta distribution of the second 
    kind (GB2) and related models using grouped data in form of income shares. 
    The GB2 family is a general class of distributions that provides an accurate 
    fit to income data. 'GB2group' includes functions to estimate the GB2, the 
    Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 
    'GB2group' deploys two different econometric strategies to estimate these 
    parametric distributions, the equally weighted minimum distance (EWMD) estimator and the  
    optimally weighted minimum distance (OMD) estimator. Asymptotic standard errors are reported for the 
    OMD estimates. Standard errors of the EWMD estimates are obtained by Monte 
    Carlo simulation. See Jorda et al. (2018) <arXiv:1808.09831> for a detailed 
    description of the estimation procedure.
DNA methylation of 5-methylcytosine (5mC) is the result of a multi-step, enzyme-dependent process. Predicting these sites in-vitro is laborious, time consuming as well as costly. This ' Gb5mC-Pred ' package is an in-silico pipeline for predicting DNA sequences containing the 5mC sites. It uses a machine learning approach which uses Stochastic Gradient Boosting approach for prediction of the sequences with 5mC sites. This package has been developed by using the concept of Navarez and Roxas (2022) <doi:10.1109/TCBB.2021.3082184>.
Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) <doi:10.1214/11-BJPS139>, Hamza & Vallois (2016) <doi:10.1016/j.spl.2016.03.014>, Chen & Novick (1984) <doi:10.3102/10769986009002163>.
Supplies a set of functions to interface with bikeshare data
    following the General Bikeshare Feed Specification, allowing users to query
    and accumulate tidy datasets for specified cities/bikeshare programs.
A high performance interface to the Global Biodiversity
  Information Facility, 'GBIF'.  In contrast to 'rgbif', which can
  access small subsets of 'GBIF' data through web-based queries to
  a central server, 'gbifdb' provides enhanced performance for R users
  performing large-scale analyses on servers and cloud computing
  providers, providing full support for arbitrary 'SQL' or 'dplyr'
  operations on the complete 'GBIF' data tables (now over 1 billion
  records, and over a terabyte in size). 'gbifdb' accesses a copy
  of the 'GBIF' data in 'parquet' format, which is already readily
  available in commercial computing clouds such as the Amazon Open
  Data portal and the Microsoft Planetary Computer, or can be 
  accessed directly without downloading, or downloaded
  to any server with suitable bandwidth and storage space.
  The high-performance techniques for local and remote access 
  are described in <https://duckdb.org/why_duckdb>
  and <https://arrow.apache.org/docs/r/articles/fs.html> respectively.
Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
	association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ),
    Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence
    Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented 
    in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests.
    The GBJ has been shown to outperform other tests in genetic association studies when signals
    are correlated and moderately sparse. Please see the vignette for a quickstart guide or 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.
Estimation and analysis of group-based multivariate trajectory models (Nagin, 2018 <doi:10.1177/0962280216673085>; Magrini, 2022 <doi:10.1007/s10182-022-00437-9>). The package implements an Expectation-Maximization (EM) algorithm allowing unbalanced panel and missing values, and provides several functionalities for prediction and graphical representation.
Basic infrastructure and several algorithms for 1d-4d bin packing
    problem. This package provides a set of c-level classes and solvers for
    1d-4d bin packing problem, and an r-level solver for 4d bin packing problem,
    which is a wrapper over the c-level 4d bin packing problem solver.
    The 4d bin packing problem solver aims to solve bin packing problem, a.k.a
    container loading problem, with an additional constraint on weight.
    Given a set of rectangular-shaped items, and a set of rectangular-shaped bins
    with weight limit, the solver looks for an orthogonal packing solution
    such that minimizes the number of bins and maximize volume utilization.
    Each rectangular-shaped item i = 1, .. , n is characterized by length l_i,
    depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin
    j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j,
    and weight limit w_j.
    The item can be rotated into any orthogonal direction, and no further
    restrictions implied.
Provides utilities for processing Rd objects and files.
        Extract argument descriptions and other parts of the help pages
        of functions.
Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
An implementation of hyperparameter optimization for Gradient
    Boosted Trees on binary classification and regression problems. The current
    version provides two optimization methods: Bayesian optimization and random
    search. Instead of giving the single best model, the final output is an 
    ensemble of Gradient Boosted Trees constructed via the method of ensemble 
    selection.
Plot density and distribution functions with automatic selection of
       suitable regions. Numerically invert (compute quantiles) distribution
       functions. Simulate real and complex numbers from distributions of their
       magnitude and arguments. Optionally, the magnitudes and/or arguments may
       be fixed in almost arbitrary ways. Create polynomials from roots given in
       Cartesian or polar form. Small programming utilities: check if an object
       is identical to NA, count positional arguments in a call, set
       intersection of more than two sets, check if an argument is unnamed,
       compute the graph of S4 classes in packages.
Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.
Aligns peak based on peak retention times and matches homologous peaks
    across samples. The underlying alignment procedure comprises three sequential steps.
    (1) Full alignment of samples by linear transformation of retention times to 
    maximise similarity among homologous peaks (2) Partial alignment of peaks within 
    a user-defined retention time window to cluster homologous peaks (3) Merging rows
    that are likely representing homologous substances (i.e. no sample shows peaks in 
    both rows and the rows have similar retention time means). The algorithm is described in detail
    in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
'GPU'/CPU Benchmarking on Debian-package based systems
 This package benchmarks performance of a few standard linear algebra
 operations (such as a matrix product and QR, SVD and LU decompositions)
 across a number of different 'BLAS' libraries as well as a 'GPU' implementation.
 To do so, it takes advantage of the ability to 'plug and play' different
 'BLAS' implementations easily on a Debian and/or Ubuntu system.  The current
 version supports
  - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline
  - Atlas which are tuned but typically configure single-threaded
  - Atlas39 which are tuned and configured for multi-threaded mode
  - 'Goto Blas' which are accelerated and multi-threaded
  - 'Intel MKL' which is a commercial accelerated and multithreaded version.
 As for 'GPU' computing, we use the CRAN package
  - 'gputools'
 For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be
 used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10.
Provides methods for model selection, estimation, bootstrap inference, and simulation for the 
    multilevel factor model, based on the principal component estimation and generalised 
    canonical correlation approach. Details can be found in "Generalised Canonical Correlation 
    Estimation of the Multilevel Factor Model." Lin and Shin (2023) <doi:10.2139/ssrn.4295429>.
Contains the Global Charcoal database data. Data include charcoal
    series (age, depth, charcoal quantity, associated units and methods) and
    information on sedimentary sites (localisation, depositional environment, biome,
    etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.
Implements a generalized coordinate descent (GCD) algorithm
    for computing the solution paths of the hybrid Huberized support vector
    machine (HHSVM) and its generalizations. Supported models include the
    (adaptive) LASSO and elastic net penalized least squares, logistic
    regression, HHSVM, squared hinge loss SVM and expectile regression.
Generalized competing event model based on Cox PH model and Fine-Gray model.
    This function is designed to develop optimized risk-stratification methods for competing
    risks data, such as described in:
    1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F,
    and Mell LK (2014) <DOI:10.1016/j.ijrobp.2014.03.047>.
    2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose
    BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>.
    3. Lunn, Mary, and Don McNeil (1995) <DOI:10.2307/2532940>.
Scrapes Google Citation pages and creates data frames of 
    citations over time.
Provides a variety of functions to analyze and model
    geostatistical count data with Gaussian copulas, including
 1) data simulation and visualization; 
 2) correlation structure assessment (here also known as the Normal To Anything); 
 3) calculate multivariate normal rectangle probabilities; 
 4) likelihood inference and parallel prediction at predictive locations.
 Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.
Estimation of covariance matrices as solutions of 
             continuous time Lyapunov equations. 
             Sparse coefficient matrix and diagonal noise are estimated 
             with a proximal gradient 
             method for an l1-penalized loss minimization problem.
             Varando G, Hansen NR (2020) <arXiv:2005.10483>.
Orders panels in scatterplot matrices and parallel coordinate
 displays by some merit index. Package contains various indices of merit,
 ordering functions, and enhanced versions of pairs and parcoord which
 color panels according to their merit level.
Likelihood inference in Gaussian copula marginal
        regression models.
An implementation of the International Bureau of Weights and Measures (BIPM) generalized consensus estimators used to assign the reference value in a key comparison exercise. This can also be applied to any interlaboratory study. Given a set of different sources, primary laboratories or measurement methods this package provides an evaluation of the variance components according to the selected statistical method for consensus building. It also implements the comparison among different consensus builders and evaluates the participating method or sources against the consensus reference value. Based on a diverse set of references, DerSimonian-Laird (1986) <doi:10.1016/0197-2456(86)90046-2>, for a complete list of references look at the reference section in the package documentation.
Data sets used in the book "R Graphics Cookbook" by Winston
    Chang, published by O'Reilly Media.
Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) <doi:10.1002/sim.8855>.
Easy import, manipulation, and model-free analysis of
    microbial growth curve data, as commonly output by plate readers.
    Tools for reshaping common plate reader outputs into 'tidy' formats and
    merging them with design information, making data easy to work with using 
    'gcplyr' and other packages. Also streamlines common growth curve
    processing steps, like smoothing and calculating derivatives, and
    facilitates model-free characterization and analysis of growth data.
    See methods at <https://mikeblazanin.github.io/gcplyr/>.
Analyze the default risk of credit portfolios. Commonly known models, 
		like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
		The portfolio loss distribution can be achieved either by simulation or analytically 
		in case of the classic CreditRisk+ model. Models are only implemented to respect losses
		caused by defaults, i.e. migration risk is not included. The package structure is kept
		flexible especially with respect to distributional assumptions in order to quantify the
		sensitivity of risk figures with respect to several assumptions. Therefore the package
		can be used to determine the credit risk of a given portfolio as well as to quantify
		model sensitivities.
Provides implementation of the generic composite similarity measure
    (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The
    implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations
    of the structural similarity (SSIM) and the composite similarity measure based
    on means, standard deviations, and correlation coefficient (CMSC), are included.
Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md <https://github.com/rikenbit/gcTensor>, for details of the method.
Geographical detectors for measuring spatial stratified heterogeneity,
             as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and 
             Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the
             optimal discretization of continuous data, four primary functions of 
             geographical detectors, comparison of size effects of spatial unit and 
             the visualizations of results. 
             To use the package and to refer the 
             descriptions of the package, methods and case datasets, please cite 
             Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>.
             The model has been applied in factor exploration of road performance and 
             multi-scale spatial segmentation for network data, as described in 
             Yongze Song (2018) <doi:10.3390/rs10111696> and 
             Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users
    define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and 
    resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, 
    applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, 
    exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features.  
    All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. 
    See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.
Interface to the Raster API of the 'Geospatial Data Abstraction
    Library' ('GDAL') supporting manual creation of uninitialized datasets,
    creation from existing raster as template, low level I/O, configuration of
    virtual raster (VRT), coordinate transformation, and access to 'gdalwarp'
    for reprojection. Includes several 'GDAL' algorithms and functions for
    working with spatial reference systems. Calling signatures resemble the
    native C, C++ and Python APIs provided by the 'GDAL' project
    (<https://gdal.org>). Bindings are implemented via exposed C++ class
    encapsulating a 'GDALDataset' and its associated 'GDALRasterBand' objects,
    plus several stand-alone functions. Additional functionality includes:
    class 'RunningStats' for efficient summary statistics on large data
    streams; class 'CmbTable' for counting unique combinations of integers
    with a hash table; raster 'combine()' to identify and count unique pixel
    combinations across multiple input layers; raster 'calc()' to evaluate
    an R expression on a stack of layers with pixel coordinates available as
    variables in the expression; and raster display using base 'graphics'.
    'gdalraster' may be suitable for applications that primarily need low-level
    I/O or prefer a direct 'GDAL' API. The additional functionality is somewhat
    aimed at thematic data analysis but may have other utility.
R's 'sf' package ships with self-contained 'GDAL'
    executables, including a bare bones interface to several
    'GDAL'-related utility programs collectively known as the 'GDAL
    utilities'. For each of those utilities, this package provides an
    R wrapper whose formal arguments closely mirror those of the
    'GDAL' command line interface. The utilities operate on data
    stored in files and typically write their output to other
    files. Therefore, to process data stored in any of R's more common
    spatial formats (i.e. those supported by the 'sf' and 'terra'
    packages), first write them to disk, then process them with the
    package's wrapper functions before reading the outputted results
    back into R. GDAL function arguments introduced in GDAL version
    3.5.2 or earlier are supported.
Various R programming tools for data manipulation, including
  medical unit conversions, combining objects, character vector operations,
  factor manipulation, obtaining information about R objects, generating
  fixed-width format files, extracting components of date & time objects,
  operations on columns of data frames, matrix operations, operations on
  vectors, operations on data frames, value of last evaluated expression, and a
  resample() wrapper for sample() that ensures consistent behavior for both
  scalar and vector arguments.
Many tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>), such as MCA variants (Specific Multiple Correspondence Analysis, Class Specific Analysis), many graphical and statistical aids to interpretation (structuring factors, concentration ellipses, inductive tests, bootstrap validation, etc.) and multiple-table analysis (Multiple Factor Analysis, between- and inter-class analysis, Principal Component Analysis and Correspondence Analysis with Instrumental Variables, etc.).
Functions to explore datasets from the Global Biodiversity Information Facility (GBIF - <https://www.gbif.org/>) using a Shiny interface.
The GDELT V1 Event data set is over 41 GB now and growing 250 MB 
    a month. The number of source articles has increased over time and unevenly 
    across countries. This package makes it easy to download a subset of that 
    data, then normalize that data to facilitate valid time series analysis.
Tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. GDI was first employed by Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999) <doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass estimation of (extinct) animals. The package 'gdi' aims to make this technique as convenient and versatile as possible. The core functions of 'gdi' provide utilities for automatically measuring diameters from digital silhouettes provided as image files and calculating volume via graphic double integration with simple elliptical, superelliptical (following Motani 2001 <doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex cross-sectional models. Additionally, the package provides functions for estimating the center of mass position (COM), the moment of inertia (I) for 3D shapes and the second moment of area (Ix) of 2D cross-sections, as well as for visualization of results.
Functions for performing graphical difference testing.     
             Differences are generated between raster images.
             Comparisons can be performed between different package
             versions and between different R versions.
Cross-validated eigenvalues are estimated by
    splitting a graph into two parts, the training and the test graph.
    The training graph is used to estimate eigenvectors, and
    the test graph is used to evaluate the correlation between the training
    eigenvectors and the eigenvectors of the test graph.
    The correlations follow a simple central limit theorem that can
    be used to estimate graph dimension via hypothesis testing, see
    Chen et al. (2021) <arXiv:2108.03336> for details.
A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) <DOI:10.18637/jss.v093.i14>, Ma and de la Torre (2019) <DOI:10.1111/emip.12262>, Ma (2019) <DOI:10.1007/978-3-030-05584-4_29> and de la Torre and Akbay (2019).
Provides classes and functions to calculate various 
             distance measures and routes in heterogeneous geographic 
             spaces represented as grids. The package implements measures
             to model dispersal histories first presented by van Etten and
             Hijmans (2010) <doi:10.1371/journal.pone.0012060>. Least-cost
             distances as well as more complex distances based on (constrained)
             random walks can be calculated. The distances implemented in 
             the package are used in geographical genetics, accessibility 
             indicators, and may also have applications in other fields of
             geospatial analysis.
Retrieve datasets from the 'Global Data Lab' website <https://globaldatalab.org>
    directly into R data frames. Functions are provided to reference available options
    (indicators, levels, countries, regions) as well.
A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) <doi:10.1111/geb.13459> Ferrier S, Manion G, Elith J, Richardson K (2007) <doi:10.1111/j.1472-4642.2007.00341.x>.
Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation
    includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02>
    for the identification of both the number of lags to be used
    in the generalized dynamic principal components as well as the number of components required
    for a given reconstruction accuracy.
Convert GDP time series data from one unit to
    another. All common GDP units are included, i.e. current and constant
    local currency units, US$ via market exchange rates and international
    dollars via purchasing power parities.
Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.
The method aims to identify important factors in screening experiments by aggregation over random models as studied in Singh and Stufken (2022) <doi:10.48550/arXiv.2205.13497>. This package provides functions to run the Gauss-Dantzig selector on screening experiments when interactions may be affecting the response. Currently, all functions require each factor to be at two levels coded as +1 and -1.
Tools are provided to compute metrics of 
  formatted strings and to check the availability of a font. 
  Another set of functions is provided to support the collection 
  of fonts from 'Google Fonts' in a cache. Their use is simple within 
  'R Markdown' documents and 'shiny' applications but also with graphic 
  productions generated with the 'ggiraph', 'ragg' and 'svglite' packages 
  or with tabular productions from the 'flextable' package.
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 J. von Neumann (1945, A Model of General Economic Equilibrium. The Review of Economic Studies, 13. pp. 1-9), J. G. Kemeny, O. Morgenstern and G. L. Thompson (1956, A Generalization of the von Neumann Model of an Expanding Economy, Econometrica, 24, pp. 115-135) et al. By the way, J. G. Kemeny is a co-inventor of the computer language BASIC.
Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).
Contains the framework of the estimation, sampling, and hypotheses testing for two special distributions (Exponentiated Exponential-Pareto and Exponentiated Inverse Gamma-Pareto) within the family of Generalized Exponentiated Composite distributions. The detailed explanation and the applications of these two distributions were introduced in Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.1080/03610926.2022.2050399>, Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/math10111895>, and Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/app13010645>.
Includes a collection of geographical analysis functions aimed primarily at ecology and conservation science studies, allowing processing of both point and raster data. Future versions will integrate species threat datasets developed by the authors.
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/id/eprint/18460/>.
Provides adaptive association tests for SNP level, gene level and pathway level analyses.
Estimation of generalized linear models with
    correlated/clustered observations by use of generalized estimating
    equations (GEE). See e.g. Halekoh and Højsgaard, (2005,
    <doi:10.18637/jss.v015.i02>), for details. Several types of
    clustering are supported, including exchangeable variance
    structures, AR1 structures, M-dependent, user-specified variance
    structures and more. The model fitting computations are performed
    using modified code from the 'geeM' package, while the interface
    and output objects have been written to resemble the 'geepack'
    package. The package also contains additional tools for working
    with and inspecting results from the 'geepack' package, e.g. a
    'confint' method for 'geeglm' objects from 'geepack'.
Population-averaged models have been increasingly used in the design and analysis of 
             cluster randomized trials (CRTs). To facilitate the applications of population-averaged 
             models in CRTs, the package implements the generalized estimating equations (GEE) and 
             matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal 
             mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the 
             general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by 
             Li et al. (2021) <doi:10.1093/biostatistics/kxaa056>
             specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives 
             a simple and efficient estimating equations approach based on the cluster-period means to 
             estimate the intervention effects as well as correlation parameters. In addition, the package 
             also provides functions for generating correlated binary data with specific mean vector and 
             correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or 
             the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.
Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) <doi:10.1016/j.jmva.2013.09.003>.
GEE estimation of the parameters in mean structures with possible
    correlation between the outcomes. User-specified mean link and variance
    functions are allowed, along with observation weighting. The 'M' in the name
    'geeM' is meant to emphasize the use of the Matrix package, which allows for an
    implementation based fully in R.
Causal mediation analysis for a single exposure/treatment and a
    single mediator, both allowed to be either continuous or binary. The package
    implements the difference method and provides point and interval estimates as
    well as testing for the natural direct and indirect effects and the mediation
    proportion. Nevo, Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.
Generalized estimating equations solver for parameters in
    mean, scale, and correlation structures, through mean link,
    scale link, and correlation link. Can also handle clustered
    categorical responses. See e.g. Halekoh and Højsgaard, (2005,
    <doi:10.18637/jss.v015.i02>), for details.
Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.
Analyze small-sample clustered or longitudinal data with binary 
    outcome using modified generalized estimating equations with bias-adjusted
    covariance estimator. The package provides any combination of three 
    modified generalized estimating equations and 11 bias-adjusted covariance 
    estimators.
Provides a general, flexible framework for estimating parameters
    and empirical sandwich variance estimator from a set of unbiased estimating
    equations (i.e., M-estimation in the vein of Stefanski & Boos (2002)
    <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002)
    are published in the corresponding Journal of Statistical Software paper
    "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020)
    <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample 
    variance corrections.
Functions to compute generalized eigenvalues and eigenvectors,
             the generalized Schur decomposition and
             the generalized Singular Value Decomposition of a matrix pair,
             using Lapack routines.
Methods for fitting macroevolutionary models to phylogenetic trees
	Pennell (2014) <doi:10.1093/bioinformatics/btu181>.
Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions.
Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).
Represents generalized geometric ellipsoids with the "(U,D)" representation. It allows degenerate
	and/or unbounded ellipsoids, together with methods for linear and duality transformations, and for plotting.
	Thus ellipsoids are naturally extended to include lines, hyperplanes, points, cylinders, etc.
	This permits exploration of a variety to statistical issues that can be visualized using ellipsoids
	as discussed by Friendly, Fox & Monette (2013), Elliptical Insights: Understanding Statistical Methods 
	Through Elliptical Geometry <doi:10.1214/12-STS402>.
Implements several extensions of the elastic net regularization
    scheme. These extensions include individual feature penalties for the L1 term,
    feature-feature penalties for the L2 term, as well as translation coefficients
    for the latter.
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. Reference: Oskar Hagen, Benjamin Flueck, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago F. Rangel, Loic Pellissier (2021) "gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity" <doi:10.1371/journal.pbio.3001340>.
A collection of functions for processing 'Gen5' 2.06 exported data.
    'Gen5' is an essential data analysis software for BioTek plate readers <https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>. This package contains functions for data cleaning, 
    modeling and plotting using exported data from 'Gen5' version 2.06. It exports
    technically correct data defined in (Edwin de Jonge and Mark van der Loo 
    (2013) <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>) for customized analysis. It 
    contains Boltzmann fitting for general kinetic analysis.  
    See <https://www.github.com/yanxianUCSB/gen5helper> for more information,
    documentation and examples.
Implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.archives-ouvertes.fr/hal-00764996>.
R based genetic algorithm for binary and floating point
        chromosomes.
Defines classes and methods that can be used
  to implement genetic algorithms for feature selection.  The idea is
  that we want to select a fixed number of features to combine into a
  linear classifier that can predict a binary outcome, and can use a
  genetic algorithm heuristically to select an optimal set of features.
Provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.
Allows users to generate a gendered language score according to the gendered language dictionary in Roberts and Utych (2019) <doi:10.1177/1065912919874883>.
Set of functions to create datasets using a correlation matrix.
Infers state-recorded gender categories from first names and dates of birth using historical
    datasets. By using these datasets instead of lists of male and female names,
    this package is able to more accurately infer the gender of a name, and it
    is able to report the probability that a name was male or female. GUIDELINES:
    This method must be used cautiously and responsibly. Please be sure to see the
    guidelines and warnings about usage in the 'README' or the package documentation.
    See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.
A method to predict and report gender from Brazilian first names
    using the Brazilian Institute of Geography and Statistics' Census data (<https://censo2010.ibge.gov.br/nomes/>).
Implementation of functions, which combines binomial calculation 
    and data visualisation, to analyse the differences in publishing authorship
    by gender described in Day et al. (2020) <doi:10.1039/C9SC04090K>.
    It should only be used when self-reported gender is unavailable.
Provides tools for quantitative analysis in gender studies, including functions to calculate various gender inequality metrics such as the Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI), and Gender Empowerment Measure (GEM). Also includes extracted real datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center <https://hdr.undp.org/data-center/documentation-and-downloads> and the World Bank Gender Data Portal <https://genderdata.worldbank.org/indicators/>. References: Miller, Kevin; Vagins, Deborah J. (2021) <https://eric.ed.gov/?id=ED596219>. Jacques Charmes & Saskia Wieringa (2003) <doi:10.1080/1464988032000125773>. Gaëlle Ferrant (2010) <https://shs.hal.science/halshs-00462463/>.
Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
Segmentation and classification procedures for data from the 'Activinsights GENEActiv' <https://activinsights.com/technology/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing.
    Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.
Functions and analytics for GENEA-compatible accelerometer data into R objects. 
             See topic 'GENEAread' for an introduction to the package. 
             See <https://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.
The GeneCycle package implements the approaches of Wichert
        et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki 
        et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al.
        (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically 
        expressed genes from gene expression time series data.
A function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293,  GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>. More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, <doi:10.1007/978-1-0716-1839-4_12>.
Implementation of several generalized F-statistics. The
    current version includes a generalized F-statistic based on the
    flexible isotonic/monotonic regression or order restricted hypothesis
    testing. Based on: Y. Lai (2011) <doi:10.1371/journal.pone.0019754>.
Import genome variants data and perform gene haplotype Statistics, 
  visualization and phenotype association with 'R'.
A pipeline with high specificity and sensitivity in extracting 
  proteins from the RefSeq database (National Center for Biotechnology 
  Information). Manual identification of gene families is highly 
  time-consuming and laborious, requiring an iterative process of manual and 
  computational analysis to identify members of a given family. The pipelines 
  implements an automatic approach for the identification of gene families 
  based on the conserved domains that specifically define that family. See 
  Die et al. (2018) <doi:10.1101/436659> for more information and examples.
An analysis toolkit focused on genes. It mainly includes five
    features (search, convert, analysis, plot, and export).
    The user just needs to input feature id ('entrez', 'symbol', 'ensembl' or
    'uniprot') to retrieve feature information and 'PubMed'<https://pubmed.ncbi.nlm.nih.gov/> records, 
    to convert id types, to easily handle gene enrichment analysis and publication-level figures, 
    to plot group interaction and export results as sheets in one 'excel'
    file to easily share and communicate with others.
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).
Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies.
Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Function gmcmtx0() computes a more reliable (general) 
    correlation matrix. Since causal paths from data are important for all sciences, the
    package provides many sophisticated functions. causeSummBlk() and causeSum2Blk()
    give easy-to-interpret causal paths.  Let Z denote control variables and compare 
    two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 
    says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent"
    than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These
    inequalities between many absolute values are quantified by four orders of 
    stochastic dominance. Our third criterion Cr3, for the causal path X to Y,
    requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|.
    The function parcorVec() reports generalized partials between the first
    variable and all others.  The package provides several R functions including
    get0outliers() for outlier detection, bigfp() for numerical integration by the
    trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
    canonRho() for generalized canonical correlations, depMeas() measures nonlinear
    dependence, and causeSummary(mtx) reports summary of causal paths among matrix 
    columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx()
    can rank several stocks. Functions whose names begin with 'boot' provide bootstrap
    statistical inference, including a new bootGcRsq() test for "Granger-causality" 
    allowing nonlinear relations. A new tool for evaluation of out-of-sample
    portfolio performance is outOFsamp(). Panel data implementation is now included.
    See eight vignettes of the package for theory, examples, and
    usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.
Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal).
A statistical hypothesis test for conditional independence. It performs nonlinear regressions on the conditioning variable and then tests for a vanishing covariance between the resulting residuals. It can be applied to both univariate random variables and multivariate random vectors. Details of the method can be found in Rajen D. Shah and Jonas Peters: The Hardness of Conditional Independence Testing and the Generalised Covariance Measure, Annals of Statistics 48(3), 1514--1538, 2020.
Functions for the hyperbolic and related distributions.
  Density, distribution and quantile functions and random number generation
  are provided for the hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, normal inverse
	Gaussian distribution and generalized inverse Gaussian distribution,
	including fitting of these distributions to data. Linear models with
        hyperbolic errors may be fitted using hyperblmFit.
Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.
A fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation.
  Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) <url:<https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2F17-AOS1652 >>.
  Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) <arXiv:2008.02904>.
  Reinhard Furrer and Roman Flury and Florian Gerber (2022) <url:<https://CRAN.R-project.org/package=spam >>.
Perform the Blinder-Oaxaca decomposition for generalized linear 
   model with bootstrapped standard errors. The twofold and threefold 
   decomposition are given, even the generalized linear model output in each group.
Allows users to quickly and easily generate fake data containing
    Personally Identifiable Information (PII) through convenience functions.
Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802>. This package's workhorse is the 'mlr3' framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802> for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported.
In order to reduce potential package dependencies and
    conflicts, generics provides a number of commonly used S3 generics.
Estimate gender from names in Spanish and Portuguese. 
    Works with vectors and dataframes. The estimation works not only
    for first names but also full names. The package relies on a
    compilation of common names with it's most frequent associated 
    gender in both languages which are used as look up tables for gender
    inference.
Simulating single cell RNA-seq data with complicated structure. This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) <doi:10.1186/s13059-017-1305-0>). 'GeneScape' incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes.
Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying 
    gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), 
    'KEGG' (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), 
    'WikiPathway' (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), 
    'MsigDb' (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), 
    'Reactome' (David et al. (2011) <doi:10.1093/nar/gkq1018>),
    'MeSH' (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), 
    'DisGeNET' (Janet et al. (2017) <doi:10.1093/nar/gkw943>),
    'Disease Ontology' (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), 
    'Network of Cancer Genes' (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and 
    'COVID-19' (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>).
    Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. 
    The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of 
    terms ID and term description.
Genome-wide association study (GWAS) performed with SLOPE,
    short for Sorted L-One Penalized Estimation, a
    method for estimating the vector of coefficients in a linear model.
    In the first step of GWAS, single nucleotide polymorphisms (SNPs) are 
    clumped according to their correlations and distances. 
    Then, SLOPE is performed on the data where each clump has
    one representative.
    Malgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su 
    and Emmanuel Candes (2014) 
    "SLOPE - Adaptive Variable Selection via Convex Optimization" 
    <arXiv:1407.3824>.
Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>).
  Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.
Data from multi environment agronomic trials, which are often 
  carried out by plant breeders, can be analyzed with the tools offered by this 
  package such as the Additive Main effects and Multiplicative Interaction model 
  or 'AMMI' ('Gauch' 1992, ISBN:9780444892409) 
  and the Site Regression model or 'SREG' ('Cornelius' 1996, 
  <doi:10.1201/9780367802226>). Since these methods present a poor performance 
  under the presence of outliers and missing values, this package includes 
  robust versions of the 'AMMI' model ('Rodrigues' 2016, 
  <doi:10.1093/bioinformatics/btv533>), and also imputation techniques 
  specifically developed for this kind of data ('Arciniegas-Alarcón' 2014, 
  <doi:10.2478/bile-2014-0006>).
Classes and methods for handling genetic data. Includes
        classes to represent genotypes and haplotypes at single markers
        up to multiple markers on multiple chromosomes. Function
        include allele frequencies, flagging homo/heterozygotes,
        flagging carriers of certain alleles, estimating and testing
        for Hardy-Weinberg disequilibrium, estimating and testing for
        linkage disequilibrium, ...
Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
Implementation of spatial graph-theoretic genetic gravity models.
    The model framework is applicable for other types of spatial flow questions.
    Includes functions for constructing spatial graphs, sampling and summarizing
    associated raster variables and building unconstrained and singly constrained
    gravity models.
Includes the reference implementation of Genie - a hierarchical
    clustering algorithm that links two point groups in such a way that
    an inequity measure (namely, the Gini index) of the cluster sizes
    does not significantly increase above a given threshold.
    This method most often outperforms many other data segmentation approaches
    in terms of clustering quality as tested on a wide range of benchmark
    datasets. At the same time, Genie retains the high speed of the single
    linkage approach, therefore it is also suitable for analysing larger data sets.
    For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>).
    For an even faster and more feature-rich implementation, including,
    amongst others, noise point detection, see the 'genieclust' package.
The American Association Research (AACR) Project 
    Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma 
    Collaborative represents a multi-year, multi-institution effort
    to build a pan-cancer repository of linked clinico-genomic data. 
    The genomic and clinical data are provided in multiple releases (separate
    releases for each cancer cohort with updates following data corrections), 
    which are stored on the data sharing platform 
    'Synapse' <https://www.synapse.org/>. 
    The 'genieBPC' package provides a seamless way to obtain the
    data corresponding to each release from 'Synapse' and to prepare
    datasets for analysis.
A retake on the Genie algorithm
    (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) - a robust
    hierarchical clustering method
    (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).
    Now faster and more memory efficient; determining the whole hierarchy
    for datasets of 10M points in low dimensional Euclidean spaces or
    100K points in high-dimensional ones takes only 1-2 minutes.
    Allows clustering with respect to mutual reachability distances
    so that it can act as a noise point detector or a robustified version of
    'HDBSCAN*' (that is able to detect a predefined number of
    clusters and hence it does not dependent on the somewhat
    fragile 'eps' parameter).
    The package also features an implementation of inequality indices
    (the Gini, Bonferroni index), external cluster validity measures
    (e.g., the normalised clustering accuracy and partition similarity scores
    such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information,
    and the pair sets index),
    and internal cluster validity indices (e.g., the Calinski-Harabasz,
    Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices).
    See also the 'Python' version of 'genieclust' available on 'PyPI', which
    supports sparse data, more metrics, and even larger datasets.
Implements readers and writers for file formats associated with genetics data.  Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations.  Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files).  There are also make functions for FAM and BIM tables with default values to go with simulated genotype data.
Provides tools to interact nicely with the 'Genius' API 
    <https://docs.genius.com/>. 
    Search hosted content, extract associated metadata and retrieve lyrics with ease.
Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations. See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) <doi:10.1186/s12859-015-0581-5>.
Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive 'RStudio' plot for better guessing dynamically of initial values for ease of included optimization and simulating.
Provides routines to estimate the Mixture Transition Distribution Model based on Raftery (1985) <http://www.jstor.org/stable/2345788> and Nicolau (2014) <doi:10.1111/sjos.12087> specifications, for multivariate data. Additionally, provides a 
             function for the estimation of a new model for multivariate non-homogeneous Markov chains. This new specification, Generalized Multivariate Markov Chains (GMMC) was proposed by Carolina Vasconcelos and Bruno Damasio and considers (continuous or discrete) covariates exogenous to the Markov chain.
Generalized meta-analysis is a technique for estimating parameters associated with a multiple regression model through meta-analysis of studies which may have information only on partial sets of the regressors. It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) <doi:10.1093/biomet/asz030>.The current version (0.2.0) is updated to address some of the stability issues in the previous version (0.1).
Using the DNA sequence and gene annotation files provided in 
            'ENSEMBL' <https://www.ensembl.org/index.html>, 
            the functions implemented in the package try to find the DNA sequences and 
            protein sequences of any given genomic loci, and to find the genomic coordinates 
            and protein sequences of any given protein locations, which are the frequent
            tasks in the analysis of genomic and proteomic data.
Calculates Agresti's generalized odds ratios.
  For a randomly selected pair of observations
  from two groups, calculates the odds that
  the second group will have a higher scoring outcome
  than that of the first group.
  Package provides hypothesis testing for if this odds
  ratio is significantly different to 1 (equal chance).
Evaluates likelihood ratio tests for alleged ancestry. Implements the methods of Tvedebrink et al (2018) <doi:10.1016/j.tpb.2017.12.004>.
Individual-based simulations forward in time,
    simulating how patterns in ancestry along the genome change after
    admixture. Full description can be found in Janzen (2021)
    <doi:10.1111/2041-210X.13612>.
Local structure in genomic data often induces dependence between observations taken at different genomic locations.  Ignoring this dependence leads to underestimation of the standard error of parameter estimates.  This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.
Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure(Cabrera et al (2012) <doi:10.1534/g3.112.002618>). All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user.
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.
Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids.
	Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and
	Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny().
It provides an interesting solution for handling a high number 
	of segmentation variables in partial least squares structural equation 
	modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, 
	and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient 
	test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) 
	to detect the path coefficients responsible for the identified differences). 
	The package also allows running the hybrid multi-group approach (Lamberti (2021) 
	<doi:10.1007/s11135-021-01096-9>).
Power and sample size calculations for genetic association studies allowing 
    for misspecification of the model of genetic susceptibility.
    "Hum Hered. 2019;84(6):256-271.<doi:10.1159/000508558>. Epub 2020 Jul 28."
    Power and/or sample size can be calculated for logistic (case/control study design) 
    and linear (continuous phenotype) regression models, using additive, dominant, 
    recessive or degree of freedom coding of the genetic covariate while assuming 
    a true dominant, recessive or additive genetic effect. In addition, power and 
    sample size calculations can be performed for gene by environment interactions.  
    These methods are extensions of Gauderman (2002) 
    <doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973>
    and are described in: 
    Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations 
    for Genetic Association Studies in the Presence of Genetic Model Misspecification. 
    American Society of Human Genetics. 
    October 2018, San Diego.
The genridge package introduces generalizations of the standard univariate
  ridge trace plot used in ridge regression and related methods.  These graphical methods
  show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
  coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
  both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
  predictors.
Performs search for global minimum of a very complex non-linear
    objective function with a very large number of optima.
Implementation of the Generalized Score Matching estimator in Yu et al. (2019) <https://jmlr.org/papers/v20/18-278.html> for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) <doi:10.1214/16-EJS1126>, with the addition of a diagonal multiplier.
Performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.
For educational purposes to demonstrate the importance of multiple regression. The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results.
Define and compute with generalized spherical distributions - multivariate probability
 laws that are specified by a star shaped contour (directional behavior) and a radial component.
 The methods are described in Nolan (2016) <doi:10.1186/s40488-016-0053-0>.
Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated.
Generation of survival data with one (binary)
  time-dependent covariate.  Generation of survival data arising
  from a progressive illness-death model.
The GenSVM classifier is a generalized multiclass support vector
	machine (SVM). This classifier aims to find decision boundaries that
	separate the classes with as wide a margin as possible. In GenSVM, the
	loss function is very flexible in the way that misclassifications are
	penalized.  This allows the user to tune the classifier to the dataset
	at hand and potentially obtain higher classification accuracy than
	alternative multiclass SVMs.  Moreover, this flexibility means that
	GenSVM has a number of other multiclass SVMs as special cases. One of
	the other advantages of GenSVM is that it is trained in the primal
	space, allowing the use of warm starts during optimization.  This
	means that for common tasks such as cross validation or repeated model
	fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den
	Burg and P.J.F. Groenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.
Implement a coherent and flexible protocol for animal color tagging. 'GenTag' provides a simple computational routine with low CPU usage to create color sequences for animal tag. First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications.
Make it easy to create simplified trial summary (TS) domain
  based on FDA FDA guide
  <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.
Two arms clinical trials required sample size is calculated in the comprehensive parametric context. The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) <doi:10.1201/9781315183084>, Wittes, J (2002) <doi:10.1093/epirev/24.1.39>, Sato, T (2000) <doi:10.1002/1097-0258(20001015)19:19%3C2689::aid-sim555%3E3.0.co;2-0>, Lachin J M, Foulkes, M A (1986) <doi:10.2307/2531201>, Whitehead J(1993) <doi:10.1002/sim.4780122404>, Julious SA (2023) <doi:10.1201/9780429503658>.
Defines window or bin boundaries for the analysis of genomic data.
    Boundaries are based on the inflection points of a cubic smoothing spline
    fitted to the raw data. Along with defining boundaries, a technique to
    evaluate results obtained from unequally-sized windows is provided.
    Applications are particularly pertinent for, though not limited to, genome
    scans for selection based on variability between populations (e.g. using
    Wright's fixations index, Fst, which measures variability in subpopulations
    relative to the total population).
The purpose is to account for the random displacements 
 (jittering) of true survey household cluster center coordinates in geostatistical 
 analyses of Demographic and Health Surveys program (DHS) data. Adjustment for 
 jittering can be implemented either in the spatial random effect, or in the 
 raster/distance based covariates, or in both. Detailed information about the methods 
 behind the package functionality can be found in two preprints.
 Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2202.11035v2>. 
 Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2211.07442v1>.
Collection of tools that facilitates data access and workflow for spatial analysis of Argentina. Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English.
Functions to fit geostatistical data. The data can be
        continuous, binary or count data and the models implemented are
        flexible. Conjugate priors are assumed on some parameters while
        inference on the other parameters can be done through a full
        Bayesian analysis of by empirical Bayes methods.
Easy access to official spatial data sets of Brazil as 'sf' objects 
             in R. The package includes a wide range of geospatial data available
             at various geographic scales and for various years with harmonized
             attributes, projection and fixed topology.
Tools for solving common geocaching puzzle types, and other
    Geocaching-related tasks.
Spatio-temporal causal inference based on point process data. 
    You provide the raw data of locations and timings of treatment and 
    outcome events, specify counterfactual scenarios, and the package 
    estimates causal effects over specified spatial and temporal windows.
    See Papadogeorgou, et  al. (2022) <doi:10.1111/rssb.12548>.
Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed.
    The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).
Stores small spatial datasets used to teach basic spatial analysis
    concepts. Datasets are based off of the 'GeoDa' software workbook and data
    site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin
    and team at the University of Chicago. Datasets are stored as 'sf' objects.
Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.
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, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y>.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Provides geographical faceting functionality for 'ggplot2'.
    Geographical faceting arranges a sequence of plots of data for different
    geographical entities into a grid that preserves some of the geographical
    orientation.
Kriging based methods are used for predicting functional data 
             (curves) with spatial dependence.
Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <http://geo.stat.fi/geoserver/wfs>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.
A Kriging method for functional datasets with spatial dependency.
    This functional Kriging method avoids the need to estimate the
    trace-variogram, and the curve is estimated by minimizing a quadratic
    form. The curves in the functional dataset are smoothed using Fourier
    series. The functional Kriging of this package is a modification of the
    method proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>.
Implementation of the ordinary functional kriging method 
    proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>. This
    implements an alternative method to estimate the trace-variogram using
    Fourier Smoothing and Gaussian Quadrature.
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.
The American Community Survey (ACS)
    <https://www.census.gov/programs-surveys/acs> offers geodatabases with
    geographic information and associated data of interest to researchers
    in the area. The goal of this package is to generate objects that
    allow us to access and consult the information available in various
    formats, such as in GeoPackage format or in multidimensional ROLAP
    star format.
Turn irregular polygons (such as geographical regions) into regular or hexagonal grids.
    This package enables the generation of regular (square) and hexagonal grids through the package 
    'sp' and then assigns the content of the existing polygons to the new grid using 
    the Hungarian algorithm, Kuhn (1955) (<doi:10.1007/978-3-540-68279-0_2>). 
    This prevents the need for manual generation of hexagonal grids or regular grids 
    that are supposed to reflect existing geography.
Geographical spatial analysis of cropland connectivity.
    Allows users to visualize risk index plots for a given set of crops.
    The functions are developed as an extension to analysis from Xing et al (2021) <doi:10.1093/biosci/biaa067>.
    The primary function is sean() and is indicative of how sensitive the risk analysis is to parameters using kernel models.
    The Package currently supports crops sourced from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) <doi:10.1029/2007gb002947> "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and
    International Food Policy Research Institute (2019) <doi:10.7910/DVN/PRFF8V> "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4".
    This analysis produces 3 maps - mean, variance, and difference for the crop risk index. It applies distance functions and graph operations on a network to calculate risk index.
    There are multiple ways in which functions can be used - generate final outcome and then the intermediate outcomes for more sophisticated use cases.
    Refer to vignettes.
    sean() will set some global variables which can be accessed using $ prefix. These values are propagated to other functions for performing operations such as distance matrix calculation.
    parameters.yaml stores the parameters and values and can be accessed using get_parameters(). Refer it's usage.
    The objective of this package is to support risk analysis using cropland connectivity on 10 parameters -
    host crops, density threshold, aggregation and distance method, resolution, geographic extent, link threshold, kernel models, network metrics and maps.
    These parameters serves as an input and are used different phases of analysis workflow.
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'; and serializing to/from 'Geobuf' binary 'GeoJSON' 
    format.
Convert data to 'GeoJSON' or 'TopoJSON' from various R
    classes, including vectors, lists, data frames, shape files, and
    spatial classes.  'geojsonio' does not aim to replace packages like
    'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to
    simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'.
Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://datatracker.ietf.org/doc/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>).
A compilation of tools to complete common tasks for studying gerrymandering. 
    This focuses on the geographic tool side of common problems, such as linking 
    different levels of spatial units or estimating how to break up units. Functions 
    exist for creating redistricting-focused data for the US.
Set of routines for making map projections (forward and inverse), topographic maps, perspective plots, geological maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map.
Tools to easily visualize geographic data of Morocco.
    This package interacts with data available through the
    'geomarocdata' package, which is available in a 'drat' 
    repository.   The size of the 'geomarocdata' package is
    approximately 12 MB.
Provides eigenvector-based (geometric) forecast
    combination methods; also includes simple approaches (simple average,
    median, trimmed and winsorized mean, inverse rank method) and regression-based
    combination. Tools for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity).
Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution.
Provides facilities to 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.
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.
The 'geomod' does spatial prediction of the Geotechnical soil properties. 
    It predicts the spatial distribution of Geotechnical properties of soil e.g. shear strength, 
    permeability, plasticity index, Standard Penetration Test (SPT) counts, etc. The output of the prediction takes the form of a 
    map or a series of maps. It uses the interpolation technique where a single or statistically “best” 
    estimate of spatial occurrence soil property is determined. The interpolation is based on both the 
    sampled data and a variogram model for the spatial correlation of the sampled data. 
    The single estimate is produced by a Kriging technique.
Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) simulation and inference  for random fields using standard likelihood and a likelihood approximation  method called  weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial  and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.
Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://github.com/mongodb/mongo-python-driver> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.
Read, manipulate, and digitize landmark data, generate shape
    variables via Procrustes analysis for points, curves and surfaces, perform
    shape analyses, and provide graphical depictions of shapes and patterns of
    shape variation.
A 'ggplot2' extension that allows text to follow curved paths.
    Curved text makes it easier to directly label paths or neatly annotate in 
    polar co-ordinates.
Multidimensional systems allow complex queries to be carried
    out in an easy way. The geographical dimension, together with the
    temporal dimension, plays a fundamental role in multidimensional
    systems. Through this package, vector geographic data layers can be
    associated to the attributes of geographic dimensions, so that the
    results of multidimensional queries can be obtained directly as vector
    layers.  The multidimensional structures on which we can define the
    queries can be created from a flat table or imported directly using
    functions from this package.
The web service at <https://www.geonames.org/> provides a number of spatial data queries, including 
    administrative area hierarchies, city locations and some country postal code queries. A (free) username
    is required and rate limits exist.
Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and expose it to OGC CSW.
Provides classes and methods for handling networks or 
  graphs whose nodes are geographical (i.e. locations in the globe).
  The functionality includes the creation of objects of class geonetwork
  as a graph with node coordinates, the computation of network measures,
  the support of spatial operations (projection to different Coordinate
  Reference Systems, handling of bounding boxes, etc.) and the plotting of
  the geonetwork object combined with supplementary cartography for spatial 
  representation.
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 an R API to the Open Source Geometry Engine
  ('GEOS') library (<https://libgeos.org/>) and a vector format 
  with which to efficiently store 'GEOS' geometries. High-performance functions 
  to extract information from, calculate relationships between, and
  transform geometries are provided. Finally, facilities to import 
  and export geometry vectors to other spatial formats are provided.
This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016)<doi:10.1002/9781118814963.ch13>, and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018)<doi:10.21082/jpptp.v2n2.2018.p101-110>.
Provides an R interface to the GeoServer REST API, allowing to upload 
 and publish data in a GeoServer web-application and expose data to OGC Web-Services. 
 The package currently supports all CRUD (Create,Read,Update,Delete) operations
 on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes,
 layers, styles, as well as vector data upload operations. For more information about 
 the GeoServer REST API, see <https://docs.geoserver.org/stable/en/user/rest/>.
Functionality for adding the geological timescale to bivariate plots.
Find the smallest circle that contains all longitude and latitude input points. From the generated center and radius, variable side polygons can be created, navigation based on bearing and distance can be applied, and more. Based on a modified version of Welzl's algorithm for smallest circle. Distance calculations are based on the haversine formula. Calculations for distance, midpoint, bearing and more are derived from <https://www.movable-type.co.uk>.
Understanding spatial association is essential for spatial 
             statistical inference, including factor exploration and spatial prediction. 
             Geographically optimal similarity (GOS) model is an effective method 
             for spatial prediction, as described in Yongze Song (2022) 
             <doi:10.1007/s11004-022-10036-8>. GOS was developed based on 
             the geographical similarity principle, as described in Axing Zhu (2018) 
             <doi:10.1080/19475683.2018.1534890>. GOS has advantages in 
             more accurate spatial prediction using fewer samples and 
             critically reduced prediction uncertainty.
R binds 'GeoSpark' <http://geospark.datasyslab.org/> extending 'sparklyr' 
    <https://spark.rstudio.com/> R package to make distributed 'geocomputing' easier. Sf is a
    package that provides [simple features] <https://en.wikipedia.org/wiki/Simple_Features> access
    for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring 
    the same simple features access like sf but running on Spark distributed system.
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis 
        functions (optimization, prediction and cross-validation), summary
        statistics from cross-validation, pocket plot, and design of
        optimal sampling networks through sequential and simultaneous
        points methods.
Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
For Bayesian inference with spatial data, provides exploratory spatial analysis tools, multiple spatial model specifications, spatial model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health surveillance data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
A collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as 'compositions', 'robCompositions', 'sp', 'gstat' and 'RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at <https://tinyurl.com/UCLgeostats/>.
Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector'
    objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian
    geostatistical models use Maximum Likelihood Estimation.  For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The 'RandomFields' package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.
It analyzes raster maps and other information as input/output
    files from the Hydrological Distributed Model GEOtop. It contains functions
    and methods to import maps and other keywords from geotop.inpts file. Some
    examples with simulation cases of GEOtop 2.x/3.x are presented in the package.
    Any information about the GEOtop Distributed Hydrological Model source code
    is available on www.geotop.org. Technical details about the model are
    available in Endrizzi et al (2014) <https://gmd.copernicus.org/articles/7/2831/2014/gmd-7-2831-2014.html>.
Provides functions and methods for: splitting large raster objects
             into smaller chunks, transferring images from a binary format into raster 
             layers, transferring raster layers into an 'RData' file, calculating the 
             maximum gap (amount of consecutive missing values) of a numeric vector, 
             and fitting harmonic regression models to periodic time series. The homoscedastic
             harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) <doi:10.1080/014311600209814>.
The toolbox have functions to load and process geographic
    information for Uruguay.  And extra-function to get address
    coordinates and orthophotos through the uruguayan 'IDE' API
    <https://www.gub.uy/infraestructura-datos-espaciales/tramites-y-servicios/servicios/sistema-unico-direcciones-geograficas>.
Simulation, estimation and inference for the geopolitical volatility (GEOVOL) model of Engle and Campos-Martins (2020) <doi:10.2139/ssrn.3685213>, where GEOVOL is modelled as a latent multiplicative volatility factor with heterogeneous factor loadings. Estimation is carried out as a maximization-maximization procedure, where GEOVOL and the GEOVOL loadings are estimated iteratively until convergence.
Contains the development of a tool that provides a web-based
    graphical user interface (GUI) to perform Techniques from a subset of
    spatial statistics known as geographically weighted (GW) models.
    Contains methods described by Brunsdon et al., 1996
    <doi:10.1111/j.1538-4632.1996.tb00936.x>, Brunsdon et al., 2002
    <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011
    <doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007
    <doi:10.1111/j.1538-4632.2007.00709.x>.
Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.
Encode and decode the Google Encoded Polyline Algorithm Format (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Implements a new multiple imputation method that draws
    imputations from a latent joint multivariate normal model which
    underpins generally structured data. This model is constructed using a
    sequence of flexible conditional linear models that enables the
    resulting procedure to be efficiently implemented on high dimensional
    datasets in practice. See Robbins (2021) <arXiv:2008.02243>.
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' <https://libgit2.org> with
    support for SSH and HTTPS remotes. All functions in 'gert' use basic R data 
    types (such as vectors and data-frames) for their arguments and return values.
    User credentials are shared with command line 'git' through the git-credential
    store and ssh keys stored on disk or ssh-agent.
Visualising publication activity per gene based on a gene list and  a user-defined set of keywords utilising R 'rentrez' <https://cran.r-project.org/package=rentrez/> that provides 'NCBI' <https://www.ncbi.nlm.nih.gov> database search like in 'PubMed' <https://pubmed.ncbi.nlm.nih.gov>. It summarises the retrieved number of publications for each gene in an overview bar plot (saved as 'png' file) and by choice a summary table (as 'csv') that including the most recent 100 'PubMed' IDs and publication titles.
Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.
Reproducible, programmatic retrieval of datasets from the
    GESIS Data Archive.  The GESIS Data Archive <https://search.gesis.org>  
    makes available thousands of invaluable datasets, but researchers using
    these datasets are caught in a bind.  The archive's terms and conditions
    bar dissemination of downloaded datasets to third parties, but to ensure 
    that one's work can be reproduced, assessed, and built upon by others, one
    must provide access to the raw data one has employed.  The 'gesisdata'
    package cuts this knot by providing registered users with programmatic,
    reproducible access to GESIS datasets from within 'R'.
Promote access to the GESLA
    <https://gesla787883612.wordpress.com> (Global Extreme Sea Level
    Analysis) dataset, a higher-frequency sea-level record data from all
    over the world. It provides functions to download it entirely, or
    query subsets directly into R, without the need of downloading the
    full dataset. Also, it provides a built-in web-application, so that
    users can apply basic filters to select the data of interest,
    generating informative plots, and showing the selected sites.
The method focuses on a single environmental exposure and induces 
    a main-effect-before-interaction hierarchical structure for the joint selection of interaction terms 
    in a regularized regression model. For details see Zemlianskaia et al. (2021) <arxiv:2103.13510>.
Provides a suite of function-building tools centered around a
  (forward) composition operator, %>>>%, which extends the semantics of the
  'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables
  you to construct composite functions that can be inspected and transformed as
  list-like objects. In conjunction with %>>>%, a compact function constructor,
  fn(), and a partial-application constructor, partial(), are also provided;
  both support quasiquotation.
Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).
Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) <doi:10.1515/em-2015-0005> and Dukes and Vansteelandt <doi:10.1093/aje/kwx347>. The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories. The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable.
Implementation of global envelopes for a set of general d-dimensional vectors T
    in various applications. A 100(1-alpha)% global envelope is a band bounded by two
    vectors such that the probability that T falls outside this envelope in any of the d
    points is equal to alpha. Global means that the probability is controlled simultaneously
    for all the d elements of the vectors. The global envelopes can be used for graphical
    Monte Carlo and permutation tests where the test statistic is a multivariate vector or
    function (e.g. goodness-of-fit testing for point patterns and random sets, functional
    analysis of variance, functional general linear model, n-sample test of correspondence
    of distribution functions), for central regions of functional or multivariate data (e.g.
    outlier detection, functional boxplot) and for global confidence and prediction bands
    (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See
    Myllymäki and Mrkvička (2020) <arXiv:1911.06583>,
    Myllymäki et al. (2017) <doi: 10.1111/rssb.12172>,
    Mrkvička and Myllymäki (2022) <arXiv:2008.10108>,
    Mrkvička et al. (2017) <doi: 10.1007/s11222-016-9683-9>,
    Mrkvička et al. (2020) <doi: 10.14736/kyb-2020-3-0432>,
    Mrkvička et al. (2021) <doi: 10.1007/s11009-019-09756-y>,
    Mrkvička et al. (2022) <doi: 10.1002/sim.9236>,
    Mrkvička et al. (2016) <doi: 10.1016/j.spasta.2016.04.005>,
    Myllymäki et al. (2021) <doi: 10.1016/j.spasta.2020.100436>,
    Dai et al. (2022) <doi: 10.5772/intechopen.100124>, and
    Dvořák and Mrkvička (2022) <doi: 10.1007/s00180-021-01134-y>.
Dynamically retrieve data from the web to render HTML tables
    on inspection in R Markdown HTML documents.
Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the 'memoise' package and
    , multicore/multisession with 'furrr' and format of output data (long/wide).
Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.
Reads annual financial reports including assets, liabilities, dividends history, stockholder composition and much more from Bovespa's DFP, FRE and FCA systems <http://www.b3.com.br/pt_br/produtos-e-servicos/negociacao/renda-variavel/empresas-listadas.htm>.
 These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing.
Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange 
            <https://www.b3.com.br/>. 
            All data is downloaded and imported from CVM's public ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/>.
The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design.
    The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.
Reads corporate data such as board composition and compensation for companies traded at B3, 
             the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from the ftp site <http://dados.cvm.gov.br/dados/CIA_ABERTA/DOC/FRE/>.
A micro-package for getting your 'IP' address, either the 
    local/internal or the public/external one. Currently only 'IPv4' addresses
    are supported.
Tool for import and process data from 'Lattes' curriculum platform (<http://lattes.cnpq.br/>). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in 'Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. 'getLattes' package allows work with 'Lattes' data exported to XML format.
A simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.
Quantifying systematic heterogeneity in meta-analysis using R.
    The M statistic aggregates heterogeneity information across multiple
    variants to, identify systematic heterogeneity patterns and their direction
    of effect in meta-analysis. It's primary use is to identify outlier studies,
    which either show "null" effects or consistently show stronger or weaker
    genetic effects than average across, the panel of variants examined in a
    GWAS meta-analysis. In contrast to conventional heterogeneity metrics
    (Q-statistic, I-squared and tau-squared) which measure random heterogeneity
    at individual variants, M measures systematic (non-random)
    heterogeneity across multiple independently associated variants. Systematic
    heterogeneity can arise in a meta-analysis due to differences in the study
    characteristics of participating studies. Some of the differences may
    include: ancestry, allele frequencies, phenotype definition, age-of-disease
    onset, family-history, gender, linkage disequilibrium and quality control
    thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical
    statistical theory, documentation and examples.
Package designed to be used with Rscript to write
    '#!' shebang scripts that accept short and long flags/options.
    Many users will prefer using instead the packages optparse or argparse
    which add extra features like automatically generated help option and usage,
    support for default values, positional argument support, etc.
This is a command-line argument parser which wraps the 
    powerful Perl module Getopt::Long and with some adaptations for easier use
	in R. It also provides a simple way for variable interpolation in R.
A micro-package for reading "passwords", i.e.  reading
    user input with masking, so that the input is not displayed as it 
    is typed.  Currently we have support for 'RStudio', the command line
    (every OS), and any platform where 'tcltk' is present.
Allows get address and port 
	of the free proxy server, from one of two services
	<http://gimmeproxy.com/> or <https://getproxylist.com/>. 
	And it's easy to redirect your Internet connection through
	a proxy server.
Imports time series data from the 'Quandl' database <https://data.nasdaq.com/>. The package uses the  'json api' at <https://data.nasdaq.com/search>, local caching ('memoise' package) and the tidy format by default. 
   Also allows queries of databases, allowing the user to see which time series are available for each database id. In short, it is an alternative to package 'Quandl', with faster data importation in the tidy/long format.
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03>.
Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the 'gets' package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.
An implementation of SPRE (standardised predicted random-effects)
    statistics in R to explore heterogeneity in genetic association meta-
    analyses, as described by Magosi et al. (2019) 
    <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision 
    weighted residuals that indicate the direction and extent with which 
    individual study-effects in a meta-analysis deviate from the average 
    genetic effect. Overly influential positive outliers have the potential 
    to inflate average genetic effects in a meta-analysis whilst negative 
    outliers might lower or change the direction of effect. See the 'getspres' 
    website for documentation and examples 
    <https://magosil86.github.io/getspres/>.
Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <https://www.tesourodireto.com.br/>.
A function to retrieve the system timezone on Unix systems
 which has been found to find an answer when 'Sys.timezone()' has failed.
 It is based on an answer by Duane McCully posted on 'StackOverflow', and
 adapted to be callable from R. The package also builds on Windows, but
 just returns NULL.
A simple wrapper for 'Wikipedia' data. Specifically, this 
    package looks to fill a gap in retrieving text data in a tidy format that can be used for Natural Language Processing.
A novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.
Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.
Generates experiments - simulating structured or experimental data as: 
             completely randomized design, randomized block design, latin square design, 
             factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; 
             Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; 
             Montgomery, 2001, ISBN:0471316490).
Download geyser eruption and observation data from the GeyserTimes
  site (<https://geysertimes.org>) and optionally store it locally. The vignette
  shows a simple analysis of downloading, accessing, and summarizing the data.
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.
Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL '<https://oops.uni-oldenburg.de/id/eprint/4290>').
Supports analyses using the Global Forest Change dataset released
    by Hansen et al. gfcanalysis was originally written for the Tropical Ecology 
    Assessment and Monitoring (TEAM) Network. For additional details on the 
    Global Forest Change dataset, see: Hansen, M. et al. 2013. "High-Resolution 
    Global Maps of 21st-Century Forest Cover Change." Science 342 (15 
    November): 850-53. The forest change data and more information on the 
    product is available at <http://earthenginepartners.appspot.com>.
Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
    Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.
Performs test procedures for general hypothesis testing problems for four multivariate coefficients of variation (Ditzhaus and Smaga, 2023 <arXiv:2301.12009>). We can verify the global hypothesis about equality as well as the particular hypotheses defined by contrasts, e.g., we can conduct post hoc tests. We also provide the simultaneous confidence intervals for contrasts.
Implemented are three Wald-type statistic and respective
  permuted versions for null hypotheses formulated in terms of cumulative hazard rate functions, medians and the concordance measure, respectively, in the general framework of survival factorial designs with possibly heterogeneous survival and/or censoring distributions, for crossed designs with an arbitrary number of factors and nested designs with up to three factors.
	Ditzhaus, Dobler and Pauly (2020) <doi:10.1177/0962280220980784> 
	Ditzhaus, Janssen, Pauly (2020) <arXiv: 2004.10818v2>
	Dobler and Pauly (2019) <doi:10.1177/0962280219831316>.
The philosophy in the package is described in Stasny (1988) <doi:10.2307/1391558> and Gutierrez, A., Trujillo, L. & Silva, N. (2014), <ISSN:1492-0921> to estimate the gross flows under complex surveys using a Markov chain approach with non response.
Focuses on data collecting, analyzing and visualization in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites such as MEP (Ministry of Environmental Protection of China, <https://www.mee.gov.cn>), water 
  related projects identification and environmental data visualization.
Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.
Fiducial framework to perform inference on the quantiles for a generalized Pareto distribution model and on the parameters of the Pareto exceedance distribution, assuming the exceedance threshold is a known or unknown parameter. Reference: Damian V. Wandler & Jan Hannig (2012) <doi:10.1007/s10687-011-0127-9>.
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>.
Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables.
    Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model,
    respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. 
    This model can be employed in social and behavioral sciences, economy and finance, and  genomics, 
    to extract interpretable nonlinear factors. More details can be referred to 
    Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2021) <doi:10.1080/01621459.2021.1999818>.
Download 'Google' fonts and generate 'CSS' to use in 'rmarkdown' documents and 
  'shiny' applications. Some popular fonts are included and ready to use.
Implements the 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.
Implements the G-Formula method for causal inference with time-varying treatments and
  confounders using Bayesian multiple imputation methods, as described by
  Bartlett, Olarte Parra and Daniel (2023) <arXiv:2301.12026>. It creates multiple synthetic imputed
  datasets under treatment regimes of interest using the 'mice' package. These can then be analysed
  using rules developed for analysing multiple synthetic datasets.
Penalized parametric change-point detection by functional pruning dynamic programming algorithm. The successive means are constrained using a graph structure with edges defining the nature of the changes These changes can be unconstrained (type std), up or down constrained (type up and down) or constrained by a minimal size jump (type abs). The type null means that the graph allows us to stay on the same segment. To each edge we can associate some additional properties: a minimal gap size, a penalty, some robust parameters (K,a) for biweight (K) and Huber losses (K and a). The user can also constrain the inferred means to lie between some minimal and maximal values. Data is modeled by a cost with possible use of a robust loss, biweight and Huber (see edge parameters K and a). These costs should have a quadratic, log-linear or a log-log representation. This includes quadratic Gaussian cost (type = 'mean'), log-linear cost (type = 'variance', 'poisson' or 'exp') and log-log cost (type = 'negbin'). More details in the paper published in the Journal of Statistical Software: <doi:10.18637/jss.v106.i06>.
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.
Providing publication-ready graphs for Multiple sequence alignment. Moreover, it provides a unique solution for visualizing the multiple sequence alignment without the need to do the alignment in each run which is a big limitation in other available packages.
'D&D' alignment charts show 9 boxes with values for good
    through evil and values for chaotic through lawful. This package
    easily creates these alignment charts from user-provided image paths
    and alignment values.
Extra geoms and scales for 'ggplot2', including geom_cloud(),
  a Normal density cloud replacement for errorbars;
  transforms ssqrt_trans and pseudolog10_trans, which are loglike but 
  appropriate for negative data; interp_trans() and warp_trans() which
  provide scale transforms based on interpolation;
  and an infix compose operator for scale transforms.
Alluvial plots use variable-width ribbons and stacked bar plots to
    represent multi-dimensional or repeated-measures data with categorical or
    ordinal variables; see Riehmann, Hanfler, and Froehlich (2005)
    <doi:10.1109/INFVIS.2005.1532152> and Rosvall and Bergstrom (2010)
    <doi:10.1371/journal.pone.0008694>.
    Alluvial plots are statistical graphics in the sense of Wilkinson (2006)
    <doi:10.1007/0-387-28695-0>; they share elements with Sankey diagrams and
    parallel sets plots but are uniquely determined from the data and a small
    set of parameters. This package extends Wickham's (2010)
    <doi:10.1198/jcgs.2009.07098> layered grammar of graphics to generate
    alluvial plots from tidy data.
The R package 'ggplot2' is a plotting system based on the grammar of graphics.
    'GGally' extends 'ggplot2' by adding several functions
    to reduce the complexity of combining geometric objects with transformed data.
    Some of these functions include a pairwise plot matrix, a two group pairwise plot
    matrix, a parallel coordinates plot, a survival plot, and several functions to
    plot networks.
A compendium of new geometries, coordinate systems, statistical 
    transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, 
    univariate average shifted histograms, a new map coordinate system based on the 
    'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of 
    geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly'
    compatibility and the 'StateFace' open source font 'ProPublica'. Further new 
    functionality includes lollipop charts, dumbbell charts, the ability to encircle
    points and coordinate-system-based text annotations.
Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.
The grammar of graphics as implemented in the 'ggplot2' package has
    been successful in providing a powerful API for creating static 
    visualisation. In order to extend the API for animated graphics this package
    provides a completely new set of grammar, fully compatible with 'ggplot2' 
    for specifying transitions and animations in a flexible and extensible way.
Geoms for placing arrowheads at multiple points along a segment, not just at the end; position function to shift starts and ends of arrows to avoid exactly intersecting points.
Mapping tools that convert place names to coordinates on the fly.
    These 'ggplot2' extensions make maps from a data frame where one of the
    columns contains place names, without having to directly work with the
    underlying geospatial data and tools. The corresponding map data must be
    registered with 'cartographer' either by the user or by another package.
Provides two methods of plotting categorical scatter plots such
    that the arrangement of points within a category reflects the density of
    data at that region, and avoids over-plotting.
Simplify 'ggplot2' visualisation with 'ggblanket' wrapper functions.
Algebra of operations for blending, copying, adjusting, and 
    compositing layers in 'ggplot2'. Supports copying and adjusting the 
    aesthetics or parameters of an existing layer, partitioning a layer 
    into multiple pieces for re-composition, applying affine transformations
    to layers, and combining layers (or partitions of layers) using blend modes
    (including commutative blend modes, like multiply and darken). Blend 
    mode support is particularly useful for creating plots with overlapping 
    groups where the layer drawing order does not change the output; 
    see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.
A set of geometries to make line plots a little bit nicer. Use 
    along with 'ggplot2' to:
    - Improve the clarity of line plots with many overlapping lines
    - Draw more realistic worms.
A 'ggplot2'-consistent approach to generating 2D displays of  volumetric brain imaging data.
  Display data from multiple NIfTI images using standard 'ggplot2' conventions such scales, limits, and
  themes to control the appearance of displays. The resulting plots are returned as 'patchwork' objects,
  inheriting from 'ggplot', allowing for any standard modifications of display aesthetics supported by 'ggplot2'.
An implementation of scale functions for setting axis breaks of a 'gg' plot.
When comparing discrete data mini bubble plots allow displaying 
   more information than traditional bubble plots via colour, shape or labels. 
   Exact overlapping coordinates will be transformed so they surround the
   original point circularly without overlapping. This is implemented as a 
   position_surround() function for 'ggplot2'.
Saves a 'ggplot' object into multiple files, each with a layer 
    added incrementally. Generally to be used in presentation slides.
    Flexible enough to allow different file types for the final complete plot,
    and intermediate builds.
A geom for ggplot to create bump plots. Can be good to use for showing rank over time.
R provides fantastic tools for changepoint
    analysis, but plots generated by the tools do 
    not have the 'ggplot2' style. This tool, however, 
    combines 'changepoint', 'changepoint.np' and 'ecp'
    together, and uses 'ggplot2' to visualize changepoints.
Streamline the creation of common charts by taking care of a lot of
    data preprocessing and plot customization for the user. Provides a
    high-level interface to create plots using 'ggplot2'.
Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data
    to human-like faces. Inspired by Chernoff (1973) <doi:10.1080/01621459.1973.10482434>.
Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.
William S. Cleveland's book 'Visualizing Data' is a classic piece 
	of literature on Exploratory Data Analysis. Although it was written 
	several decades ago, its content is still relevant as it proposes several 
	tools which are useful to discover patterns and relationships among the data 
	under study, and also to assess the goodness of fit o a model.  This package 
	provides functions to produce the 'ggplot2' versions of the visualization tools 
	described in this book and is thought to be used in the context of courses on 
	Exploratory Data Analysis.
The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for
    reordering the correlation matrix and displays the significance level
    on the plot. It also includes a function for computing a matrix of
    correlation p-values.
Corset plots are a visualization technique used strictly to visualize repeat measures 
  at 2 time points (such as pre- and post- data). The distribution of measurements are visualized at 
  each time point, whilst the trajectories of individual change are visualized by connecting the pre- 
  and post- values linearly. These lines can be coloured to represent the magnitude of change, or 
  other user-defined value. This method of visualization is ideal for showing the heterogeneity of 
  data, including differences by sub-groups. The package relies on 'ggplot2' allowing for easy 
  integration so that users can customize their visualizations as required. Users can create corset 
  plots using data in either wide or long format using the functions gg_corset() or gg_corset_elongated(), respectively.
Tidy, analyze, and plot directed acyclic graphs (DAGs).
    'ggdag' is built on top of 'dagitty', an R package that uses the
    'DAGitty' web tool (<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().
Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment 
    made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>),
    the seasonal adjustment software officially recommended to the members of the European Statistical System and
    the European System of Central Banks.
This is a set of tools for dendrograms and
    tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
    clearly separate data from the presentation.
    Unfortunately the plot method for dendrograms plots
    directly to a plot device without exposing the data.
    The 'ggdendro' package resolves this by making available
    functions that extract the dendrogram plot data. The package
    provides implementations for 'tree', 'rpart', as well as diana and agnes
    (from 'cluster') diagrams.
The 'ggplot2' package provides simple functions for visualizing contours
  of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators 
  as well as more interpretable visualizations based on highest density regions instead of
  the traditional height of the estimated density surface.
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for
    visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as 
    frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as 
    bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include
    but are not limited to: points with multiple uncertainty intervals, 
    eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>,
    density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>,
    quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>,
    complementary cumulative distribution function 
    barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>,
    and fit curves with multiple uncertainty ribbons.
Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.
Generate commonly used plots in the field of design of experiments using 'ggplot2'.
  'ggDoE' currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot,
  regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs,
  contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.
A data visualization design that provides comparison between 
    two (Double) data sources (usually on a par with each other) on one 
    reformed heatmap, while inheriting 'ggplot2' features.
Create biplots for GGE (genotype plus genotype-by-environment) and
    GGB (genotype plus genotype-by-block-of-environments) models. 
    See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.
Provides a series of aliases to commonly used but difficult 
    to remember 'ggplot2' sequences.
Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within the archived package 'GGEBiplotGUI'.
Interactively edit 'ggplot2' layer and theme aesthetics definitions.
Compute marginal effects and adjusted predictions from statistical
    models and returns the result as tidy data frames. These data frames are 
    ready to use with the 'ggplot2'-package. Effects and predictions can be 
    calculated for many different models. Interaction terms, splines and 
    polynomial terms are also supported. The main functions are ggpredict(), 
    ggemmeans() and ggeffect(). There is a generic plot()-method to plot the 
    results using 'ggplot2'.
Implements an extension of 'ggplot2' and visualizes the symbolic data with multiple plot which can be adjusted by more general and flexible input arguments. It also provides a function to transform the classical data to symbolic data by both clustering algorithm and customized method.
Extension of 'ggplot2' providing layers, scales and preprocessing functions
    useful to represent behavioural variables that are recorded over multiple animals and days.
    This package is part of the 'rethomics' framework <https://rethomics.github.io/>.
Collection of functions and layers to enhance 'ggplot2'. The 
    flagship function is 'ggMarginal()', which can be used to add marginal
    histograms/boxplots/density plots to 'ggplot2' scatterplots.
Contains functions to create life history parameter plots from raw data. 
	The plots are created using 'ggplot2', and calculations done using the 'tidyverse'
	collection of packages. The package contains references to FishBase 
	(Froese R., Pauly. D., 2023) <https://www.fishbase.se/>.
A 'ggplot2' extension to fit text into a box by growing, shrinking or wrapping the text.
Flowcharts can be a useful way to visualise complex processes. This package 
    uses the layered grammar of graphics of 'ggplot2' to create simple flowcharts.
A 'ggplot2' extension that provides tools for automatically
    creating scales to focus on subgroups of the data plotted 
    without losing other information.
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'.
Useful functions and utilities for 'ggplot' object (e.g., geometric layers, themes, and utilities to edit the object).
Provides a range of filters that can be applied to layers from the 
    'ggplot2' package and its extensions, along with other graphic elements such 
    as guides and theme elements. The filters are applied at render time and 
    thus uses the exact pixel dimensions needed.
The function gggap() streamlines the creation of segments on
    the y-axis of 'ggplot2' plots which is otherwise not a trivial task to
    accomplish.
A 'ggplot2' extension for drawing gene arrow maps.
Creates a reproducible 'ggplot2' object by storing the data and calls.
Moon charts are like pie charts except that the proportions are
    shown as crescent or gibbous portions of a circle, like the lit and unlit
    portions of the moon. As such, they work best with only one or two groups.
    'gggibbous' extends 'ggplot2' to allow for plotting multiple moon charts in
    a single panel and does not require a square coordinate system.
An extension of 'ggplot2' that makes it easy to add
             raw 'grid' output, such as customised annotations, to a 
             'ggplot2' plot.
A 'ggplot2' extension that does a variety of little
    helpful things.  The package extends 'ggplot2' facets through
    customisation, by setting individual scales per panel, resizing panels
    and providing nested facets.  Also allows multiple colour and fill
    scales per plot. Also hosts a smaller collection of stats, geoms and axis 
    guides.
Reproduce the halfnorm() function found in the 'faraway' package 
    using the 'ggplot2' API.
A 'ggplot2' extension for easy plotting of half-half geom combinations. Think half boxplot and half jitterplot, or half violinplot and half dotplot.
Provides 'ggplot2' framework for visualising Highest Density Regions (HDR) 
    <doi:10.1080/00031305.1996.10474359>. This work is based on
    the package 'hdrcde'<https://pkg.robjhyndman.com/hdrcde/> and 
    displays highest density regions in 'ggplot2' for one and two dimensions and
    univariate densities conditional on one covariate.
A Humanitarian Data Exchange (HDX) theme, color palettes, and
    scales for 'ggplot2' to allow users to easily follow the HDX visual design
    guide, including convenience functions for for loading and using the
    Source Sans 3 font.
The flexibility and excellence of 'ggplot2' is unquestionable, 
    so many drawing tools basically need 'ggplot2' as the operating object. 
    In order to develop a heatmap drawing system based on ggplot2, we developed 
    this tool, mainly to solve the heatmap puzzle problem and the flexible 
    connection between the heatmap and the 'ggplot2' object.
    The advantages of this tool are as follows: 
    1. More flexible label settings; 
    2. Realize the linkage of heatmap and 'ggplot2' drawing system, 
       which is helpful for operations such as puzzles; 
    3. Simple and easy to operate; 
    4. Optimization of clustering tree visualization.
Make it easier to explore data with highlights.
A set of functions that help to create plots based on Hilbert curves. 
    Hilbert curves are used to map one dimensional data into the 2D plane.
    The package provides a function that generate a 2D coordinate from an 
    integer position. As a specific use case the package provides a function 
    that allows mapping a character column in a data frame into 2D space 
    using 'ggplot2'. This allows visually comparing long lists of URLs, words,
    genes or other data that has a fixed order and position.
Wrapper around geom_histogram() of 'ggplot2' to plot the histogram of a numeric vector. This is especially useful, since qplot() was deprecated in 'ggplot2' 3.4.0.
A user-friendly, highly customizable R package for building
    horizon plots in the 'ggplot2' environment.
Supports image files and graphic objects to be visualized in
    'ggplot2' graphic system.
Provides two new layer types for displaying image data as layers
  within the Grammar of Graphics framework. Displays images using either a
  rectangle interface, with a fixed bounding box, or a point interface using a
  central point and general size parameter. Images can be given as local
  JPEG or PNG files, external resources, or as a list column containing
  raster image data.
Visualise the results of F test to compare two variances, Student's t-test, test of equal or given proportions, Pearson's chi-squared test for count data and test for association/correlation between paired samples.
Extensions to 'ggplot2' providing low-level debug tools: statistics
    and geometries echoing their data argument. Layer manipulation: deletion,
    insertion, extraction and reordering of layers. Deletion of unused variables
    from the data object embedded in "ggplot" objects.
A 'ggplot2' extension that enables visualization of IP
    (Internet Protocol) addresses and networks. The address space is
    mapped onto the Cartesian coordinate system using a space-filling
    curve. Offers full support for both IPv4 and IPv6 (Internet Protocol
    versions 4 and 6) address spaces.
A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://activinsights.com/> and GENEA devices (not for sale), .csv-export data from  'Actigraph' <https://theactigraph.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'.
Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' <https://activinsights.com/>, binary data from GENEA devices (not for sale), and .cwa-format and .wav-format data from 'Axivity' <https://axivity.com>. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.
Provides stat_isotonic() to add weighted univariate isotonic regression curves.
Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
Provides multiple palettes based on pride flags with tailored themes.
Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics.  
  Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.
Provides 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.
Helper to add insets based on geom_sf() from 'ggplot2'.
    This package gives you a drop-in replacement for geom_sf() that supports
    adding a zoomed inset map without having to create and embed a separate plot.
A quick and easy way of plotting the columns of two matrices or 
    data frames against each other using 'ggplot2'. Although 'ggmatplot' doesn't 
    provide the same flexibility as 'ggplot2', it can be used as a workaround for 
    having to wrangle wide format data into long format for plotting with
    'ggplot2'.
Tools for assessing and diagnosing convergence of
    Markov Chain Monte Carlo simulations, as well as for graphically display
    results from full MCMC analysis. The package also facilitates the graphical
    interpretation of models by providing flexible functions to plot the
    results against observed variables, and functions to work with
    hierarchical/multilevel batches of parameters
    (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Enhance a 'mice' imputation workflow with visualizations for
    incomplete and/or imputed data. The plotting functions produce
    'ggplot' objects which may be easily manipulated or extended. Use
    'ggmice' to inspect missing data, develop imputation models, evaluate
    algorithmic convergence, or compare observed versus imputed data.
Fit penalized multivariable linear mixed models with a single 
    random effect to control for population structure in genetic association 
    studies. The goal is to simultaneously fit many genetic variants at the 
    same time, in order to select markers that are independently associated 
    with the response. Can also handle prior annotation information, 
    for example, rare variants, in the form of variable weights. For more 
    information, see the website below and the accompanying paper: 
    Bhatnagar et al., "Simultaneous SNP selection and adjustment for 
    population structure in high dimensional prediction models", 2020, 
    <DOI:10.1371/journal.pgen.1008766>.
Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>, 
  including the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>, 
  seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>,
  exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>, 
  smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>,
  logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>,
  Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>,
  and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions
  for computing variable inclusion probabilities, multiple regression coefficients, and 
  statistical inference <doi:10.1214/15-EJS1031>.
Estimate non-regularized Gaussian graphical models, Ising models, 
  and mixed graphical models. The current methods consist of multiple regression, 
  a non-parametric bootstrap  <doi:10.1080/00273171.2019.1575716>, and Fisher z 
  transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, 
  predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.
Mosaic plots in the 'ggplot2' framework. Mosaic
    plot functionality is provided in a single 'ggplot2' layer by calling
    the geom 'mosaic'.
Extract and visualize motif information from XML file from MEME software. 
    In biology, a motif is a nucleotide or amino acid sequence pattern that is widespread 
    and usually assumed to be related to specifical biological functions. 
    There exist many software was used to discover motif sequences from a set of nucleotide 
    or amino acid sequences. MEME is almost the most used software to detect motif. 
    It's difficult for biologists to extract and visualize the location of a motif 
    on sequences from the results from MEME software.
Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.
Estimation of partial correlation matrix using ridge penalty 
    followed by thresholding and reestimation. Under multivariate Gaussian 
    assumption, the matrix constitutes an Gaussian graphical model (GGM).
Graph estimation in Gaussian Graphical Models, following the method
  developed by C. Giraud, S. Huet and N. Verzelen (2012) <doi:10.1515/1544-6115.1625>.
  The main functions return the adjacency matrix of an undirected graph
  estimated from a data matrix.
Create plots that combine a phylogeny and frequency dynamics.
    Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo".
    Inspired by similar plots in publications of the labs of RE Lenski and JE
    Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose
    code this package exploits).
It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.
Geometries to plot network objects with 'ggplot2'.
Use multiple fill and colour scales in 'ggplot2'.
Uses 'ggplot2' to create normally distributed
    violin plots with specified means and standard deviations. This
    function can be useful in showing hypothetically normal distributions
    and confidence intervals.
Provides a theme, a discrete color palette, and continuous scales
    to make 'ggplot2' look like 'gnuplot'. This may be helpful if you use both
    'ggplot2' and 'gnuplot' in one project.
Allows plotting data on bathymetric maps using 'ggplot2'. Plotting
  oceanographic spatial data is made as simple as feasible, but also flexible
  for custom modifications. Data that contain geographic information from 
  anywhere around the globe can be plotted on maps generated by the basemap()
  or qmap() functions using 'ggplot2' layers separated by the '+' operator. The 
  package uses spatial shape- ('sf') and raster ('stars') files, geospatial 
  packages for R to manipulate, and the 'ggplot2' package to plot these 
  files. The package ships with low-resolution spatial data files and 
  higher resolution files for detailed maps are stored in the 
  'ggOceanMapsLargeData' repository on GitHub and downloaded automatically 
  when needed.
Discrete scales for the colorblind-friendly 'Okabe-Ito'
    palette, including 'color', 'fill', and 'edge_colour'. 'ggokabeito'
    provides 'ggplot2' and 'ggraph' scales to easily use the 'Okabe-Ito'
    palette in your data visualizations.
The 'vegan' package includes several functions for adding features
  to ordination plots: ordiarrows(),  ordiellipse(), ordihull(),
  ordispider() and ordisurf(). This package adds these same   features to 
  ordination plots made with 'ggplot2'. In addition, gg_ordibubble() sizes
  points relative to the value of an environmental variable.
Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.
Create groups of 'ggplot2' layers that can be easily migrated from one plot
    to another, reducing redundant code and improving the ability to format many
    plots that draw from the same source 'ggpacket' layers.
Facilitates the creation of page layout
    visualizations in which words are represented as rectangles with sizes
    relating to the length of the words. Which then is divided in lines
    and pages for easy overview of up to quite large texts.
Create hammock plots, parallel sets, and common angle plots
    with 'ggplot2'.
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'.
A 'ggplot2' extension that enables robust image grobs in
    panels and theme elements.
Provides 'ggplot2' geoms filled with various patterns.  Includes a patterned version of every 'ggplot2' geom that has a region that can be filled with a pattern.  Provides a suite of 'ggplot2' aesthetics and scales for controlling pattern appearances.  Supports over a dozen builtin patterns (every pattern implemented by 'gridpattern') as well as allowing custom user-defined patterns.
Modern Parallel Coordinate Plots have been introduced in the 1980s as 
    a way to visualize arbitrarily many numeric variables. This Grammar of Graphics implementation
    also incorporates categorical variables into the plots in a principled manner. 
    By separating the data managing part from the visual rendering, we give full access
    to the users while keeping the number of parameters manageably low.
Implements methods to plot periodic data in any arbitrary range on the fly.
Provides a convenient way to analyze and visualize 'PICRUSt2' output with pre-defined plots and functions. Allows for generating statistical plots about microbiome functional predictions and offers customization options. Features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. Streamlines the 'PICRUSt2' analysis and visualization process. For more details, see Yang et al. (2023) <doi:10.1093/bioinformatics/btad470>.
Create pie, donut and rose pie plot with 'ggplot2'.
Enables users to create simple plots of biological culture plates as well as microplates. Both continuous and discrete values can be plotted onto the plate layout.
Provides the ggplot binning layer stat_summaries_hex(),
    which functions similar to its singular form,
    but allows the use of multiple statistics per bin.
    Those statistics can be mapped to multiple bin aesthetics.
A system for 'declaratively' creating graphics, based on "The
    Grammar of Graphics". You provide the data, tell 'ggplot2' how to map
    variables to aesthetics, what graphical primitives to use, and it
    takes care of the details.
Selected utilities, in particular 'geoms' and 'stats'
    functions, extending the 'ggplot2' package. This package imports
    functions from 'EnvStats' <doi:10.1007/978-1-4614-8456-1>, 'GGally'
    <doi:10.5281/zenodo.5009047> and 'ggpp'
    <https://CRAN.R-project.org/package=ggpp> and then exports them.  This
    package also contains modified code from 'ggquickeda'
    <https://CRAN.R-project.org/package=ggquickeda> for Kaplan-Meier lines
    and ticks additions to plots.  All functions are tested to make sure
    that they work reliably.
A dataset about movies. This was previously contained in ggplot2,
   but has been moved its own package to reduce the download size of ggplot2.
An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package.
    You can learn each steps of making plot by clicking your mouse without coding.
    You can get resultant code for the plot.
Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.
Convert plot function call (using expression or formula) to 'grob' or 'ggplot' object that compatible to the 'grid' and 'ggplot2' ecosystem. With this package, we are able to e.g. using 'cowplot' to align plots produced by 'base' graphics, 'ComplexHeatmap', 'eulerr', 'grid', 'lattice', 'magick', 'pheatmap', 'vcd' etc. by converting them to 'ggplot' objects.
Convenience functions for smooth conversion from 'ggplot' to 'plotly' where the conversion using ggplotly() usually gives an unexpected labels. The package ease the process of making a 'plotly' figures generated from 'ggplot2' object more aesthetic in terms of labels and customizability.
Extensions to 'ggplot2' respecting the grammar of graphics
    paradigm. Statistics: locate and tag peaks and valleys; label plot with the
    equation of a fitted polynomial or other types of models; labels
    with P-value, R^2 or adjusted R^2 or information criteria for fitted models;
    label with ANOVA table for fitted models; label with summary for fitted
    models. Model fit classes for which suitable methods are provided by package
    'broom' and 'broom.mixed' are supported. Scales and stats to build volcano
    and quadrant plots based on outcomes, fold changes, p-values and false 
    discovery rates.
At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling
  activities in order to reduce the overall effort required for generating such plots.
  For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox,
  called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient
  for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP
  <doi:10.1002/psp4.12161>. While not required, you can get/install the 'R' 'lixoftConnectors'
    package in the 'Monolix' installation, as described at the following url
    <https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/>.
    When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required
    Chart Data instead of exporting it from the 'Monolix' gui.
A cross between a 2D density plot and a scatter plot,
    implemented as a 'ggplot2' geom. Points in the scatter plot are 
    colored by the number of neighboring points. This is useful to
    visualize the 2D-distribution of points in case of overplotting.
A collection of geometries and stats for
    'ggplot2'. Currently it supports geom_pointless() which adds
    minimal emphasis to your plots. Or just some visual sugar. 
    geom_lexis() draws a 45° lifeline of an event that mimics 
    lexis diagrams. geom_chaikin() applies Chaikin's corner
    cutting algorithm to a path.
A 'ggplot2' extension for implementing parliament charts and several other useful visualizations.
Provides basic graphing functions to fully demonstrate
    point-to-point connections in a polar coordinate space.
Tools for working with polygons with holes in 'ggplot2', with a
    new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding'
    rules.
Extensions to 'ggplot2' respecting the grammar of graphics 
    paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to 
    plots using native data coordinates, while geom_table_npc(), geom_plot_npc()
    and geom_grob_npc() do the same using "npc" coordinates through new 
    aesthetics "npcx" and "npcy". Statistics: select observations based on 2D 
    density. Positions: radial nudging away from a center point and nudging away
    from a line or curve; combined stacking and nudging; combined dodging and
    nudging.
Provides various themes, palettes, and other functions
    that are used to customise ggplots to look like they were made in 'GraphPad 
    Prism'. The 'Prism'-look is achieved with theme_prism() and 
    scale_fill|colour_prism(), axes can be changed with custom guides like 
    guide_prism_minor(), and significance indicators added with add_pvalue().
The 'ggplot2' package is excellent and flexible for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.
Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, 
    and adds the resulting p-values to the plot with an annotation bar.
    Visualizing group differences are frequently performed by boxplots, bar plots, etc.
    Statistical test results are often needed to be annotated on these plots. 
    This package provides a convenient function that works on 'ggplot2' objects, 
    performs the desired statistical test between groups of interest and annotates the test results on the plot.
Plot single and faceted type quality control charts
  for 'ggplot'.
A quantile-quantile plot can be used to compare a sample of p-values 
  to the uniform distribution.  But when the dataset is big (i.e. > 1e4 p-values),
  plotting the quantile-quantile plot can be slow.  geom_QQ uses all the data to
  calculate the quantiles, but thins it out in a way that focuses on points near zero 
  before plotting to speed up plotting and decrease file size, when vector graphics are stored.
Quickly and easily perform exploratory data analysis by uploading your
     data as a 'csv' file. Start generating insights using 'ggplot2' plots and
     'table1' tables with descriptive stats, all using an easy-to-use point and click 
     'Shiny' interface.
An extension of 'ggplot2' to provide quiver plots to visualise vector fields. 
    This functionality is implemented using a geom to produce a new graphical layer, which
    allows aesthetic options. This layer can be overlaid on a map to improve visualisation
    of mapped data.
Extend 'ggplot2' facets to panel layouts arranged in a grid
    with ragged edges. facet_ragged_rows() groups panels in rows of
    (potentially) varying lengths, facet_ragged_cols() does the same for
    columns. These can be useful, for example, to represent nested or
    partially crossed relationships between faceting variables.
The 'geom_rain()' function adds different geoms together using 'ggplot2' to create raincloud plots.
Graphic elements for exploring Random Forests using the 'randomForest' or
    'randomForestSRC' package for survival, regression and classification forests and
    'ggplot2' package plotting.
The grammar of graphics as implemented in ggplot2 is a poor fit for
    graph and network visualizations due to its reliance on tabular data input.
    ggraph is an extension of the ggplot2 API tailored to graph visualizations
    and provides the same flexible approach to building up plots layer by layer.
Intended for both technical and non-technical users to create
    interactive data visualizations through a web browser GUI without writing any
    code.
Given a group of genomes and their relationship with each other, the package clusters the genomes and selects the most representative members of each cluster. Additional data can be provided to the prioritize certain genomes. The results can be printed out as a list or a new phylogeny with graphs of the trees and distance distributions also available. For detailed introduction see: Thomas H Clarke, Lauren M Brinkac, Granger Sutton, and Derrick E Fouts (2018), GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models, Bioinformatics, bty300, <doi:10.1093/bioinformatics/bty300>.
Rasterize only specific layers of a 'ggplot2' plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without loosing vector properties of the scale-sensitive information.
You can use this function to easily draw a combined histogram and restricted cubic spline. 
    The function draws the graph through 'ggplot2'. RCS fitting requires the use of the rcs() function of the 'rms' package. 
    Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.
Provides 'ggplot2' extensions for political map making. Implements
    new geometries for groups of simple feature geometries. Adds palettes and scales 
    for red to blue color mapping and for discrete maps. Implements tools for easy 
    label generation and placement, automatic map coloring, and themes.
Provides text and label geoms for 'ggplot2' that help to avoid
    overlapping text labels. Labels repel away from each other and away from the
    data points.
An R package for creating panels of diagnostic plots for residuals from a model 
    using ggplot2 and plotly to analyze residuals and model assumptions from a variety of 
    viewpoints. It also allows for the creation of interactive diagnostic plots.
The Graphical Group Ridge 'GGRidge' package package classifies ridge regression predictors in disjoint groups of conditionally correlated variables and derives different penalties (shrinkage parameters) for these groups of predictors. It combines the ridge regression method with the graphical model for high-dimensional data (i.e. the number of predictors exceeds the number of cases) or ill-conditioned data (e.g. in the presence of multicollinearity among predictors). The package reduces the mean square errors and the extent of over-shrinking of predictors as compared to the ridge method.Aldahmani, S. and Zoubeidi, T. (2020) <DOI:10.1080/00949655.2020.1803320>.
Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
The risk plot may be one of the most commonly used figures in 
    tumor genetic data analysis. We can conclude the following two points: 
    Comparing the prediction results of the model with the real survival situation 
    to see whether the survival rate of the high-risk group is lower than that of the 
    low-level group, and whether the survival time of the high-risk group is 
    shorter than that of the low-risk group. The other is to compare the heat 
    map and scatter plot to see the correlation between the predictors and the 
    outcome.
Creates bar plots with rounded corners using 'ggplot2'.
    The code in this package was adapted from a solution provided by 
    Stack Overflow user 'sthoch' in the following post
    <https://stackoverflow.com/questions/62176038/r-ggplot2-bar-chart-with-round-corners-on-top-of-bar>.
Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Works with 'ggplot2' to add a Van Gogh color palette to the user’s repertoire.
 It also has a function that work alongside 'ggplot2' to create more interesting data visualizations and add contextual information to the user’s plots.
The function combines a scatter plot with ridgelines to better visualise the distribution between sample groups. The plot is created with 'ggplot2'.
A collection of 'ggplot2' color palettes inspired by
    plots in scientific journals, data visualization libraries,
    science fiction movies, and TV shows.
Provides 'ggplot2' 'stats' that estimate seasonally adjusted series 
    and rolling summaries such as rolling average on the fly for time series.
Some useful functions that can use 'grid' and 'ggplot2' to plot sectors and interact with 'Seurat' to plot gene expression percentages. Also, there are some examples of how to draw sectors in 'ComplexHeatmap'.
Contains 'ggplot2' geom for plotting brain atlases using 
    simple features. The largest component of the package is the data 
    for the two built-in atlases. Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.
Mainly contains a plotting function ggseg3d(), 
    and data of two standard brain atlases (Desikan-Killiany and aseg). 
    By far, the largest bit of the package is the data for each of the atlases.
    The functions and data enable users to plot tri-surface mesh plots of
    brain atlases, and customise these by projecting colours onto the brain
    segments based on values in their own data sets. Functions are wrappers
    for 'plotly'. Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.
A set of wrapper functions that mainly re-produces most of the 
  sequence plots rendered with TraMineR::seqplot(). Whereas 'TraMineR' uses base 
  R to produce the plots this library draws on 'ggplot2'. 
  The plots are produced on the basis of a sequence object defined  
  with TraMineR::seqdef(). The package automates the reshaping and plotting 
  of sequence data. Resulting plots are of class 'ggplot', i.e. components 
  can be added and tweaked using '+' and regular 'ggplot2' functions.
A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), 
    geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more 
    aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a 
    neon glow around lines to get a steampunk style.
The grammar of graphics as shown in 'ggplot2' has provided
  an expressive API for users to build plots. 'ggside' extends 'ggplot2' 
  by allowing users to add graphical information about one of the main panel's
  axis using a familiar 'ggplot2' style API with tidy data. This package is
  particularly useful for visualizing metadata on a discrete axis, or summary
  graphics on a continuous axis such as a boxplot or a density distribution.
Enrich your 'ggplots' with group-wise comparisons.
    This package provides an easy way to indicate if two groups are
    significantly different. Commonly this is shown by a bracket on top
    connecting the groups of interest which itself is annotated with the
    level of significance (NS, *, **, ***).  The package provides a single
    layer (geom_signif()) that takes the groups for comparison and the
    test (t.test(), wilcox.text() etc.) as arguments and adds the
    annotation to the plot.
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 multiple polygonal point layer for easily discernible shapes, 
             we developed the package, it is like the 'geom_point' of 'ggplot2'.
             It can be used to draw the scatter plot.
Provides new statistics, new geometries and new positions for 
    'ggplot2' and a suite of functions to facilitate the creation of 
    statistical plots.
Extension of 'ggplot2', 'ggstatsplot' creates graphics with
    details from statistical tests included in the plots themselves. It
    provides an easier syntax to generate information-rich plots for
    statistical analysis of continuous (violin plots, scatterplots,
    histograms, dot plots, dot-and-whisker plots) or categorical (pie and
    bar charts) data. Currently, it supports the most common types of
    statistical approaches and tests: parametric, nonparametric, robust,
    and Bayesian versions of t-test/ANOVA, correlation analyses,
    contingency table analysis, meta-analysis, and regression analyses.
    References: Patil (2021) <doi:10.21105/joss.03236>.
Make smoothed stacked area charts in 'ggplot2'. Stream plots are useful to show magnitude trends over time.
Provides an extension to 'ggplot2' (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two 
    types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean.
    These plots contain multiple user-defined confidence areas with varying colours, 
    defined by the underlying t-distribution used to compute standard confidence intervals for 
    the mean of the normal distribution when the variance is unknown.
    Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the 
    shape (horizontal width) is defined by the probability density function of the t-distribution.
Functions for survey data including svydesign objects from the 'survey' package that call 'ggplot2' to make bar charts, histograms, boxplots, and hexplots of survey data.
Ease the creation of time-to-event (i.e. survival) endpoint
    figures. The modular functions create figures ready for publication.
    Each of the functions that add to or modify the figure are written as
    proper 'ggplot2' geoms or stat methods, allowing the functions from
    this package to be combined with any function or customization from
    'ggplot2' and other 'ggplot2' extension packages.
Offers various swiss maps as data frames and 'ggplot2' objects and gives the
    possibility to add layers of data on the maps. Data are publicly available
    from the swiss federal statistical office.
A collection of palettes and themes for 'ggplot2', offering a light, pastel aesthetic. Syntax follows the 'viridis' package.
Extends the functionality of 'ggplot2', providing the capability
    to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally,
    'ggtern' has implemented several NEW geometries which are unavailable to the
    standard 'ggplot2' release. For further examples and documentation, please
    proceed to the 'ggtern' website.
A 'ggplot2' extension that enables the rendering of
    complex formatted plot labels (titles, subtitles, facet labels,
    axis labels, etc.). Text boxes with automatic word wrap are also
    supported.
Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
Some extra themes, geoms, and scales for 'ggplot2'.
    Provides 'ggplot2' themes and scales that replicate the look of plots
    by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata',
    'Excel', and 'The Wall Street Journal', among others.
    Provides 'geoms' for Tufte's box plot and range frame.
Designed to customize 'ggplot' graphics according to the institutional identity of the University of Ljubljana.
Annotation of 'ggplot2' plots with arbitrary 'TikZ' code, using absolute data or relative plot coordinates.
Provides additional display mediums for time series visualisations.
Provides 'ggplot2' geoms that allow groups of data points to be
    outlined or highlighted for emphasis. This is particularly useful when
    working with dense datasets that are prone to overplotting.
Add trendline and confidence interval of linear or nonlinear regression model and
    show equation to 'ggplot' as simple as possible. For a general overview of the methods used in
	  this package, see Ritz and Streibig (2008) <doi:10.1007/978-0-387-09616-2> and 
	  Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.
A collection of several geoms to create graphics, using
    'ggplot2' and the Cartesian coordinate system. You use the familiar
    mapping 'Grammar of Graphics' without the need to do another
    transformation into polar coordinates.
An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and 'GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available.
Replace the standard x-axis in 'ggplots' with a combination matrix
  to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize
  the overlap of sets as an alternative to Venn diagrams. 
  This package provides a simple way to produce such plots using 'ggplot2'. 
  In addition it can convert any categorical axis into a combination
  matrix axis.
An easy-to-use way to draw pretty venn diagram by 'ggplot2'.
Easy-to-use functions to generate 2-7 sets Venn plot in publication quality. 
  'ggVennDiagram' plot Venn using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets 
  Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which 
  are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to 
  integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections 
  of them, then separately plot Venn in three components: set edges, set labels, and regions.
  From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar.
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'.
Provides a word cloud text geom for 'ggplot2'. Texts
    are placed so that they do not overlap as in 'ggrepel'.  The algorithm
    used is a variation around the one of 'wordcloud2.js'.
The 'ggplot2' package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid 'ggplot2' commands.
Minimal client to access the 'GitHub' 'API'.
Haplotype calling from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. The package was first described in Bioinformatics by Utsunomiya et al. (2016, <doi:10.1093/bioinformatics/btw356>). Since the v2 release, the package provides functions for unsupervised and supervised detection of ancestry tracks. The methods implemented in these functions were described in an article published in Methods in Ecology and Evolution by Utsunomiya et al. (2020, <doi:10.1111/2041-210X.13467>). The source code for v3 was modified for improved performance and inclusion of new functionality, including analysis of unphased data, runs of homozygosity, sampling methods for virtual gamete mating, mixed model fitting and GWAS.
'GitHub' apps provide a powerful way to manage fine grained
    programmatic access to specific 'git' repositories, without having to
    create dummy users, and which are safer than a personal access token
    for automated tasks. This package extends the 'gh' package to let you
    authenticate and interact with 'GitHub' <https://docs.github.com/en/rest/overview>
    in 'R' as an app.
Functions are provided for quantifying evolution and selection on complex traits. 
              The package implements effective handling and analysis algorithms scaled for 
              genome-wide data and calculates a composite statistic, denoted Ghat, which is used 
              to test for selection on a trait. The package provides a number of simple examples 
              for handling and analysing the genome data and visualising the output and results. 
              Beissinger et al., (2018) <doi:10.1534/genetics.118.300857>.
Interface for the GitHub API that enables efficient 
    management of courses on GitHub. It has a functionality for 
    managing organizations, teams, repositories, and users on GitHub 
    and helps automate most of the tedious and repetitive tasks 
    around creating and distributing assignments.
A statistical hypothesis test for conditional independence.
    Given residuals from a sufficiently powerful regression, it tests whether 
    the covariance of the residuals is vanishing. It can be applied to both
    discretely-observed functional data and multivariate data. 
    Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas
    Peters (2022) <doi:10.1111/rssb.12544>.
Colour palettes inspired by Studio Ghibli <https://en.wikipedia.org/wiki/Studio_Ghibli> 
    films, ported to R for your enjoyment.
Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.
Functions for multiple knockoff inference using summary statistics, e.g. Z-scores. The knockoff inference is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. This package provides a procedure which performs knockoff inference without ever constructing individual knockoffs (GhostKnockoff). It additionally supports multiple knockoff inference for improved stability and reproducibility. Moreover, it supports meta-analysis of multiple overlapping studies.
A 'GraphQL' client, with an R6 interface for initializing
    a connection to a 'GraphQL' instance, and methods for constructing
    queries, including fragments and parameterized queries. Queries
    are checked with the 'libgraphqlparser' C++ parser via the
    'gaphql' package.
Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.
Detailed functionality for working
        with the univariate and multivariate Generalized Hyperbolic
        distribution and its special cases (Hyperbolic (hyp), Normal
        Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
        and Gaussian distribution). Especially, it contains fitting
        procedures, an AIC-based model selection routine, and functions
        for the computation of density, quantile, probability, random
        variates, expected shortfall and some portfolio optimization
        and plotting routines as well as the likelihood ratio test. In
        addition, it contains the Generalized Inverse Gaussian
        distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. 
        Quantitative risk management: Concepts, techniques and tools. 
        Princeton University Press, Princeton (2005).
Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG).
    To learn how to use it, check the vignettes for a quick tutorial.
    Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300>
    together with those relevant references from the one listed below.
    The package is based on the research developed at the Chair of Systems Design, ETH Zurich.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <arXiv:1607.02441>.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>.
    Casiraghi, G., (2017) <arXiv:1702.02048>
    Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>
    Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>.
    Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>.
    Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.
'Giac'
    <https://www-fourier.ujf-grenoble.fr/~parisse/giac/doc/en/cascmd_en/cascmd_en.html>
    is a general purpose symbolic algebra software. It powers the
    graphical interface 'Xcas'. This package allows to execute 'Giac'
    commands in 'R'.
A plain 'Rcpp' wrapper of 'MeCab' that can segment Chinese,
    Japanese, and Korean text into tokens. The main goal of this package
    is to provide an alternative to 'tidytext' using morphological
    analysis.
Build a map of path-based geometry, this is a simple description of the number
 of parts in an object and their basic structure. Translation and restructuring operations for 
 planar shapes and other hierarchical types require a data model with a record of the underlying
 relationships between elements. The gibble() function creates a geometry map, a simple record of 
 the underlying structure in path-based hierarchical types. There are methods for the planar shape 
 types in the 'sf' and 'sp' packages and for types in the 'trip' and 'silicate' packages.
An iterative algorithm that improves the proximity matrix (PM) from a random forest (RF) and the resulting clusters as measured by the silhouette score.
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.
Retrieving regional plant checklists, species traits and
  distributions, and environmental data from the Global Inventory of Floras and
  Traits (GIFT). More information about the GIFT database can be found at
  <https://gift.uni-goettingen.de/about> and the map of available floras can be
  visualized at <https://gift.uni-goettingen.de/map>. The API and associated
  queries can be accessed according the following scheme:
  <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.
Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.
A framework and functions to create 'MOODLE' quizzes. 'GIFTr' takes dataframe of questions of
    four types: multiple choices, numerical, true or false and short answer questions, and exports a text 
    file formatted in 'MOODLE' GIFT format. You can prepare a spreadsheet in any software and import 
    it into R to generate any number of questions with 'HTML', 'markdown' and 'LaTeX' support.
A Gibbs sampler corresponding to a Group 
    Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. 
    Hyperparameters in the GIGG prior specification can either be fixed by the 
    user or can be estimated via Marginal Maximum Likelihood Estimation.
    Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar 
    Mukherjee (2021) <arXiv:2102.10670>.
Generator and density function for the
  Generalized Inverse Gaussian (GIG) distribution.
Provides a simple to use, intuitive, and
  extensible interface to several stochastic simulation
  algorithms for generating simulated trajectories of finite
  population continuous-time model. Currently it implements
  Gillespie's exact stochastic simulation algorithm (Direct
  method) and several approximate methods (Explicit tau-leap,
  Binomial tau-leap, and Optimized tau-leap). The package also
  contains a library of template models that can be run as demo
  models and can easily be customized and extended. Currently the
  following models are included, 'Decaying-Dimerization' reaction
  set, linear chain system, logistic growth model, 'Lotka'
  predator-prey model, Rosenzweig-MacArthur predator-prey model,
  'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model.
  Pineda-Krch et al. (2008) <doi:10.18637/jss.v025.i12>.
A fast, scalable, and versatile framework for
    simulating large systems with Gillespie's Stochastic Simulation
    Algorithm ('SSA').  This package is the spiritual successor to the
    'GillespieSSA' package originally written by Mario Pineda-Krch.
    Benefits of this package include major speed improvements (>100x),
    easier to understand documentation, and many unit tests that try to
    ensure the package works as intended. Cannoodt and Saelens et al. (2021) 
    <doi:10.1038/s41467-021-24152-2>.
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.
Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
Simplifies the process of creating essential visualizations
    in R, offering a range of plotting functions for common chart types
    like violin plots, pie charts, and histograms.  With an intuitive
    interface, users can effortlessly customize colors, labels, and
    styles, making it an ideal tool for both beginners and experienced
    data analysts. Whether exploring datasets or producing quick visual
    summaries, this package provides a streamlined solution for
    fundamental graphics in R.
Supplemental tools for the 'gimme' R package. It contains an interactive graphical user interface, allowing for the flexible specification of a variety of both basic and advanced options. It will expand to include a variety of tools for navigating output.
This is a set of functions to retrieve information about GIMMS
    NDVI3g files currently available online; download (and re-arrange, in the 
    case of NDVI3g.v0) the half-monthly data sets; import downloaded files from 
    ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based 
    on the widespread 'raster' package; conduct quality control; and generate 
    monthly composites (e.g., maximum values) from the half-monthly input data. 
    As a special gimmick, a method is included to conveniently apply the 
    Mann-Kendall trend test upon 'Raster*' images, optionally featuring 
    trend-free pre-whitening to account for lag-1 autocorrelation.
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.
Providing various equations to calculate Gini coefficients.
    The methods used in this package can be referenced from Brown MC (1994) <doi: 10.1016/0277-9536(94)90189-9>.
An implementation of 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.
Density function and generation of random variables from the Generalized Inverse Normal (GIN) distribution from Robert (1991) <doi:10.1016/0167-7152(91)90174-P>. Also provides density functions and generation from the GIN distribution truncated to positive or negative reals. Theoretical guarantees supporting the sampling algorithms and an application to Bayesian estimation of network formation models can be found in the working paper Ding, Estrada and Montoya-Blandón (2023) <https://www.smontoyablandon.com/publication/networks/network_externalities.pdf>.
A workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework.
    References: 
            Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.
Maximum likelihood estimation under relational models, with or without the overall effect.
The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean
    and the standard deviation of a quantitative variable. It also flags 
    potential outliers (together with their frequencies) that are c standard deviations away from the mean.
Find the permutation symmetry group such that the covariance
    matrix of the given data is approximately invariant under it.
    Discovering such a permutation decreases the number of observations
    needed to fit a Gaussian model, which is of great use when it is
    smaller than the number of variables. Even if that is not the case,
    the covariance matrix found with 'gips' approximates the actual
    covariance with less statistical error. The methods implemented in
    this package are described in Graczyk et al. (2022)
    <doi:10.1214/22-AOS2174>.
Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general 
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices. 
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the 
 likelihood using MCMC samplers for large lattices.
Tools to download data from the GISCO (Geographic Information
    System of the Commission) Eurostat database
    <https://ec.europa.eu/eurostat/web/gisco>. Global and European map
    data available.  This package is in no way officially related to or
    endorsed by Eurostat.
A collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.
Work with 'GitHub' 'gists' from 'R' (e.g., 
    <https://en.wikipedia.org/wiki/GitHub#Gist>, 
    <https://docs.github.com/en/github/writing-on-github/creating-gists/>). A 'gist'
    is simply one or more files with code/text/images/etc. This package allows
    the user to create new 'gists', update 'gists' with new files, rename files,
    delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists',
    open a 'gist' in your default browser, get embed code for a 'gist', list
    'gist' 'commits', and get rate limit information when 'authenticated'. Some
    requests require authentication and some do not. 'Gists' website: 
    <https://gist.github.com/>.
Interface to the 'libgit2' library, which is a pure C
    implementation of the 'Git' core methods. Provides access to 'Git'
    repositories to extract data and running some basic 'Git'
    commands.
The git2rdata package is an R package for writing and reading
    dataframes as plain text files.  A metadata file stores important
    information.  1) Storing metadata allows to maintain the classes of
    variables.  By default, git2rdata optimizes the data for file storage.
    The optimization is most effective on data containing factors.  The
    optimization makes the data less human readable.  The user can turn
    this off when they prefer a human readable format over smaller files.
    Details on the implementation are available in vignette("plain_text",
    package = "git2rdata").  2) Storing metadata also allows smaller row
    based diffs between two consecutive commits.  This is a useful feature
    when storing data as plain text files under version control.  Details
    on this part of the implementation are available in
    vignette("version_control", package = "git2rdata").  Although we
    envisioned git2rdata with a git workflow in mind, you can use it in
    combination with other version control systems like subversion or
    mercurial.  3) git2rdata is a useful tool in a reproducible and
    traceable workflow.  vignette("workflow", package = "git2rdata") gives
    a toy example.  4) vignette("efficiency", package = "git2rdata")
    provides some insight into the efficiency of file storage, git
    repository size and speed for writing and reading.
An interactive git user interface from the R command line. Intuitive
         tools to make commits, branches, remotes, and diffs an integrated part
         of R coding. Built on git2r, a system installation of git is not required
         and has default on-premises remote option.
Query, set, delete credentials from the 'git' credential
    store. Manage 'GitHub' tokens and other 'git' credentials. This
    package is to be used by other packages that need to authenticate to
    'GitHub' and/or other 'git' repositories.
Read all commit messages of your local git repository and
    sort them according to tags or specific text pattern into chapters of
    a HTML book using 'bookdown'. The git history book presentation helps
    organisms required to testify for every changes in their source code,
    in relation to features requests.
'Gitea' is a community managed, lightweight code hosting solution 
  were projects and their respective git repositories can be managed 
  <https://gitea.io>. This package gives an interface to the 'Gitea' API to 
  access and manage repositories, issues and organizations directly in R.
An 'Rstudio' addin for version control that allows users to clone
    repositories, create and delete branches, and sync forks on GitHub, GitLab, etc.
    Furthermore, the addin uses the GitLab API to allow instructors to create
    forks and merge requests for all students/teams with one click of a button.
Automates the process of adding,
    committing, and pushing changes to a 'git' repository
    using commit messages generated by passing the git diff output 
    to the 'OpenAI'
    'GPT-3.5 Turbo' model (<https://platform.openai.com/docs/models/gpt-3>).
Provides an helpful way to install packages hosted on GitHub.
This is a 'GitHub' API wrapper for R. <https://docs.github.com/en/rest> It uses the 'gh' package but has things wrapped up for convenient use cases.
Simple interface to query gitignore.io to fetch
    gitignore templates that can be included in the .gitignore file. More
    than 450 templates are currently available.
Provides R functions to access the API of the project and
    repository management web application 'GitLab'. For many common tasks
    (repository file access, issue assignment and status, commenting)
    convenience wrappers are provided, and in addition the full API can be
    used by specifying request locations. 'GitLab' is open-source software
    and can be self-hosted or used on <https://about.gitlab.com>.
Provides helpers to add 'Git' links to 'shiny'
    applications, 'rmarkdown' documents, and other 'HTML' based resources.
    This is most commonly used for 'GitHub' ribbons.
A light-weight, dependency-free, application programming interface
    (API) to access system-level 'Git' commands from within 'R'. Contains wrappers
    and defaults for common data science workflows as well as
    'Zsh' plugin aliases. A generalized API syntax is also available.
    A system installation of 'Git' <https://git-scm.com/downloads> is required.
In computationally demanding data analysis pipelines,
  the 'targets' R package (2021, <doi:10.21105/joss.02959>) maintains
  an up-to-date set of results while skipping tasks that do not need to rerun.
  This process increases speed and increases trust in the final end product.
  However, it also overwrites old output with new output, and past
  results disappear by default. To preserve historical output, the 'gittargets'
  package captures version-controlled snapshots of the data store,
  and each snapshot links to the underlying commit of the source code.
  That way, when the user rolls back the code to a previous branch or commit,
  'gittargets' can recover the data contemporaneous with that commit so that
  all targets remain up to date.
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.  Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.
An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Functions for the g-and-k and generalised g-and-h distributions.
Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
A simple way to interact with and extract data from
  the official 'Google Knowledge Graph' API <https://developers.google.com/knowledge-graph/>.
Imports the 'gkmSVM' v2.0 functionalities into R <https://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
    Users should note that the suggested packages 'rtracklayer', 'GenomicRanges', 'BSgenome', 'BiocGenerics', 
    'Biostrings', 'GenomeInfoDb', 'IRanges', and 'S4Vectors' are all BioConductor packages <https://bioconductor.org>.
Kernel regularized least squares, also known as kernel ridge regression, 
    is a flexible machine learning method. This package implements this method by 
    providing a smooth term for use with 'mgcv' and uses random sketching to 
    facilitate scalable estimation on large datasets. It provides additional 
    functions for calculating marginal effects after estimation and for use with 
    ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), 
    and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2023)
    <arXiv:2209.14355> provide further details.
Efficient design matrix free  lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a  component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs.
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.
Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arXiv:2007.08623v1>.
Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to
    search job statistics, employer statistics, and job progression,
    where 'Glassdoor' provides a breakdown of other jobs a person did
    after their current one.
Estimation of a sparse inverse covariance matrix using a lasso (L1)
             penalty. Facilities are provided for estimates along a path of values
	     for the regularization parameter.
A fast and improved implementation of the graphical LASSO.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Fits multiple-group latent class analysis (LCA) for exploring 
    differences between populations in the data with a multilevel structure. 
    There are two approaches to reflect group differences in glca: 
    fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>;
    Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA 
    (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).
Enables calculation of image textures (Haralick 1973) 
    <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices 
    (GLCMs). Supports processing images that cannot fit in memory.
Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.
The generalised lambda distribution, or Tukey lambda distribution, 
  provides a wide variety of shapes with one functional form.   
  This package provides random numbers, quantiles, probabilities, 
  densities and density quantiles for four different types of the distribution,
  the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden
  and Loots 2009) and FM5 - see documentation for details.
  It provides the density function, distribution function, and Quantile-Quantile 
  plots.  
  It implements a variety of estimation methods for the distribution, 
  including diagnostic plots. 
  Estimation methods include the starship (all 4 types), 
  method of L-Moments for the GPD and FKML types, and a 
  number of methods for only the FKML type.  
  These include maximum likelihood, maximum product of spacings, 
  Titterington's method, Moments, Trimmed L-Moments and 
  Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" <doi:10.1016/j.csda.2007.06.021>, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" <doi:10.1111/1467-842X.00089>, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" <doi:10.22237/jmasm/1130803560>, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" <doi:10.1016/j.csda.2006.06.008>, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" <doi:10.18637/jss.v021.i09>, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" <doi:10.1016/j.csda.2009.02.014>, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" <doi:10.1201/b10159>, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" <doi:10.1201/b10159>, Su (2015)   "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.
Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of  the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.  References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.
Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
    Huang (2014) <doi:10.1080/01621459.2013.824892>.
    Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
    Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
Global and individual tests for pleiotropy and direct effects in Mendelian randomization studies. Refer to J. Y. Dai, U. Peters, X. Wang, J. Kocarnik et al. AJE (2018) <doi:10.1093/aje/kwy177>.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.
Routines for log-linear models of incomplete contingency tables,
             including some latent class models, via EM and Fisher scoring
             approaches. Allows bootstrapping. See Espeland and Hui (1987)
             <doi:10.2307/2531553> for general approach.
Analysis of multivariate data using generalized linear latent variable models (gllvm). 
      Estimation is performed using either 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() [generalised linear model], glm.nb() [negative binomial model],
	polr() [ordinal logistic model], multinom() [multinomial model] and tobit() [tobit model],
	svyglm() [survey-weighted generalised linear models], lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive  sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
Find all hierarchical models of specified generalized linear
    model with information criterion (AIC, BIC, or AICc) within specified
    cutoff of minimum value.  Alternatively, find all such graphical models.
    Use branch and bound algorithm so we do not have to fit all models.
Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.
In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix.
Provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference.
Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 
    'partykit'. The fitting algorithm is described in more detail in Fokkema,
    Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).
Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple
   and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values,
   less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint().
   See Wilks (1938) <doi:10.1214/aoms/1177732360> for the LRT chi-square approximation.
   See Rao (1948) <doi:10.1017/S0305004100023987> for Rao's score test.
   See Wald (1943) <doi:10.2307/1990256> for Wald's test.
Efficient algorithms for fitting regularization paths for
        linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.
Fits generalized linear mixed models for a single grouping factor under
    maximum likelihood approximating the integrals over the random effects with an 
    adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) 
    <doi:10.1080/10618600.1995.10474663>.
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, 
	see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>.
	See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.
Binomial and Poisson regression for clustered data, fixed
        and random effects with bootstrapping.
Fits high dimensional penalized generalized linear 
    mixed models using 
    the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. 
    The purpose of the package is to perform variable selection on both the fixed and 
    random effects simultaneously for generalized linear mixed models.
    The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and
    supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm
    is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>.
    The techniques used in the minimization portion of the procedure (the M-step) are
    derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) 
    <doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015)
    <doi:10.1007/s11222-013-9424-2>), with
    appropriate modifications to account for the estimation and penalization of
    the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, 
    and LASSO penalties.
Specification, analysis, simulation, and fitting of generalised linear mixed models. 
  Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models, 
  non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily,
  robust and bias-corrected standard error estimation, power calculation, data simulation, and more. 
  See <https://samuel-watson.github.io/glmmr-web/> for a detailed manual.
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. 
    Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.
A Bayesian model selection approach for generalized linear mixed models.
    Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. 
    Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix.
    'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics.
    'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.
Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.
Fit linear and generalized linear mixed models with various
    extensions, including zero-inflation. The models are fitted using maximum
    likelihood estimation via 'TMB' (Template Model Builder). Random effects are
    assumed to be Gaussian on the scale of the linear predictor and are integrated
    out using the Laplace approximation. Gradients are calculated using automatic
    differentiation.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model,  multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead.  The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.
Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal
 response dataset for fitting continuation ratio models for datasets  where the number of covariates exceeds the sample size or when
 there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
Cross validation informed Relaxed LASSO, Artificial Neural Network (ANN), gradient boosting machine ('xgboost'), Recursive Partitioning ('RPART') or step wise regression models are fit.  It fits all these model as extensions of linear, logistic and Cox regression models.  The package can fit all these models in a single call, and performs nested cross validation allowing the user to evaluate and compare the performances of these different models.  The package fits these models using other packages including 'glmnet', 'survival', 'xgboost', 'rpart' and 'torch'.  For the relaxed lasso models 'glmnetr' uses 'stat' and 'survival' to obtain stable model fits, and obtain these often more quickly.  This too might be achieved using the 'path=TRUE' option in 'glmnet'.
    While the package fits nested cross validation for the lasso and other models, it does not fit the general elastic net model.  If you are fitting not a relaxed lasso model but an elastic-net model, then the R-packages 'nestedcv' <https://cran.r-project.org/package=nestedcv>, 'glmnetSE' <https://cran.r-project.org/package=glmnetSE> or others may provide greater functionality when performing a nested CV.
    As with the 'glmnet' package, this package passes most relevant information to the output object which can be evaluated using plot, summary() and predict() functions.  The 'glmnetr' package has some features and functionality that we find useful, but omits some of the functionality of 'glmnet' as well.  Use of the 'glmnetr' package has many similarities to the 'glmnet' package and it is recommended that the user of 'glmnetr' first become familiar with the 'glmnet' package <https://cran.r-project.org/package=glmnet>, with the "An Introduction to glmnet" and "The Relaxed Lasso" being especially helpful in this regard.
Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or
    'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value)
    for selected coefficients with no shrinkage applied for them. Model
    performance can be evaluated on test data and an automated alpha
    selection is implemented for Elastic Net. Parallelized computation is
    used to speed up the process. The methods are described in Friedman et
    al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011)
    <doi:10.18637/jss.v039.i05>.
Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.
Contains all the data and functions used in Generalized
    Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create
    all models, tables, and plots are included for each data set.
A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
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.
Creates ideal data for all distributions in the generalized
    linear model framework.
Extremely efficient procedures for fitting regularization path with l0, l1, and truncated lasso penalty for linear regression and logistic regression models. This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized.
Set of tools to the statistical analysis of data using: (1) normal linear models; (2) generalized linear models; (3) negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion; (4) beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) Zero-inflated and zero-altered regression models to deal with zero-excess in count data; (6) generalized estimating equations for cluster correlated data.
We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The relevant paper
    is available on arXiv: <arXiv:2105.14328>.
A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user's criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://github.com/adimajo/manuscrit_these/releases/>.
Automated model selection and model-averaging. Provides a
        wrapper for glm and other functions, automatically generating
        all possible models (under constraints set by the user) with
        the specified response and explanatory variables, and finding
        the best models in terms of some Information Criterion (AIC,
        AICc or BIC). Can handle very large numbers of candidate
        models. Features a Genetic Algorithm to find the best models
        when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
             including parametric links and heteroscedastic latent variables.
Provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by 'VGAM' package), beta regression model (estimated by 'betareg' package) and negative binomial regression model (estimated by 'MASS' package). Since most of functions implemented in 'glmxdiag' already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.
Second-order summary statistics K- and pair-correlation functions describe
    interactions in point pattern data. This package provides computations to estimate those
    statistics on inhomogeneous point processes, using the methods of
    in T Shaw, J Møller, R Waagepetersen, 2020 <arXiv:2004.00527>.
It provides more configurations on the option values such as validation
    and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown" or "free") objects in R expressions
    by code inspection using various strategies (ordered, liberal, or
    conservative).  The objective of this package is to make it as simple as
    possible to identify global objects for the purpose of exporting them in
    parallel, distributed compute environments.
Google offers public access to global search volumes from its
    search engine through the Google Trends portal. The package downloads
    these search volumes provided by Google Trends and uses them to
    measure and analyze the distribution of search scores across countries
    or within countries. The package allows researchers and analysts to
    use these search scores to investigate global trends based on patterns
    within these scores. This offers insights such as degree of
    internationalization of firms and organizations or dissemination of
    political, social, or technological trends across the globe or within
    single countries.  An outline of the package's methodological
    foundations and potential applications is available as a working
    paper: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3969013>.
Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.
Tools for the generalized logistic distribution (Type I,
             also known as skew-logistic distribution), encompassing
	     basic distribution functions (p, q, d, r, score), maximum
	     likelihood estimation, and structural change methods.
Add glossaries to markdown and quarto documents by tagging individual words. Definitions can be provided inline or in a separate file.
Read examples with interlinear glosses from files
    or from text and print them in a way compatible with both
    Latex and HTML outputs.
Streamlined workflows for geolinguistic analysis, including:
    accessing global linguistic and cultural databases, data import, data
    entry, data cleaning, data exploration, mapping, visualization and
    export.
Provides a framework for creating plots with glowing points.
R Interface to C API of GLPK, depends on GLPK Version >= 4.42.
Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An implementation of interpreted string literals, inspired by
    Python's Literal String Interpolation
    <https://www.python.org/dev/peps/pep-0498/> and Docstrings
    <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted
    String Literals
    <https://docs.julialang.org/en/v1.3/manual/strings/#Triple-Quoted-String-Literals-1>.
Ease the transition between R vectors and markdown
    text. With 'gluedown' and 'rmarkdown', users can create traditional
    vectors in R, glue those strings together with the markdown syntax,
    and print those formatted vectors directly to the document. This
    package primarily uses GitHub Flavored Markdown (GFM), an offshoot of
    the unambiguous CommonMark specification by John MacFarlane (2019)
    <https://spec.commonmark.org/>.
Provides residual global fit indices for generalized latent variable models.
Useful utilities in N-glycan data analysis. This package tries
    to fill the gap in N-glycan data analysis by providing easy
    to use functions for basic operations on data
    (see <https://en.wikipedia.org/wiki/Glycomics> for more
    details on Glycomics). At the moment 'glycanr' is mostly oriented
    to data obtained by UPLC (Ultra Performance Liquid Chromatography)
    and LCMS (Liquid chromatography–mass spectrometry)
    analysis of Plasma and IgG glycome.
Provides a simple and intuitive high-level language, with which
    you can create music easily. Takes care of all the dirty technical
    details in converting your music to musical scores and audio files.
    Works in 'R Markdown' documents <https://rmarkdown.rstudio.com/>,
    R 'Jupyter Notebooks' <https://jupyter.org/>, and 'RStudio'
    <https://www.rstudio.com/>, so you can embed generated music
    anywhere. Internally, uses 'MusicXML' <https://www.musicxml.com/> to
    represent musical scores, and 'MuseScore' <https://musescore.org/> to
    convert 'MusicXML'.
Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.
Performs genomic mediation
    analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale
    mediation analysis and adaptively selects potential confounding variables to
    adjust for each mediation test from a pool of candidate confounders. The package
    is tailored for but not limited to genomic mediation analysis (e.g., cis-gene
    mediating trans-gene regulation pattern where an eQTL, its cis-linking gene
    transcript, and its trans-gene transcript play the roles as treatment, mediator
    and the outcome, respectively), restricting to scenarios with the presence of
    cis-association (i.e., treatment-mediator association) and random eQTL (i.e.,
    treatment).
An interface to the 'Gmail' 'RESTful' API.  Allows access to
    your 'Gmail' messages, threads, drafts and labels.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described
    multiple test procedures.
A lightweight fork of 'gMCP' with functions for graphical
    described multiple test procedures introduced in
    Bretz et al. (2009) <doi:10.1002/sim.3495> and
    Bretz et al. (2011) <doi:10.1002/bimj.201000239>.
    Implements a flexible function using 'ggplot2' to create
    multiplicity graph visualizations.
    Contains instructions of multiplicity graph and graphical testing for
    group sequential design, described in
    Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>,
    with necessary unit testing using 'testthat'.
A template for a geometallurgical database and a fast and easy
    interface for accessing it is provided in this package.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response (Dag et al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).
Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.
    Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms
    that performs gradually complicated polynomial models and selecting the best solution by an external criterion.
    In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and
    selecting an optimal structure of model or network.
    The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.
Fast algorithms for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
Some methods for the inference and clustering of univariate and 
             multivariate functional data, using a generalization of Mahalanobis
             distance, along with some functions useful for the analysis of functional data.
             For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
Support for geostatistical analysis of multivariate data, 
         in particular data with restrictions, e.g. positive amounts, 
         compositions, distributional data, microstructural data, etc. 
         It includes descriptive analysis and modelling for such data, both 
         from a two-point Gaussian perspective and multipoint perspective.
         The methods mainly follow Tolosana-Delgado, Mueller and van den
         Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
Gaussian mixture graphical models include Bayesian networks and
    dynamic Bayesian networks (their temporal extension) whose local probability
    distributions are described by Gaussian mixture models. They are powerful
    tools for graphically and quantitatively representing nonlinear dependencies
    between continuous variables. This package provides a complete framework to
    create, manipulate, learn the structure and the parameters, and perform
    inference in these models. Most of the algorithms are described in the PhD
    thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.
Tools for making the descriptive "Table 1" used in medical
    articles, a transition plot for showing changes between categories
    (also known as a Sankey diagram), flow charts by extending the grid package,
    a method for variable selection based on the SVD, Bézier lines with arrows
    complementing the ones in the 'grid' package, and more.
High performance trainers for parameterizing and clustering weighted data. The Gaussian mixture (GM) module includes the conventional EM (expectation maximization) trainer, the component-wise EM trainer, the minimum-message-length EM trainer by Figueiredo and Jain (2002) <doi:10.1109/34.990138>. These trainers accept additional constraints on mixture weights, covariance eigen ratios and on which mixture components are subject to update. The K-means (KM) module offers clustering with the options of (i) deterministic and stochastic K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii) Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse representation of data input. The package improved the typical implementations of GM and KM algorithms in various aspects. It is carefully crafted in multithreaded C++ for modeling large data for industry use.
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Likelihood-based boosting approaches for generalized mixed models are provided.
The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.
An implementation of maximum simulated likelihood method for the
    estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>.
    Specifically, it allows estimating models with continuous heterogeneity
    such as the mixed multinomial logit and the generalized multinomial logit.
    It also allows estimating models with discrete heterogeneity such as the
    latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Make 2D and 3D plots of linear programming (LP), 
    integer linear programming (ILP), or mixed integer linear programming (MILP) models 
    with up to three objectives. Plots of both the solution and criterion space are possible.
    For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models 
    (see vignettes for an overview). The package also contains an function for checking if a point
    is inside the convex hull.
Manage data from 'oTree' experiments.  Import 'oTree' data and
    clean them up by using functions to deal with messy data, dropouts, and
    other problematic cases. Create IDs, calculate the time, transfer
    variables between app data frames, and delete sensitive information.
    You can also check your experimental data before running the
    experiment. Information on 'oTree' is found in Chen, D. L., Schonger, M., 
    & Wickens, C. (2016) <doi: 10.1016/j.jbef.2015.12.001>.
Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).
Symbolic calculation (addition or multiplication) and evaluation of multivariate polynomials with rational coefficients.
Functions for graph matching via nodes' degree profiles are provided in this package. The models we can handle include Erdos-Renyi random graphs and stochastic block models(SBM). More details are in the reference paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <arXiv:2006.03284>.
A system for fitting Gompertz Curve in a Time Series Data.
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, Student's t mixture vector autoregressive, and Gaussian and Student's t
    mixture vector autoregressive models, quantile residual tests, graphical diagnostics,
    simulations, forecasting, and estimation of generalized impulse response function and generalized 
    forecast error variance decomposition.
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>,
    Savi Virolainen (2022) <arXiv:2007.04713>,
    Savi Virolainen (2022) <arXiv:2109.13648>.
Fit joint models of survival and multivariate longitudinal data. The longitudinal
    data is specified by generalised linear mixed models. The joint models are fit via maximum
    likelihood using an approximate expectation maximisation algorithm. 
    Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.
Implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>.
             The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals. 
             The 'gmwmx' package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.
Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots, for the underlying manuscript see Fischer, Oja (2015) <doi:10.18637/jss.v065.i09>.
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables.  Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>.
Compute standard and generalized Nash Equilibria of non-cooperative games. 
 Optimization methods available are nonsmooth reformulation, fixed-point formulation, 
 minimization problem and constrained-equation reformulation. 
 See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.
Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures.
A variety of functions to fit linear and nonlinear
    regression with a large selection of distributions.
Functions to specify and fit generalized nonlinear models,
    including models with multiplicative interaction terms such as the
    UNIDIFF model from sociology and the AMMI model from crop science, and
    many others.  Over-parameterized representations of models are used
    throughout; functions are provided for inference on estimable
    parameter combinations, as well as standard methods for diagnostics
    etc.
Tools to set up, train, store, load, investigate and analyze
             generative neural networks. In particular, functionality for
	     generative moment matching networks is provided.
Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>.
    It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.
The goal of 'gnonadd' is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.
Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.
Estimation of gross output production functions and productivity in the presence of numerous fixed (nonflexible) and a single flexible input using the nonparametric identification strategy specified in Gandhi, Navarro, and Rivers (2020) <doi:10.1086/707736>. Monte Carlo evidence from the paper demonstrates high performance in estimating production function elasticities.
Provides tools for interacting with the 'geographic name resolution service' ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The 'GNRS' is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The 'GNRS' resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the 'GNRS' attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The 'GNRS' works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the 'GNRS' does not attempt to match state or county.
Homogenize GNSS (Global Navigation Satellite System) time-series. The general model is a segmentation in the mean model including a periodic function and considering monthly variances,  see Quarello (2020) <arXiv:2005.04683>.
Read data files readable by 'gnumeric' into 'R'. Can read
        whole sheet or a range, from several file formats, including
        the native format of 'gnumeric'. Reading is done by using
        'ssconvert' (a file converter utility included in the 'gnumeric'
        distribution <http://www.gnumeric.org>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.
This function converts mfpr, numeric, or character strings representing numbers to bigq format without loss of precision.
Solves goal programming problems of the weighted and  
    lexicographic type, as well as combinations of the two, as described 
    by Ignizio (1983) <doi:10.1016/0305-0548(83)90003-5>. Allows for 
    a simple human-readable input describing the problem as a series 
    of equations. Relies on the 'lpSolve' package to solve the underlying 
    linear optimisation problem.
Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.
A collection of functions useful in (vegetation) community analyses and ordinations. Includes automatic species selection for ordination diagrams, NMDS stress/scree plots, species response curves and rank-abundance curves as well as calculation and sorting of synoptic tables.
Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.
Harness the power of 'quanteda', 'data.table' & 'stringi' to quickly generate 'tm' DocumentTermMatrix
             and TermDocumentMatrix data structures.
A post-estimation method for categorical response models (CRM). 
    Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), 
    vglm() and glm() are currently supported. Available tests include the 
    Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic 
    regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal 
    models. The proportional odds, adjacent-category, and constrained continuation-ratio 
    models are particularly supported at ordinal level. Tests for the proportional 
    odds assumptions in ordinal models are also possible with the Brant and the 
    Likelihood-Ratio tests. Moreover, several summary measures of predictive strength 
    (Pseudo R-squared), and some useful error metrics, including, the brier 
    score, misclassification rate and logloss are also available for the 
    binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) 
    <http://www.statmod.org/workshops_archive_proceedings_2018.html>.
Graphical tools and goodness-of-fit tests for right-censored data:
             1. Kolmogorov-Smirnov, Crámer-von Mises, and Anderson-Darling tests
                based on the empirical distribution function for complete data and their 
                extensions  for  right-censored data. 
             2. Generalized chi-squared-type tests  based on the squared difference between 
                observed and expected counts using random cells with right-censored data.
             3. A series of graphical tools such as probability or cumulative hazard 
                plots to guide the decision about the parametric model that best
                fits the data.
Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.
Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test a sample against Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.
Implementation of several goodness-of-fit tests for functional
    data. Currently, mostly related with the functional linear model with
    functional/scalar response and functional/scalar predictor. The package
    allows for the replication of the data applications considered in
    García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga
    (2021) <doi:10.1111/sjos.12486>.
We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.
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.
Provision of classes and methods for estimating generalized
   orthogonal GARCH models. This is an alternative approach to CC-GARCH models
   in the context of multivariate volatility modeling.
Tools for fitting statistical network models to dynamic network data.
   Can be used for fitting both dynamic network actor models ('DyNAMs') and
   relational event models ('REMs').
   Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>,
   Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>,
   Stadtfeld and Block (2017) <doi:10.15195/v4.a14>,
   Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.
Implements the Goldilocks adaptive trial design for a time to event
    outcome using a piecewise exponential model and conjugate Gamma prior
    distributions. The method closely follows the article by Broglio and
    colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore
    the operating characteristics of different trial designs.
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.
Estimates hazard ratios and mortality
    differentials for doubly-truncated data without population
    denominators.
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>.
Fit Good regression models to count data (Tur et al., 2021) <arXiv:2105.01557>. The package provides functions for model estimation and model prediction. Density, distribution function, quantile function and random generation for the Good distribution are also provided.
Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length and curvature).
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.
Collect  marketing data from 'Google Ads' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Interact with the Google Analytics 
  APIs <https://developers.google.com/analytics/>, including 
  the Core Reporting API (v3 and v4), Management API, User Activity API
  GA4's Data API and Admin API and Multi-Channel Funnel API.
Create R functions that interact with OAuth2 Google APIs 
    <https://developers.google.com/apis-explorer/> easily,
    with auto-refresh and Shiny compatibility.
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/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.
Send error reports to the Google Error Reporting service <https://cloud.google.com/error-reporting/> and view errors and assign error status in the Google Error Reporting user interface.
Images are provided as an array dataset of 2D image thumbnails from Google Image Search <https://www.google.com/search>.
  This array data may be suitable for a training data of machine learning or deep learning as a first trial.
Allows you to retrieve information from the 'Google Knowledge Graph' API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The 'Knowledge Graph Search' API lets you find entities in the 'Google Knowledge Graph'. The API uses standard 'schema.org' types and is compliant with the 'JSON-LD' specification.
Call 'Google Cloud' machine learning APIs for text and speech tasks.
  Call the 'Cloud Translation' API <https://cloud.google.com/translate/> for detection 
  and translation of text, the 'Natural Language' API <https://cloud.google.com/natural-language/> to 
  analyse text for sentiment, entities or syntax, the 'Cloud Speech' API 
  <https://cloud.google.com/speech/> to transcribe sound files to text and 
  the 'Cloud Text-to-Speech' API <https://cloud.google.com/text-to-speech/> to turn text 
  into sound files.
Interact with Google's Cloud Natural Language API
    <https://cloud.google.com/natural-language/> (v1) via R. The API has
    four main features, all of which are available through this
    R package: syntax analysis and part-of-speech tagging, entity
    analysis, sentiment analysis, and language identification.
Encodes simple feature ('sf') objects and coordinates, and decodes polylines 
    using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Provides a collection of functions to set up 'Google Public Data Explorer'
  <https://www.google.com/publicdata/> data visualization tool with your own data,
  building automatically the corresponding DataSet Publishing Language file, or
  DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to
  be published in 'Public Data Explorer'.
Provides an easy to use interface to the 'Google
	Pub/Sub' REST API <https://cloud.google.com/pubsub/docs/reference/rest>.
This is a wrapper for the command line tool 'googler', which can be
    found at the following URL: <https://github.com/jarun/googler>.
Interact with Google Sheets through the Sheets API v4
    <https://developers.google.com/sheets/api>. "API" is an acronym for
    "application programming interface"; the Sheets API allows users to
    interact with Google Sheets programmatically, instead of via a web
    browser. The "v4" refers to the fact that the Sheets API is currently
    at version 4. This package can read and write both the metadata and
    the cell data in a Sheet.
Interact with the 'Google Tag Manager' API <https://developers.google.com/tag-platform/tag-manager/api/v2>, enabling scripted deployments and updates across multiple tags, triggers, variables and containers.
Create geographically referenced traffic data from the Google Maps JavaScript API <https://developers.google.com/maps/documentation/javascript/examples/layer-traffic>.
R interface to Google's chart tools, allowing users
    to create interactive charts based on data frames. Charts
    are displayed locally via the R HTTP help server. A modern
    browser with an Internet connection is required. The data 
    remains local and is not uploaded to Google.
Provides a mechanism to plot a 'Google Map' from 'R' and overlay
    it with shapes and markers. Also provides access to 'Google Maps' APIs,
    including places, directions, roads, distances, geocoding, elevation and
    timezone.
Implementation of multilayered visualizations for enhanced
    graphical representation of functional analysis data. It combines and integrates
    omics data derived from expression and functional annotation enrichment
    analyses. Its plotting functions have been developed with an hierarchical
    structure in mind: starting from a general overview to identify the most
    enriched categories (modified bar plot, bubble plot) to a more detailed one
    displaying different types of relevant information for the molecules in a given
    set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).
Informal implementation of some algorithms from Graph
  Theory and Combinatorial Optimization which arise in the subject
  "Graphs and Network Optimization" from first course of the EUPLA
  (Escuela Universitaria Politecnica de La Almunia) degree of Data
  Engineering in Industrial Processes.
  References used are:
  Cook et al (1998, ISBN:0-471-55894-X),
  Korte, Vygen (2018) <doi:10.1007/978-3-662-56039-6>,
  Hromkovic (2004) <doi:10.1007/978-3-662-05269-3>,
  Hartmann, Weigt (2005, ISBN:978-3-527-40473-5).
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models.
Implements the generalized order-restricted information criterion
    approximation (GORICA), an AIC-like information criterion that can be
    utilized to evaluate informative hypotheses specifying directional
    relationships between model parameters in terms of (in)equality
    constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper,
    2021), <doi:10.31234/osf.io/t3c8g>. The GORICA is applicable not only to
    normal linear models, but also to generalized linear models (GLMs), 
    generalized linear mixed models (GLMMs), structural equation models
    (SEMs), and contingency tables. For contingency tables, restrictions on cell
    probabilities can be non-linear.
Toolkit to analyse experimental agriculture data, 
    from data synthesis to model selection and visualisation. 
    The package is named after W.S. Gosset aka ‘Student’, a pioneer 
    of modern statistics in small sample experimental design and analysis.
Add a scroll back to top 'Font Awesome' icon 
    <https://fontawesome.com/> in 'rmarkdown' documents and 'shiny'
    apps thanks to 'jQuery GoTop' <https://scottdorman.blog/jquery-gotop/>.
A suite of custom R Markdown formats and templates
    for authoring web pages styled with the GOV.UK Design System.
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/>).
Functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.
Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.
Gaussian process regression models, a.k.a. Kriging models, are
    applied to global multi-objective optimization of black-box functions.
    Multi-objective Expected Improvement and Step-wise Uncertainty Reduction
    sequential infill criteria are available. A quantification of uncertainty
    on Pareto fronts is provided using conditional simulations.
Derivative Free Gradient Projection Algorithms for Factor Rotation.
    For more details see ?GPArotateDF. Theory for these functions can be found in
    the following publications:
    Jennrich (2004) <doi:10.1007/BF02295647>.
    Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.
Gradient Projection Algorithms for Factor Rotation. 
    For details see ?GPArotation. When using this package, please cite:
    Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.
    "Gradient Projection Algorithms and Software 
    for Arbitrary Rotation Criteria in Factor Analysis".
Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'.
An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.
Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/> and Diallel analysis as per Griffing, B. (1956) <https://www.publish.csiro.au/bi/pdf/BI9560463>.
Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint <arXiv:2105.03454>.
Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
Functionalities for modelling functional data with 
    multidimensional inputs, multivariate functional data, and non-separable 
    and/or non-stationary covariance structure of function-valued processes. 
    In addition, there are functionalities for functional regression models where 
    the mean function depends on scalar and/or functional covariates and 
    the covariance structure depends on functional covariates. 
    The development version of the package can be found on 
    <https://github.com/gpfda/GPFDA-dev>.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic 
    methods. Includes utilities for public key encryption, creating and verifying
    digital signatures, and managing your local keyring. Some functionality 
    depends on the version of GnuPG that is installed on the system. On Windows
    this package can be used together with 'GPG4Win' which provides a GUI for 
    managing keys and entering passphrases.
Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) <doi:10.1007/s10898-018-0688-0>. M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) <arXiv:1902.06565v2>.
Functions for fitting and doing predictions with
    Gaussian process models using Vecchia's (1988) approximation. 
    Package also includes functions for reordering input locations, 
    finding ordered nearest neighbors (with help from 'FNN' package), 
    grouping operations, and conditional simulations.
    Covariance functions for spatial and spatial-temporal data
    on Euclidean domains and spheres are provided. The original 
    approximation is due to Vecchia (1988) 
    <http://www.jstor.org/stable/2345768>, and the reordering and
    grouping methods are from Guinness (2018) 
    <doi:10.1080/00401706.2018.1437476>.
    Model fitting employs a Fisher scoring algorithm described
    in Guinness (2019) <arXiv:1905.08374>.
Compute the GPIC index as described in Pham (2020)
    <doi:10.35542/osf.io/ajz5v>.
Tools to build and work with bilateral generalized-mean
    price indexes (and by extension quantity indexes), and indexes composed of
    generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS).
    Covers the core mathematical machinery for making bilateral price indexes,
    computing price relatives, detecting outliers, and decomposing indexes,
    with wrapper for all common (and many uncommon) index-number
    formulas. Implements and extends many of the methods in
    Balk (2008, ISBN:978-1-107-40496-0),
    von der Lippe (2001, ISBN:3-8246-0638-0), and the
    CPI manual (2020, ISBN:978-1-51354-298-0).
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.
Build Open Geospatial Consortium 'GeoPackage' files (<https://www.geopackage.org/>). 'GDAL' utilities for reading and writing spatial data are provided by the 'terra' package. Additional 'GeoPackage' and 'SQLite' features for attributes and tabular data are implemented with the 'RSQLite' package.
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').
We provides functions that employ splines to estimate generalized 
             partially linear single index models (GPLSIM), which extend the generalized linear 
             models to include nonlinear effect for some predictors. Please see Y. (2017) at
             <doi:10.1007/s11222-016-9639-0> and Y., and R. (2002) at <doi:10.1198/016214502388618861> for more details.
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.
          See 'Cyprien Mbogning et al.'(2014)<doi:10.1186/2043-9113-4-6> and 
         'Cyprien Mbogning et al.'(2015)<doi:10.1159/000380850> 
          for an overview of all the methods implemented in this package.
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). The method is described in the article :
    Mangiarotti S. and Huc M. (2019) <doi:10.1063/1.5081448>.
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.
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.
General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) <arXiv:2201.06808>. Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) <doi:10.1214/ss/1038425655>. They are also related to the O-splines of O'Sullivan (1986) <doi:10.1214/ss/1177013525> via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) <doi:10.1007/s11222-022-10178-z>. It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework.
Focused on extracting important data from track points such as speed, distance, elevation difference and azimuth.(PLAZA, J. et al., 2022) <doi:10.1016/j.applanim.2022.105643>.
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. 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 of global positioning system exchange format files for
    navigation purposes. 
    Methods can be found at <doi:10.1111/2041-210X.13572>.
Large language models are readily accessible via API. This
    package lowers the barrier to use the API inside of your development
    environment.  For more on the API, see
    <https://platform.openai.com/docs/introduction>.
An R interface to the 'GPTZero' API (<https://gptzero.me/docs>). Allows 
    users to classify text into human and computer written with probabilities. Formats
    the data into data frames where each sentence is an observation. Paragraph-level
    and document-level predictions are organized to align with the sentences.
GPUs are great resources for data analysis, especially in statistics and linear algebra. Unfortunately, very few packages connect R to the GPU, and none of them are transparent enough to run the computations on the GPU without substantial changes to the code. The maintenance of these packages is cumbersome: several of the earlier attempts have been removed from their respective repositories. It would be desirable to have a properly maintained R package that takes advantage of the GPU with minimal changes to the existing code. We have developed the GPUmatrix package (available on CRAN). GPUmatrix mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single(FP32) and double(FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU. GPUmatrix relies on either the Torch or Tensorflow R packages to perform the GPU operations. We have demonstrated its usefulness for several statistical applications and machine learning applications: non-negative matrix factorization, logistic regression and general linear models. We have also included a comparison of GPU and CPU performance on different matrix operations.
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>.
Process open standard GPX files into data.frames for further use and analysis in R.
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>.
Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).
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.
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.
An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially.
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.
Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify-vignettes.netlify.app/>. Citation: <doi:10.5281/zenodo.5136508>.
Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.
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.
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.
Numerical integration with Gram polynomials (based on <arXiv:2106.14875> [math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University of Birmingham, UK]).
Interactively applies the Guidelines for Reporting About Network Data (GRAND) to an 'igraph' object, and generates a uniform narrative or tabular description of the object.
Nucleotide conversion sequencing experiments have been
  developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such 
  experiments require specialized tools for primary processing such as GRAND-SLAM,
  (see 'Jürges et al' <doi:10.1093/bioinformatics/bty256>) and specialized tools for 
  downstream analyses. 'grandR' provides a comprehensive toolbox for quality control,
  kinetic modeling, differential gene expression analysis and visualization of such data.
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>.
This small collection of functions provides what we call elemental graphics for display of analysis of variance
    results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) 
    <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. 
    The term elemental derives from the fact that each function is aimed at construction of
    graphical displays that afford direct visualizations of data with respect to the fundamental 
    questions that drive the particular analysis of variance methods. These functions can be 
    particularly helpful for students and non-statistician analysts. But these methods should be
    quite generally helpful for work-a-day applications of all kinds, as they can help to identify
    outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.
A minimal set of routines to calculate the 'Grantham' distance
    <doi:10.1126/science.185.4154.862>. The 'Grantham' distance attempts to
    provide a proxy for the evolutionary distance between two amino acids
    based on three key chemical properties: composition, polarity and
    molecular volume. In turn, evolutionary distance is used as a proxy for
    the impact of missense mutations. The higher the distance, the more
    deleterious the substitution is expected to be.
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.
Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format.
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>.
Graph clustering using an agglomerative algorithm to maximize the
  integrated classification likelihood criterion and a mixture of stochastic
  block models. The method is described in the article "Model-based clustering 
  of multiple networks with a hierarchical algorithm" by 
  T. Rebafka (2022) <arXiv:2211.02314>.
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 (2020) <doi:10.1111/rssb.12355>.
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 the user 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.
Estimation and display of various types of population attributable fraction and impact fractions.  As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions.
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.
Perform segmentation of graph-based signals. Assume a noisy observation
    of a signal two values correspond to vertices on a graph. Assume the true 
    value of the signal is piece-wise constant (where each 'piece' is a
    connected subgraph). The main function, agraph(), computes the
    segmentation of the signal. The package also includes a wrapper around the competing
    method flsa() (from package 'flsa').
    More information about this method in Goepp and van de Kassteele (2022) 
    "Graph-Based Spatial Segmentation of 
    Health-Related Areal Data" <doi:10.48550/arXiv.2206.06752>.
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.
Graphical approach provides a useful framework for multiplicity adjustment in clinical trials with multiple endpoints. This package includes statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design.
    See Zhang, F. and Gou, J. (2023). Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment, Technical Report.
Facilitates the citation of R packages used in analysis
    projects. Scans project for packages used, gets their citations, and
    produces a document with citations in the preferred bibliography
    format, ready to be pasted into reports or manuscripts. Alternatively,
    'grateful' can be used directly within an 'R Markdown' or 'Quarto' document.
Provides a coherent interface and implementation for creating
    grouped date classes. This package is part of the RECON
    (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
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 or tiles by setting their placement (at particular meridians and parallels)
    and extent (along parallels and meridians). Labels are created independently of
    lines.
Generates synthetic time series based on various univariate time series models
  including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., 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.
Using Australian Bureau of Statistics indices, provides functions
    that convert historical, nominal statistics to real, contemporary values 
    without worrying about date input quality, performance, or the ABS catalogue.
Wrappers for functions in the 'gRain' package to emulate some 'RHugin' 
  functionality, allowing the building of Bayesian networks consisting on discrete 
  chance nodes incrementally, through   adding nodes, edges and conditional probability 
  tables, the setting of evidence,   both 'hard' (boolean) or 'soft' (likelihoods), 
  querying marginal probabilities   and normalizing constants, and generating sets of
  high-probability configurations. Computations will typically not be so fast as they are
  with 'RHugin', but this package should assist users without access to 'Hugin' to use
  code written to use 'RHugin'.
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.
Computes the gravitational and magnetic anomalies generated by
  3-D vertical rectangular prisms at specific observation points using the
  method of Plouff (1976) <doi:10.1190/1.1440645>.
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.
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.
Implement maximum likelihood estimation for Poisson generalized
  linear models with grouped and right-censored count data. Intended to be used
  for analyzing grouped and right-censored data, which is widely applied in
  many branches of social sciences. The algorithm implemented is described
  in Fu et al., (2021) <doi:10.1111/rssa.12678>.
A tool for registering (aligning) gene expression profiles
    between two species (reference data and data to transform).
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <DOI:10.1007/s11222-017-9786-y>.
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 design number (1) to further optimize balance,
    (8) Binary pair matching plus design number (3) to further optimize balance,
    (9) Hadamard designs,
    (10) Simultaneous Multiple Kernels.
    In (1-9) we allow for three objective functions:
    Mahalanobis distance,
    Sum of absolute differences standardized and
    Kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in 
    Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>,
    Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance 
    Optimization" (2021) <arXiv:2012.03330>.
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.
Methods to calculate sensitivities of financial option prices for
 European, geometric and arithmetic Asian, and American options, with various
 payoff functions in the Black Scholes model, and in more general jump diffusion
 models. A shiny app to interactively plot the results is included. Furthermore,
 methods to compute implied volatilities are provided for a wide range of option
 types and  custom payoff functions. Classical formulas are implemented for
 European options in the Black Scholes Model, as is presented in Hull, J. C.
 (2017), Options, Futures, and Other Derivatives.
 In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see
 Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential
 Lévy processes. <doi:10.1007/s11009-023-10014-5>. For American
 options, the Binomial Tree  Method is implemented, as is presented in Hull,
 J. C. (2017).
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.
These Bayesian models written in the 'Stan' probabilistic language can be used to interpret green crab trapping and environmental DNA monitoring data, either independently or jointly. Detailed model information is found in Keller (2022) <doi:10.1002/eap.2561>.
Tools and methods to apply the model Geospatial Regression Equation
    for European Nutrient losses (GREEN); 
    Grizzetti et al. (2005) <doi:10.1016/j.jhydrol.2004.07.036>; 
    Grizzetti et al. (2008);
    Grizzetti et al. (2012) <doi:10.1111/j.1365-2486.2011.02576.x>; 
    Grizzetti et al. (2021) <doi:10.1016/j.gloenvcha.2021.102281>.
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 functions 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.
Functions which make using the Generalized Regression Estimator(GREG)
    J.N.K. Rao, Isabel Molina, (2015) <doi:10.3390/f11020244> 
    and the Generalized Regression Estimator Operating on Resolutions of Y (GREGORY) easier. 
    The functions are designed to work well within a forestry context, and estimate multiple 
    estimation units at once. Compared to other survey estimation packages, this function has greater flexibility when 
    describing the linear model.
We propose two distribution-free test statistics based on between-sample edge counts and measure the degree of relevance by standardized counts. Users can set edge costs in the graph to compare the parameters of the distributions. Methods for comparing distributions are as described in: Xiaoping Shi (2021) <arXiv:2107.00728>.
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>.
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.
A 'greta' extension for analysing transition matrices and 
  ordinary differential equations representing dynamical systems. Provides 
  functions for analysing transition matrices by iteration, and solving 
  ordinary differential equations. This is an extension to the 'greta' 
  software, Golding (2019) <doi:10.21105/joss.01601>.
Provides a syntax to create and combine Gaussian process kernels 
  in 'greta'. You can then them to define either full rank or sparse Gaussian 
  processes. This is an extension to the 'greta' software, Golding (2019) 
  <doi:10.21105/joss.01601>.
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, R Markdown and Quarto. 'gretl' ('Gnu' Regression, 'Econometrics', and Time-series Library) is a statistical software for Econometric analysis.  This package does not only integrate 'gretl' and 'R' but also serves  as a 'gretl' Knit-Engine for 'knitr' package. Write all your 'gretl' commands in 'R', 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.
Testing, Implementation and Forecasting of Grey Model (GM(1, 1)). For method details see Hsu, L. and Wang, C. (2007). <doi:10.1016/j.techfore.2006.02.005>.
Forest-based statistical estimation and inference.
  GRF provides non-parametric methods for heterogeneous treatment effects estimation
  (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables),
  as well as least-squares regression, quantile regression, and survival regression,
  all with support for missing covariates.
Several group factor analysis algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008> , Generalised Canonical Correlation Estimation by Lin and Shin (2022) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the approach we recently proposed, named Weighted Projection Estimation.
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.
Estimates grid type bivariate copula functions, calculates some association measures and provides several copula graphics.
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) <doi:10.1145/3388440.3412415>.
 Joint grid discretization is applicable as a data transformation step
 to prepare data for model-free inference of association, function, or
 causality.
Can be used for optimal transport between two-dimensional grids with respect to separable cost functions of l^p form. It utilizes the Frank-Wolfe algorithm to approximate so-called pivot measures: one-dimensional transport plans that fully describe the full transport, see G. Auricchio (2021) <arXiv:2105.07278>. For these, it offers methods for visualization and to extract the corresponding transport plans and costs. Additionally, related functions for one-dimensional optimal transport are available.
Provides 'grid' grobs that fill in a user-defined area with various patterns.  Includes enhanced versions of the geometric and image-based patterns originally contained in the 'ggpattern' package as well as original 'pch', 'polygon_tiling', 'regular_polygon', 'rose', 'text', 'wave', and 'weave' patterns plus support for custom user-defined patterns.
Simulation tool to facilitate determination of
    required sample size to achieve category saturation
    for studies using multiple repertory grids in conjunction with
    content analysis.
An easy way to create responsive layouts with just a few lines of code. You can create boxes that are draggable and resizable and load predefined Layouts. The package serves as a wrapper to allow for easy integration of the 'gridstack.js' functionalities <https://github.com/gridstack/gridstack.js>.
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" <https://vwo.com/downloads/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).
This General Regression Neural Networks Package uses various distance functions.
     It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper 
     Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions, 
     although "euclidean" distance is used traditionally.
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 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 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 at that date).
    Load packages  from CRAN, GitHub, or Gitlab.
Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.
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 or collapse them to fewer groups.
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).
The 'Grouphmap' was implemented in R, an open-source programming environment, and was released under the provided website. The difference analysis is based on the 'limma' package, which can cover gene and protein expression profiles (Reference: Matthew E Ritchie , Belinda Phipson , Di Wu , Yifang Hu , Charity W Law , Wei Shi , Gordon K Smyth (2015) <doi:10.1093/nar/gkv007>). The GO enrichment analysis is based on the 'clusterProfiler' package and supports three common species: human, mouse, and yeast (Reference: Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He (2012) <doi:10.1089/omi.2011.0118>). The results of batch difference analysis and enrichment analysis are output in separate folders for easy viewing and further visualization of the results during the process. The results returned a heatmap in R and exported to 3 folders named DEG, go, and merge.
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.
Computes probabilities related to group sequential designs for
    normally distributed test statistics. Enables to derive critical
    boundaries, power, drift, and confidence intervals of such designs.
    Supports the alpha spending approach by Lan-DeMets (1994)
    <doi:10.1002/sim.4780131308>.
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.
Provides an expectation-maximization (EM) algorithm using the approach introduced in Warasi (2021) <doi:10.1080/03610918.2021.2009867>. The EM algorithm can be used to estimate the prevalence (overall proportion) of a disease and to estimate a binary regression model from among the class of generalized linear models based on group testing data. The estimation framework we consider offers a flexible and general approach; i.e., its application is not limited to any specific group testing protocol. Consequently, the EM algorithm can model data arising from simple pooling as well as advanced pooling such as hierarchical testing, array testing, and quality control pooling. Also, provided are functions that can be used to conduct the Wald tests described in Buse (1982) <doi:10.1080/00031305.1982.10482817> and to simulate the group testing data described in Kim et al. (2007) <doi:10.1111/j.1541-0420.2007.00817.x>.
A procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.
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.
Run grass growth simulations using a grass growth model based 
    on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting 
    Dynamics of Biomass, Structure and Digestibility of Herbage in Managed 
    Permanent Pastures. 1. Model Description." (2006) 
    <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in
    this package contains a few additions to the above cited version of ModVege,
    such as simulations of management decisions, and influences of snow cover.
    As such, the model is fit to simulate grass growth in mountainous 
    regions, such as the Swiss Alps. The package also contains routines for 
    calibrating the model and helpful tools for analysing model outputs and 
    performance.
Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.
Identifies implausible anthropometric (e.g., height,
    weight) measurements in irregularly spaced longitudinal datasets, such as those from electronic health records.
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.
Assists in the plotting and functional smoothing of traits 
    measured over time and the extraction of features from these traits, 
    implementing the SET (Smoothing and Extraction of Traits) method 
    described in Brien et al. (2020) Plant Methods, 16. Smoothing of 
    growth trends for individual plants using natural cubic smoothing 
    splines or P-splines is available for removing transient effects and 
    segmented smoothing is available to deal with discontinuities in 
    growth trends. There are graphical tools for assessing the adequacy 
    of trait smoothing, both when using this and other packages, such as 
    those that fit nonlinear growth models. A range of per-unit (plant, 
    pot, plot) growth traits or features can be extracted from the 
    data, including single time points, interval growth rates and other 
    growth statistics, such as maximum growth or days to maximum growth. 
    The package also has tools adapted to inputting data from 
    high-throughput phenotyping facilities, such 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.
Create a grid-based graphviz using the following functions:
    1 - Creating the data.frame where the nodes are;
    2 - Adding and editing nodes;
    3 - Plotting these nodes.
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 generalized linear models with group elastic net penalties. Implements group lasso, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), logistic regression (binomial), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.
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.  For more
  information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>,
  Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang
  (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015)
  <doi:10.1111/biom.12300>, or visit the package homepage
  <https://pbreheny.github.io/grpreg/>.
Provides tools for sparse regression modelling with grouped predictors using the group 
    subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver 
    near optimal estimates. The group subset penalty can be combined with a group lasso or ridge 
    penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping 
    groups.
Design of group sequential trials, including non-binding futility analysis
  at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).
Group SLOPE (Group Sorted L1 Penalized Estimation) 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).
    For additional information about the implemented methods please see
    Brzyski, Gossmann, Su, Bogdan (2018) <doi:10.1080/01621459.2017.1411269>.
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.
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.
Simulation and analysis of graded response data with different types of estimators. Also, an interactive shiny application is provided with graphics for characteristic and information curves. Samejima (2018) <doi:10.1007/978-1-4757-2691-6_5>.
Statistical testing procedures for detecting
  GxE (gene-environment) interactions. The main focus lies on
  GRSxE interaction tests that aim at detecting GxE interactions
  through GRS (genetic risk scores). Moreover, a novel testing
  procedure based on bagging and OOB (out-of-bag) predictions is
  implemented for incorporating all available observations at
  both GRS construction and GxE testing (Lau et al., 2023,
  <doi:10.1038/s41598-023-28172-4>).
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.
River hydrograph separation and daily runoff time series analysis. Provides
  various filters to separate baseflow and quickflow. Implements advanced separation 
  technique by Rets et al. (2022) <doi:10.1134/S0097807822010146> which involves 
  meteorological data to reveal genetic components of the runoff: ground, rain, thaw 
  and spring (seasonal thaw). High-performance C++17 computation, annually aggregated 
  variables, statistical testing and numerous plotting functions for high-quality 
  visualization.
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.
Provides 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,
	as described in Gerber and Gsponer (2016) <doi: 10.18637/jss.v069.i11>.
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.
Analysis frameworks for pathway heterogeneity and pathway activity evaluation in single-cell data, including scRNA-seq data and spatial genomics data.
Derives group sequential clinical trial designs and describes
    their properties. Particular focus on time-to-event, binary, and
    continuous outcomes. Largely based on methods described in
    Jennison, Christopher and Turnbull, Bruce W., 2000,
    "Group Sequential Methods with Applications to Clinical Trials"
    ISBN: 0-8493-0316-8.
The goal of 'gsDesign2' is to enable fixed or group sequential
    design under non-proportional hazards. To enable highly flexible enrollment,
    time-to-event and time-to-dropout assumptions, 'gsDesign2' offers piecewise
    constant enrollment, failure rates, and dropout rates for a stratified
    population. This package includes three methods for designs:
    average hazard ratio, weighted logrank tests in Yung and Liu (2019)
    <doi:10.1111/biom.13196>, and MaxCombo tests.
    Substantial flexibility on top of what is in the 'gsDesign' package
    is intended for selecting boundaries.
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>).
Mark your interesting genes on plot and support more parameters to handle your own gene set enrichment analysis plot.
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.
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>.
R implementation of the 'Octave' package 'signal', containing
    a variety of signal processing tools, such as signal generation and
    measurement, correlation and convolution, filtering, filter design,
    filter analysis and conversion, power spectrum analysis, system
    identification, decimation and sample rate change, and windowing.
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.
An R interface to nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.
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.
It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).
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>.
Proposes a new file format ('gson') for storing gene set and related information, and provides read, write and other utilities to process this file format.
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".
Generalization of supervised principal component regression (SPCR; 
    Bair et al., 2006, <doi:10.1198/016214505000000628>) to support continuous, 
    binary, and discrete variables as outcomes and predictors 
    (inspired by the 'superpc' R package <https://cran.r-project.org/package=superpc>).
A group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System <doi:10.1080/01621459.2016.1219261> for the details.
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. 
    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.
Mapper-based survival analysis with transcriptomics data is designed to carry out. 
			Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) 
			where survival data is taken into account in the filtering function. More 
			details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, 
			A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap 
			identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics
			and methylation studies" <doi:10.1101/2022.09.08.507080>.
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>.
Implementations of the algorithms present article 
    Generalized Spatial-Time Sequence Miner, original title
    (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio
    ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de
    Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD -
    Simpósio Brasileiro de Banco de Dados, 2021 <doi:10.5753/sbbd.2021.17891>).
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 3.06-16-0 (commit '657216dd4f5ea079b5f0e021a4163e2d26893371', dated 2022-10-11, 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.
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 built up sequentially.
Computes the  probability density, survival function,
   the hazard rate functions and generates random samples from the 
   GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. 
   The likelihood estimates, the randomized quantile (Louzada, F., et al. 
   (2020) <doi:10.1109/ACCESS.2020.3040525>)
   residuals and the normally transformed randomized survival 
   probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>)
   residuals are obtained for the GTDL model.
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.
New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data.
    Song, H. and Chen, H. (2022) 
    <arXiv:2205.13787>.
Provides additional functions for creating beautiful tables
    with 'gt'. The functions are generally wrappers around boilerplate or
    adding opinionated niche capabilities and helpers functions.
A bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the 'gtfs2emis' model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) <doi:10.31219/osf.io/8m2cy>.
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.
Tools for the development of packages related to General
    Transit Feed Specification (GTFS) files. Establishes a standard for
    representing GTFS feeds using R data types. Provides fast and flexible
    functions to read and write GTFS feeds while sticking to this
    standard. Defines a basic 'gtfs' class which is meant to be extended
    by packages that depend on it. And offers utility functions that
    support checking the structure of GTFS objects.
Use 'GTFS' (General Transit Feed Specification) data for routing
    from nominated start and end stations, for extracting 'isochrones', and
    travel times from any nominated start station to all other stations.
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).
Insert tables created by the 'gt' R package into 'Microsoft Word' 
    documents. This gives users the ability to add to their existing word documents
    the tables made in 'gt' using the familiar 'officer' package and syntax from
    the 'officeverse'.
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'),
  - apply title capitalization rules to a character vector ('capwords').
The goal of this package is to translate between different languages without any Google API authentication which is pain and you must pay for the key, This package is free and lightweight.
Creates tables suitable for regulatory agency submission by
    leveraging the 'gtsummary' package as the back end. Tables can be exported
    to HTML, Word, PDF and more. Highly customized outputs are
    available by utilizing existing styling functions from 'gtsummary' as
    well as custom options designed for regulatory tables.
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 2021, de acuerdo al Servicio de Registro Civil.
    English: Chilean baby names registered from 1920 to 2021
    by the Civil Registry Service.
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 geospatial 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>.
A nice GUI for financial DErivatives in R.
Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.
'Guild AI' is an open-source tool for managing machine learning
    experiments. It's for scientists, engineers, and researchers who want to
    run scripts, compare results, measure progress, and automate machine
    learning workflow. 'Guild AI' is a light weight, external tool that runs
    locally. It works with any framework, doesn't require any changes to
    your code, or access to any web services. Users can easily record
    experiment metadata, track model changes, manage experiment artifacts,
    tune hyperparameters, and share results. 'Guild AI' combines features
    from 'Git', 'SQLite', and 'Make' to provide a lab notebook for machine
    learning.
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.
This 'R' package provides a single procedure guix.install(),
  which allows users to install 'R' packages via 'Guix' right from within
  their running 'R' session.  If the requested 'R' package does not exist
  in 'Guix' at this time, the package and all its missing dependencies
  will be imported recursively and the generated package definitions
  will be written to ~/.Rguix/packages.scm.  This record of imported
  packages can be used later to reproduce the environment, and to add
  the packages in question to a proper 'Guix' channel (or 'Guix' itself).
  guix.install() not only supports installing packages from CRAN, but
  also from Bioconductor or even arbitrary 'git' or 'mercurial'
  repositories, replacing the need for installation via 'devtools'.
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).
Uses jackknife and bootstrap methods to quantify the sampling uncertainty in goodness-of-fit statistics. Full details are in Clark et al. (2021), "The abuse of popular performance metrics in hydrologic modeling", Water Resources Research, <doi:10.1029/2020WR029001>.
A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances,  Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and  (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.
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 <https://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 (see Albert, C., Vogel, S. and Ashauer, R. (2016) <doi:10.1371/journal.pcbi.1004978>).
Light procedures for learning 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.
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.
Simplify your R data analysis and data visualization workflow by turning your data frame into an interactive 'Tableau'-like interface, leveraging the 'graphic-walker' JavaScript library and the 'htmlwidgets' package.
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), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).
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.
'GWAS' R 'API' Data Download.
    This package provides easy access to the 'NHGRI'-'EBI' 'GWAS' Catalog data by
    accessing the 'REST' 'API' <https://www.ebi.ac.uk/gwas/rest/docs/api/>.
Provides methods to Get Water Attributes Visually in R ('gwavr'). This allows the user to point and click on areas within the United States and get back hydrological data, e.g. flowlines, catchments, basin boundaries, comids, etc.
Fit a regression model for when the response variable is presented as a ratio or proportion. This adjustment can occur globally, with the same estimate for the entire study space, or locally, where a beta regression model is fitted for each region, considering only influential locations for that area. Da Silva, A. R. and Lima, A. O. (2017) <doi:10.1016/j.spasta.2017.07.011>.
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>.
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. At this point only 'gWidgets2tcltk' is viable.
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>).
An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017)<doi:10.1553/giscience2017_01_s36> and are described in greater detail in (Tsutsumida et al., 2019)<doi:10.5194/ica-abs-1-372-2019> and (Percival et al., 2021)<arXiv:2101.03491>.
Geographically weighted panel regression is grounded in a branch of spatial statistics. Using geographically weights, the geographically weighted panel regression is try to solve the residuals from panel regression clustering spatially. To investigate whether the residuals cluster spatially, the Moran's I test is also improved. Furthermore, three local statistic tests are contained to help the users select model. The major references are Fotheringham et al. (2003, ISBN:978-0-470-85525-6) and Beenstock and Felsenstein (2019, ISBN:978-3-030-03614-0).
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>), a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) and a WQS with 2 indices (Renzetti et al. (2023) <doi:10.3389/fpubh.2023.1289579>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.
It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of
             LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that 
             captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).<DOI:10.1068/a40256>.
             The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables
             are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations
             and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. 
             Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between 
             the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex 
             spatial patterns and large datasets with multiple predictor variables.
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. See Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for more details.
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.
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.
A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).
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.
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>.
Hyperbolic geometry in the Minkowski model and the Poincaré
    model. The methods are based on the gyrovector space theory developed
    by A. A. Ungar that can be found in the book 'Analytic Hyperbolic
    Geometry: Mathematical Foundations And Applications'
    <doi:10.1142/5914>. The package provides functions to plot
    three-dimensional hyperbolic polyhedra and to plot hyperbolic tilings
    of the Poincaré disk.
We provide a toolbox to conduct a Bayesian meta-analysis for estimating the current expansion rate of the Universe, called the Hubble constant H0, via time delay cosmography. The input data are Fermat potential difference and time delay estimates. For a robust inference, we assume a Student's t error for these inputs. Given these inputs, the meta-analysis produces posterior samples of the model parameters including the Hubble constant via Metropolis-Hastings within Gibbs. The package provides an option to implement repelling-attracting Metropolis-Hastings within Gibbs in a case where the parameter space has multiple modes.
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), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection,
    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.
Several functions are provided that simplify using 'h2o' 
             package. Currently, a function for extracting the AutoML 
             model parameter is provided, alongside a function for computing 
             F-Measure statistics at any given threshold. For more information 
             about 'h2o' package see <https://h2o.ai/>.
Implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and 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.
Provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile 'h3-js' <https://github.com/uber/h3-js> and 'V8' <https://github.com/jeroen/v8>.
'H3' is a hexagonal hierarchical spatial index developed by 'Uber' <https://h3geo.org/>.
  This package exposes the source code of 'H3' (written in 'C') to routines that are callable through 'R'.
A Haar-Fisz algorithm for Poisson intensity estimation.
	Will denoise Poisson distributed sequences where
        underlying intensity is not constant. Uses the multiscale
        variance-stabilization method called the Haar-Fisz transform.
        Contains functions to carry out the forward and inverse
        Haar-Fisz transform and denoising on near-Gaussian sequences.
        Can also carry out cycle-spinning.
	Main reference: Fryzlewicz, P. and Nason, G.P. (2004)
	"A Haar-Fisz algorithm for Poisson intensity estimation."
        Journal of Computational and Graphical Statistics,
        13, 621-638. <doi:10.1198/106186004X2697>.
Based on landscape connectivity, spatial boundaries were
        identified using community detection algorithm at grid level. Methods 
        using raster as input and the value of each cell of the raster is the 
        "smoothness" to indicate how easy the cell connecting with neighbor cells. 
        Details about the 'habCluster' package methods can be found in Zhang et al.
        <bioRxiv:2022.05.06.490926>.
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.
A collection of cancer transcriptomics gene signatures as well as a 
    simple and tidy interface to compute single sample enrichment scores either 
    with the original procedure or with three alternatives:
    the "combined z-score" of Lee et al. (2008) <doi:10.1371/journal.pcbi.1000217>,
    the "single sample GSEA" of Barbie et al. (2009) <doi:10.1038/nature08460> and 
    the "singscore" of Foroutan et al. (2018) <doi:10.1186/s12859-018-2435-4>.
    The 'get_sig_info()' function can be used to retrieve information about each 
    signature implemented.
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>. 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available.
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) (10.1093/bioinformatics/btaa587). '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.
Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices.
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.
Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at <https://github.com/kharchenkolab/hahmmr/>.
Initializes a class that obtains API credentials and provides
    a method to use those credentials to make GET requests to the 'Hakai'
    API server. Usage instructions are documented at
    <https://hakaiinstitute.github.io/hakai-api/>.
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>. This
  implementation of the highly adaptive lasso algorithm was described by Hejazi,
  Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.
An algorithm for flexible conditional density estimation based on
    application of pooled hazard regression to an artificial repeated measures
    dataset constructed by discretizing the support of the outcome variable. To
    facilitate non/semi-parametric estimation of the conditional density, the
    highly adaptive lasso, a nonparametric regression function shown to reliably
    estimate a large class of functions at a fast convergence rate, is utilized.
    The pooled hazards data augmentation formulation implemented was first
    described by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. To
    complement the conditional density estimation utilities, tools for efficient
    nonparametric inverse probability weighted (IPW) estimation of the causal
    effects of stochastic shift interventions (modified treatment policies),
    directly utilizing the density estimation technique for construction of the
    generalized propensity score, are provided. These IPW estimators utilize
    undersmoothing (sieve estimation) of the conditional density estimators in
    order to achieve the non/semi-parametric efficiency bound.
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.
Build better balance in causal inference models. 'halfmoon'
    helps you assess propensity score models for balance between groups
    using metrics like standardized mean differences and visualization
    techniques like mirrored histograms. 'halfmoon' supports both
    weighting and matching techniques.
Provides methods for implementing hierarchical age length keys
    to estimate fish ages from lengths using data borrowing. Users can create
    hierarchical age length keys and use them to assign ages given length.
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.
Shiny-App that allows to annotate vectors of texts to predefined categories by hand.
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>.
Perform statistical writership analysis of scanned handwritten documents.
             Webpage provided at: <https://csafe-isu.github.io/handwriter/index.html>.
Some useful functions for simply manipulating and analyzing data with data.frame format.
  It mainly includes the following sections: ReformatDataframe (reformat dataframe with the modifiers), InteractDataframe, and Post-VCF (for downstream analysis for data generated from 'vcftools' Petr et al (2011) <doi:10.1093/bioinformatics/btr330>
  or 'plink' Chang et al (2015) <doi:10.1186/s13742-015-0047-8>.
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.
A simple and time saving multiple linear regression function (OLS) with interpretation, optional bootstrapping, effect size calculation and all tested requirements.
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.
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. Case-parent triads: Estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Annals of Human Genetics (2006) 70, pp. 382-396".
Haplotype and covariate relative risks in case-control data are estimated by weighted logistic regression. Diplotype probabilities, which are estimated by EM computation with progressive insertion of loci, are utilized as weights. French et al. (2006) <doi:10.1002/gepi.20161>.
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.
Used for predicting a genotype’s allelic state at a specific locus/QTL/gene. This is accomplished by using both a genotype matrix and a separate file which has categorizations about loci/QTL/genes of interest for the individuals in the genotypic matrix. A training population can be created from a panel of individuals who have been previously screened for specific loci/QTL/genes, and this previous screening could be summarized into a category. Using the categorization of individuals which have been genotyped using a genome wide marker platform, a model can be trained to predict what category (haplotype) an individual belongs in based on their genetic sequence in the region associated with the locus/QTL/gene. These trained models can then be used to predict the haplotype of a locus/QTL/gene for individuals which have been genotyped with a genome wide platform yet not genotyped for the specific locus/QTL/gene. This package is based off work done by Winn et al 2021. For more specific information on this method, refer to <doi:10.1007/s00122-022-04178-w>.
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.
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.
Automatic open data acquisition from resources of IGN
    ('Institut National de Information Geographique et forestiere')
    (<https://www.ign.fr/>). Available datasets include various types of
    raster and vector data, such as digital elevation models, state
    borders, spatial databases, cadastral parcels, and more. There also
    access to point clouds data ('LIDAR') and specifics API
    (<https://apicarto.ign.fr/api/doc/>).
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.
By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.
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) 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. The functionality of the package is explained in detail in a related JSS paper <doi:10.18637/jss.v064.i03>.
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.
Several functions are provided to harmonize CN8 (Combined Nomenclature 
	8 digits) and PC8 (Production Communautaire 8 digits) product codes over 
	time and the classification systems HS6 and BEC. Harmonization of CN8 
	codes are possible by default from 1995 to 2022 and of PC8 from 2001 to 
	2021, respectively.
Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.
'Harmony' is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try 'Harmony' at <https://harmonydata.ac.uk/app/> and you can read our blog at <https://harmonydata.ac.uk/blog/> or at <https://fastdatascience.com/how-does-harmony-work/>. Documentation at <https://harmonydata.ac.uk/harmony-r-released/>.
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.
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.
The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. 
    The  Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for 
    any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to 
    the number of lags involved in the test and therefore it should be used with extra caution.
    The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.
A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) 
  test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve
  two distinct purposes. Initially, the test seeks to determine whether there exists a 
  statistically significant difference between the distribution of forecast errors, and 
  secondly it exploits the principles of stochastic dominance to determine whether the 
  forecasts with the lower error also reports a stochastically smaller error than forecasts 
  from a competing model, and thereby enables distinguishing between the predictive accuracy 
  of forecasts. KSPA test has been described in : Hassani and Silva (2015) <doi:10.3390/econometrics3030590>.
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.
Implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.
The conditional treatment effect for competing risks data in observational studies is estimated. While it is described as a constant difference between the hazard functions given the covariates, we do not assume specific functional forms for the covariates. Rava, D. and Xu, R. (2021) <arXiv:2112.09535>.
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.
Implements hierarchically regularized entropy balancing proposed by Xu and Yang (2022) <doi:10.1017/pan.2022.12>. The method adjusts the covariate distributions of the control group to match those of the treatment group. 'hbal' automatically expands the covariate space to include higher order terms and uses cross-validation to select variable penalties for the balancing conditions.
Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte
    Carlo via 'Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the ideological 
    positions of survey respondents and political actors on a common scale using positional survey 
    data. The hierarchical versions of the Bayesian 'AM' model included in this package outperform 
    other versions both in terms of yielding meaningful posterior distributions for respondent 
    positions and in terms of recovering true respondent positions in simulations. The package 
    contains functions for preparing data, fitting models, extracting estimates, plotting key 
    results, and comparing models using cross-validation. The default model is described in 
    Bølstad (2023) <doi:10.1017/pan.2023.18>.
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>.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.
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
    mean squared errors, as well as some model selection measures. Additional functions
    provide means to compute aggregate estimates and mean squared errors, to minimally
    adjust the small area estimates to benchmarks at a higher aggregation
    level, and to graphically compare different sets of small area estimates.
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.
Simulate and analyze hierarchical composite endpoints. Win odds is the main analysis method. See Gasparyan SB et al (2022) "Design and Analysis of Studies Based on Hierarchical Composite Endpoints: Insights from the DARE-19 Trial." Therapeutic Innovation & Regulatory Science 56: 785–794. <doi:10.1007/s43441-022-00420-1>.
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.
A collection of datasets of human-computer interaction (HCI) experiments. 
    Each dataset is from an HCI paper, with all fields described and the original publication 
    linked. All paper authors of included data have consented to the inclusion of their data 
    in this package. The datasets include data from a range of HCI studies, such as pointing 
    tasks, user experience ratings, and steering tasks. 
    Dataset sources: 
    Bergström et al. (2022) <doi:10.1145/3490493>;
    Dalsgaard et al. (2021) <doi:10.1145/3489849.3489853>;
    Larsen et al. (2019) <doi:10.1145/3338286.3340115>;
    Lilija et al. (2019) <doi:10.1145/3290605.3300676>;
    Pohl and Murray-Smith (2013) <doi:10.1145/2470654.2481307>;
    Pohl and Mottelson (2022) <doi:10.3389/frvir.2022.719506>.
Univariate agglomerative hierarchical clustering with a comprehensive list of choices of a linkage function in O(n*log n) time. The better algorithmic time complexity is paired with an efficient 'C++' implementation.
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.
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.
It provides functions to design historical controlled trials with survival outcome by group sequential method. The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) <doi:10.1002/pst.1756> and Jianrong Wu, Yimei Li (2020) <doi:10.1080/10543406.2019.1684305>.
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.
Ridge regression provide biased estimators of the regression parameters with lower variance. The HDBRR ("High Dimensional Bayesian Ridge Regression") function fits Bayesian Ridge regression without MCMC, this one uses the SVD or QR decomposition for the posterior computation.
A two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the “double robustness” property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics <doi:10.1080/07350015.2020.1811102>.
Efficient implementations of the following multiple changepoint detection algorithms: Efficient Sparsity Adaptive Change-point estimator by Moen, Glad and Tveten (2023) <arXiv:2306.04702> , Informative Sparse Projection for Estimating Changepoints by Wang and Samworth (2017) <doi:10.1111/rssb.12243>, and the method of Pilliat et al (2023) <doi:10.1214/23-EJS2126>.
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>.
'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.
Some methods to manipulate 'HDF5' files, extending the 'hdf5r' package. Reading and writing R objects to 'HDF5' formats follow the specification of 'AnnData' <https://anndata.readthedocs.io/en/latest/fileformat-prose.html>.
Provides a forecasting method that maps vast numbers of
    (scalar-valued) signals of any type into an aggregate density forecast
    in a time-varying and computationally fast manner. The method proceeds
    in two steps: First, it transforms a predictive signal into a density
    forecast. Second, it combines the generated candidate density
    forecasts into an ultimate density forecast. The methods are explained
    in detail in Adaemmer et al. (2023) <doi:10.2139/ssrn.4342487>.
Provides an interface to 'HDFql' <https://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 
    <https://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 correlation-based batch process for fast, accurate imputation for 
    high dimensional missing data problems via chained random forests.
    See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on 'hdImpute',
    Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on 'missForest', 
    and Mayer (2022) <https://github.com/mayer79/missRanger> for more on 'missRanger'.
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 computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) <doi:10.1007/s11634-021-00457-4>. 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. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.
Joint models have been widely used to study the associations between longitudinal biomarkers and a survival outcome. However, existing joint models only consider one or a few longitudinal 
    biomarkers and cannot deal with high-dimensional longitudinal biomarkers. This package can be used to fit our recently developed penalized joint model that can handle high-dimensional longitudinal biomarkers. 
    Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the longitudinal biomarkers on the survival outcome, which allows for variable selection. 
    Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.
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 (2021) <doi:10.1016/j.jmva.2021.104897>; 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>.
We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). <doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008) <doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008) <doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X. (2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.
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 suite of functions for performing mediation
    analysis with high-dimensional mediators. In addition to centralizing code 
    from several existing packages for high-dimensional mediation analysis, we 
    provide organized, well-documented functions for a handle of methods 
    which, though programmed their original authors, have not previously been 
    formalized into R packages or been made presentable for public use. The 
    methods we include cover a broad array of approaches and objectives, and are
    described in detail by both our companion manuscript---"Methods for 
    Mediation Analysis with High-Dimensional DNA Methylation Data: Possible 
    Choices and Comparison"---and the original publications that proposed them. 
    The specific methods offered by our package include the Bayesian sparse 
    linear mixed model (BSLMM) by Song et al. (2019); high-dimensional mediation
    analysis (HDMA) by Gao et al. (2019); high-dimensional multivariate 
    mediation (HDMM) by Chén et al. (2018); high-dimensional linear mediation 
    analysis (HILMA) by Zhou et al. (2020); high-dimensional mediation analysis
    (HIMA) by Zhang et al. (2016); latent variable mediation analysis (LVMA) by 
    Derkach et al. (2019); mediation by fixed-effect model (MedFix) by 
    Zhang (2021); pathway LASSO by Zhao & Luo (2022); principal component 
    mediation analysis (PCMA) by Huang & Pan (2016); and sparse principal 
    component mediation analysis (SPCMA) by Zhao et al. (2020). Citations for 
    the corresponding papers can be found in their respective functions.
Hign-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023)<doi:10.1080/07350015.2023.2191676>. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR). In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) <doi:10.1080/01621459.2021.1970569>, the Projected estimation (PE) method by Yu et al. (2022)<doi:10.1016/j.jeconom.2021.04.001>. In addition, the methods for determining the pair of factor numbers are also given.
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.
Contains functions to construct high-dimensional orthogonal maximin distance designs in two, four, eight, and sixteen levels from rotating the Kronecker product of sub-Hadamard matrices.
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>.
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> and <doi:10.18637/jss.v107.i10>.
Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.
Factor models have been widely applied in areas such as economics and finance, and the well-known heavy-tailedness of macroeconomic/financial data should be taken into account when conducting factor analysis. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber PCA. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm. In this package we also provide the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022) and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al. (2023).
Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.
Constructs shrinkage estimators of high-dimensional mean-variance portfolios and performs 
    high-dimensional tests on optimality of a given portfolio. The techniques developed in 
    Bodnar et al. (2018) <doi:10.1016/j.ejor.2017.09.028>, Bodnar et al. (2019) 
    <doi:10.1109/TSP.2019.2929964>, Bodnar et al. (2020) <doi:10.1109/TSP.2020.3037369> 
    are central to the package. They provide simple and feasible estimators and tests for optimal 
    portfolio weights, which are applicable for 'large p and large n' situations where p is the 
    portfolio dimension (number of stocks) and n is the sample size. The package also includes tools
    for constructing portfolios based on shrinkage estimators of the mean vector and covariance matrix
    as well as a new Bayesian estimator for the Markowitz efficient frontier recently developed by 
    Bauder et al. (2021) <doi:10.1080/14697688.2020.1748214>.
Contains seven datasets used in the chapters and exercises of Paul, Alice (2023) "Health Data Science in R" <https://alicepaul.github.io/health-data-science-in-r/>.
Allows to detect spatial clusters of abnormal values on multivariate or functional data. Martin KULLDORFF and Lan HUANG and Kevin KONTY (2009) <doi:10.1186/1476-072X-8-58>, Inkyung JUNG and Ho Jin CHO (2015) <doi:10.1186/s12942-015-0024-6>, Lionel CUCALA and Michael GENIN and Caroline LANIER and Florent OCCELLI (2017) <doi:10.1016/j.spasta.2017.06.001>, Lionel CUCALA and Michael GENIN and Florent OCCELLI and Julien SOULA (2019) <doi:10.1016/j.spasta.2018.10.002>, Camille FREVENT and Mohamed-Salem AHMED and Matthieu MARBAC and Michael GENIN (2021) <doi:10.1016/j.spasta.2021.100550>, Zaineb SMIDA and Lionel CUCALA and Ali GANNOUN and Ghislain Durif (2022) <doi:10.1016/j.csda.2021.107378>, Camille FREVENT and Mohamed-Salem AHMED and Sophie DABO-NIANG and Michael GENIN (2023) <doi:10.1093/jrsssc/qlad017>.
A method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, 'HDStIM' labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.
Procedures for high-dimensional time series analysis including factor analysis proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by Chang, Jiang and Shao (2021) preprint, principal component analysis proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, identifying conintegration proposed by Zhang, Robinson and Yao (2019) <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2021) <doi:10.1093/biomet/asab034> and white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>.
The Tweedie lasso model implements an iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing solution paths of the (grouped) lasso and the (grouped) elastic net methods.
Create dynamic, data-driven text. Given two values, a list of 
    talking points is generated and can be combined using string
    interpolation. Based on the 'glue' package.
Health Calculator helps to find different parameters like basal metabolic rate, body mass index etc. related to fitness and health of a person.
Antitrust analysis of
    healthcare markets. Contains functions to implement the 
    semiparametric estimation technique described in Raval, Rosenbaum,
    and Tenn (2017) "A Semiparametric Discrete Choice Model: An Application
    to Hospital Mergers" <doi:10.1111/ecin.12454>.
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).
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.
Hospital machine learning and ai data analysis workflow tools, modeling, and automations. 
    This library provides many useful tools to review common administrative 
    hospital data. Some of these include predicting length of stay, and readmits.
    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.
Makes it easy to download a large number of files such as PDF files 
    and CSV files, while automatically slowing down requests, letting you know 
    where it is up to, and adjusting for files that have already been downloaded.
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.
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>.
A set of tools supporting more flexible heatmaps. The graphics is grid-like using
  the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions
  constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming
  and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set,
  e.g., clustered and ordered by a supervised clustering method.
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 for defining, detecting, and categorising the extreme events 
    known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016) <doi: 10.1016/j.pocean.2015.12.014> 
    and Hobday et al. (2018) <https://www.jstor.org/stable/26542662>. The functions in this package work on both air 
    and water temperature data. These detection algorithms may be used on non-temperature data as well.
It performs maximum likelihood estimation for the Heckman selection model (Normal or Student-t) using an EM-algorithm <doi:10.1016/j.jmva.2021.104737>. It also performs influence diagnostic through global and local influence for four possible perturbation schema.
This tool identifies hydropeaking events from raw time-series flow record, a rapid flow variation induced by the hourly-adjusted electricity market. The novelty of 'HEDA' is to use vector angle instead of the first-order derivative to detect change points which not only largely improves the computing efficiency but also accounts for the rate of change of the flow variation. More details <doi:10.1016/j.jhydrol.2021.126392>.
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>.
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.
Template R package with minimal setup to use Rust code in R without
    hacks or frameworks. Includes basic examples of importing cargo dependencies,
    spawning threads and passing numbers or strings from Rust to R. Cargo crates
    are automatically 'vendored' in the R source package to support offline
    installation. The GitHub repository for this package has more details and also 
    explains how to set up CI. This project was first presented at 'Erum2018' to 
    showcase R-Rust integration <https://jeroen.github.io/erum2018/>; for a real
    world use-case, see the 'gifski' package on 'CRAN'.
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, tide level, 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 APIs in the Helsinki
    region in Finland. Current data sources include the Service Map API,
    Linked Events API, and Helsinki Region Infoshare statistics API.
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>).
Import and classify canopy fish-eye images, estimate angular gap fraction and derive canopy attributes like leaf area index and openness.  Additional information is provided in the study by Chianucci F., Macek M. (2023) <doi:10.1016/j.agrformet.2023.109470>.
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.
Hierarchical exponential-family random graph models with local dependence. See Schweinberger and Luna (2018) <doi:10.18637/jss.v085.i01>.
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.
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 nonparametric 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 Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.
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. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) <doi:10.1016/j.spasta.2020.100413>.
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>.
A comprehensive suite of spatial 
  functions created to analyze and assess data heterogeneity and climate variability 
  in spatial datasets. This package is specifically designed to address the challenges associated
  with characterizing and understanding complex spatial patterns in environmental and climate-related data.
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., 2022), 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. (2022), 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. (2022). Gaussian Graphical 
             Model-based Heterogeneity Analysis via Penalized Fusion. 
             Biometrics, 78 (2), 524-535.
A multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the 'heteromixgm' package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2022) <doi:10.48550/arXiv.2210.13140>.
Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. 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.
Homomorphic encryption (Brakerski and Vaikuntanathan (2014) <doi:10.1137/120868669>) using Ring Learning with Errors (Lyubashevsky et al. (2012) <https://eprint.iacr.org/2012/230>) is a form of Learning with Errors (Regev (2005) <doi:10.1145/1060590.1060603>) using polynomial rings over finite fields. Functions to generate the required polynomials (using 'polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided.
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>.
Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.
Functions for the fitting and summarizing of heteroscedastic t-regression.
Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.
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 codes (natural persons and 
    organizations); 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.  Take The Best was first described in: Gigerenzer, G.
    & Goldstein, D. G. (1996) <doi:10.1037/0033-295X.103.4.650>.  All
    of these heuristics were run on many data sets and analyzed in:
    Gigerenzer, G., Todd, P. M., & the ABC Group (1999).
    <ISBN:978-0195143812>.
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.
Scavenge the web for possible hex logos for CRAN packages.
Contains all the hex font files from the 'GNU Unifont Project' <https://unifoundry.com/unifont/> compressed by 'xz'.  'GNU Unifont' is a duospaced bitmap font that attempts to cover all the official Unicode glyphs plus several of the artificial scripts in the '(Under-)ConScript Unicode Registry' <http://www.kreativekorp.com/ucsur/>.  Provides a convenience function for loading in several of them at the same time as a 'bittermelon' bitmap font object for easy rendering of the glyphs in an 'R' terminal or graphics device.
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.
Provides functionality to download and cache files from 'Hugging Face Hub' <https://huggingface.co/models>. 
    Uses the same caching structure so files can be shared between different client libraries.
A data only package containing commercial domestic flights that
    departed Houston (IAH and HOU) in 2011.
Provides functions for the estimation, plotting, predicting and cross-validation of hierarchical feature regression models as described in Pfitzinger (2021) <arXiv:2107.04831>.
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. Journal of Machine Learning Research 15(Oct):3297-3331.
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.
Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.
A set of routines to quickly download and import the 'HGNC'
    data set on mapping of gene symbols to gene entries in other popular
    databases or resources.
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.
It is used to travel graphs, by using DFS and BFS to get the path from node to each leaf node.
  Depth first traversal(DFS) is a recursive algorithm for searching all the vertices of a graph or tree data structure.
  Traversal means visiting all the nodes of a graph.
  Breadth first traversal(BFS) algorithm is used to search a tree or graph data structure for a node that meets a set of criteria.
  It starts at the tree’s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level.
  Also, it provides the matrix which is reachable between each node.
  Implement reference about Baruch Awerbuch (1985) <doi:10.1016/0020-0190(85)90083-3>.
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.
This model divides coefficients into three types,
        i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022)<doi:10.1177/23998083211063885>.
        If data have spatial hierarchical structures (especially are overlapping on some locations),
        it is worth trying this model to reach better fitness.
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>.
In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. 
             Reference: 
             Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.
Develops algorithms for fitting, prediction, simulation 
  and initialization of the hidden hybrid Markov/semi-Markov model, 
  introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, 
  which also includes several tools for handling missing data, 
  nonparametric mixture of B-splines emissions (Langrock et al., 2015
  <doi:10.1111/biom.12282>), fitting regime switching regression 
  (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive 
  hidden hybrid Markov/semi-Markov model, spline-based nonparametric 
  estimation of additive state-switching models  
  (Langrock et al., 2018 <doi:10.1111/stan.12133>)
  and many other useful tools 
  (read for more description: Amini et al., 2022 <doi:10.1007/s00180-022-01248-x> and its 
  arxiv version: <arXiv:2109.12489>).
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.
A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including:
    (1) fixed effects and coefficients of covariates,
    (2) environmental random effects, and its corresponding variance, 
    (3) genetic variance, 
    (4) residual variance, 
    (5) heritability, 
    (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals,
    (7) SNP effect size, 
    (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, 
    (9) posterior probability of association of the genomic window (WPPA), 
    (10) posterior inclusive probability (PIP). 
    The functions are not limited, we will keep on going in enriching it with more features. 
    References: Meuwissen et al. (2001) <doi:10.1093/genetics/157.4.1819>; Gustavo et al. (2013) <doi:10.1534/genetics.112.143313>; Habier et al. (2011) <doi:10.1186/1471-2105-12-186>; Yi et al. (2008) <doi:10.1534/genetics.107.085589>; Zhou et al. (2013) <doi:10.1371/journal.pgen.1003264>; Moser et al. (2015) <doi:10.1371/journal.pgen.1004969>; Lloyd-Jones et al. (2019) <doi:10.1038/s41467-019-12653-0>; Henderson (1976) <doi:10.2307/2529339>; Fernando et al. (2014) <doi:10.1186/1297-9686-46-50>.
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 Badr et al. (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.
Generates HIDECAN plots that summarise and combine 
  the results of genome-wide association studies (GWAS) and transcriptomics 
  differential expression analyses (DE), along with manually curated candidate genes of interest. The HIDECAN plot 
  is presented in Angelin-Bonnet et al. (2023) (currently in review).
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.
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.
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.
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://academic.oup.com/genetics/article/144/4/1933/6017091>).
    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://academic.oup.com/genetics/article/206/4/2085/6072590>.
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.
Machine learning portfolio allocation strategies based on hierarchical clustering methods. 
 The implemented methods are:
  Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059> and
  Hierarchical clustering-based asset allocation (Raffinot, 2017) 
  <DOI: 10.3905/jpm.2018.44.2.089>.
Provides semiparametric sufficient dimension reduction for central mean subspaces for heterogeneous data defined by combinations of binary factors (such as chronic conditions). Subspaces are estimated to be hierarchically nested to respect the structure of subpopulations with overlapping characteristics. This package is an implementation of the proposed methodology of Huling and Yu (2021) <doi:10.1111/biom.13546>.
A wrapper for the 'Highcharts' library including
    shortcut functions to plot R objects. 'Highcharts'
    <https://www.highcharts.com/> is a charting library offering
    numerous chart types with a simple configuration syntax.
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. A detailed vignette can be found in the open-access paper 
    "Analyzing Intraday Financial Data in R: The highfrequency Package" 
    by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>).
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.
Code Syntax Highlighting made easy for code snippets or complete
    files. Whether you're documenting your data analysis or creating interactive
    'shiny' apps.
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.
Perform high dimensional Feature Selection in the presence of survival outcome.
            Based on Feature Selection method and different survival analysis, it will obtain the best 
            markers with optimal threshold levels according to their effect on disease progression
            and produce the most consistent level according to those threshold values. 
            The functions' methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039>  and Bhattacharjee  et al (2021) <arXiv:2012.02102>.
The classical Markowitz's mean-variance portfolio formulation ignores 
    heavy tails and skewness. High-order portfolios use higher order moments to
    better characterize the return distribution. Different formulations and fast 
    algorithms are proposed for high-order portfolios based on the mean, variance, 
    skewness, and kurtosis.
    The package is based on the papers:
    R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via 
    Successive Convex Approximation Algorithms." <arXiv:2008.00863>.
    X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable 
    High-Order Portfolios Design via Parametric Skew-t Distribution." <arXiv:2206.02412>.
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>.
R interface to 'HiGHS', an optimization solver for solving mixed integer 
        optimization problems with quadratic or linear objective and linear constraints.
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.
HIGHT(HIGh security and light weigHT) algorithm is a block cipher encryption algorithm developed to provide confidentiality in computing environments that demand low power consumption and lightweight, such as RFID(Radio-Frequency Identification) and USN(Ubiquitous Sensor Network), or in mobile environments that require low power consumption and lightweight, such as smartphones and smart cards. Additionally, it is designed with a simple structure that enables it to be used with basic arithmetic operations, XOR, and circular shifts in 8-bit units. This algorithm was designed to consider both safety and efficiency in a very simple structure suitable for limited environments, compared to the former 128-bit encryption algorithm SEED. In December 2010, it became an ISO(International Organization for Standardization) standard. The detailed procedure is described in Hong et al. (2006) <doi:10.1007/11894063_4>.
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.
Provides utilities for encoding and decoding coordinates to/from Hilbert curves
  based on the iterative encoding implementation described in Chen et al. (2006) <doi:10.1002/spe.793>.
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>.
Calculate taxonomic, functional and phylogenetic diversity measures 
    through Hill Numbers proposed by Chao, Chiu and Jost (2014) 
    <doi:10.1146/annurev-ecolsys-120213-091540>.
A set of tools to create georeferenced hillshade relief 
    raster maps using ray-tracing and other advanced hill-shading 
    techniques. It includes a wrapper function to create a georeferenced,
    ray-traced hillshade map from a digital elevation model, and other
    functions that can be used in a rayshader pipeline.
Allows to estimate and test high-dimensional mediation effects based on advanced mediator screening and penalized regression techniques. Methods used in the package refer to Zhang H, Zheng Y, Zhang Z, Gao T, Joyce B, Yoon G, Zhang W, Schwartz J, Just A, Colicino E, Vokonas P, Zhao L, Lv J, Baccarelli A, Hou L, Liu L. Estimating and Testing High-dimensional Mediation Effects in Epigenetic Studies. Bioinformatics. (2016) <doi:10.1093/bioinformatics/btw351>. PMID: 27357171.
For supersonic aircraft, flying subsonic over land,
    find the best route between airports. Allow for coastal buffer and
    potentially closed regions. Use a minimal model of aircraft
    performance: the focus is on time saved versus subsonic flight, rather
    than on vertical flight profile. For modelling and forecasting, not for planning your
    flight!
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. <arXiv:1305.0717>.
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.
An 'RStudio' Addin for Hippie Expand (AKA Hippie Code
  Completion or Cyclic Expand Word). This type of completion searches for 
  matching tokens within the user's current source editor file, regardless of
  file type. By searching only within the current source file, 'hippie' offers a
  fast way to identify and insert completions that appear around the user's
  cursor.
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>.
In some cases you will have data in a histogram format, where
  you have a vector of all possible observations, and a vector of how many
  times each observation appeared. You could expand this into a single 1D
  vector, but this may not be advisable if the counts are extremely large.
  'HistDat' allows for the calculation of summary statistics without the need
  for expanding your data.
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.
Historical borrowing in clinical trials can improve
  precision and operating characteristics. This package supports
  a hierarchical model and a mixture model to borrow historical
  control data from other studies to better characterize the
  control response of the current study. It also quantifies
  the amount of borrowing through benchmark models (independent
  and pooled). Some of the methods are discussed by
  Viele et al. (2013) <doi:10.1002/pst.1589>.
Historical borrowing in clinical trials can improve
  precision and operating characteristics. This package supports
  a longitudinal hierarchical model to borrow historical
  control data from other studies to better characterize the
  control response of the current study. It also quantifies
  the amount of borrowing through longitudinal benchmark models (independent
  and pooled). The hierarchical model approach to historical borrowing
  is discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.
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.
A histogram slider input binding for use in 'Shiny'. Currently supports creating histograms from numeric, date, and 'date-time' vectors.
Automatically tracks and makes programmatically available code
    evaluation history in R sessions and dynamic documents.
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. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) <doi:10.1016/j.ejor.2012.08.026>. van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) <doi:10.1016/j.ejor.2014.06.036>.
We provide the monthly number of HIV and antiretroviral therapy (ART) cases of male, female, children and transgender as well as for the whole of Pakistan reported at various treatment centers in Pakistan from January 2016 to December 2021. Related works include: 
  a) Imran, M., Nasir, J. A., & Riaz, S. (2018). Regional pattern of HIV cases in Pakistan. Journal of Postgraduate Medical Institute, 32(1), 9-13. 
  <https://jpmi.org.pk/index.php/jpmi/article/view/2108>.
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).
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 for the hierarchical kernel extreme value process of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>, including simulation, estimation and spatial extrapolation. The spatial latent variable model <DOI:10.1214/11-STS376> is also included.
Methods to make inference about the Hurst-Kolmogorov (fractional Gaussian
    noise, fGn) and the AR(1) process. Related time series trend tests are also
    included.
Functions, Shiny apps and data for the book "Introduction to Statistics" by
   Wolfgang Karl Härdle, Sigbert Klinke, and Bernd Rönz (2015) <doi:10.1007/978-3-319-17704-5>.
A streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database <https://haplostats.org/haplostats>. High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting 'HLAMatchMaker' <http://www.epitopes.net> versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality.
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>.
Higher-order latent trait theory (item response theory). We
    implement the generalized partial credit model with a second-order latent
    trait structure. Latent regression can be done on the second-order latent
    trait. For a pre-print of the methods,
    see, "Latent Regression in Higher-Order Item Response Theory with the R
    Package hlt" <https://mkleinsa.github.io/doc/hlt_proof_draft_brmic.pdf>.
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>.
Fitting hidden Markov models of learning under the cognitive diagnosis framework.
  The estimation of the hidden Markov diagnostic classification model,
  the first order hidden Markov model, the reduced-reparameterized unified learning model,
  and the joint learning model for responses and response times.
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 (<https://www.fertilitydata.org>), The Japanese Mortality Database (<https://www.ipss.go.jp/p-toukei/JMD/index-en.html>), 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.
A set of objects and functions for Bayes Linear emulation and history matching.
  Core functionality includes automated training of emulators to data, diagnostic functions
  to ensure suitability, and a variety of proposal methods for generating 'waves' of points.
  For details on the mathematical background, there are many papers available on the topic
  (see references attached to function help files); for details of the functions in this package,
  consult the manual or help files.
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,
	recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.
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.
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. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions.
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>.
Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", <https://depmix.github.io/hmmr/>.
Fitting hidden Markov models using automatic differentiation 
  and Laplace approximation, allowing for fast inference and flexible covariate
  effects (including random effects and smoothing splines) on model parameters.
  The package is described by Michelot (2022) <arXiv:2211.14139>.
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>).
The HMS (Hierarchic Memetic Strategy) is a composite global optimization
  strategy consisting of a multi-population evolutionary strategy and some
  auxiliary methods. The HMS makes use of a dynamically-evolving data structure
  that provides an organization among the component populations. It is a tree
  with a fixed maximal height and variable internal node degree. Each component
  population is governed by a particular evolutionary engine. This package
  provides a simple R implementation with examples of using different genetic
  algorithms as the population engines. References: J. Sawicki, M. Łoś,
  M. Smołka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.
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.
The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.
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.
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.
Allows to evaluate Higher Order Assortativity of complex networks defined through objects of class 'igraph' from the package of the same name. The package returns a result also for directed and weighted graphs. References, Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2017) <doi:10.1016/j.ejor.2017.04.028> Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2021) <doi:10.1016/j.jbusres.2019.10.008> Arcagni, A., Cerqueti, R., & Grassi, R. (2023) <doi:10.48550/arXiv.2304.01737>.
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, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project <https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/home>, CSIRO <https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data: <doi:10.3334/CDIAC/ATG.009>.
Built by Hodges lab members for current and future Hodges lab members. Other individuals are welcome to use as well. Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code.
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.
Holistic generalized linear models (HGLMs) extend generalized linear models (GLMs) by enabling the possibility to add further constraints to the model. The 'holiglm' package simplifies estimating HGLMs using convex optimization. Additional information about the package can be found in the reference manual, the 'README' and the accompanying paper <doi:10.48550/arXiv.2205.15447>.
Offers a convenient way to compute parameters 
    in the framework of the theory of vocational choice introduced by
    J.L. Holland, (1997). A comprehensive summary to this theory 
    of vocational choice is given in Holland, J.L. (1997). Making vocational
    choices. A theory of vocational personalities and work environments. 
    Lutz, FL: Psychological Assessment.
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.
A 'shiny' application, which allows you to perform single- and
    multi-omics analyses using your own omics datasets. After the upload 
    of the omics datasets and a metadata file, single-omics is performed
    for feature selection and dataset reduction. These datasets are used 
    for pairwise- and multi-omics analyses, where automatic tuning is 
    done to identify correlations between the datasets - the end goal 
    of the recommended 'Holomics' workflow.
    Methods used in the package were implemented in the package 'mixomics'
    by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752>
    and are described there in further detail.
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 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>.
Most common exact, asymptotic and resample based tests are provided for testing the 
            homogeneity of variances of k normal distributions under normality. 
            These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. 
            Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters.  
            Bartlett, M. S. (1937) <doi:10.1098/rspa.1937.0109> 
            Bhandary, M., & Dai, H. (2008) <doi:10.1080/03610910802431011>
            Brown, M. B., & Forsythe, A. B. (1974).<doi:10.1080/01621459.1974.10482955>
            Chang, C. H., Pal, N., & Lin, J. J. (2017) <doi:10.1080/03610918.2016.1202277>
            Gokpinar E. & Gokpinar F. (2017) <doi:10.1080/03610918.2014.955110>
            Liu, X., & Xu, X. (2010) <doi:10.1016/j.spl.2010.05.017>
            Levene, H. (1960) <https://cir.nii.ac.jp/crid/1573950400526848896>
            Gökpınar, E. (2020) <doi:10.1080/03610918.2020.1800037>.
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.
Implements the Brakerski-Fan-Vercauteren (BFV, 2012) <https://eprint.iacr.org/2012/144>, Brakerski-Gentry-Vaikuntanathan (BGV, 2014) <doi:10.1145/2633600>, and Cheon-Kim-Kim-Song (CKKS, 2016) <https://eprint.iacr.org/2016/421.pdf> schema for Fully Homomorphic Encryption, as well as several helper functions.
A utility to quickly obtain clean and tidy men's
    basketball play by play data. Provides functions to access
    live play by play and box score data from ESPN<https://www.espn.com> with shot locations
    when available. It is also a full NBA Stats API<https://www.nba.com/stats/> wrapper.
    It is also a scraping and aggregating interface for Ken Pomeroy's 
    men's college basketball statistics website<https://kenpom.com>. It provides users with an
    active subscription the capability to scrape the website tables and
    analyze the data for themselves.
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.
Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) <doi:10.32614/RJ-2022-055>.
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 <https://link.springer.com/article/10.1007/BF02289447>). 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.
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).
Automatically displays the order and spatial weighting matrix of the distance between locations. This concept was derived from the research of Mubarak, Aslanargun, and Siklar (2021) <doi:10.52403/ijrr.20211150> and Mubarak, Aslanargun, and Siklar (2022) <doi:10.17654/0972361722052>. Distance data between locations can be imported from 'Ms. Excel', 'maps' package or created in 'R' programming directly. This package also provides 5 simulations of distances between locations derived from fictitious data, the 'maps' package, and from research by Mubarak, Aslanargun, and Siklar (2022) <doi:10.29244/ijsa.v6i1p90-100>.
Set of tools to help interested researchers to build hospital networks 
  from data on hospitalized patients transferred between hospitals. Methods provided 
  have been used in Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715>, 
  and Nekkab N, Crépey P, Astagneau P, Opatowski L, Temime L. (2020) <doi:10.1038/s41598-020-71212-6>.
A data set of the Portuguese 'NHS' hospitals.
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.
Functions to compute the semi-axes lengths and coordinate points of Hotelling ellipse. Bro and Smilde (2014) <DOI:10.1039/c3ay41907j>. Brereton (2016) <DOI:10.1002/cem.2763>.
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.
Audio interactivity within 'shiny' applications using 'howler.js'. Enables the
    status of the audio player to be sent from the UI to the server, and events such as
    playing and pausing the audio can be triggered from the server.
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.
When performing multiple imputations, while 5-10 imputations are
             sufficient for obtaining point estimates, a larger number of
             imputations are needed for proper standard error estimates.
             This package allows you to calculate how many imputations are
             needed, following the work of von Hippel (2020)
             <doi:10.1177/0049124117747303>.
Helping to calculate cricket specific problems in a tidy & simple manner.
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.
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.
	Stability analysis: see Rumyantsev and Morozov (2017) <doi:10.1007/s10479-015-1917-2>,
	workload recursion: see Rumyantsev (2014) <doi:10.1109/PDCAT.2014.36>.
Provides two functions that implement the one-sided and
    two-sided versions of the Hodrick-Prescott filter. The one-sided
    version is a Kalman filter-based implementation, whereas the two-
    sided version uses sparse matrices for improved efficiency.
    References:
    Hodrick, R. J., and Prescott, E. C. (1997) <doi:10.2307/2953682>
    Mcelroy, T. (2008) <doi:10.1111/j.1368-423X.2008.00230.x>
    Meyer-Gohde, A. (2010) <https://ideas.repec.org/c/dge/qmrbcd/181.html>
    For more references, see the vignette.
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.
Provides a nonparametric smoothed kernel density estimator for the future conditional hazard when time-dependent covariates are present. It also provides pointwise and uniform confidence bands and a bandwidth selection.
Miscellaneous convenience functions and wrapper functions 
	to convert frequencies between Hz, semitones, mel and Bark, 
	to create a matrix of dummy columns from a factor, 
	to determine whether x lies in range [a,b], 
	and to add a bracketed line to an existing plot.
	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).
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.
A program that conducts group variable selection for quantile and robust mean 
              regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for
              group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss.
              Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check 
              function can be approximated by the Huber loss for the median and the tilted version of 
              Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and
              has also been shown to be statistical consistent.
Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" <arXiv:2104.12597>.
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.
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 (2020) "Design of Experiments for 
    Fine-Mapping Quantitative Trait Loci in Livestock Populations" 
    <doi:10.1186/s12863-020-00871-1>.
    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>.
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 and others 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.
We present this package for fitting structural equation models using the hierarchical likelihood method. This package allows extended structural equation model, including dynamic structural equation model. We illustrate the use of our packages with well-known data sets. Therefore, this package are able to handle two serious problems inadmissible solution and factor indeterminacy <doi:10.3390/sym13040657>.
Implementation of S4 class of sets and multisets of numbers.
    The implementation is based on the hash table from the package 'hash'.
    Quick operations are allowed when the set is a dynamic object.
    The implementation is discussed in detail in Ceoldo and Wit (2023) <arXiv:2304.09809>.
To test the homogeneity of stratum effects in stratified paired binary data.
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data.
Spatial heterogeneity can be specified in various ways. 'hspm' is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of 'hspm' deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) <doi:10.1111/j.1467-9787.2009.00618.x>, Bivand and Piras, (2015) <doi:10.18637/jss.v063.i18>, Drukker et al., (2013) <doi:10.1080/07474938.2013.741020>, Kelejian and Prucha, (2010) <doi:10.1016/j.jeconom.2009.10.025>).
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. 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. For parameter estimation, at least one sire has to be double 
    heterozygous at the investigated pairs of SNPs.
    Based on recombination rates, genetic distances between markers can be 
    estimated. Markers with unusually large recombination rate to markers in 
    close proximity (i.e. putatively misplaced markers) shall be discarded in 
    this derivation.
    A workflow description is attached as vignette.
    *A pipeline is available at GitHub*
    <https://github.com/wittenburg/hsrecombi>
    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>).
Fast, model-agnostic implementation of different H-statistics
    introduced by Jerome H. Friedman and Bogdan E. Popescu (2008)
    <doi:10.1214/07-AOAS148>.  These statistics quantify interaction
    strength per feature, feature pair, and feature triple.  The package
    supports multi-output predictions and can account for case weights.
    In addition, several variants of the original statistics are provided.
    The shape of the interactions can be explored through partial
    dependence plots or individual conditional expectation plots. 'DALEX'
    explainers, meta learners ('mlr3', 'tidymodels', 'caret') and most
    other models work out-of-the-box.
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>.
Estimating heterogeneous treatment effects with tree-based machine
    learning algorithms and visualizing estimated results in flexible and 
    presentation-ready ways. For more information, see Brand, Xu, Koch, 
    and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package 
    first started as a fork of the 'causalTree' package on 'GitHub' and we 
    greatly appreciate the authors for their extremely useful and free package.
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 plain texts by stripping off all html tags.
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 valid HTML tag strings for HTML5 elements documented by Mozilla. 
    Attributes are passed as named lists, with names being the attribute name and values being the attribute value. 
    Attribute values are automatically double-quoted. To declare a DOCTYPE, wrap html() with function doctype().
    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 common word processors. The package also contains a few
    text formatting functions that help outputting text compatible with HTML/LaTeX.
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.
Detection of haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes that are associated with a phenotype. Methods for implementing HTRX are described in Yang Y, Lawson DJ (2023) <doi:10.1093/bioadv/vbad038> and Barrie W, Yang Y, Attfield K E, et al (2022) <doi:10.1101/2022.09.23.509097>.
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).
A novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity.
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 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.
Testing and documenting code that communicates with remote servers
    can be painful. This package helps with writing tests for packages that
    use 'httr2'. It enables testing all of the logic
    on the R sides of the API without requiring access to the
    remote service, and it also allows recording real API responses to use as
    test fixtures. The ability to save responses and load them offline also
    enables writing 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).
Tools for creating and modifying HTTP requests, then
    performing them and processing the results. 'httr2' is a modern
    re-imagining of 'httr' that uses a pipe-based interface and solves
    more of the problems that API wrapping packages face.
Collection of functions to help retrieving data from
    'Hub'Eau' the free and public French National APIs on water
    <https://hubeau.eaufrance.fr/>.
Provides functions to access data from the US Department of Housing and Urban Development <https://www.huduser.gov/portal/dataset/fmr-api.html>.
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.
An open-source R package to deploys reproducible and flexible labels using layers. 
  The 'huito' package is part of the 'inkaverse' project for developing different procedures and
  tools used in plant science and experimental designs. 
  Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
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
             (Natalia Novoselova, Cristina Della Beffa, Junxi Wang, Jialiang Li, Frank Pessler, Frank Klawonn
             (2014) <doi:10.1093/bioinformatics/btu086>).
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.
Contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling using Stan.
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.
The HURRECON model estimates wind speed, wind direction, enhanced 
    Fujita scale wind damage, and duration of EF0 to EF5 winds as a function 
    of hurricane location and maximum sustained wind speed. Results may be 
    generated for a single site or an entire region. Hurricane track and 
    intensity data may be imported directly from the US National Hurricane 
    Center's HURDAT2 database. For details on the original version of the 
    model written in Borland Pascal, see: Boose, Chamberlin, and Foster (2001) 
    <doi:10.1890/0012-9615(2001)071[0027:LARIOH]2.0.CO;2> and Boose, Serrano, 
    and Foster (2004) <doi:10.1890/02-4057>.
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.
Facilitates building topology preserving maps for rich multivariate data. See <https://en.wikipedia.org/wiki/Voronoi_diagram> for more information. Credits to Mu Sigma for their continuous support throughout the development of the package.
Fits models for genotypic disequilibria, as described in
  Huttley and Wilson (2000) <doi:10.1093/genetics/156.4.2127>, 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.
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.
Inference concerning equilibrium and random mating in 
    autopolyploids. Methods are available to test for equilibrium 
    and random mating at any even ploidy level (>2) in the presence
    of double reduction at biallelic loci. For autopolyploid populations
    in equilibrium, methods are available to estimate the degree of 
    double reduction. We also provide functions to calculate genotype 
    frequencies at equilibrium, or after one or several rounds of 
    random mating, given rates of double reduction. The main function is
    hwefit(). This material is based upon work supported by the
    National Science Foundation under Grant No. 2132247. The opinions,
    findings, and conclusions or recommendations expressed are those of
    the author and do not necessarily reflect the views of the National
    Science Foundation. For details of these methods, see 
    Gerard (2022a) <doi:10.1111/biom.13722> and
    Gerard (2022b) <doi:10.1101/2022.08.11.503635>.
The half-weight index gregariousness (HWIG) is an association 
             index used in social network analyses. It extends the half-weight 
             association index (HWI), correcting for level of gregariousness 
             in individuals. It is calculated using group by individual 
             data according to methods described in Godde et al. (2013) 
             <doi:10.1016/j.anbehav.2012.12.010>.
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.
Programmatic interface to the Harmonized World Soil Database 
    'HWSD' web services (<https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247>).
    Allows for easy downloads of 'HWSD' soil data directly to your R workspace 
    or your computer. Routines for both single pixel data downloads and
    gridded data are provided.
Provides methods to test whether time series is consistent
	with white noise. Two new tests based on Haar wavelets and general
	wavelets described by Nason and Savchev (2014)
	<doi:10.1002/sta4.69> are provided and, for comparison purposes
	this package also implements the
	B test of Bartlett (1967) <doi:10.2307/2333850>. Functionality
	is provided to compute an approximation to the theoretical
	power of the general wavelet test in the case of general
	ARMA alternatives.
The Hybrid design is a combination of model-assisted design (e.g., the modified 
             Toxicity Probability Interval design) with dose-toxicity model-based design for 
             phase I dose-finding studies. The hybrid design controls the overdosing 
             toxicity well and leads to a recommended dose closer to the true maximum tolerated 
             dose (MTD) due to its ability to calibrate for an intermediate dose. More details 
             can be found in Liao et al. 2022 <doi:10.1002/ijc.34203>.
A set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz").
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>.
Using hybrid data, this package created a vividly colored hybrid heat map.
    The input is two files which are auto-selected.
    The first file has three columns, the first two for pairs of species,
    with the third column for the hybrid experiment code (an integer).
    The second file is a list of code and their descriptions in two 
    columns. The output is a figure showing the hybrid heat map with a color legend.
Method and tool for generating hybrid time series forecasts using
        an error remodeling approach. These forecasting approaches utilize a recursive 
        technique for modeling the linearity of the series using a linear method 
        (e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster
        using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps: 
        firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step, 
        and finally, the forecasts from both the steps are combined to produce the final output. This method additionally 
        provides the confidence intervals as needed. Ten different models can be implemented using this package.
        This package generates different types of hybrid error correction models for time series forecasting 
        based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020), 
        Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022)
        <doi:10.1016/S0925-2312(01)00702-0> <doi:10.1016/j.physa.2019.121266> 
        <doi:10.1016/j.chaos.2020.109850> <doi:10.1109/IJCNN52387.2021.9533747> 
        <doi:10.1007/978-3-030-72834-2_29> <doi:10.1007/s11071-021-07099-3>.
An S4 class and several functions which utilize internally stored
    datasets and gauging data enable 1d water level interpolation. The S4 class
    (WaterLevelDataFrame) structures the computation and visualisation
    of 1d water level information along the German federal waterways Elbe and
    Rhine. 'hyd1d' delivers 1d water level data - extracted from the 'FLYS'
    database - and validated gauging data - extracted from the hydrological
    database 'HyDaBa' - package-internally. For computations near real time
    gauging data are queried externally from the 'PEGELONLINE REST API' 
    <https://pegelonline.wsv.de/webservice/dokuRestapi>.
Raster based flood modelling internally using 'hyd1d', an R package
    to interpolate 1d water level and gauging data. The package computes flood
    extent and durations through strategies originally developed for 'INFORM',
    an 'ArcGIS'-based hydro-ecological modelling framework. It does not provide
    a full, physical hydraulic modelling algorithm, but a simplified, near real
    time 'GIS' approach for flood extent and duration modelling. Computationally
    demanding annual flood durations have been computed already and data
    products were published by Weber (2022) <doi:10.1594/PANGAEA.948042>.
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, 
    plotting a Moody diagram, matching a pump characteristic curve to a system 
    curve, and solving for flows in a pipe network using the Hardy-Cross method. 
    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). Some gradually- and 
    rapidly-varied flow functions are included. For the Manning equation
    solutions, this package uses modifications of original code from the 'iemisc' 
    package by Irucka Embry.
Estimates frictional constants for hydraulic analysis 
    of rivers. This HYDRaulic ROughness CALculator (HYDROCAL) was previously 
    developed as a spreadsheet tool and accompanying documentation by McKay and
    Fischenich (2011, <https://erdc-library.erdc.dren.mil/jspui/bitstream/11681/2034/1/CHETN-VII-11.pdf>).
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.
Events from individual hydrologic time series are extracted, and events from multiple time series can be matched to each other.
	Tang, W. & Carey, S. K. (2017) <doi:10.1002/hyp.11185>.
	Kaur, S., Horne, A., Stewardson, M.J., Nathan, R., Costa, A.M., Szemis, J.M., & Webb, J.A. (2017) <doi:10.1080/24705357.2016.1276418>.
	Ladson, A., Brown, R., Neal, B., & Nathan, R. J. (2013) <doi:10.7158/W12-028.2013.17.1>.
Contains one function for drawing Piper diagrams (also
    called Piper-Hill diagrams) of water analyses for major ions.
A collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) <doi:10.3133/ofr20191096>), Cormen and Leiserson (2022) <ISBN:9780262046305> and Verdin and Verdin (1999) <doi:10.1016/S0022-1694(99)00011-6>.
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.
An important environmental impact on running water ecosystems 
    is caused by hydropeaking - the discontinuous release of turbine water 
    because of peaks of energy demand. An event-based algorithm is implemented 
    to detect flow fluctuations referring to increase events (IC) and decrease 
    events (DC). For each event, a set of parameters related to the fluctuation 
    intensity is calculated. The framework is introduced in Greimel et al. (2016) 
    "A method to detect and characterize sub-daily flow fluctuations" 
    <doi:10.1002/hyp.10773> and can be used to identify different fluctuation 
    types according to the potential source: e.g., sub-daily flow fluctuations 
    caused by hydropeaking, rainfall, or snow and glacier melt.
    This is a companion to the package 'hydroroute', which is used to detect and 
    follow hydropower plant-specific hydropeaking waves at the sub-catchment 
    scale and to describe how hydropeaking flow parameters change along the
    longitudinal flow path as proposed and validated in Greimel et al. (2022).
Statistical functions used in the French 'HydroPortail' <https://hydro.eaufrance.fr/>.
    This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities.
    Technical details are available (in French) in Renard (2016) <https://hal.inrae.fr/hal-02605318>.
Implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) <doi:10.1002/rra.3978>. The identification of associated events is based on the event detection implemented in 'hydropeak'.
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 records (with special features for Argentina and Chile data-sets).
Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.
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.
Facilitates the analysis and evaluation of hydrologic model output and 
    time-series data with functions focused on comparison of modeled (simulated) and observed data, 
    period-of-record statistics, and trends.
A suite of routines for the hyperdirichlet distribution;
   supersedes the 'hyperdirichlet' package.  Uses 'disordR' discipline.
Maintenance has been discontinued for this package. It has been
	     superseded by 'GeneralizedHyperbolic'. 'GeneralizedHyperbolic' 
	     includes all the functionality of 'HyperbolicDist' and more 
	     and is based on a more rational design. 'HyperbolicDist' 
	     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.
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.
Work with model files (setup, input, output) from
    the hydrological catchment model HYPE: Streamlined file import and export, standard 
    evaluation plot routines, diverse post-processing and aggregation routines 
    for hydrological model analysis.
An implementation of Random Forest-based two-sample tests as introduced in Hediger & Michel & Naef (2020) <arXiv:1903.06287>.
Add, share and manage annotations for 'Shiny' applications and R Markdown documents via 'hypothes.is'.
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 et al. (2019) <doi:10.1016/j.jml.2019.104038> and Rabe et al. (2020) <doi:10.21105/joss.02134>.
Functions for generating tables required for drawing and calculating hypsometric curves and hypsometric integrals.
    These functions accept as input the DEM of the region of interest (your watershed) and a spatial data frame file specifying delineation of sub-catchments within the watershed.
    They then generate output in the form of PNG images and HTML files contained in a folder named "HYPSO_OUTPUT" created in the current directory.
    S. K. Sharma, S. Gajbhiye, et al. (2018) <doi:10.1007/978-981-10-5801-1_19>.
    Omvir Singh, A. Sarangi, and Milap C. Sharma (2006) <doi:10.1007/s11269-008-9242-z>.
    James A. Vanderwaal and Herbert Ssegane (2013) <doi:10.1111/jawr.12089>.
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.
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.
Estimate parameters of the hysteretic threshold autoregressive
    (HysTAR) model, using conditional least squares.
    In addition, you can generate time series data from the HysTAR model.
    For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.
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.
Up-to-date data from the 'Unicode CLDR Project' (where 'CLDR'
    stands for 'Common Locale Data Repository') are available here as a series
    of easy-to-parse datasets. Several functions are provided for extracting
    key elements from the tabular datasets.
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 (<https://www.reconverse.org/).
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.
Provides functions to conduct a model-agnostic asymptotic hypothesis test for the identification of interaction effects in black-box machine learning models. The null hypothesis assumes that a given set of covariates does not contribute to interaction effects in the prediction model. The test statistic is based on the difference of variances of partial dependence functions (Friedman (2008) <doi:10.1214/07-AOAS148> and Welchowski (2022) <doi:10.1007/s13253-021-00479-7>) with respect to the original black-box predictions and the predictions under the null hypothesis. The hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply as the null distribution does not require resampling or refitting black-box prediction models.
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>.
Implementation of some of the formulations for the thermodynamic
	and transport properties released by the International Association for
	the Properties of Water and Steam (IAPWS). More specifically, the
	releases R1-76(2014), R5-85(1994), R6-95(2018), R7-97(2012), R8-97,
	R9-97, R10-06(2009), R11-07(2019), R12-08, R15-11, R16-17(2018),
	R17-20 and R18-21 at <http://iapws.org>.
An implementation of the International Association for the
    Properties of Water (IAPWS) Formulation 1995 for the Thermodynamic Properties of Ordinary
    Water Substance for General and Scientific Use and on the releases for viscosity,
    conductivity, surface tension and melting pressure.
Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>).
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.
A statistical method based on Bayesian Additive Regression Trees with Global 
    Standard Error Permutation Test (BART-G.SE) for descriptor selection 
    and symbolic regression. It finds the symbolic formula of the regression function 
    y=f(x) as described in Ye, Senftle, and Li (2023) <arXiv:2110.10195>.
A collection of useful functions and datasets for the Data Science
  Course at IBAW in Lucerne.
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.
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. Applications include calculation of realised
    relatedness coefficients and IBD segment distributions. 'ibdsim2' is
    part of the 'ped suite' collection of packages for pedigree analysis.
    A detailed presentation of the 'ped suite', including a separate
    chapter on 'ibdsim2', is available in the book 'Pedigree analysis in
    R' (Vigeland, 2021, ISBN:9780128244302). A 'shiny' app for visualising
    and comparing IBD distributions is available at
    <https://magnusdv.shinyapps.io/ibdsim2-shiny/>.
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.
The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. However, the existing methods of IBFS do not always provide a good feasible solution which can reduce the number of iterations to find the optimal solution. This initial basic feasible solution can be obtained by using any of the following methods. 
             a) North West Corner Method. 
             b) Least Cost Method. 
             c) Row Minimum Method. 
             d) Column Minimum Method. 
             e) Vogel's Approximation Method.
             etc.
             For more technical details about the algorithms please refer below URLs.
             <https://theintactone.com/2018/05/24/ds-u2-topic-8-transportation-problems-initial-basic-feasible-solution/>.
             <https://www.brainkart.com/article/Methods-of-finding-initial-Basic-Feasible-Solutions_39037/>.
             <https://myhomeworkhelp.com/row-minima-method/>.
             <https://myhomeworkhelp.com/column-minima-method/>.
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.
        For executing R-functions in a multi-node environment in parallel the idaTApply() function
        requires the 'SparkR' package (<https://spark.apache.org/docs/latest/sparkr.html>).
        The optional 'ggplot2' package is needed for the plot.idaLm() function only.
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.
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.
Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). <https://pubmed.ncbi.nlm.nih.gov/12069320/>. Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).<doi:10.1007/s11356-012-1359-1>. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). <doi:10.1007/s11356-013-2169-9>. Minato N. (2022). <https://minato.sip21c.org/msb/>.
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 full or subsampling 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. 
             See 'Cyprien Mbogning' and 'Philippe Broet' (2016)<doi:10.1186/s12859-016-1090-x> for 
             an overview about the methods implemented in this package.
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.
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
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 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>.
Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection.
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.
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.
Estimates the intraclass correlation coefficient (ICC) for count data to assess repeatability (intra-methods concordance) and concordance (between-method concordance). In the concordance setting, the ICC is equivalent to the concordance correlation coefficient estimated by variance components. The ICC is estimated using the estimates from generalized linear mixed models. The within-subjects distributions considered are: Poisson; Negative Binomial with additive and proportional extradispersion; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial with additive and proportional extradispersion. The statistical methodology used to estimate the ICC with count data can be found in Carrasco (2010) <doi:10.1111/j.1541-0420.2009.01335.x>.
The functions compute 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 prior to computation of the double-entry intraclass correlation (McCrae, 2008). See Furr (2010) <doi:10.1080/00223890903379134> or McCrae (2008) <doi:10.1080/00223890701845104> for details.
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>.
Estimates the intraclass correlation coefficient for trajectory data using a matrix of distances between trajectories. The distances implemented are the extended Hausdorff distances (Min et al. 2007) <doi:10.1080/13658810601073315> and the discrete Fréchet distance (Magdy et al. 2015) <doi:10.1109/IntelCIS.2015.7397286>.
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.
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.
Provides user-friendly and configurable print debugging via a
    single function, ic(). Wrap an expression in ic() to print the
    expression, its value and (where available) its source location.
    Debugging output can be toggled globally without modifying code.
Estimate test-retest reliability for complex sampling strategies 
    and extract variances using IntraClass Effect Decomposition. Developed by
    Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" <doi:10.7554/eLife.35718> 
    Also includes functions to simulate data based on sampling strategy. 
    Unofficial version release name: "Good work squirrels".
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, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). 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.
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.
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>.
The app will calculate the ICER (incremental cost-effectiveness 
             ratio) Rawlins (2012) <doi:10.1016/B978-0-7020-4084-9.00044-6> from the mean 
             costs and quality-adjusted life years (QALY) Torrance and Feeny (2009) <doi:10.1017/S0266462300008461>
             for a set of treatment options, and draw the efficiency frontier 
             in the costs-effectiveness plane. The app automatically identifies 
             and excludes dominated and extended-dominated options from the ICER 
             calculation.
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.
Programmatic connection to the 'OpenAltimetry API' <https://openaltimetry.org/data/swagger-ui/> to download and process 'ATL03' (Global Geolocated Photon Data), 'ATL06' (Land Ice Height), 'ATL07' (Sea Ice Height), 'ATL08' (Land and Vegetation Height), 'ATL10' (Sea Ice Freeboard), 'ATL12' (Ocean Surface Height) and 'ATL13' (Inland Water Surface Height) 'ICESat-2' Altimeter Data. The user has the option to download the data by selecting a bounding box from a 1- or 5-degree grid globally utilizing a shiny application. The 'ICESat-2' mission collects altimetry data of the Earth's surface. The sole instrument on 'ICESat-2' is the Advanced Topographic Laser Altimeter System (ATLAS) instrument that measures ice sheet elevation change and sea ice thickness, while also generating an estimate of global vegetation biomass. 'ICESat-2' continues the important observations of ice-sheet elevation change, sea-ice freeboard, and vegetation canopy height begun by 'ICESat' in 2003.
Provides user tokens for ICES web services that require
  authentication and authorization.  Web services covered by this
  package are ICES VMS database, the ICES DATSU web services, and the
  ICES SharePoint site.
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 Interact with the ICES Data Submission Utility (DATSU)
  <https://datsu.ices.dk/web/index.aspx>.
Run quality checks on data sets using the same checks that are conducted
  on the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk/web/index.aspx>.
R interface to access the web services of the ICES Stock Assessment
             Graphs database <https://sg.ices.dk>.
R interface to access the web services of the ICES Stock Database <https://sd.ices.dk>.
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.
Links to the ICES Vessel Monitoring System (VMS) and logbook database web services 
  <https://data.ices.dk/vms/webservices> to allow users to download summaries and data products.
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>.
It 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.
Calculate various information criteria in literature for "lm" and "glm" objects.
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.
An implementation of 'Ichimoku Kinko Hyo', also commonly known as
    'cloud charts'. Static and interactive visualizations with tools for
    creating, backtesting and development of quantitative 'ichimoku' strategies.
    As described in Sasaki (1996, ISBN:4925152009), the technique is a refinement
    on candlestick charting, originating from Japan and now in widespread use in
    technical analysis worldwide. Translating as 'one-glance equilibrium chart',
    it allows the price action and market structure of financial securities to
    be determined 'at-a-glance'. Incorporates an interface with the OANDA
    fxTrade API <https://developer.oanda.com/> for retrieving historical and
    live streaming price data for major currencies, metals, commodities,
    government bonds and stock indices.
A GUI designed to support the analysis of financial-economic time
    series data.
Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.
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>.
Implementation of icosahedral grids in three dimensions. The spherical-triangular tessellation can be set to create grids with custom resolutions. Both the primary triangular and their inverted penta-hexagonal grids can be calculated. Additional functions are provided that allow plotting of the grids and associated data, the interaction of the grids with other raster and vector objects, and treating the grids as a graphs.
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 (2021+). 
            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, <https://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.
Implementation of tandem clustering with invariant coordinate 
              selection with different scatter matrices and several choices for the 
              selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2022) <arXiv:2212.06108>.
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.
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>.
Functions for converting time series of spatial abundance or density 
    data in raster format to vector fields of population movement using the digital 
    image correlation technique. More specifically, the functions in the package 
    compute cross-covariance using discrete fast Fourier transforms for computational 
    efficiency. Vectors in vector fields point in the direction of highest two 
    dimensional cross-covariance. The package has a novel implementation of the 
    digital image correlation algorithm that is designed to detect persistent 
    directional movement when image time series extend beyond a sequence of 
    two raster images.
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>.
Provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". 
             The datasets can be used to present various methods of data analysis and statistical modeling.
             Functions for data visualization are also implemented.
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
             <https://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.
Provides access to the 
    Idea Data Center (IDC) application for conducting 
    nonresponse bias analysis (NRBA). The IDC NRBA app is an
    interactive, browser-based Shiny application that can be used to 
    analyze survey data with respect to response rates,
    representativeness, and nonresponse bias. This app provides a user-friendly
    interface to statistical methods implemented by the 'nrba' package.
    Krenzke, Van de Kerckhove, and Mohadjer (2005) 
    <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>
    and Lohr and Riddles (2016) 
    <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>
    provide an overview of the statistical methods implemented in the application.
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.
When added to an existing shiny app, users may subset any
    developer-chosen R data.frame on the fly. That is, users are empowered to
    slice & dice data by applying multiple (order specific) filters using the
    AND (&) operator between each, and getting real-time updates on the number
    of rows effected/available along the way. Thus, any downstream processes
    that leverage this data source (like tables, plots, or statistical procedures)
    will re-render after new filters are applied. The shiny module’s user interface has
    a 'minimalist' aesthetic so that the focus can be on the data &
    other visuals. In addition to returning a reactive (filtered) data.frame,
    'IDEAFilter' as also returns 'dplyr' filter statements used to actually slice
    the data.
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>.
Collection of tools to automate the processing of data collected though the IDEA4 method (see Zahm et al. (2018) <doi:10.1051/cagri/2019004> ). Starting from the original data collecting files this packages provides functions to compute IDEA indicators, draw modern and aesthetic plots, and produce a wide range of reporting materials.
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.
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. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, 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.
Interpreting the differences between mean scale scores across various
    forms of an assessment can be challenging. This difficulty arises from different
    mappings between raw scores and scale scores, complex mathematical relationships,
    adjustments based on judgmental procedures, and diverse equating functions applied
    to different assessment forms. An alternative method involves running simulations
    to explore the effect of incrementing raw scores on mean scale scores. The
    'idmact' package provides an implementation of this approach based on the
    algorithm detailed in Schiel (1998)
    <https://www.act.org/content/dam/act/unsecured/documents/ACT_RR98-01.pdf> which
    was developed to help interpret differences between mean scale scores on the
    American College Testing (ACT) assessment. The function idmact_subj() within
    the package offers a framework for running simulations on subject-level scores.
    In contrast, the idmact_comp() function provides a framework for conducting
    simulations on composite scores.
Utilities to work with data from the Internal Displacement
    Monitoring Centre (IDMC), with convenient functions for loading events
    data from the IDMC API and transforming events data to daily
    displacement estimates.
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.
A systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. 'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs.
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.
Most existing approaches for network reconstruction can only infer an overall network 
	and, also, fail to capture a complete set of network properties. To address these issues, 
	a new model has been developed, which converts static data into their 'dynamic' form. 
	'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, 
	omnidirectional and personalized networks. For more information on functional 
	clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, 
	Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, 
	see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) 
	<doi:10.1080/19490976.2022.2106103>.
Implements a nonparametric maximum likelihood method for assessing 
  potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection 
  under staggered enrollment and time-varying community transmission, allowing 
  crossover of placebo volunteers to the vaccine arm. 
  Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) 
  <doi:10.1093/cid/ciab630>.
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. Details are 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) 
             as described in Yongze Song (2021) <doi:10.1080/13658816.2021.1882680>. 
             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.
A fragmentation spectra detection pipeline for high-throughput LC/HRMS data processing using peaklists generated by the 'IDSL.IPA' workflow <doi:10.1021/acs.jproteome.2c00120>. The 'IDSL.CSA' package can deconvolute fragmentation spectra from Composite Spectra Analysis (CSA), Data Dependent Acquisition (DDA) analysis, and various Data-Independent Acquisition (DIA) methods such as MS^E, All-Ion Fragmentation (AIF) and SWATH-MS analysis. The 'IDSL.CSA' package was introduced in <doi:10.1021/acs.analchem.3c00376>.
The 'IDSL.FSA' package was designed to annotate standard .msp (mass spectra format) and .mgf (Mascot generic format) files using mass spectral entropy similarity, dot product (cosine) similarity, and normalized Euclidean mass error (NEME) followed by intelligent pre-filtering steps for rapid spectra searches. 'IDSL.FSA' also provides a number of modules to convert and manipulate .msp and .mgf files. The 'IDSL.FSA' workflow was integrated in the 'IDSL.CSA' and 'IDSL.NPA' packages introduced in <doi:10.1021/acs.analchem.3c00376>.
A multi-layered untargeted pipeline for high-throughput LC/HRMS data processing to extract signals of organic small molecules. The package performs ion pairing, peak detection, peak table alignment, retention time correction, aligned peak table gap filling, peak annotation and visualization of extracted ion chromatograms (EICs) and total ion chromatograms (TICs). The 'IDSL.IPA' package was introduced in <doi:10.1021/acs.jproteome.2c00120> .
A tiny parser to extract mass spectra data and metadata table of mass spectrometry acquisition properties from mzML, mzXML and netCDF files introduced in <doi:10.1021/acs.jproteome.2c00120>.
A pipeline to process nominal mass spectrometry data to create .msp files for untargeted analyses.
A simplified version of the 'IDSL.UFA' package to calculate isotopic profiles and adduct formulas from molecular formulas with no dependency on other R packages for online tools and educational mass spectrometry courses. The 'IDSL.SUFA' package also provides an ancillary module to process user-defined adduct formulas.
A pipeline to annotate chromatography peaks from the 'IDSL.IPA' workflow <doi:10.1021/acs.jproteome.2c00120> with molecular formulas of a prioritized chemical space using an isotopic profile matching approach. The 'IDSL.UFA' workflow only requires mass spectrometry level 1 (MS1) data for formula annotation. The 'IDSL.UFA' methods was described in <doi:10.1021/acs.analchem.2c00563> .
A pipeline to annotate a number of peaks from the 'IDSL.IPA' peaklists using an exhaustive chemical enumeration-based approach. This package can perform elemental composition calculations using the following 15 elements : C, B, Br, Cl, K, S, Si, N, H, As, F, I, Na, O, and P.
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/>.
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 collection of Irucka Embry's miscellaneous USGS data sets (USGS
    Parameter codes with fixed values, USGS global time zone codes, and US Air
    Force Global Engineering Weather Data). Irucka created these data sets
    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,
    Construction Measurements, GNU Octave compatible functions, Python
    compatible function, Trigonometric functions in degrees and function in
    radians, Geometry, Statistics, Mortality Calculators, Quick Search, etc.).
Miscellaneous data sets [Chemistry, Engineering Economics,
    Environmental/Water Resources Engineering, Nuclear Accidents, US
    Presidential Elections, and US Continental Congress Presidents].
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>.
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®' 'Cytek®'). Provides an easy and simple way to read and write .fcs, .rif, .cif and .daf files. Information such as masks, features, regions and populations set within these files can be retrieved for each single cell. 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, manipulate 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.
Automatically detects Copy Number Variations (CNV) from Next Generation Sequencing data using a machine learning algorithm, Isolation forest. More details about the method can be found in the paper by Cabello-Aguilar (2022) <doi:10.1101/2022.01.03.474771>.
Provides utility functions to deal with Italian fiscal
    code ('codice fiscale').
Implementation of two multi-criteria decision making methods (MCDM): Intuitionistic Fuzzy Synthetic Measure (IFSM) and Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IFTOPSIS) for intuitionistic fuzzy data sets for multi-criteria decision making problems. References describing the methods: Jefmański (2020) <doi:10.1007/978-3-030-52348-0_4>; Jefmański, Roszkowska, Kusterka-Jefmańska (2021) <doi:10.3390/e23121636>.
Compute static, onestep and multistep time series forecasts for 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.
Iterated Function Systems Estimator as in Iacus and La Torre (2005) <doi:10.1155/JAMDS.2005.33>.
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 and summary data based Mendelian randomization.
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.
Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.
Compute distributional quantities for an
    Integrated Gamma (IG) or Integrated Gamma Limit (IGL) copula, such
    as a cdf and density. Compute corresponding conditional quantities
    such as the cdf and quantiles. Generate
    data from an IG or IGL copula. See the vignette for formulas,
    or for a derivation, see Coia, V (2017) "Forecasting of Nonlinear 
    Extreme Quantiles Using Copula Models." PhD Dissertation, 
    The University of British Columbia.
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 plots, lasagna plots and ambulatory glucose profile report.
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).  Version 3 includes information from 1815 to 2014.
Launches a shiny application generating code to
    view tables in several ways,
    import/export tables,
    modify tables,
    make some basic graphics.
  'IGoR' is a graphic user interface designed to help beginners
  using simple functions around table management and exploration.
  Inspired by 'Rcmdr', 'IGoR' is a code generator that, with simple inputs under a Shiny application,
  provides R code mainly built around the 'tidyverse' or some packages in the direct line of the Mosaic project:
  the 'rio' and 'ggformula' packages.
  The generated code doesn't depend on IGoR and can be manually modified by the user or copied elsewhere.
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.
Versatile tools and data for graph matching analysis with various forms of prior information
    that supports working with 'igraph' objects, matrix objects, or lists of either.
This program facilitates exporting igraph graphs to the
        SoNIA file format
The 13th generation International Geomagnetic Reference Field (IGRF).
 A standard spherical harmonic representation of the Earth's main field.
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.
Provides a set of tools to
  i) identify geographic areas with significant change over time in drug utilization, and 
  ii) characterize common change over time patterns among the time series for multiple geographic areas.
  For reference, see below:
    1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) <doi:10.1504/IJCBDD.2018.10011910>
    2. Wu, S., Wu, H. (2013) <doi:10.1186/1471-2105-14-6>
    3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) <doi:10.1016/j.idm.2016.07.001>.
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.
A set of tools for writing documents
    according to Geneva Graduate Institute conventions and regulations.
    The most common use is for writing and compiling theses or thesis
    chapters, as drafts or for examination with correct preamble formatting. 
    However, the package also offers users to create HTML presentation
    slides with 'xaringan', complete problem sets, format posters, and, 
    for course instructors, prepare a syllabus.
    The package includes additional functions for institutional color palettes,
    an institutional 'ggplot' theme, a function for counting manuscript words,
    and a bibliographical analysis toolkit.
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.
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>.
The Importance Index (I.I.) can determine the loss and solution sources for a system in certain knowledge areas (e.g., agronomy), when production (e.g., fruits) is known (Demolin-Leite, 2021). Events (e.g., agricultural pest) can have different magnitudes (numerical measurements), frequencies, and distributions (aggregate, random, or regular) of event occurrence, and I.I. bases in this triplet (Demolin-Leite, 2021) <https://cjascience.com/index.php/CJAS/article/view/1009/1319>. Usually, the higher the magnitude and frequency of aggregated distribution, the greater the problem or the solution (e.g., natural enemies versus pests) for the system (Demolin-Leite, 2021). However, the final production of the system is not always known or is difficult to determine (e.g., degraded area recovery). A derivation of the I.I. is the percentage of Importance Index-Production Unknown (% I.I.-PU) that can detect the loss or solution sources, when production is unknown for the system (Demolin-Leite, 2024) <DOI:10.1590/1519-6984.253218>.
Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.
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.
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.
Linear regression when covariates include missing values by embedding the 
    correlation information between covariates. Especially for block missing data,
    it works well. 'ILSE' conducts imputation and regression simultaneously and iteratively. 
    More details can be referred to 
    Huazhen Lin, Wei Liu and Wei Lan. (2021) <doi:10.1080/07350015.2019.1635486>.
Provides functionality to perform a likelihood-free method for estimating the parameters of complex models
    that results in a simulated sample from the posterior distribution of model parameters given targets. The method begins
    with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution
    of model parameters. Accepted points result in model predictions that are within the initially specified tolerance
    intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of
    multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the
    acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the
    adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) <arXiv:1804.02090>.
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>.
    Provides as well functionality to put cursive text in images upright using the approach defined in the paper 'A new normalization technique for cursive handwritten words' by  Vinciarelli A. and Luettin J. (2001) <doi:10.1016/S0167-8655(01)00042-3>.
The goal of 'image2data' is to extract images and return
    them into a data set, 
    especially for teaching data manipulation and data visualization. 
    Basically, the eponymous function takes an 
    image file ('png', 'tiff', 'jpeg', 'bmp') and turn it into a data set,
    pixels being rows (subjects) and columns (variables) being their coordinate positions (x- and y-axis) and their respective color (in hex codes). 
    The function can return a complete image or a range of color (i.e., contour, silhouette).
    The data can then be manipulated as would any data set by either creating other related variables (to hide the image) or as a genuine toy data set.
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.
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'.
A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <arXiv:1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.
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.
Simulate general insurance policies, losses and loss emergence. The functions contemplate 
  deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative
  to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency
  distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation 
  uses standard loss distributions for claim amounts.
Provides fast application of image filters to data matrices,
    using R and C++ algorithms.
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>.
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.
Relocates oversampled data from a specific oversampling method to
    cover area determined by pure and proper class cover catch digraphs (PCCCD). 
    It prevents any data to be generated in class overlapping area.
Provides a set of fast, chainable image-processing operations
             which are applicable to images of two, three or four dimensions,
             particularly medical images.
Containerizes cytometry data and allows for S4 class structure to extend slots related to cell morphology, spatial coordinates, phenotype network information, and unique cellular labeling.
Cluster sampling is a valuable approach when constructing a comprehensive list of individual units is challenging. It provides operational and cost advantages. This package is designed to test the efficiency of cluster sampling in terms cluster variance and design effect in context to crop surveys. This package has been developed using the algorithm of Iqbal et al. (2018) <doi:10.19080/BBOAJ.2018.05.555673>.
Index of Multiple Deprivation for UK nations at various 
    geographical levels. In England, deprivation data is for Lower Layer Super
    Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities
    based on data from <https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019>.
    In Wales, deprivation data is for Lower Layer Super Output Areas, Middle 
    Layer Super Output Areas, Wards, and Local Authorities based on data from
    <https://gov.wales/welsh-index-multiple-deprivation-full-index-update-ranks-2019>.
    In Scotland, deprivation data is for Data Zones, Intermediate Zones, and 
    Council Areas based on data from <https://simd.scot>. In Northern Ireland,
    deprivation data is for Super Output Areas and Local Government Districts
    based on data from <https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-2017-nimdm2017>.
    The 'IMD' package also provides the composite UK index developed by
    <https://github.com/mysociety/composite_uk_imd>.
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.
A straightforward interface for accessing the IMF 
    (International Monetary Fund) data JSON API, 
    available at <https://data.imf.org/>. This package offers direct access to 
    the primary API endpoints: Dataflow, DataStructure, and CompactData. 
    And, it provides an intuitive interface for exploring available 
    dimensions and attributes, as well as querying individual time-series datasets. 
    Additionally, the package implements a rate limit on API calls to reduce the 
    chances of exceeding service limits (limited to 10 calls every 5 seconds) 
    and encountering response errors.
Tree height is an important dendrometric variable and forms the basis of vertical structure of a forest stand. This package will help to fit and validate various non-linear height diameter models for assessing the underlying relationship that exists between tree height and diameter at breast height in case of conifer trees. This package has been implemented on Naslund, Curtis, Michailoff, Meyer, Power, Michaelis-Menten and Wykoff non linear models using algorithm of  Huang et al.  (1992) <doi:10.1139/x92-172> and Zeide et al. (1993) <doi:10.1093/forestscience/39.3.594>.
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.
Estimating height of forest plant is one of the key challenges of recent times. This package will help to fit and validate AI (Artificial Intelligence) based machine learning algorithms for estimation of height of conifer trees based on diameter at breast height as explanatory variable using algorithm of Paul et al. (2022) <doi:10.1371/journal.pone.0270553>..
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> .
Toolkit for Poly-pharmacology Research of Traditional Chinese Medicine. Based on the
    biological descriptors and drug-disease interaction networks, it can
    analyze the potential poly-pharmacological mechanisms of Traditional Chinese Medicine and be
    used for drug-repositioning in Traditional Chinese Medicine.
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 and 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.
Fitting and validation of machine learning algorithms
             for volume prediction of trees, currently for conifer trees based on
			 diameter at breast height and height as explanatory variables.
A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell
    T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and
    visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq)
    and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods,
    such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes
    and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor
    databases and clonotype tracking in vaccination and cancer studies. A successor to our
    previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.
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>).
Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>.
Allows easy creation of CSS layouts (grid and flexbox)
  directly from R without added CSS.
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.
Functions to read, process and analyse accelerometer
    data related to mechanical loading variables. This package is
    developed and tested for use with raw accelerometer data from
    triaxial 'ActiGraph' <https://actigraphcorp.com> accelerometers.
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>).
Support for implicit expansion of arrays in operations involving
    arrays of mismatching sizes. This pattern is known as "broadcasting" in
    'Python' and  "implicit expansion" in 'Matlab' and is explained for example in
    the article "Array programming with NumPy" by C. R. Harris et al. (2020)
    <doi:10.1038/s41586-020-2649-2>.
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 between bookmaker odds and probabilities. Eight 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.
The Percentage of Importance Indice (Percentage_I.I.) bases in magnitudes, frequencies, and distributions of occurrence of an event (DEMOLIN-LEITE, 2021) <http://cjascience.com/index.php/CJAS/article/view/1009/1350>. This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(ks1 x c1 x ds1)/SUM (ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)] x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) <DOI: 10.1590/1519-6984.253215>.
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/>.
An imprecise inference presented in the study of Walley (1996) <doi:10.1111/j.2517-6161.1996.tb02065.x> is one of the statistical reasoning methods when prior information is unavailable. Functions and utils needed for illustrating this inferential paradigm are implemented for classroom teaching and further comprehensive research. Two imprecise models are demonstrated using multinomial data and 2x2 contingency table data. The concepts of prior ignorance and imprecision are discussed in lower and upper probabilities. Representation invariance principle, hypothesis testing, decision-making, and further generalization are also illustrated.
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.
Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) <doi:10.1126/science.aag1322>. Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc.
A variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein. More details can be found in chapter 7 of Saleh, A. K. Md. E. (2006) <ISBN: 978-0-471-56375-4>.
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 general workflow of most imputation methods is quite
    similar. The aim of this package is to provide parts of this general
    workflow to make the implementation of imputation methods easier. The
    heart of an imputation method is normally the used model. These models
    can be defined using the 'parsnip' package or customized
    specifications. The rest of an imputation method are more technical
    specification e.g. which columns and rows should be used for
    imputation and in which order. These technical specifications can be
    set inside the imputation functions.
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.
Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) <doi:10.1002/sim.9919> for an overview.
Implements imputation methods using EM and Data Augmentation for
    multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
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.
Set of functions to impute missing rare earth data, calculate La 
    and Pr concentrations and Ce anomalies in zircons based on the
    Chondrite-Onuma and Chondrite-Lattice of Carrasco-Godoy and 
    Campbell (2023) <doi:10.1007/s00410-023-02025-9> and the Logarithmic regression from  
    Zhong et al. (2019) <doi:10.1007/s00710-019-00682-y>.
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.
Volume prediction is one of challenging task in forestry research. This package is a comprehensive toolset designed for the fitting and validation of various linear and nonlinear allometric equations (Linear, Log-Linear, Inverse, Quadratic, Cubic, Compound, Power and Exponential) used in the prediction of conifer tree volume. This package is particularly useful for forestry professionals, researchers, and resource managers engaged in assessing and estimating the volume of coniferous trees. This package has been developed using the algorithm of Sharma et al. (2017) <doi:10.13140/RG.2.2.33786.62407>.
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.
Offers a pipe-friendly alternative to the 'dplyr' functions
    case_when() and if_else(), as well as a number of user-friendly
    simplifications for common use cases.  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 outputs to the same type, meaning you no longer have to worry
    about using specific typed variants of NA or explicitly declaring
    integer outputs, and evaluate outputs somewhat lazily, so you don't
    waste time on long operations that won't be used.
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>. For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) <doi:10.18637/jss.v099.i09>.
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 (<https://www.reconverse.org>).
Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort.
Make empirical Bayes incidence curves from reported case data using a specified delay distribution.
Functions to generate incidence matrices and bipartite graphs that have (1) a fixed fill rate, (2) given marginal sums, (3) marginal sums that follow given distributions, or (4) represent bill sponsorships in the US Congress <doi:10.31219/osf.io/ectms>. It can also generate an incidence matrix from an adjacency matrix, or bipartite graph from a unipartite graph, via a social process mirroring team, group, or organization formation <doi:10.48550/arXiv.2204.13670>.
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>.
Fit parametric models for time-to-event data that show an initial
    'incubation period', i.e., a variable delay phase where the hazard is zero. The
    delayed Weibull distribution serves as foundational data model. The
    specific method of 'MPSE' (maximum product of spacings estimation) is used for parameter
    estimation. Bootstrap confidence intervals for parameters and significance
    tests in a two group setting are provided.
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.
Estimates weights to make a continuous-valued exposure statistically independent of a vector of pre-treatment covariates using the method proposed in Huling, Greifer, and Chen (2021) <arxiv:2107.07086>.
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.
Extract and replace elements using indices that start from zero (rather than one), as is common in mathematical notation and other programming languages.
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.
Allows the construction selection indices based on estimated breeding values in animal and plant breeding and to calculate several analytic measures around to assess its impact on genetic and phenotypic progress. The methodology thereby allows to analyze genetic gain of traits in the breeding goal which are not part of the actual index and automatically computes several analytic measures. It further allows to retrospectively derive realized economic weights from observed genetic trends. The framework is described in Simianer, H., Heise, J., Rensing, S., Pook, T. Geibel, J. and Reimer, C. (2023) <doi:10.1186/s12711-023-00807-0>.
Computation of test statistics of independence between  (continuous) innovations of time series. They Can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.
Set of routines for influence diagnostics by using case-deletion in ordinary least 
    squares, ridge estimation [Walker and Birch (1988). <doi:10.1080/00401706.1988.10488370>] and 
    least absolute deviations (LAD) regression [Sun and Wei (2004). <doi:10.1016/j.spl.2003.08.018>].
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>].
Simplifies the generation of customized R Markdown PDF templates.
    A template may include an individual logo, typography, geometry or color
    scheme. The package provides a skeleton with detailed instructions for
    customizations. The skeleton can be modified by changing defaults in the
    'YAML' header, by adding additional 'LaTeX' commands or by applying dynamic
    adjustments in R. Individual corporate design elements, such as a title page, can be added as R functions that produce 'LaTeX' code.
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  and multidimensional 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.
Companion package to the book "industRial data science", 
    J.Ramalho (2021) <https://j-ramalho.github.io/industRial/>. 
    Provides data sets and functions to complete the case studies and contains 
    the book original Rmd files and tutorials.
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>.
Set of wrapper and helper functions to facilitate interaction with
    the Statistics Portugal (Instituto Nacional de Estatistica - INE) API
    (<https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_api&INST=322751522&xlang=en>).
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Decomposition of income inequality by groups formed of individuals 
    possessing similar characteristics (e.g., sex, education, age) and their 
    income sources at the same time.
    Decomposition of the Theil index is based on Giammatteo, M. (2007) 
    <https://www.lisdatacenter.org/wps/liswps/466.pdf>.
    Decomposition of the squared coefficient of variation is based on
    Garcia-Penalosa, C., & Orgiazzi, E. (2013) <doi:10.1111/roiw.12054>.
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>.
Performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the 'hdi' package.
Provides simple functions to compute and plot two types 
    (sample-size- and coverage-based) rarefaction and extrapolation curves for species
    diversity (Hill numbers) based on individual-based abundance data or sampling-unit-
    based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent
    theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution)
    for an introduction of the R package.
The objective of this package is to perform inference using
    an expressive statistical grammar that coheres with the tidy design
    framework.
A method for inferring cell-specific gene regulatory network from single-cell sequencing data.
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) 
        <doi: 10.1097/00001648-200301000-00026> and 
        Talbot et al. (2011) <doi: 10.1002/sim.4334>.
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>.
Implementation of methods Extremum Surface Estimator (ESE) and 
   Extremum Distance Estimator (EDE) to identify the inflection point of a curve .
   Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. 
    McCracken (2022) <doi:10.1101/2022.12.30.522131>.
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 constraint and effective network size by Burt (2000) <doi:10.1016/S0191-3085(00)22009-1>; algorithm 
    to identify key players by Borgatti (2006) <doi:10.1007/s10588-006-7084-x>; and the 
    bridging algorithm by Valente and Fujimoto (2010) <doi:10.1016/j.socnet.2010.03.003>. 
    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.
'InfluxDB' 2.x time-series database client. Supports both 'InfluxDB' OSS (<https://portal.influxdata.com/downloads/>) and Cloud (<https://cloud2.influxdata.com/>) version.
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).
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>.
After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test.  The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>.  The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374  <doi:10.1093/biomet/79.2.367>.
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> .
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. A first approach was described in Rousset et al. (2017 <doi:10.1111/1755-0998.12627>) but the package implements more advanced methods.
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.
Sports Injury Data analysis aims to identify and describe the
  magnitude of the injury problem, and to gain more insights (e.g. determine
  potential risk factors) by statistical modelling approaches. The 'injurytools'
  package provides standardized routines and utilities that simplify such
  analyses. It offers functions for data preparation, informative visualizations 
  and descriptive and model-based analyses.
Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <https://www.r-inla.org>.
Facilitates spatial and general latent Gaussian modeling using
  integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>).
  Additionally, extends the GAM-like model class to more general nonlinear predictor
  expressions, and implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data.
  Model components are specified with general inputs and mapping methods to the
  latent variables, and the predictors are specified via general R expressions,
  with separate expressions for each observation likelihood model in
  multi-likelihood models. A prediction method based on fast Monte Carlo sampling
  allows posterior prediction of general expressions of the latent variables.
  Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019)
  <doi:10.1111/2041-210X.13168>.
Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).
Prepare objects to implement models over spatial and 
  spacetime domains with the 'INLA' package (<https://www.r-inla.org>).
  These objects contain data to for the 'cgeneric' interface in
  'INLA', enabling fast parallel computations.
  We implemented the spatial barrier model, see Bakka et. al. (2019) 
  <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal 
  models in Lindgren et. al. (2023) <arXiv:2006.04917>. 
  Details are provided in the available vignettes and from the URL bellow.
A collection of functions for creating color schemes.
    Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory Project Office.
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.
Contains bibliographic information for the U.S. Geological Survey
    (USGS) Idaho National Laboratory (INL) Project Office.
Interpretability methods to analyze the behavior and
    individual predictions of modern neural networks. Implemented methods
    are: 'Connection Weights' described by Olden et al. (2004)
    <doi:10.1016/j.ecolmodel.2004.03.013>, Layer-wise Relevance
    Propagation ('LRP') described by Bach et al. (2015)
    <doi:10.1371/journal.pone.0130140>, Deep Learning Important Features
    ('DeepLIFT') described by Shrikumar et al.  (2017) <arXiv:1704.02685>
    and gradient-based methods like 'SmoothGrad' described by Smilkov et
    al. (2017) <arXiv:1706.03825>, 'Gradient x Input' described by
    Baehrens et al. (2009) <arXiv:0912.1128> or 'Vanilla Gradient'.
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>.
David Onstad provided us with this insect disease database, sometimes referred to as the 'Ecological Database of the Worlds Insect Pathogens' or EDWIP. Files have been converted from 'SQL' to csv, and ported into 'R' for easy exploration and analysis. Thanks to the Macroecology of Infectious Disease Research Coordination Network (RCN) for funding and support. Data are also served online in a static format at <https://edwip.ecology.uga.edu/>.
Using embedded sdmx queries, get the data of more than 150 000 insee series from 'bdm' macroeconomic database.
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 'InterVA' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
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.
This is a shiny app used for the statistical classifying and analysing
    pre-clinical tumour responses.
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>.
Collect  marketing data from Instagram Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
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 Posit/RStudio-sponsored CRAN mirror. Lastly, the
    chosen package(s) is/are loaded. The function 'load_package' simply loads
    the provided package(s). 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.
Similar to 'rstantools' for 'rstan',
  the 'instantiate' package builds pre-compiled
  'CmdStan' models into CRAN-ready statistical modeling R packages.
  The models compile once during installation,
  the executables live inside the file systems of their respective packages,
  and users have the full power and convenience of
  'cmdstanr' without any additional compilation.
  This approach saves time, allows R package developers
  to migrate from 'rstan' to the more modern
  'cmdstanr', and fits well with centrally maintained
  R installations where users have trouble installing
  their own packages, diagnosing compilation errors,
  and setting environment variables.
  Packages 'rstantools', 'cmdstanr', 'stannis', and
  'stanapi' are similar Stan clients with different objectives.
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.
Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
The methods in this package adds to the functionality of  the 'intamap' package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
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. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.
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} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. 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).
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.
Four datasets are provided here from the 'Intendo' game
    'Super Jetroid'. It is data from the 2015 year of operation and it comprises
    a revenue table ('all_revenue'), a daily users table ('users_daily'), a user
    summary table ('user_summary'), and a table with data on all user sessions
    ('all_sessions'). These core datasets come in different sizes, and, each of
    them has a variant that was intentionally made faulty (totally riddled with
    errors and inconsistencies). This suite of tables is useful for testing with
    packages that focus on data validation and data documentation.
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.
Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. 
    This package is based on the following research: Eckardt and Mateu (2018) <doi:10.1080/10618600.2017.1391695>. Eckardt and Mateu (2021) <doi:10.1007/s11749-020-00720-4>.
Power analysis for regression models which test the interaction of
    two independent variables on a single dependent variable. Includes options 
    for continuous, binary, or ordinal variables, as well as correlated 
    interacting variables. Also includes options to specify variable reliability.
    Power analyses can be done either analytically or via simulation.  Includes 
    tools for simulating single data sets and visualizing power analysis results.
    The primary functions are power_interaction_r2() and power_interaction(). 
    Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
    Olino TM (2022). "Tutorial: Power analyses for interaction effects in 
    cross-sectional regressions." <doi:10.31234/osf.io/5ptd7>.
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 tool to calculate and plot estimates from models 
    in which an interaction between the main predictor and a continuous covariate has been specified.
    Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); 
    Durrleman S, Simon R. (1989) <doi:10.1002/sim.4780080504>; Greenland S. (1995) <doi:10.1097/00001648-199507000-00005>.
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.
Chi-square tests are computed with corrections.
Various functions and a Shiny app to enrich the results of Multiple Correspondence Analysis with interpretive axes and planes (see Moschidis, Markos, and Thanopoulos, 2022; <doi:10.1108/ACI-07-2022-0191>).
We developed the Integrated and Robust Deconvolution algorithm to infer cell-type proportions from target bulk RNA-seq data. This package is able to effectively integrate deconvolution results from multiple scRNA-seq datasets and calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Moreover, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system.
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.
Performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator.
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.
  Linear interpolation is implemented in 
  interp::interp(..., method="linear"), 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 irregular grid spline
  interpolation (akima::interp(..., linear=FALSE)) is now also
  available via interp::interp(..., method="akima").
  Estimators for partial derivatives are now also available in 
  interp::locpoly(), these are a prerequisite for the spline interpolation.  
  The basic part is a GPLed triangulation algorithm (sweep hull 
  algorithm by David Sinclair) providing the starting point for the
  irregular grid interpolator. As side effect this algorithm is also
  used to provide replacements for almost all functions of the tripack
  package which also suffers from the same ACM license restrictions.  
  All functions are designed to be backward compatible with their 
  akima / tripack counterparts.
Plots the conditional coefficients ("marginal effects") of
    variables included in multiplicative interaction terms.
Some interpolation methods taken from 'Boost': barycentric
    rational interpolation, modified Akima interpolation, PCHIP (piecewise
    cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom
    splines.
Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
Estimate confidence intervals for mean, proportion, mean difference 
   for unpaired and paired samples and proportion difference. Plot the confidence 
   intervals. Generate documents explaining the statistical result step by step.
High resolution mass spectrometry yields often large data
    sets of spectra from compounds which are not present in available
    libraries. These spectra need to be annotated and
    interpreted.'InterpretMSSpectrum' provides a set of functions to
    perform such tasks for Electrospray-Ionization and
    Atmospheric-Pressure-Chemical-Ionization derived data in positive and
    negative ionization mode.
Compute permutation- based performance measures and create partial
    dependence plots for (cross-validated) 'randomForest' and 'ada' models.
A set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1017/pan.2021.19>.
Generates three inter-related genomic datasets : methylation, gene expression and protein expression.
Provides an R version of the 'InterVA4' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Provides an R version of the 'InterVA5' software (<http://www.byass.uk/interva/>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].
Perform fast and memory efficient time-weighted averaging of values 
    measured over intervals into new arbitrary intervals. 
	This package is useful in the context of data measured or represented
	as constant values over intervals on a one-dimensional discrete axis 
	(e.g. time-integrated averages of a curve over defined periods). 
	This package was written specifically to deal with air 
	pollution data recorded or predicted as averages over sampling periods. 
	Data in this format often needs to be shifted to non-aligned periods 
	or averaged up to periods of longer duration (e.g. averaging data measured over 
	sequential non-overlapping periods to calendar years).
A user-friendly toolbox for doing the statistical analysis of
  interval-valued responses in questionnaires measuring intrinsically
  imprecise human attributes or features (attitudes, perceptions, opinions,
  feelings, etc.). In particular, this package provides S4 classes, methods,
  and functions in order to compute basic arithmetic and statistical operations
  with interval-valued data; prepare customized plots; associate each
  interval-valued response to its equivalent Likert-type and visual analogue
  scales answers through the minimum theta-distance and the mid-point criteria;
  analyze the reliability of respondents' answers from the internal consistency
  point of view by means of Cronbach's alpha coefficient; and simulate
  interval-valued responses in this type of questionnaires. The package also
  incorporates some real-life data that can be used to illustrate its working
  with several non-trivial reproducible examples. The methodology used in this
  package is based in many theoretical and applied publications from
  SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and
  Comparison of Distributions of Random Elements) Research Group
  (<https://bellman.ciencias.uniovi.es/smire+codire/>)
  from the University of Oviedo (Spain).
Tools for working with and comparing sets of points and intervals.
Manipulate integer-bounded intervals including finding overlaps, piling and merging.
Performs hypothesis testing
    using the interval estimates (e.g., confidence intervals). The
    non-overlapping interval estimates indicates the statistical
    significance. References to these procedures can be found at
    Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>,
    Bonett and Seier (2003) <doi:10.1198/0003130032323>, and
    Lemm (2006) <doi:10.1300/J082v51n02_05>.
Creation of tables of summary statistics or counts for clinical data (for 'TLFs'). 
  These tables can be exported as in-text table (with the 'flextable' package) for a Clinical Study Report 
  (Word format) or a 'topline' presentation (PowerPoint format), 
  or as interactive table (with the 'DT' package) to an html document for clinical data review.
The 'inti' package is part of the 'inkaverse' project for developing 
    different procedures and tools used in plant science and experimental designs. 
    The mean aim of the package is to support researchers during the planning of
    experiments and data collection (tarpuy()), data analysis and graphics (yupana())
    , and technical writing. 
    Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
An interval-valued extension of ordinary and simple kriging.
    Optimization of the function is based on a generalized interval distance.
    This creates a non-differentiable cost function that requires a
    differentiable approximation to the absolute value function. This
    differentiable approximation is optimized using a Newton-Raphson algorithm
    with a penalty function to impose the constraints. Analyses in the package
    are driven by the 'intsp' and 'intgrd' 
    classes, 
    which are interval-valued extensions of
    'SpatialPointsDataFrame' and 'SpatialPixelsDataFrame' respectively. 
    The package includes several wrappers to functions in the 
    'gstat' and 'sp' packages.
This workflow takes analyte levels from two different types of analytes (e.g. gene expression and metabolite abundance), meta-information on each analyte type, and sample outcome and metadata to identify analyte pairs that are significantly associated with a continuous or discrete outcome (e.g. drug response or tumor type). The following references describe the methods in this package: (1) Jalal K. Siddiqui, et al. (2018) <doi:10.1186/s12859-018-2085-6>, (2) Andrew Patt, et al. (2019) <doi:10.1007/978-1-4939-9027-6_23>.
Provides a key-value store data structure. The keys are
    integers and the values can be any R object. This is like a list but
    indexed by a set of integers, not necessarily contiguous and possibly
    negative. The implementation uses a 'R6' class. These containers are 
    not faster than lists but their usage can be more convenient for 
    certain situations.
Implementing the interventional effects for mediation analysis for up to 3 mediators.
    The methods used are based on VanderWeele, Vansteelandt and Robins (2014) <doi:10.1097/ede.0000000000000034>, 
    Vansteelandt and Daniel (2017) <doi:10.1097/ede.0000000000000596> and Chan and Leung (2020; unpublished manuscript, available on request from the author of this package).
    Linear regression, logistic regression and Poisson regression are used for continuous, binary 
    and count mediator/outcome variables respectively.
Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.
Models, analyzes, and forecasts financial intraday signals. This package
    currently supports a univariate state-space model for intraday trading volume provided
    by Chen (2016) <doi:10.2139/ssrn.3101695>.
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.  Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).
Performs Goodness of Fit for regression models 
  using Integrated Regression method. Works for several 
  different fitting techniques.
Estimate the proportions of the null and the reproducibility and
    non-reproducibility of the signal group for the input data set. The Bayes factor
    calculation and EM (Expectation Maximization) algorithm procedures are also
    included.
Provides functions to estimate the intrinsic dimension of a dataset 
  via likelihood-based approaches. Specifically, the package implements the 
  'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. 
  In addition, the first reference contains an extended vignette on the usage of 
  the 'TWO-NN' and 'Hidalgo' models. References: 
    Denti (2023, <doi:10.18637/jss.v106.i09>); 
    Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); 
    Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>);
    Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); 
    Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.
Functions for evaluating and testing asset pricing models, including
    estimation and testing of factor risk premia, selection of "strong" risk 
    factors (factors having nonzero population correlation with test asset
    returns), heteroskedasticity and autocorrelation robust covariance matrix
    estimation and testing for model misspecification and identification. 
    The functions for estimating and testing factor risk 
    premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass 
    approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) 
    <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk
    premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The 
    functions for selecting the "strong" risk factors are based on the Oracle
    estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the 
    factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. 
    The functions for evaluating model misspecification implement the HJ
    model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>,
    which is a modification of the prominent Hansen-Jagannathan (1997)
    <doi:10.1111/j.1540-6261.1997.tb04813.x> distance.
    The functions for testing model identification 
    specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> 
    and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test 
    to the regression coefficient matrix of test asset returns on risk factors.
    Finally, the function for heteroskedasticity and autocorrelation robust 
    covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912>
    covariance estimator.
Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics – A novel and practical method for estimating the “intrinsic” value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>.
Evaluating if values
  of vectors are within different open/closed intervals
  (`x %[]% c(a, b)`), or if two closed
  intervals overlap (`c(a1, b1) %[]o[]% c(a2, b2)`).
  Operators for negation and directional relations also implemented.
Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.
Integration of disparate datasets is needed in order to make efficient use of all available data and thereby address the issues currently threatening biodiversity.
   Data integration is a powerful modeling framework which allows us to combine these datasets together into a single model, yet retain the strengths of each individual dataset.
   We therefore introduce the package, 'intSDM': an R package designed to help ecologists develop a reproducible workflow of integrated species distribution models, using data both provided from the user as well as data obtained freely online.
   An introduction to data integration methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
Contains implementations of
    integrative survival analysis routines, including
    regular Cox cure rate model proposed by
    Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531>
    via an EM algorithm proposed by
    Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>,
    regularized Cox cure rate model with elastic net penalty following
    Masud et al. (2018) <doi:10.1177/0962280216677748>, and
    Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and
    weighted concordance index for cure models proposed by
    Asano and Hirakawa (2017) <doi:10.1080/10543406.2017.1293082>.
Provides tools for importing, merging, and analysing data from 
  international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).
Enum-type representation of vectors and representation
  of intervals, including a method of coercing variables in data frames.
Provides an up-to-date version of the 'InvaCost' database 
    (<doi:10.6084/m9.figshare.12668570>) in R, and
    several functions to analyse the costs of invasive alien species 
    (<doi:10.1111/2041-210X.13929>).
Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.
Vector operations between grapes: An infix-only package! The 'invctr' functions perform common and less common operations on vectors, data frames matrices and list objects:
    - Extracting a value (range), or, finding the indices of a value (range).
    - Trimming, or padding a vector with a value of your choice.
    - Simple polynomial regression.
    - Set and membership operations.
    - General check & replace function for NAs, Inf and other values.
Simulate inventory policies with and without forecasting, facilitate inventory analysis calculations such as  stock levels and re-order points,pricing and promotions calculations. 
  The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations.
    The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization.
  The functions are referenced from :
  1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. <isbn10: 135–136, 152>.
  2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. <isbn: 0-07- 2231265-3. Chapter 4>.
  3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. <isbn: 978-0471119470>. 
  4-Ballou, R.H. Business Logistics Management. <isbn: 978-0130661845>. Chapter 9.
  5-MIT Micromasters Program. 
  6- Columbia University  course for supply and demand analysis.
  8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W
  For further details or correspondence :<www.linkedin.com/in/haythamomar>, <www.rescaleanalytics.com>.
Determination of the optimal policy in inventory problems from a game-theoretic perspective.
Reverse engineer a regular expression pattern for the characters
    contained in an R object. Individual characters can be categorised into
    digits, letters, punctuation or spaces and encoded into run-lengths. This
    can be used to summarise the structure of a dataset or identify non-standard
    entries. Many non-character inputs such as numeric vectors and data frames
    are supported.
Provides composable invertible transforms for
    (sparse) matrices.
Functions to facilitate inverse estimation (e.g., calibration) in
    linear, generalized linear, nonlinear, and (linear) mixed-effects models. A
    generic function is also provided for plotting fitted regression models with
    or without confidence/prediction bands that may be of use to the general
    user. For a general overview of these methods, see Greenwell and Schubert 
    Kabban (2014) <doi:10.32614/RJ-2014-009>.
Light weight implementation of the standard distribution
  functions for the inverse gamma distribution, wrapping those for the gamma
  distribution in the stats package.
Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) <http://rcea.org/RePEc/pdf/wp23-11.pdf> .  
    The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.
Fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
Contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019)  <doi:10.1007/s42519-018-0027-2>. The package can also be used as a teaching demo for introductory Bayesian courses.
Interaction and analysis of multiple response data,
    along with other tools for analysing these types of data including
    missing value analysis and calculation of standard errors for
    a range of covariance matrix results (proportions, multinomial,
    independent samples, and multiple response).
Simple plotting function(s) for exploratory data analysis with flexible options allowing for easy plot customisation. The goal is to make it easy for beginners to start exploring a dataset through simple R function calls, as well as provide a similar interface to summary statistics and inference information. Includes functionality to generate interactive HTML-driven graphs. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a suite of functions to use with regression models, including summaries, residual plots, and factor comparisons. Used as part of the Model Fitting module of 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding.
Provides a collection of functions for working with time series data, including functions for drawing, decomposing, and forecasting. Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) <doi:10.1016/j.ijforecast.2003.09.015>, Winters (1960) <doi:10.1287/mnsc.6.3.324>, Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".
One function to read files. One function to write files. One
    function to direct plots to screen or file. Automatic file format inference
    and directory structure creation.
Calculates fundamental IO matrices (Leontief, Wassily W. (1951) <doi:10.1038/scientificamerican1051-15>); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) <doi:10.1080/09535319200000013>, Blair and Miller (2009) <ISBN:978-0-521-73902-3>, Antras et al (2012) <doi:10.3386/w17819>, Hummels, Ishii, and Yi (2001) <doi:10.1016/S0022-1996(00)00093-3>); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) <doi:10.1080/09535319300000017>, Sonis, Hewings, and Guo (2006) <doi:10.1080/09535319600000002>); and a variety of table operators.
The data analysis module for the Iterative Optimization Heuristics
    Profiler ('IOHprofiler'). This module provides statistical analysis methods for the 
    benchmark data generated by optimization heuristics, which can be visualized through a 
    web-based interface. The benchmark data is usually generated by the 
    experimentation module, called 'IOHexperimenter'. 'IOHanalyzer' also supports
    the widely used 'COCO' (Comparing Continuous Optimisers) data format for benchmarking.
The benchmarking module for the Iterative Optimization Heuristics
    Profiler ('IOHprofiler'). This module provides benchmarking in the 'IOHprofiler'
    format, which can be visualized using the 'IOHanalyzer' module.
Addresses the 'log of zero' by developing a new family of 
    estimators called iterated Ordinary Least Squares. 
    This family nests standard approaches such as log-linear and 
    Poisson regressions, offers several computational advantages, 
    and corresponds to the correct way to perform the popular 
    log(Y + 1) transformation. For more details about how to use it, 
    see the notebook at: <https://www.davidbenatia.com/>.
Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.
Provides item exclusion procedure, which is a formal method to 
    test 'Indifference Of iNdicator' (ION). When a latent personality 
    trait-outcome association is assumed, then the association strength 
    should not depend on which subset of indicators (i.e. items) has been 
    chosen to reflect the trait. Personality traits are  often measured 
    (reflected) by  a sum-score of a certain set of indicators. 
    Item exclusion procedure randomly excludes items from a sum-score and 
    tests, whether the sum-score - outcome correlation changes. ION has been 
    achieved, when any item can be excluded from the sum-score without the 
    sum-score - outcome correlation substantially changing . For more details, 
    see Vainik, Mottus et. al, (2015) "Are Trait-Outcome Associations Caused
    by Scales or Particular Items? Example Analysis of Personality Facets and
    BMI",European Journal of Personality  DOI: <10.1002/per.2009> .
Collection of functions for IO Psychologists.
Density, spectral density, and regression estimation using infinite
    order flat-top kernels.
Pre-processing and basic analytical tasks related to working
    with Eurostat's symmetric input-output tables and provide basic
    input-output economics calculations. The package is part of rOpenGov
    <http://ropengov.github.io/> to open source open government initiatives.
Routines and tools for assessing the quality of content
    analysis on the basis of the Iota Reliability Concept. The concept is
    inspired by item response theory and can be applied to any kind of
    content analysis which uses a standardized coding scheme and discrete
    categories.  It is also applicable for content analysis conducted by
    artificial intelligence.  The package provides reliability measures
    for a complete scale as well as for every single category. Analysis of
    subgroup-invariance and error corrections are implemented. This
    information can support the development process of a coding scheme and
    allows a detailed inspection of the quality of the generated data.
    Equations and formulas working in this package are part of Berding et
    al. (2022)<doi:10.3389/feduc.2022.818365> and Berding and Pargmann
    (2022) <doi:10.30819/5581>.
Basic I/O tools for streaming and data parsing.
Provides S4 classes for Internet Protocol (IP) versions 4 and 6 addresses and efficient methods for 'IP' addresses comparison, arithmetic, bit manipulation and lookup. Both 'IPv4' and 'IPv6' arbitrary ranges are also supported as well as domain lookup and 'whois' query.
Enables the user to find the country, region, district, city, coordinates, zip code, time zone, ISP, domain name, connection type, area code, weather, Mobile Country Code, Mobile Network Code, mobile brand name, elevation, usage type, address type, IAB category and Autonomous system information that any IP address or hostname originates from. Supported IPv4 and IPv6.
        Please visit <https://www.ip2location.com> to learn more. You may also want to visit <https://lite.ip2location.com> for free database download.
        This package requires 'IP2Location Python' module. At the terminal, please run 'pip install IP2Location' to install the module.
Enable user to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits.
    The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit <https://lite.ip2location.com> for free database download.
Converts character vectors between phonetic
    representations.  Supports IPA (International Phonetic Alphabet),
    X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and
    ARPABET (used by the CMU Pronouncing Dictionary).
Classes and functions for working with IP (Internet Protocol)
    addresses and networks, inspired by the Python 'ipaddress' module.
    Offers full support for both IPv4 and IPv6 (Internet Protocol versions
    4 and 6) address spaces. It is specifically designed to work well with
    the 'tidyverse'.
A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) <doi:10.1101/2020.03.21.001206>.
Read data from 'LimeSurvey'
    (<https://www.limesurvey.org/>)
    in a comfortable way.
    Heavily inspired by 'limer'
    (<https://github.com/cloudyr/limer/>),
    which lacked a few comfort features for me.
An R client for the 'ipbase.com' IP Geolocation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://ipbase.com/docs> .
Provides tools for passing messages between R processes. 
    Shiny examples are provided showing how to perform useful tasks such as: 
    updating reactive values from within a future, progress bars for long running 
    async tasks, and interrupting async tasks based on user input.
An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.
Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials.
Basic checks needed with an individual level participant data from randomised controlled trial. This 
    checks files for existence, read access and individual columns for formats. The checks on format is currently implemented for gender and age formats.
An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. ’s digitize package (2011)  <doi:10.32614/RJ-2011-004> . For more complex and tangled together graphs, digitizing software, such as 'DigitizeIt' (for MAC or windows) or 'ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) <doi:10.1186/1471-2288-12-9> with some modifications.
Functions are provided to interpolate geo-referenced point data via
    Inverse Path Distance Weighting. Useful for coastal marine applications where
    barriers in the landscape preclude interpolation with Euclidean distances.
Allows direct access to the macroeconomic, 
             financial and regional database maintained by 
             Brazilian Institute for Applied Economic Research ('Ipea').
             This R package uses the 'Ipeadata' API. For more information, 
             see <http://www.ipeadata.gov.br/>.
Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) <doi:10.1002/9780470316757> and Ratkowsky and Reddy (2017) <doi:10.1093/aesa/saw098> for details.
Contains data on Post-Secondary Institution Statistics in 2020 <https://nces.ed.gov/ipeds/use-the-data>. The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice.
Starting from user-supplied institutional data, these scripts 
    transform, aggregate, and reshape the information to produce 
    key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) 
    through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes.  
    Final files are saved locally to a location of the user's choice. 
    User-friendly readable files can also be produced for purposes of data review and validation.
A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.
A fast (C) implementation of the iterative proportional fitting
    procedure.
Performs iterative proportional updating given a seed table and
  an arbitrary number of marginal distributions. This is commonly used in
  population synthesis, survey raking, matrix rebalancing, and other
  applications. For example, a household survey may be weighted to match the
  known distribution of households by size from the census. An origin/
  destination trip matrix might be balanced to match traffic counts.
  The approach used by this package is based on a paper from
  Arizona State University (Ye, Xin, et. al. (2009)
  <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf>).
  Some enhancements have been made to their work including primary and 
  secondary target balance/importance, general marginal agreement, and weight 
  restriction.
Algorithms and utility functions for indoor positioning using fingerprinting techniques. 
    These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data 
    sets, estimation of positions,comparison of the performance of different models, and graphical 
    visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or
    probabilistic fingerprinting are implemented in this package to perform analysis
    and estimations over RSSI data sets.
When you want to install R package or download file from GitHub, 
    but you can't access GitHub, this package helps you install R packages or 
    download file from GitHub via the proxy website <https://mirror.ghproxy.com/> 
    or <https://gh-proxy.com/>, which is in real-time sync with GitHub.
Combining genomic prediction with Monte Carlo simulation, three different 
    strategies are implemented to select parental lines for multiple traits in plant 
    breeding. The selection strategies include (i) GEBV-O considers only genomic 
    estimated breeding values (GEBVs) of the candidate individuals; (ii) GD-O 
    considers only genomic diversity (GD) of the candidate individuals; and (iii) 
    GEBV-GD considers both GEBV and GD. The above method can be seen in Chung PY, 
    Liao CT (2020) <doi:10.1371/journal.pone.0243159>. Multi-trait genomic best 
    linear unbiased prediction (MT-GBLUP) model is used to simultaneously estimate 
    GEBVs of the target traits, and then a selection index is adopted to evaluate 
    the composite performance of an individual.
An R client for the 'iplookupapi.com' IP Lookup API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://iplookupapi.com/docs> .
Provides functions and data sets to accompany the  book 'Integrated Population Models: Theory and Ecological Applications with R and JAGS' by Michael Schaub and Marc Kéry (ISBN: 9780128205648).
Flexibly implements Integral Projection Models using a
  mathematical(ish) syntax. This package will not help with the vital rate
  modeling process, but will help convert those regression models into an
  IPM. 'ipmr' handles density dependence and environmental stochasticity, with a
  couple of options for implementing the latter. In addition, provides functions
  to avoid unintentional eviction of individuals from models. Additionally, 
  provides model diagnostic tools, plotting functionality, 
  stochastic/deterministic simulations, and analysis tools.
  Integral projection models are described in depth by Easterling et al. (2000) 
  <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) 
  <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>,
  and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. 
  Williams et al. (2012) <doi:10.1890/11-2147.1> discuss the problem of 
  unintentional eviction.
Calculation of key bacterial growth curve parameters using fourth degree polynomial functions. 
    Six growth curve parameters are provided including peak growth rate, doubling time, lag time, maximum growth, and etc.
    'ipolygrowth' takes time series data from individual biological samples (with technical replicates) or multiple samples.
Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.
Let us consider a sample of patients who can suffer from several diseases simultaneously, in a given set of diseases. The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient.
Improved predictive models by indirect classification and
  bagging for classification, regression and survival problems 
  as well as resampling based estimators of prediction error.
Provides methods to perform and analyse I-prior regression models.
    Estimation is done either via direct optimisation of the log-likelihood or 
    an EM algorithm.
Functions that wrap popular phylogenetic software for sequence
    alignment, masking of sequence alignments, and estimation of phylogenies and
    ancestral character states.
Estimates the density of a spatially distributed animal population 
  sampled with an array of passive detectors, such as traps. Models incorporating 
  distance-dependent detection are fitted by simulation and inverse prediction 
  as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.
Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets.
Contains data sets, programmes and illustrations 
    discussed in the book, "Introduction to Probability, Statistics and R:
    Foundations for Data-Based Sciences."  Sahu (2023, isbn:978-3-031-37864-5)
    describes the methods in detail.
An easy way to work with census, survey, and geographic data
    provided by IPUMS in R. Generate and download data through the IPUMS
    API and load IPUMS files into R with their associated metadata to
    make analysis easier. IPUMS data describing 1.4 billion individuals
    drawn from over 750 censuses and surveys is available free of charge
    from the IPUMS website <https://www.ipums.org>.
Generate plots based on the Item Pool Visualization concept for
    latent constructs. Item Pool Visualizations are used to display the
    conceptual structure of a set of items (self-report or psychometric).
    Dantlgraber, Stieger, & Reips (2019) <doi:10.1177/2059799119884283>.
Functions to estimate the probability to receive the observed treatment, based on
    individual characteristics. The inverse of these probabilities can be used as weights when
	estimating causal effects from observational data via marginal structural models. Both point
	treatment situations and longitudinal studies can be analysed. The same functions can be used to
	correct for informative censoring.
Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or  modelled  using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.
An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighted (IPW) estimation of average treatment effect (ATE) with misclassified binary outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals.
An implementation of the MaxLFQ algorithm by
    Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive
    pipeline for processing proteomics data in data-independent acquisition mode
    (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>).
    It offers additional options for protein quantification using
    the N most intense fragment ions, using all fragment ions, and
    a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160).
    In general, the tool can be used to integrate multiple 
    proportional observations into a single quantitative value.
Builds statistical control charts with exact limits for
        univariate and multivariate cases.
Functions to import and handle infrared spectra (import from '.csv' and
  Thermo Galactic's '.spc', baseline correction, binning, clipping,
  interpolating, smoothing, averaging, adding, subtracting, dividing, 
  multiplying, plotting).
Iterated race is an extension of the Iterated F-race method for
             the automatic configuration of optimization algorithms, that is,
             (offline) tuning their parameters by finding the most appropriate
             settings given a set of instances of an optimization problem.
             M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle,
             and M. Birattari (2016) <doi:10.1016/j.orp.2016.09.002>.
Graphical visualization tools for analyzing the data produced by 'irace'. The 'iraceplot' package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of 'irace' and their performance. The functions just require the log file generated by 'irace' and, in some cases, they can be used with user-provided data.
Provides a flexible integrative algorithm that allows information from prior data, such as protein protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference.
Fit a predictive model with the iteratively reweighted boosting (IRBoost) that minimizes the robust loss functions in the CC-family (concave-convex). The convex optimization is conducted by functional descent boosting algorithm in the R package 'xgboost'. The IRBoost reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include the robust generalized linear models and extensions, where the mean is related to the predictors by boosting, and robust accelerated failure time models. The package supersedes the R package 'ccboost'. Wang (2021) <arXiv:2101.07718>.
Check the irrepresentable condition (IRC) in both L1-regularized regression <doi:10.1109/TIT.2006.883611>
            and Gaussian graphical models. The IRC requires that the important and unimportant variables 
            are not correlated, at least not all that much, and it is necessary for consistent model
            selection. Exploring the IRC as a function of the number of variables, assumed sparsity,
            and effect size can provide valuable insights into the model selection 
            properties of L1-regularization.
Provides implementation of various correlation coefficients of common use in
  Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient
  as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008)
  <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b
  developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.
An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>.
    Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.
Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.
Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.
Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.
Provides examples of code for analyzing data or accomplishing tasks that may be useful to institutional or educational researchers.
Fits a double logistic function to NDVI time series and calculates 
             instantaneous rate of green (IRG) according to methods described
             in Bischoff et al. (2012) <doi:10.1086/667590>.
Provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.
Classes and functions for metrics calculation as part of the
             'IRIS DMC MUSTANG' project. The functionality in this package 
             builds upon the base classes of the 'IRISSeismic' package.
             Metrics include basic statistics as well as higher level
             'health' metrics that can help identify problematic seismometers.
Provides classes and methods for seismic data analysis. The
             base classes and methods are inspired by the python code found in
             the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and 
             methods support data returned by web services provided by the 'IRIS DMC'
             <http://service.iris.edu/>.
The R kernel for the 'Jupyter' environment executes R code which the front-end
    ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
Fast and memory efficient methods for truncated singular value
    decomposition and principal components analysis of large sparse and dense
    matrices.
The Row-column designs are widely recommended for experimental situations when there are two well-identified factors that are cross-classified representing known sources of variability. These designs are expected to result a gain in accuracy of estimating treatment comparisons in an experiment as they eliminate the effects of the row and column factors. However, these designs are not readily available when the number of treatments is more than the levels of row and column blocking factors. This package named 'iRoCoDe' generates row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs. (Mcsorley, J.P., Phillips, N.C., Wallis, W.D. and Yucas, J.L. (2005).<doi:10.1007/s10623-003-6149-9>).
Imbalanced domain learning has almost exclusively focused on solving 
    classification tasks, where the objective is to predict cases labelled with a 
    rare class accurately. Such a well-defined approach for regression tasks lacked 
    due to two main factors. First, standard regression tasks assume that each value 
    is equally important to the user. Second, standard evaluation metrics focus on 
    assessing the performance of the model on the most common cases. This package 
    contains methods to tackle imbalanced domain learning problems in regression 
    tasks, where the objective is to predict extreme (rare) values.
    The methods contained in this package are: 1) an automatic and non-parametric 
    method to obtain such relevance functions; 2) visualisation tools; 3) suite of 
    evaluation measures for optimisation/validation processes; 4) the squared-error 
    relevance area measure, an evaluation metric tailored for imbalanced regression tasks.
    More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.
Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson's A, Kendall's W, Cohen's Kappa, ...
Calculates various chance-corrected agreement coefficients (CAC) among 2 or more raters are provided. 
	Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 
	coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. All of these statistical 
	procedures are described in details in Gwet, K.L. (2014,ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
	Advanced Analytics, LLC.
Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).
Calculates various intraclass correlation coefficients used to quantify 
  inter-rater and intra-rater reliability.  The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented
  in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
  Advanced Analytics, LLC.
Provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi{10.1037/1082-989X.1.1.30} and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi{10.1214/aoms/1177732186}.
A collection of Item Response Theory (IRT) and Computerized 
     Adaptive Testing (CAT) functions that are used in psychometrics.
Analysis of Dichotomous and polytomous data using unidimensional Item Response Theory model (Chalmers (2012) <doi:10.18637/jss.v048.i06>) with user friendly Graphical User Interface. Suitable for  beginners who are learning Item Response Theory.
Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) <doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) <doi:10.1177/21582440211052556>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.
Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.
Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm 
    (Bock & Aitkin, 1981 <doi:10.1007/BF02293801>). 
    Within parameter estimation algorithm, several methods for latent distribution estimation are available.
    Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution.
Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.
A collection of functions useful in learning and practicing IRT,
    which can be combined into larger programs. Provides basic CTT analysis,
    a simple common interface to the estimation of item
    parameters in IRT models for binary responses with three different programs
    (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible 
    values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord,
    and the complete Hebaera method), and a rich array of parametric and 
    non-parametric (kernel) plots. Estimates and plots Haberman's interaction model
    when all items are dichotomously scored.
Implementation of analytical and sampling-based power analyses for
    the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied
    to item response theory (IRT) models that are fitted using marginal maximum
    likelihood estimation. The methods are described in our paper
    (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).
Fit unidimensional item response theory (IRT) models to a mixture 
    of dichotomous and polytomous data, calibrate online item parameters 
    (i.e., pretest and operational items), estimate examinees' abilities, 
    and examine the IRT model-data fit on item-level in different ways 
    as well as provide useful functions related to IRT analyses such as 
    IRT model-data fit evaluation and differential item functioning analysis.
    The bring.flexmirt() and write.flexmirt() functions were written by modifying 
    the read.flexmirt() function (Pritikin & Falk (2022) <doi:10.1177/0146621620929431>).
    The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() 
    and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>).
    The bisection() function was written by modifying the bisection() function 
    (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve 
    scoring in the est_score() function was written by modifying the irt.eq.tse() function 
    (González (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted 
    likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions
    of the catR package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).
Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software.
Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
Interactive shiny application for running Item Response Theory
    analysis. Provides graphics for characteristic and information curves.
The ISA is a biclustering algorithm that finds modules 
  in an input matrix. A module or bicluster is a block of the
  reordered input matrix.
We provide data sets used in the forthcoming textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2024). The package currently contains sixteen datasets and should be published in early 2024.
Reads the output of the 'PerkinElmer InForm' software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.
ISA is a metadata framework to manage an increasingly diverse set 
    of life science, environmental and biomedical experiments. In isatabr 
    methods for reading, modifying and writing of files in the ISA-Tab format
    are implemented. It also contains methods for processing assay data.
Implementation of functions to assign corresponding common job prestige scores (SIOPS, ISEI), the official job or group title and the ISCO-88 code to given ISCO-08 codes. ISCO-08 is the latest version of the International Standard Classification of Occupations which is used to organise information on labour and jobs.
Allows the user to perform approximate
 matching between the occupational classifications using concordances provided by
 the Institute for Structural Research and Faculty of Economics, University of
 Warsaw, <doi:10.1111/ecot.12145>. The crosswalks offer a complete
 step-by-step mapping of Standard Occupational Classification (2010) data to the
 International Standard Classification of Occupations (2008). We propose a
 mapping method based on the aforementioned research that converts measurements
 to the smallest possible unit of the target taxonomy, and then performs an
 aggregation/estimate to the requested degree Occupational Hierarchical level.
Implementation of a KL-based scoring rule to assess the quality of different missing value imputations in the broad sense as introduced in Michel et al. (2021)  <arXiv:2106.03742>.
Provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).
Tools for parsing 'NOAA' Integrated Surface Data ('ISD') files,
    described at <https://www.ncdc.noaa.gov/isd>. Data includes for example,
    wind speed and direction, temperature, cloud data, sea level pressure,
    and more. Includes data from approximately 35,000 stations worldwide,
    though best coverage is in North America/Europe/Australia. Data is stored
    as variable length ASCII character strings, with most fields optional.
    Included are tools for parsing entire files, or individual lines of data.
Characterisation and calibration of single or multiple Ion Selective Electrodes (ISEs); 
    activity estimation of experimental samples. Implements methods described in:
	Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) <doi:10.1002/elan.201100510>,  
	Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) <doi:10.1109/ICSENS.2017.8233898>,
	Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) <doi:10.3390/s19204544>, and
	Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020)  <doi:10.1021/acssensors.9b02133>.
The implement of integrative analysis methods based on a two-part penalization, which realizes dimension reduction analysis and mining the heterogeneity and association of multiple studies with compatible designs. The software package provides the integrative analysis methods including integrative sparse principal component analysis (Fang et al., 2018), integrative sparse partial least squares (Liang et al., 2021) and integrative sparse canonical correlation analysis, as well as corresponding individual analysis and meta-analysis versions. References: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). Integrative sparse principal component analysis. Journal of Multivariate Analysis, <doi:10.1016/j.jmva.2018.02.002>. (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). Integrative sparse partial least squares. Statistics in Medicine, <doi:10.1002/sim.8900>.
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
A toolbox for constructing potential landscapes for Ising
    networks. The parameters of the networks can be directly supplied by
    users or estimated by the 'IsingFit' package by van Borkulo and
    Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from
    empirical data. The Ising model's Boltzmann distribution is preserved
    for the potential landscape function. The landscape functions can be
    used for quantifying and visualizing the stability of network states,
    as well as visualizing the simulation process.
Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided.
Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.
Develops stochastic models based on the Theory of Island
    Biogeography (TIB) of MacArthur and Wilson (1967) <doi:10.1023/A:1016393430551>
    and extensions. It implements methods to estimate colonization and
    extinction rates (including environmental variables) given presence-absence
    data, simulates community assembly, and performs model selection.
An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.
The development of ISM was made by Warfield in 1974. 
      ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure.
      To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's.
      Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524>
      Warfield,J.N. (1974, E-ISSN:2168-2909).
Functions to support the computations carried out in
  `An Introduction to Statistical Modeling of Extreme Values' by
  Stuart Coles. The functions may be divided into the following 
  groups; maxima/minima, order statistics, peaks over thresholds
  and point processes.
Paquete creado con el fin de facilitar el cálculo y distribución del índice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodología completa está disponible en "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)].
The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For a tutorial, download at <https://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Check if an 'externalptr' is a null pointer.
    R does currently not have a native function for that purpose.
    This package contains a C function that returns TRUE in case of a null pointer.
Linear order and unimodal order (univariate)
	     isotonic regression; bivariate isotonic regression
	     with linear order on both variables.
Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers.
    The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal 'dot' format, e.g., 3E7.1CBE991A14. These are referred
    to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary
    structure (see <https://en.wikipedia.org/wiki/ISO_11784_and_ISO_11785>). There are two 'flavours' of this format, a left and a right variation. Which flavour
    a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on 
    the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically.
A fast C++ implementation to generate contour lines
    (isolines) and contour polygons (isobands) from regularly spaced grids
    containing elevation data.
In classification problems a monotone relation between some
  predictors and the classes may be assumed. In this package 'isoboost' 
  we propose new boosting algorithms, based on LogitBoost, that 
  incorporate this isotonicity information, yielding more accurate 
  and easily interpretable rules.
A set of functions to run simple and composite box-models to describe the 
    dynamic or static distribution of stable isotopes in open 
    or closed systems. The package also allows the sweeping of many 
    parameters in both static and dynamic conditions.
    The mathematical models used in this package are derived from Albarede, 1995, 
    Introduction to Geochemical Modelling, Cambridge University Press,
    Cambridge <doi:10.1017/CBO9780511622960>.
Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology <doi:10.1111/gcb.16407>.
This resource provides tools to create, compare, and post-process 
    spatial isotope assignment models of animal origin. It generates 
    probability-of-origin maps for individuals based on user-provided tissue and 
    environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] 
    <doi:10.1111/2041-210X.12147>) using the framework established in Bowen et al.
    (2010) <doi:10.1146/annurev-earth-040809-152429>). The package 'isocat' can then 
    quantitatively compare and cluster these maps to group individuals by 
    similar origin. It also includes techniques for applying four approaches 
    (cumulative sum, odds ratio, quantile only, and quantile simulation) with 
    which users can summarize geographic origins and probable distance traveled 
    by individuals. Campbell et al. [2020] establishes several of the functions
    included in this package <doi:10.1515/ami-2020-0004>.
Contains functions to check the isomorphism of multi-stage factorial designs with randomisation restrictions based on balanced spreads and balanced covering stars of PG(n-1,2) as described in Spencer, Ranjan and Mendivil (2019) <doi:10.1007/s42519-019-0064-5>.
A bunch of functions to deal with circular data under order restrictions.
ISO language, territory, currency, script and character codes.
  Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217
  currency codes, ISO 15924 script codes, and the ISO 8859 character codes
  as well as the UN M.49 area codes.
Analyzing Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) 
    measurement data to evaluate isotope ratios (IRs) is a complex process.
    The 'IsoCor' package facilitates this process and renders it reproducible 
    by providing a function to run a 'Shiny'-App locally in any web browser. 
    In this App the user can upload data files of various formats, select ion 
    traces, apply peak detection and perform calculation of IRs and delta values.
    Results are provided as figures and tables and can be exported.
    The App, therefore, facilitates data processing of ICP-MS experiments to
    quickly obtain optimal processing parameters compared to traditional 'Excel'
    worksheet based approaches. A more detailed description can be found in the
    corresponding article "Data processing made easy: standalone tool for 
    automated calculation of isotope ratio from transient signals – IsoCor" 
    by Tukhmetova et al. in Journal of Analytical Atomic Spectrometry (JAAS).
    The most recent version of 'IsoCor' can be tested online 
    at <https://jali.shinyapps.io/isocor>.
Distributional regression under stochastic order restrictions for
    numeric and binary response variables and partially ordered covariates. See
    Henzi, Ziegel, Gneiting (2020) <arXiv:1909.03725>.
Offers framework for testing for monotonic relationship between gene expression and doses in a microarray experiment. Several testing procedures including the global likelihood-ratio test (Bartholomew, 1961), Williams (1971, 1972), Marcus (1976), M (Hu et al. 2005) and the modified M (Lin et al. 2007) are used to test for the monotonic trend in gene expression with respect to doses. BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2004) FDR controlling procedures are applied to adjust the raw p-values obtained from the permutations.
This toolbox makes working with oxygen,
    carbon, and clumped isotope data reproducible and straightforward.
    Use it to quickly calculate isotope fractionation factors,
    and apply paleothermometry equations.
Implementation of Isolation kernel (Qin et al. (2019) <doi:10.1609/aaai.v33i01.33014755>).
API wrapper that contains functions to retrieve data from the 'IsoMemo' partnership databases. Web services for API: <https://isomemodb.com/api/v1/iso-data>.
Read and process isotopocule data from an Orbitrap Isotope Solutions mass spectrometer. Citation: Kantnerova et al. (in review).
Clustering algorithm developed for use with plot inventories of species. It groups plots by subsets of species rather than overall species composition. There is an unsupervised and a supervised mode, the latter accepting suggestions for species with greater weight and cluster medoids.
The function calculates the isotopic pattern (fine
        structures) for a given chemical formula.
Ternary plots made simple. This package allows to create
    ternary plots using 'graphics'. It provides functions to display the
    data in the ternary space, to add or tune graphical elements and to
    display statistical summaries. It also includes common ternary
    diagrams which are useful for the archaeologist (e.g. soil texture charts,
    ceramic phase diagram).
Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington', 'Ludwig' and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.
Provides a graphical user interface to the 'IsoplotR' package for radiometric geochronology. The GUI runs in an internet browser and can either be used offline, or hosted on a server to provide online access to the 'IsoplotR' toolbox.
Deconvolution of mixed tumour profiles into normal and cancer for each patient, using 
	the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires 
	mixed tumour profiles and a set of unmatched "basis" normal profiles.
Building isoscapes using mixed models and inferring the geographic
  origin of samples based on their isotopic ratios. This package is essentially a
  simplified interface to several other packages which implements a new
  statistical framework based on mixed models. It uses 'spaMM' for fitting and
  predicting isoscapes, and assigning an organism's origin depending on its
  isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for
  plotting the maps produced with 'terra' using 'lattice'.
IsoSpec is a fine structure calculator used for obtaining the most
    probable masses of a chemical compound given the frequencies of the composing
    isotopes and their masses. It finds the smallest set of isotopologues with
    a given probability. The probability is assumed to be that of the product of
    multinomial distributions, each corresponding to one particular element and
    parametrized by the frequencies of finding these elements in nature. These
    numbers are supplied by IUPAC - the International Union of Pure and Applied
    Chemistry. See: Lacki, Valkenborg, Startek (2020) <DOI:10.1021/acs.analchem.0c00959>
    and Lacki, Startek, Valkenborg, Gambin (2017) <DOI:10.1021/acs.analchem.6b01459>
    for the description of the algorithms used.
Nonparametric estimation on survival analysis under order-restrictions.
Contains two main functions: one for
        solving general isotone regression problems using the
        pool-adjacent-violators algorithm (PAVA); another one provides
        a framework for active set methods for isotone optimization
        problems with arbitrary order restrictions. Various types of
        loss functions are prespecified.
Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Estimates diet contributions from isotopic sources using JAGS.
    Includes estimation of concentration dependence and measurement error.
Implements Bayesian models to analyze data from tracer addition
    experiments. The implemented method was originally described in the article
    "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from
    Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020)
    <doi:10.1086/708546>.
Fast and multi-threaded implementation of
	isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>),
	extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>),
	SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>),
	fair-cut forest (Cortes (2021) <arXiv:2110:13402>),
	robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>),
	and customizable variations of them, for isolation-based outlier detection, clustered outlier detection,
	distance or similarity approximation (Cortes (2019) <arXiv:1910.12362>),
	isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>),
	and imputation of missing values (Cortes (2019) <arXiv:1911.06646>),
	based on random or guided decision tree splitting, and providing different metrics for
	scoring anomalies based on isolation depth or density (Cortes (2021) <arXiv:2111.11639>).
	Provides simple heuristics for fitting the model to categorical columns and handling missing data,
	and offers options for varying between random and guided splits, and for using different splitting criteria.
The wiDB...() functions provide an interface to the public API 
    of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: 
    build, check and submit queries, and receive and 
    unpack responses. Data analysis functions support Bayesian 
    inference of the source and source isotope composition of water 
    samples that may have experienced evaporation. Algorithms 
    adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).
This is an substitute for the %V and %u formats which are
        not implemented on Windows. In addition, the package offers
        functions to convert from standard calender format yyyy-mm-dd
        to and from ISO 8601 week format yyyy-Www-d.
A collection of several functions related to construction and analysis of incomplete split-plot designs. The package contains functions to obtain  and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are  incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments.
Allows access to data from the Rio de Janeiro Public Security Institute (ISP), such as criminal statistics, data on gun seizures and femicide. The package also contains the spatial data of Pacifying Police Units (UPPs) and Integrated Public Safety Regions, Areas and Circumscriptions.
Algorithm to handle with PCA-based missing data, where ISR is for PCA-based missing data with high correlation and DISR is for distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
This is the central location for data and tools for the development,
    maintenance, analysis, and deployment of the International Soil Radiocarbon Database
    (ISRaD). ISRaD was developed as a collaboration between the U.S. Geological Survey
    Powell Center and the Max Planck Institute for Biogeochemistry. This R package provides
    tools for accessing and manipulating ISRaD data, compiling local data using the ISRaD
    data structure, and simple query and reporting functions for ISRaD. For more detailed
    information visit the ISRaD website at: <https://soilradiocarbon.org/>.
Methodology for subgroup selection in the context of isotonic regression including methods for sub-Gaussian errors, classification, homoscedastic Gaussian errors and quantile regression. See the documentation of ISS(). Details can be found in the paper by Müller, Reeve, Cannings and Samworth (2023) <arXiv:2305.04852v2>.
You can access to open data published in Instituto Canario De Estadistica (ISTAC) APIs at <https://datos.canarias.es/api/estadisticas/>.
Launches a 'shiny' based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.
Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations.
Independent Surrogate Variable Analysis is an algorithm
        for feature selection in the presence of potential confounding
        factors (see Teschendorff AE et al 2011, <doi: 10.1093/bioinformatics/btr171>).
Data sets and scripts for text examples and exercises in 
  P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Provides two record linkage data sets on the Italian Survey on Household and Wealth, 2008 and 2010, a sample survey conducted by the Bank of Italy every two years. The 2010 survey covered 13,702 individuals, while the 2008 survey covered 13,734 individuals. The following categorical variables are included in this data set: year of birth, working status, employment status, branch of activity, town size, geographical area of birth, sex, whether or not Italian national, and highest educational level obtained. Unique identifiers are available to assess the accuracy of one’s method. Please see Steorts (2015) <DOI:10.1214/15-BA965SI> to find more details about the data set.
Functions for analyzing multiple choice items. These analyses include
  the convertion of student response into binaty data (correct/incorrect),
  the computation of the number of corrected responses and grade for each subject,
  the calculation of item difficulty and discrimination, the computation of the
  frecuency and point-biserial correlation for each distractor and the graphical
  analysis of each item.
Three methods for Individual Tree Crowns (ITCs) delineation on remote sensing data: one is based on LiDAR data in x,y,z format and one on imagery data in raster format.
The routine, itdr(), which allows to estimate the sufficient dimension reduction subspaces, i.e., central mean subspace or central subspace in regression, using Fourier transformation proposed by Zhu and Zeng (2006) <doi:10.1198/016214506000000140>, convolution transformation proposed by Zeng and Zhu (2010) <doi:10.1016/j.jmva.2009.08.004> and iterative Hessian transformation methods proposed by Cook and Li (2002) <doi:10.1214/aos/1021379861>. The function fm_xire() function provides optimal estimators by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) <doi:10.5705/ss.202020.0312>. The admmft() function selects the sufficient variables using a Fourier transform sparse inverse regression estimators proposed by Weng (2022) <doi:10.1016/j.csda.2021.107380>.
Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales). The statistics reported in this package can be found in any measurement textbook such as Crocker and Algina (2006, ISBN:9780495395911).
Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented.
    ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively.
    For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.
Fits large-scale regression models with a penalty that 
  restricts the maximum number of non-zero regression coefficients
  to a prespecified value.  While Chu et al (2020) <doi:10.1093/gigascience/giaa044>
  describe the basic algorithm, this package uses Cyclops for an efficient implementation.
Know which loop iteration the code execution is up to by including
    a single, convenient function call inside the loop.
Support for iterators, which allow a programmer to traverse
    through all the elements of a vector, list, or other collection of data.
The iterLap (iterated Laplace approximation) algorithm approximates a
             general (possibly non-normalized) probability density on R^p, by repeated
             Laplace approximations to the difference between current approximation 
             and true density (on log scale). The final approximation is a mixture of
             multivariate normal distributions and might be used for example as a
             proposal distribution for importance sampling (eg in Bayesian applications). 
             The algorithm can be seen as a computational generalization of the Laplace 
             approximation suitable for skew or multimodal densities.
A fresh take on iterators in R. Designed to be cross-compatible with the 'iterators' package, but using the 'nextOr' method will offer better performance as well as more compact code. With batteries included: includes a collection of iterator constructors and combinators ported and refined from the 'iterators', 'itertools', and 'itertools2' packages.
Iterator for generating permutations and combinations. They
    can be either drawn with or without replacement, or with distinct/ non-distinct 
    items (multiset). The generated sequences are in lexicographical order 
    (dictionary order). The algorithms to generate permutations and combinations are 
    memory efficient. These iterative algorithms enable users to process all sequences 
    without putting all results in the memory at the same time. 
    The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer
    being maintained. Users are recommended to switch to 'arrangements'.
Various tools for creating iterators, many patterned after
        functions in the Python itertools module, and others patterned
        after functions in the 'snow' package.
A port of Python's excellent itertools module to R for efficient
    looping.
Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.
The 'Interactive Tree Of Life' <https://itol.embl.de/> online server can 
    edit and annotate trees interactively. The 'itol.toolkit' package can 
    support all types of annotation templates.
Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).
Implements the Interpolate, Truncate, Project (ITP) root-finding 
    algorithm developed by Oliveira and Takahashi (2021) <doi:10.1145/3423597>.
    The user provides the function, from the real numbers to the real numbers, 
    and an interval with the property that the values of the function at its 
    endpoints have different signs. If the function is continuous over this 
    interval then the ITP method estimates the value at which the function is 
    equal to zero. If the function is discontinuous then a point of 
    discontinuity at which the function changes sign may be found. 
    The function can be supplied using either an R function or an external 
    pointer to a C++ function. Tuning parameters of the ITP algorithm can be 
    set by the user. Default values are set based on arguments in Oliveira and 
    Takahashi (2021).
Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter 
    scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing 
    XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. 
    A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. 
    Multivariate methods are implemented with appropriate data transformation.
Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999)
    as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear
    regression analysis which is particularly useful for time course data such as
    EEG, pupil dilation, gaze data (eye tracking), and articulography recordings,
    but also for behavioral data such as reaction times and response data. As time
    course measures are sensitive to autocorrelation problems, GAMMs implements
    methods to reduce the autocorrelation problems. This package includes functions
    for the evaluation of GAMM models (e.g., model comparisons, determining regions
    of significance, inspection of autocorrelational structure in residuals)
    and interpreting of GAMMs (e.g., visualization of complex interactions, and
    contrasts).
Provides a wrapper around the same API <https://app.americansocceranalysis.com/api/v1/__docs__/>
    that powers the American Soccer Analysis app.
Collection of R functions to do purely presence-only species
    distribution modeling with isolation forest (iForest) and its
    variations such as Extended isolation forest and SCiForest. See the
    details of these methods in references: Liu, F.T., Ting, K.M. and
    Zhou, Z.H. (2008) <doi:10.1109/ICDM.2008.17>, Hariri, S., Kind, M.C.
    and Brunner, R.J. (2019) <doi:10.1109/TKDE.2019.2947676>, Liu, F.T.,
    Ting, K.M. and Zhou, Z.H. (2010) <doi:10.1007/978-3-642-15883-4_18>,
    Guha, S., Mishra, N., Roy, G. and Schrijvers, O. (2016)
    <https://proceedings.mlr.press/v48/guha16.html>, Cortes, D. (2021)
    <arXiv:2110.13402>. Additionally, Shapley values are used to explain
    model inputs and outputs. See details in references: Shapley, L.S.
    (1953) <doi:10.1515/9781400881970-018>, Lundberg, S.M. and Lee, S.I.
    (2017) <https://dl.acm.org/doi/abs/10.5555/3295222.3295230>, Molnar,
    C.  (2020) <ISBN:978-0-244-76852-2>, Štrumbelj, E. and Kononenko, I.
    (2014) <doi:10.1007/s10115-013-0679-x>. itsdm also provides functions
    to diagnose variable response, analyze variable importance, draw
    spatial dependence of variables and examine variable contribution. As
    utilities, the package includes a few functions to download
    bioclimatic variables including 'WorldClim' version 2.0 (see Fick,
    S.E. and Hijmans, R.J. (2017) <doi:10.1002/joc.5086>) and
    'CMCC-BioClimInd' (see Noce, S., Caporaso, L. and Santini, M. (2020)
    <doi:10.1038/s41597-020-00726-5>.
Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <https://link.springer.com/book/10.1007/b97391>.
Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.
Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <arXiv:2103.06328>.
An implementation of randomization-based hypothesis 
    testing for three different estimands in a cluster-randomized 
    encouragement experiment. The three estimands include (1) testing
    a cluster-level constant proportional treatment effect (Fisher's
    sharp null hypothesis), (2) pooled effect ratio, and (3) average 
    cluster effect ratio. To test the third estimand, user needs to install
    'Gurobi' (>= 9.0.1) optimizer via its R API. Please refer to 
    <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.
Estimation and diagnostic tools for instrumental variables designs, which implements the guidelines proposed in Lal et al. (2023) <arXiv:2303.11399>, including bootstrapped confidence intervals, effective F-statistic, Anderson-Rubin test, valid-t ratio test, and local-to-zero tests.
Uses data and researcher's beliefs on measurement error and
    instrumental variable (IV) endogeneity to generate the space of consistent 
    beliefs across measurement error, instrument endogeneity, and instrumental 
    relevance for IV regressions. 
    Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.
Provides facilities of general to specific model selection for
    exogenous regressors in 2SLS models. Furthermore, indicator saturation
    methods can be used to detect outliers and structural breaks in the sample.
A method that estimates
    an IV-optimal individualized treatment rule. An individualized
    treatment rule is said to be IV-optimal if it minimizes the 
    maximum risk with respect to the putative IV and the set of
    IV identification assumptions. Please refer to 
    <arXiv:2002.02579> for more details on the methodology and 
    some theory underpinning the method. Function IV-PILE() uses
    functions in the package 'locClass'. Package 'locClass' can be
    accessed and installed from the 'R-Forge' repository via the following link: 
    <https://r-forge.r-project.org/projects/locclass/>.
    Alternatively, one can install the package by entering the following in R:
    'install.packages("locClass", repos="<http://R-Forge.R-project.org>")'.
Carries out instrumental variable
    estimation of causal effects, including power analysis, sensitivity analysis,
    and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.
The marginal treatment effect was introduced by Heckman and
    Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a
    choice-theoretic interpretation to instrumental variables models that
    maintain the monotonicity condition of Imbens and Angrist (1994)
    <doi:10.2307/2951620>. This interpretation can be used to extrapolate from
    the compliers to estimate treatment effects for other subpopulations. This
    package provides a flexible set of methods for conducting this
    extrapolation. It allows for parametric or nonparametric sieve estimation,
    and allows the user to maintain shape restrictions such as monotonicity. The
    package operates in the general framework developed by Mogstad, Santos and
    Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point
    identification or partial identification (bounds). In the partially
    identified case, bounds are computed using either linear programming
    or quadratically constrained quadratic programming. Support for
    four solvers is provided. Gurobi and the Gurobi R API
    can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained
    from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex'
    and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be
    obtained from <https://www.mosek.com/>. The lp_solve library is freely
    available from <http://lpsolve.sourceforge.net/5.5/>, and is included when
    installing its API 'lpSolveAPI', which is available from CRAN.
Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or 'Office' documents, including 'Excel'. Designed to work with pipes.
Compute the instrumental variables probit model using  the Amemiya's Generalized Least Squares estimators (Amemiya, Takeshi, (1978) <doi: 10.2307/1911443>).